"
- else:
- arg = repr(arg)
- text = "%s %s" % (text, arg)
- return text
-
- indent = " " * 4
-
- cur_lineno = bytecode.first_lineno
- prev_lineno = None
-
- if isinstance(bytecode, ConcreteBytecode):
- offset = 0
- for instr in bytecode:
- fields = []
- if instr.lineno is not None:
- cur_lineno = instr.lineno
- if lineno:
- fields.append(format_instr(instr))
- line = "".join(fields)
- line = format_line(offset, line)
- else:
- fields.append("% 3s %s" % (offset, format_instr(instr)))
- line = "".join(fields)
- print(line, file=stream)
-
- offset += instr.size
- elif isinstance(bytecode, Bytecode):
- labels = {}
- for index, instr in enumerate(bytecode):
- if isinstance(instr, Label):
- labels[instr] = "label_instr%s" % index
-
- for index, instr in enumerate(bytecode):
- if isinstance(instr, Label):
- label = labels[instr]
- line = "%s:" % label
- if index != 0:
- print(file=stream)
- else:
- if instr.lineno is not None:
- cur_lineno = instr.lineno
- line = format_instr(instr, labels)
- line = indent + format_line(index, line)
- print(line, file=stream)
- print(file=stream)
- elif isinstance(bytecode, ControlFlowGraph):
- labels = {}
- for block_index, block in enumerate(bytecode, 1):
- labels[id(block)] = "block%s" % block_index
-
- for block_index, block in enumerate(bytecode, 1):
- print("%s:" % labels[id(block)], file=stream)
- prev_lineno = None
- for index, instr in enumerate(block):
- if instr.lineno is not None:
- cur_lineno = instr.lineno
- line = format_instr(instr, labels)
- line = indent + format_line(index, line)
- print(line, file=stream)
- if block.next_block is not None:
- print(indent + "-> %s" % labels[id(block.next_block)], file=stream)
- print(file=stream)
- else:
- raise TypeError("unknown bytecode class")
diff --git a/spaces/TRI-ML/risk_biased_prediction/risk_biased/utils/torch_utils.py b/spaces/TRI-ML/risk_biased_prediction/risk_biased/utils/torch_utils.py
deleted file mode 100644
index 5e614b369a631d8829b5e189647f675aea1d2f46..0000000000000000000000000000000000000000
--- a/spaces/TRI-ML/risk_biased_prediction/risk_biased/utils/torch_utils.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import warnings
-
-import torch
-from torch import Tensor
-
-
-@torch.jit.script
-def torch_linspace(start: Tensor, stop: Tensor, num: int) -> torch.Tensor:
- """
- Copy-pasted from https://github.com/pytorch/pytorch/issues/61292
- Creates a tensor of shape [num, *start.shape] whose values are evenly spaced from start to end, inclusive.
- Replicates but the multi-dimensional bahaviour of numpy.linspace in PyTorch.
- """
- # create a tensor of 'num' steps from 0 to 1
- steps = torch.arange(num, dtype=torch.float32, device=start.device) / (num - 1)
-
- # reshape the 'steps' tensor to [-1, *([1]*start.ndim)] to allow for broadcastings
- # - using 'steps.reshape([-1, *([1]*start.ndim)])' would be nice here but torchscript
- # "cannot statically infer the expected size of a list in this contex", hence the code below
- for i in range(start.ndim):
- steps = steps.unsqueeze(-1)
-
- # the output starts at 'start' and increments until 'stop' in each dimension
- out = start[None] + steps * (stop - start)[None]
-
- return out
-
-
-def load_weights(
- model: torch.nn.Module, checkpoint: dict, strict=True
-) -> torch.nn.Module:
- """This function is used instead of the one provided by pytorch lightning
- because for unexplained reasons, the pytorch lightning load function did
- not behave as intended: loading several times from the same checkpoint
- resulted in different loaded weight values...
-
- Args:
- model: a model in which new weights should be set
- checkpoint: a loaded pytorch checkpoint (probably resulting from torch.load(filename))
- strict: Default to True, wether to fail if
-
- """
- if not strict:
- model_dict = model.state_dict()
- pretrained_dict = {
- k: v for k, v in checkpoint["state_dict"].items() if k in model_dict
- }
- diff1 = checkpoint["state_dict"].keys() - model_dict.keys()
- if diff1:
- warnings.warn(
- f"Found keys {diff1} in checkpoint without any match in the model, ignoring corresponding values."
- )
- diff2 = model_dict.keys() - checkpoint["state_dict"].keys()
- if diff2:
- warnings.warn(
- f"Missing keys {diff2} from the checkpoint, the corresponding weights will keep their initial values."
- )
- pretrained_dict = {
- k: v for k, v in checkpoint["state_dict"].items() if k in model_dict
- }
- model_dict.update(pretrained_dict)
- else:
- model_dict = checkpoint["state_dict"]
-
- model.load_state_dict(model_dict, strict=strict)
- return model
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pyproject_hooks/__init__.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pyproject_hooks/__init__.py
deleted file mode 100644
index ddfcf7f72f31658d75c8128de0732fbbf0e12b15..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pyproject_hooks/__init__.py
+++ /dev/null
@@ -1,23 +0,0 @@
-"""Wrappers to call pyproject.toml-based build backend hooks.
-"""
-
-from ._impl import (
- BackendInvalid,
- BackendUnavailable,
- BuildBackendHookCaller,
- HookMissing,
- UnsupportedOperation,
- default_subprocess_runner,
- quiet_subprocess_runner,
-)
-
-__version__ = '1.0.0'
-__all__ = [
- 'BackendUnavailable',
- 'BackendInvalid',
- 'HookMissing',
- 'UnsupportedOperation',
- 'default_subprocess_runner',
- 'quiet_subprocess_runner',
- 'BuildBackendHookCaller',
-]
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/tests/modeling/test_anchor_generator.py b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/tests/modeling/test_anchor_generator.py
deleted file mode 100644
index 13a808e587382216da6fe7ee957603f448172657..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/tests/modeling/test_anchor_generator.py
+++ /dev/null
@@ -1,120 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import unittest
-import torch
-
-from detectron2.config import get_cfg
-from detectron2.layers import ShapeSpec
-from detectron2.modeling.anchor_generator import DefaultAnchorGenerator, RotatedAnchorGenerator
-
-logger = logging.getLogger(__name__)
-
-
-class TestAnchorGenerator(unittest.TestCase):
- def test_default_anchor_generator(self):
- cfg = get_cfg()
- cfg.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64]]
- cfg.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.25, 1, 4]]
-
- anchor_generator = DefaultAnchorGenerator(cfg, [ShapeSpec(stride=4)])
-
- # only the last two dimensions of features matter here
- num_images = 2
- features = {"stage3": torch.rand(num_images, 96, 1, 2)}
- anchors = anchor_generator([features["stage3"]])
- expected_anchor_tensor = torch.tensor(
- [
- [-32.0, -8.0, 32.0, 8.0],
- [-16.0, -16.0, 16.0, 16.0],
- [-8.0, -32.0, 8.0, 32.0],
- [-64.0, -16.0, 64.0, 16.0],
- [-32.0, -32.0, 32.0, 32.0],
- [-16.0, -64.0, 16.0, 64.0],
- [-28.0, -8.0, 36.0, 8.0], # -28.0 == -32.0 + STRIDE (4)
- [-12.0, -16.0, 20.0, 16.0],
- [-4.0, -32.0, 12.0, 32.0],
- [-60.0, -16.0, 68.0, 16.0],
- [-28.0, -32.0, 36.0, 32.0],
- [-12.0, -64.0, 20.0, 64.0],
- ]
- )
-
- self.assertTrue(torch.allclose(anchors[0].tensor, expected_anchor_tensor))
-
- def test_default_anchor_generator_centered(self):
- # test explicit args
- anchor_generator = DefaultAnchorGenerator(
- sizes=[32, 64], aspect_ratios=[0.25, 1, 4], strides=[4]
- )
-
- # only the last two dimensions of features matter here
- num_images = 2
- features = {"stage3": torch.rand(num_images, 96, 1, 2)}
- expected_anchor_tensor = torch.tensor(
- [
- [-30.0, -6.0, 34.0, 10.0],
- [-14.0, -14.0, 18.0, 18.0],
- [-6.0, -30.0, 10.0, 34.0],
- [-62.0, -14.0, 66.0, 18.0],
- [-30.0, -30.0, 34.0, 34.0],
- [-14.0, -62.0, 18.0, 66.0],
- [-26.0, -6.0, 38.0, 10.0],
- [-10.0, -14.0, 22.0, 18.0],
- [-2.0, -30.0, 14.0, 34.0],
- [-58.0, -14.0, 70.0, 18.0],
- [-26.0, -30.0, 38.0, 34.0],
- [-10.0, -62.0, 22.0, 66.0],
- ]
- )
-
- anchors = anchor_generator([features["stage3"]])
- self.assertTrue(torch.allclose(anchors[0].tensor, expected_anchor_tensor))
-
- anchors = torch.jit.script(anchor_generator)([features["stage3"]])
- self.assertTrue(torch.allclose(anchors[0].tensor, expected_anchor_tensor))
-
- def test_rrpn_anchor_generator(self):
- cfg = get_cfg()
- cfg.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64]]
- cfg.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.25, 1, 4]]
- cfg.MODEL.ANCHOR_GENERATOR.ANGLES = [0, 45] # test single list[float]
- anchor_generator = RotatedAnchorGenerator(cfg, [ShapeSpec(stride=4)])
-
- # only the last two dimensions of features matter here
- num_images = 2
- features = {"stage3": torch.rand(num_images, 96, 1, 2)}
- anchors = anchor_generator([features["stage3"]])
- expected_anchor_tensor = torch.tensor(
- [
- [0.0, 0.0, 64.0, 16.0, 0.0],
- [0.0, 0.0, 64.0, 16.0, 45.0],
- [0.0, 0.0, 32.0, 32.0, 0.0],
- [0.0, 0.0, 32.0, 32.0, 45.0],
- [0.0, 0.0, 16.0, 64.0, 0.0],
- [0.0, 0.0, 16.0, 64.0, 45.0],
- [0.0, 0.0, 128.0, 32.0, 0.0],
- [0.0, 0.0, 128.0, 32.0, 45.0],
- [0.0, 0.0, 64.0, 64.0, 0.0],
- [0.0, 0.0, 64.0, 64.0, 45.0],
- [0.0, 0.0, 32.0, 128.0, 0.0],
- [0.0, 0.0, 32.0, 128.0, 45.0],
- [4.0, 0.0, 64.0, 16.0, 0.0], # 4.0 == 0.0 + STRIDE (4)
- [4.0, 0.0, 64.0, 16.0, 45.0],
- [4.0, 0.0, 32.0, 32.0, 0.0],
- [4.0, 0.0, 32.0, 32.0, 45.0],
- [4.0, 0.0, 16.0, 64.0, 0.0],
- [4.0, 0.0, 16.0, 64.0, 45.0],
- [4.0, 0.0, 128.0, 32.0, 0.0],
- [4.0, 0.0, 128.0, 32.0, 45.0],
- [4.0, 0.0, 64.0, 64.0, 0.0],
- [4.0, 0.0, 64.0, 64.0, 45.0],
- [4.0, 0.0, 32.0, 128.0, 0.0],
- [4.0, 0.0, 32.0, 128.0, 45.0],
- ]
- )
-
- self.assertTrue(torch.allclose(anchors[0].tensor, expected_anchor_tensor))
-
-
-if __name__ == "__main__":
- unittest.main()
diff --git a/spaces/ThirdEyeData/Network_Data_Anomaly/README.md b/spaces/ThirdEyeData/Network_Data_Anomaly/README.md
deleted file mode 100644
index 2331ac816a9361eff4741f46123f44b7d0570710..0000000000000000000000000000000000000000
--- a/spaces/ThirdEyeData/Network_Data_Anomaly/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Network Data Anomaly
-emoji: 🐢
-colorFrom: yellow
-colorTo: red
-sdk: streamlit
-sdk_version: 1.15.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Tune-A-Video-library/Tune-A-Video-Training-UI/style.css b/spaces/Tune-A-Video-library/Tune-A-Video-Training-UI/style.css
deleted file mode 100644
index c4739b4ea5fc35e774a049e3dacc443f7f0eac19..0000000000000000000000000000000000000000
--- a/spaces/Tune-A-Video-library/Tune-A-Video-Training-UI/style.css
+++ /dev/null
@@ -1,3 +0,0 @@
-h1 {
- text-align: center;
-}
diff --git a/spaces/TushDeMort/yolo/weights.py b/spaces/TushDeMort/yolo/weights.py
deleted file mode 100644
index 98e3bb4253e7be0465048f9748b58edc6d1c5b4a..0000000000000000000000000000000000000000
--- a/spaces/TushDeMort/yolo/weights.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import requests
-import os.path
-
-def weights():
-
- path = 'models/yolov7.pt'
-
- check_file = os.path.isfile(path)
-
- if check_file == True:
- pass
- else:
- URL = "https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt"
- response = requests.get(URL)
- open(path, "wb").write(response.content)
-
-if __name__ == "__main__":
- try:
- weights()
- except Exception as e:
- print(f'error is {e}')
diff --git a/spaces/TwoCH4/White-box-Cartoonization/wbc/cartoonize.py b/spaces/TwoCH4/White-box-Cartoonization/wbc/cartoonize.py
deleted file mode 100644
index 25faf1ceb95aaed9a3f7a7982d17a03dc6bc32b1..0000000000000000000000000000000000000000
--- a/spaces/TwoCH4/White-box-Cartoonization/wbc/cartoonize.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import os
-import cv2
-import numpy as np
-import tensorflow as tf
-import wbc.network as network
-import wbc.guided_filter as guided_filter
-from tqdm import tqdm
-
-
-def resize_crop(image):
- h, w, c = np.shape(image)
- if min(h, w) > 720:
- if h > w:
- h, w = int(720 * h / w), 720
- else:
- h, w = 720, int(720 * w / h)
- image = cv2.resize(image, (w, h),
- interpolation=cv2.INTER_AREA)
- h, w = (h // 8) * 8, (w // 8) * 8
- image = image[:h, :w, :]
- return image
-
-
-def cartoonize(load_folder, save_folder, model_path):
- print(model_path)
- input_photo = tf.placeholder(tf.float32, [1, None, None, 3])
- network_out = network.unet_generator(input_photo)
- final_out = guided_filter.guided_filter(input_photo, network_out, r=1, eps=5e-3)
-
- all_vars = tf.trainable_variables()
- gene_vars = [var for var in all_vars if 'generator' in var.name]
- saver = tf.train.Saver(var_list=gene_vars)
-
- config = tf.ConfigProto()
- config.gpu_options.allow_growth = True
- sess = tf.Session(config=config)
-
- sess.run(tf.global_variables_initializer())
- saver.restore(sess, tf.train.latest_checkpoint(model_path))
- name_list = os.listdir(load_folder)
- for name in tqdm(name_list):
- try:
- load_path = os.path.join(load_folder, name)
- save_path = os.path.join(save_folder, name)
- image = cv2.imread(load_path)
- image = resize_crop(image)
- batch_image = image.astype(np.float32) / 127.5 - 1
- batch_image = np.expand_dims(batch_image, axis=0)
- output = sess.run(final_out, feed_dict={input_photo: batch_image})
- output = (np.squeeze(output) + 1) * 127.5
- output = np.clip(output, 0, 255).astype(np.uint8)
- cv2.imwrite(save_path, output)
- except:
- print('cartoonize {} failed'.format(load_path))
-
-
-class Cartoonize:
- def __init__(self, model_path):
- print(model_path)
- self.input_photo = tf.placeholder(tf.float32, [1, None, None, 3])
- network_out = network.unet_generator(self.input_photo)
- self.final_out = guided_filter.guided_filter(self.input_photo, network_out, r=1, eps=5e-3)
-
- all_vars = tf.trainable_variables()
- gene_vars = [var for var in all_vars if 'generator' in var.name]
- saver = tf.train.Saver(var_list=gene_vars)
-
- config = tf.ConfigProto()
- config.gpu_options.allow_growth = True
- self.sess = tf.Session(config=config)
-
- self.sess.run(tf.global_variables_initializer())
- saver.restore(self.sess, tf.train.latest_checkpoint(model_path))
-
- def run(self, load_folder, save_folder):
- name_list = os.listdir(load_folder)
- for name in tqdm(name_list):
- try:
- load_path = os.path.join(load_folder, name)
- save_path = os.path.join(save_folder, name)
- image = cv2.imread(load_path)
- image = resize_crop(image)
- batch_image = image.astype(np.float32) / 127.5 - 1
- batch_image = np.expand_dims(batch_image, axis=0)
- output = self.sess.run(self.final_out, feed_dict={self.input_photo: batch_image})
- output = (np.squeeze(output) + 1) * 127.5
- output = np.clip(output, 0, 255).astype(np.uint8)
- cv2.imwrite(save_path, output)
- except:
- print('cartoonize {} failed'.format(load_path))
-
- def run_sigle(self, load_path, save_path):
- try:
- image = cv2.imread(load_path)
- image = resize_crop(image)
- batch_image = image.astype(np.float32) / 127.5 - 1
- batch_image = np.expand_dims(batch_image, axis=0)
- output = self.sess.run(self.final_out, feed_dict={self.input_photo: batch_image})
- output = (np.squeeze(output) + 1) * 127.5
- output = np.clip(output, 0, 255).astype(np.uint8)
- cv2.imwrite(save_path, output)
- except:
- print('cartoonize {} failed'.format(load_path))
-
-
-if __name__ == '__main__':
- model_path = 'saved_models'
- load_folder = 'test_images'
- save_folder = 'cartoonized_images'
- if not os.path.exists(save_folder):
- os.mkdir(save_folder)
- cartoonize(load_folder, save_folder, model_path)
diff --git a/spaces/Ukrania/RVC-Models/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py b/spaces/Ukrania/RVC-Models/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py
deleted file mode 100644
index ee3171bcb7c4a5066560723108b56e055f18be45..0000000000000000000000000000000000000000
--- a/spaces/Ukrania/RVC-Models/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py
+++ /dev/null
@@ -1,90 +0,0 @@
-from lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-import pyworld
-import numpy as np
-
-
-class DioF0Predictor(F0Predictor):
- def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
- self.hop_length = hop_length
- self.f0_min = f0_min
- self.f0_max = f0_max
- self.sampling_rate = sampling_rate
-
- def interpolate_f0(self, f0):
- """
- 对F0进行插值处理
- """
-
- data = np.reshape(f0, (f0.size, 1))
-
- vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
- vuv_vector[data > 0.0] = 1.0
- vuv_vector[data <= 0.0] = 0.0
-
- ip_data = data
-
- frame_number = data.size
- last_value = 0.0
- for i in range(frame_number):
- if data[i] <= 0.0:
- j = i + 1
- for j in range(i + 1, frame_number):
- if data[j] > 0.0:
- break
- if j < frame_number - 1:
- if last_value > 0.0:
- step = (data[j] - data[i - 1]) / float(j - i)
- for k in range(i, j):
- ip_data[k] = data[i - 1] + step * (k - i + 1)
- else:
- for k in range(i, j):
- ip_data[k] = data[j]
- else:
- for k in range(i, frame_number):
- ip_data[k] = last_value
- else:
- ip_data[i] = data[i] # 这里可能存在一个没有必要的拷贝
- last_value = data[i]
-
- return ip_data[:, 0], vuv_vector[:, 0]
-
- def resize_f0(self, x, target_len):
- source = np.array(x)
- source[source < 0.001] = np.nan
- target = np.interp(
- np.arange(0, len(source) * target_len, len(source)) / target_len,
- np.arange(0, len(source)),
- source,
- )
- res = np.nan_to_num(target)
- return res
-
- def compute_f0(self, wav, p_len=None):
- if p_len is None:
- p_len = wav.shape[0] // self.hop_length
- f0, t = pyworld.dio(
- wav.astype(np.double),
- fs=self.sampling_rate,
- f0_floor=self.f0_min,
- f0_ceil=self.f0_max,
- frame_period=1000 * self.hop_length / self.sampling_rate,
- )
- f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
- for index, pitch in enumerate(f0):
- f0[index] = round(pitch, 1)
- return self.interpolate_f0(self.resize_f0(f0, p_len))[0]
-
- def compute_f0_uv(self, wav, p_len=None):
- if p_len is None:
- p_len = wav.shape[0] // self.hop_length
- f0, t = pyworld.dio(
- wav.astype(np.double),
- fs=self.sampling_rate,
- f0_floor=self.f0_min,
- f0_ceil=self.f0_max,
- frame_period=1000 * self.hop_length / self.sampling_rate,
- )
- f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
- for index, pitch in enumerate(f0):
- f0[index] = round(pitch, 1)
- return self.interpolate_f0(self.resize_f0(f0, p_len))
diff --git a/spaces/Vasanthgx/Cats_vs_Dogs_vasanth/README.md b/spaces/Vasanthgx/Cats_vs_Dogs_vasanth/README.md
deleted file mode 100644
index f5e718271757ae76312a4976dfdc8b744fa240b9..0000000000000000000000000000000000000000
--- a/spaces/Vasanthgx/Cats_vs_Dogs_vasanth/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Cats Vs Dogs Vasanth
-emoji: 🚀
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Vegecken/sovits4dzl/modules/mel_processing.py b/spaces/Vegecken/sovits4dzl/modules/mel_processing.py
deleted file mode 100644
index 99c5b35beb83f3b288af0fac5b49ebf2c69f062c..0000000000000000000000000000000000000000
--- a/spaces/Vegecken/sovits4dzl/modules/mel_processing.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import math
-import os
-import random
-import torch
-from torch import nn
-import torch.nn.functional as F
-import torch.utils.data
-import numpy as np
-import librosa
-import librosa.util as librosa_util
-from librosa.util import normalize, pad_center, tiny
-from scipy.signal import get_window
-from scipy.io.wavfile import read
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
- """
- PARAMS
- ------
- C: compression factor
- """
- return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
- """
- PARAMS
- ------
- C: compression factor used to compress
- """
- return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
- output = dynamic_range_compression_torch(magnitudes)
- return output
-
-
-def spectral_de_normalize_torch(magnitudes):
- output = dynamic_range_decompression_torch(magnitudes)
- return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
- if torch.min(y) < -1.:
- print('min value is ', torch.min(y))
- if torch.max(y) > 1.:
- print('max value is ', torch.max(y))
-
- global hann_window
- dtype_device = str(y.dtype) + '_' + str(y.device)
- wnsize_dtype_device = str(win_size) + '_' + dtype_device
- if wnsize_dtype_device not in hann_window:
- hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
- y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
- y = y.squeeze(1)
-
- spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
- center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
- spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
- return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
- global mel_basis
- dtype_device = str(spec.dtype) + '_' + str(spec.device)
- fmax_dtype_device = str(fmax) + '_' + dtype_device
- if fmax_dtype_device not in mel_basis:
- mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
- mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
- spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
- spec = spectral_normalize_torch(spec)
- return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
- if torch.min(y) < -1.:
- print('min value is ', torch.min(y))
- if torch.max(y) > 1.:
- print('max value is ', torch.max(y))
-
- global mel_basis, hann_window
- dtype_device = str(y.dtype) + '_' + str(y.device)
- fmax_dtype_device = str(fmax) + '_' + dtype_device
- wnsize_dtype_device = str(win_size) + '_' + dtype_device
- if fmax_dtype_device not in mel_basis:
- mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
- mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
- if wnsize_dtype_device not in hann_window:
- hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
- y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
- y = y.squeeze(1)
-
- spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
- center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
- spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
- spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
- spec = spectral_normalize_torch(spec)
-
- return spec
diff --git a/spaces/VideoCrafter/VideoCrafter/demo_test.py b/spaces/VideoCrafter/VideoCrafter/demo_test.py
deleted file mode 100644
index 759039753e6c8caa3eb5507690e68a07e50023c7..0000000000000000000000000000000000000000
--- a/spaces/VideoCrafter/VideoCrafter/demo_test.py
+++ /dev/null
@@ -1,16 +0,0 @@
-class Text2Video():
- def __init__(self, result_dir='./tmp/') -> None:
- pass
-
- def get_prompt(self, input_text, steps=50, cfg_scale=15.0, eta=1.0, fps=16):
-
- return '01.mp4'
-
-class Image2Video:
- def __init__(self, result_dir='./tmp/') -> None:
- pass
-
- def get_image(self, input_image, input_prompt, i2v_steps=50, i2v_cfg_scale=15.0, i2v_eta=1.0, i2v_fps=16):
-
- return '01.mp4'
-
\ No newline at end of file
diff --git a/spaces/Writer/token-counter/README.md b/spaces/Writer/token-counter/README.md
deleted file mode 100644
index 45b4051524dd319c765367c7c1c118f11157bbaf..0000000000000000000000000000000000000000
--- a/spaces/Writer/token-counter/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Token Counter
-emoji: 📈
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/general_optimizer.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/general_optimizer.py
deleted file mode 100644
index f6a0487d582fe6264627d302d6580364affdf754..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/general_optimizer.py
+++ /dev/null
@@ -1,139 +0,0 @@
-from .torch_core import *
-from torch.optim import Optimizer
-import types
-
-__all__ = ['StatScope', 'Statistic', 'ConstStatistic', 'AvgStatistic', 'AvgSquare', 'GeneralOptimizer']
-
-StatScope = Enum('StatScope', 'Global Group Layer Channel Weight')
-
-@dataclass
-class Statistic():
- name:str
- param:float=0.9 # e.g. for exp moving average
- scope:StatScope=StatScope.Weight
- init:float=0. # starting value
-
- @property
- def buf(self): return f'{self.name}_buffer'
-
- def new_step(self):
- "Set state when computing statistics for Global or Group"
- raise NotImplementedError
-
- def accumulate(self, val):
- "Add `val` to statistic"
- raise NotImplementedError
-
- def update(self, state, param, val=None, step=None):
- "Update state with accumlated, or `val` (if `Weight` or `Layer` scope)"
- raise NotImplementedError
-
-class ConstStatistic(Statistic):
- @property
- def buf(self): return None
- def new_step(self): pass
- def accumulate(self): pass
- def update(self, state, param, val=None, step=None): return param
-
-@dataclass
-class CounterStat(Statistic):
- def __post_init__(self): self.init,self._buf,self.name = 0,self.name,None
- @property
- def buf(self): return self._buf
- def new_step(self): pass
- def accumulate(self, val): pass
- def update(self, state, param, val=None, step=None): return state + 1
-
-@dataclass
-class AvgStatistic(Statistic):
- decay:bool=False
- debias:bool=False
- def new_step(self): self.val,self.count = 0.,0
-
- def accumulate(self, val):
- self.count += 1
- self.val += self._get_val1(val)
-
- def _get_val1(self, val): return val.mean()
- def _get_val2(self, state, val, param): return state.add_(1-param, val) if self.decay else state.add_(val)
- def _get_val3(self, state, val, param):
- v = val.view(val.size(0), -1).mean(1)
- return state.add_(1-param, v) if self.decay else state.add_(v)
-
- def update(self, state, param, val=None, step=None):
- if self.scope == StatScope.Weight:
- # `state` is a tensor
- res = self._get_val2(state.mul_(param), val, param)
- elif self.scope == StatScope.Channel:
- # `state` is a tensor of size n_channels
- res = self._get_val3(state.mul_(param), val, param)
- # For everything else, `state` is a scalar
- elif self.scope == StatScope.Layer: res = state*param + self._get_val1(val) * (1-param if self.decay else 1.)
- elif self.count != 0: res = state*param + self.val/self.count * (1-param if self.decay else 1.)
- else: return state
- if self.debias and step is not None: res /= (1 - param ** step)
- return res
-
-class AvgSquare(AvgStatistic):
-
- def __init__(self, name:str, param:float=0.9, scope=StatScope.Weight, init:float=0., decay:bool=True, debias:bool=False):
- super().__init__(name, param=param, scope=scope, init=init, decay=decay, debias=debias)
-
- def _get_val1(self, val): return torch.norm(val).pow(2)/val.numel()
- def _get_val2(self, state, val, param):
- return state.addcmul_(1-param, val, val) if self.decay else state.addcmul_(val, val)
- def _get_val3(self, state, val, param):
- v = val.view(val.size(0), -1).mean(1)
- return state.addcmul_(1-param, v, v) if self.decay else state.addcmul_(v, v)
-
-class GeneralOptimizer(Optimizer):
- def __init__(self, params, stats=None, on_step:Callable=None):
- defaults = {s.name:s.param for s in listify(stats) if s.name is not None}
- super().__init__(params, defaults)
- self.global_stats,self.group_stats,self.layer_stats,self.channel_stats,self.weight_stats = self._split_stats(stats)
- self.init_stats()
- if on_step is not None: self.on_step = types.MethodType(on_step, self)
-
- def step(self, closure=None):
- self.update_stats()
- for i,pg in enumerate(self.param_groups):
- for p in pg['params']:
- if p.grad is not None: self.on_step(p, pg, i)
-
- def on_step(self, p, group, group_idx): p.data.add_(-group['lr'], p.grad.data)
-
- def _split_stats(self, stats):
- splits = [[stat for stat in listify(stats) if stat.scope==scope] for scope in StatScope]
- for split,s in zip([splits[0], splits[1], splits[2]+splits[3]+splits[4]], StatScope):
- if np.any([getattr(s, 'debias', False) for s in split]): split.insert(0, CounterStat('step', scope=s))
- return splits
-
- def _init_stats(self, stats, data=None):
- return {stat.buf: stat.init if data is None
- else torch.zeros_like(data) + stat.init for stat in stats if stat.buf is not None}
-
- def init_stats(self):
- self.state['global'] = self._init_stats(self.global_stats)
- for i,pg in enumerate(self.param_groups):
- self.state[f'group{i}'] = self._init_stats(self.group_stats)
- for p in pg['params']:
- self.state[p] = self._init_stats(self.layer_stats)
- self.state[p].update(self._init_stats(self.channel_stats, p.data.view(p.data.size(0), -1).mean(1)))
- self.state[p].update(self._init_stats(self.weight_stats, p.data))
-
- def _set_bufs(self, p, stats, pg, val=None):
- d = self.state[p]
- for stat in stats:
- if stat.buf is not None: d[stat.buf] = stat.update(d[stat.buf], pg[stat.name], val=val, step=d.get('step', None))
-
- def update_stats(self):
- for stat in self.global_stats: stat.new_step()
- for i,pg in enumerate(self.param_groups):
- for stat in self.group_stats: stat.new_step()
- for p in pg['params']:
- if p.grad is not None:
- for stat in self.global_stats + self.group_stats: stat.accumulate(p.grad.data)
- self._set_bufs(p, self.layer_stats+self.channel_stats+self.weight_stats, pg, p.grad.data)
- self._set_bufs(f'group{i}', self.group_stats, pg)
- self._set_bufs('global', self.global_stats, self.param_groups[0])
-
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/torch_core.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/torch_core.py
deleted file mode 100644
index 6b089e09e4e08c2b6d50b70ef3223fadae2f48cb..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/torch_core.py
+++ /dev/null
@@ -1,430 +0,0 @@
-"Utility functions to help deal with tensors"
-from .imports.torch import *
-from .core import *
-from collections import OrderedDict
-from torch.nn.parallel import DistributedDataParallel
-
-AffineMatrix = Tensor
-BoolOrTensor = Union[bool,Tensor]
-FloatOrTensor = Union[float,Tensor]
-IntOrTensor = Union[int,Tensor]
-ItemsList = Collection[Union[Tensor,ItemBase,'ItemsList',float,int]]
-LambdaFunc = Callable[[Tensor],Tensor]
-LayerFunc = Callable[[nn.Module],None]
-ModuleList = Collection[nn.Module]
-NPArray = np.ndarray
-OptOptimizer = Optional[optim.Optimizer]
-ParamList = Collection[nn.Parameter]
-Rank0Tensor = NewType('OneEltTensor', Tensor)
-SplitFunc = Callable[[nn.Module], List[nn.Module]]
-SplitFuncOrIdxList = Union[Callable, Collection[ModuleList]]
-TensorOrNumber = Union[Tensor,Number]
-TensorOrNumList = Collection[TensorOrNumber]
-TensorImage = Tensor
-TensorImageSize = Tuple[int,int,int]
-Tensors = Union[Tensor, Collection['Tensors']]
-Weights = Dict[str,Tensor]
-
-AffineFunc = Callable[[KWArgs], AffineMatrix]
-HookFunc = Callable[[nn.Module, Tensors, Tensors], Any]
-LogitTensorImage = TensorImage
-LossFunction = Callable[[Tensor, Tensor], Rank0Tensor]
-MetricFunc = Callable[[Tensor,Tensor],TensorOrNumber]
-MetricFuncList = Collection[MetricFunc]
-MetricsList = Collection[TensorOrNumber]
-OptLossFunc = Optional[LossFunction]
-OptMetrics = Optional[MetricsList]
-OptSplitFunc = Optional[SplitFunc]
-PixelFunc = Callable[[TensorImage, ArgStar, KWArgs], TensorImage]
-
-LightingFunc = Callable[[LogitTensorImage, ArgStar, KWArgs], LogitTensorImage]
-
-fastai_types = {
- AnnealFunc:'AnnealFunc', ArgStar:'ArgStar', BatchSamples:'BatchSamples',
- FilePathList:'FilePathList', Floats:'Floats', ImgLabel:'ImgLabel', ImgLabels:'ImgLabels', KeyFunc:'KeyFunc',
- KWArgs:'KWArgs', ListOrItem:'ListOrItem', ListRules:'ListRules', ListSizes:'ListSizes',
- NPArrayableList:'NPArrayableList', NPArrayList:'NPArrayList', NPArrayMask:'NPArrayMask', NPImage:'NPImage',
- OptDataFrame:'OptDataFrame', OptListOrItem:'OptListOrItem', OptRange:'OptRange', OptStrTuple:'OptStrTuple',
- OptStats:'OptStats', PathOrStr:'PathOrStr', PBar:'PBar', Point:'Point', Points:'Points', Sizes:'Sizes',
- SplitArrayList:'SplitArrayList', StartOptEnd:'StartOptEnd', StrList:'StrList', Tokens:'Tokens',
- OptStrList:'OptStrList', AffineMatrix:'AffineMatrix', BoolOrTensor:'BoolOrTensor', FloatOrTensor:'FloatOrTensor',
- IntOrTensor:'IntOrTensor', ItemsList:'ItemsList', LambdaFunc:'LambdaFunc',
- LayerFunc:'LayerFunc', ModuleList:'ModuleList', OptOptimizer:'OptOptimizer', ParamList:'ParamList',
- Rank0Tensor:'Rank0Tensor', SplitFunc:'SplitFunc', SplitFuncOrIdxList:'SplitFuncOrIdxList',
- TensorOrNumber:'TensorOrNumber', TensorOrNumList:'TensorOrNumList', TensorImage:'TensorImage',
- TensorImageSize:'TensorImageSize', Tensors:'Tensors', Weights:'Weights', AffineFunc:'AffineFunc',
- HookFunc:'HookFunc', LogitTensorImage:'LogitTensorImage', LossFunction:'LossFunction', MetricFunc:'MetricFunc',
- MetricFuncList:'MetricFuncList', MetricsList:'MetricsList', OptLossFunc:'OptLossFunc', OptMetrics:'OptMetrics',
- OptSplitFunc:'OptSplitFunc', PixelFunc:'PixelFunc', LightingFunc:'LightingFunc', IntsOrStrs:'IntsOrStrs',
- PathLikeOrBinaryStream:'PathLikeOrBinaryStream'
-}
-
-bn_types = (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d)
-bias_types = (nn.Linear, nn.Conv1d, nn.Conv2d, nn.Conv3d, nn.ConvTranspose1d, nn.ConvTranspose2d, nn.ConvTranspose3d)
-def is_pool_type(l:Callable): return re.search(r'Pool[123]d$', l.__class__.__name__)
-no_wd_types = bn_types + (nn.LayerNorm,)
-defaults.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
-AdamW = partial(optim.Adam, betas=(0.9,0.99))
-
-#Monkey-patch `torch.cuda.set_device` so that it updates `defaults.device`
-_old_torch_cuda_set_device = torch.cuda.set_device
-def _new_torch_cuda_set_device(device):
- _old_torch_cuda_set_device(device)
- defaults.device = torch.device('cuda', device) if isinstance(device, int) else device
-torch.cuda.set_device = _new_torch_cuda_set_device
-
-def tensor(x:Any, *rest)->Tensor:
- "Like `torch.as_tensor`, but handle lists too, and can pass multiple vector elements directly."
- if len(rest): x = (x,)+rest
- # XXX: Pytorch bug in dataloader using num_workers>0; TODO: create repro and report
- if is_listy(x) and len(x)==0: return tensor(0)
- res = torch.tensor(x) if is_listy(x) else as_tensor(x)
- if res.dtype is torch.int32:
- warn('Tensor is int32: upgrading to int64; for better performance use int64 input')
- return res.long()
- return res
-
-class Module(nn.Module, metaclass=PrePostInitMeta):
- "Same as `nn.Module`, but no need for subclasses to call `super().__init__`"
- def __pre_init__(self): super().__init__()
- def __init__(self): pass
-
-def np_address(x:np.ndarray)->int:
- "Address of `x` in memory."
- return x.__array_interface__['data'][0]
-
-def to_detach(b:Tensors, cpu:bool=True):
- "Recursively detach lists of tensors in `b `; put them on the CPU if `cpu=True`."
- def _inner(x, cpu=True):
- if not isinstance(x,Tensor): return x
- x = x.detach()
- return x.cpu() if cpu else x
- return recurse(_inner, b, cpu=cpu)
-
-def to_data(b:ItemsList):
- "Recursively map lists of items in `b ` to their wrapped data."
- return recurse(lambda x: x.data if isinstance(x,ItemBase) else x, b)
-
-def to_cpu(b:ItemsList):
- "Recursively map lists of tensors in `b ` to the cpu."
- return recurse(lambda x: x.cpu() if isinstance(x,Tensor) else x, b)
-
-def to_half(b:Collection[Tensor])->Collection[Tensor]:
- "Recursively map lists of tensors in `b ` to FP16."
- return recurse(lambda x: x.half() if x.dtype not in [torch.int64, torch.int32, torch.int16] else x, b)
-
-def to_float(b:Collection[Tensor])->Collection[Tensor]:
- "Recursively map lists of tensors in `b ` to FP16."
- return recurse(lambda x: x.float() if x.dtype not in [torch.int64, torch.int32, torch.int16] else x, b)
-
-def to_device(b:Tensors, device:torch.device):
- "Recursively put `b` on `device`."
- device = ifnone(device, defaults.device)
- return recurse(lambda x: x.to(device, non_blocking=True), b)
-
-def data_collate(batch:ItemsList)->Tensor:
- "Convert `batch` items to tensor data."
- return torch.utils.data.dataloader.default_collate(to_data(batch))
-
-def requires_grad(m:nn.Module, b:Optional[bool]=None)->Optional[bool]:
- "If `b` is not set return `requires_grad` of first param, else set `requires_grad` on all params as `b`"
- ps = list(m.parameters())
- if not ps: return None
- if b is None: return ps[0].requires_grad
- for p in ps: p.requires_grad=b
-
-def trainable_params(m:nn.Module)->ParamList:
- "Return list of trainable params in `m`."
- res = filter(lambda p: p.requires_grad, m.parameters())
- return res
-
-def children(m:nn.Module)->ModuleList:
- "Get children of `m`."
- return list(m.children())
-
-def num_children(m:nn.Module)->int:
- "Get number of children modules in `m`."
- return len(children(m))
-
-def range_children(m:nn.Module)->Iterator[int]:
- "Return iterator of len of children of `m`."
- return range(num_children(m))
-
-class ParameterModule(Module):
- "Register a lone parameter `p` in a module."
- def __init__(self, p:nn.Parameter): self.val = p
- def forward(self, x): return x
-
-def children_and_parameters(m:nn.Module):
- "Return the children of `m` and its direct parameters not registered in modules."
- children = list(m.children())
- children_p = sum([[id(p) for p in c.parameters()] for c in m.children()],[])
- for p in m.parameters():
- if id(p) not in children_p: children.append(ParameterModule(p))
- return children
-
-def flatten_model(m:nn.Module):
- if num_children(m):
- mapped = map(flatten_model,children_and_parameters(m))
- return sum(mapped,[])
- else:
- return [m]
-
-#flatten_model = lambda m: sum(map(flatten_model,children_and_parameters(m)),[]) if num_children(m) else [m]
-
-def first_layer(m:nn.Module)->nn.Module:
- "Retrieve first layer in a module `m`."
- return flatten_model(m)[0]
-
-def last_layer(m:nn.Module)->nn.Module:
- "Retrieve last layer in a module `m`."
- return flatten_model(m)[-1]
-
-def split_model_idx(model:nn.Module, idxs:Collection[int])->ModuleList:
- "Split `model` according to the indexes in `idxs`."
- layers = flatten_model(model)
- if idxs[0] != 0: idxs = [0] + idxs
- if idxs[-1] != len(layers): idxs.append(len(layers))
- return [nn.Sequential(*layers[i:j]) for i,j in zip(idxs[:-1],idxs[1:])]
-
-def split_model(model:nn.Module=None, splits:Collection[Union[nn.Module,ModuleList]]=None):
- "Split `model` according to the layers in `splits`."
- splits = listify(splits)
- if isinstance(splits[0], nn.Module):
- layers = flatten_model(model)
- idxs = [layers.index(first_layer(s)) for s in splits]
- return split_model_idx(model, idxs)
- return [nn.Sequential(*s) for s in splits]
-
-def get_param_groups(layer_groups:Collection[nn.Module])->List[List[nn.Parameter]]:
- return [sum([list(trainable_params(c)) for c in l.children()], []) for l in layer_groups]
-
-def split_no_wd_params(layer_groups:Collection[nn.Module])->List[List[nn.Parameter]]:
- "Separate the parameters in `layer_groups` between `no_wd_types` and bias (`bias_types`) from the rest."
- split_params = []
- for l in layer_groups:
- l1,l2 = [],[]
- for c in l.children():
- if isinstance(c, no_wd_types): l2 += list(trainable_params(c))
- elif isinstance(c, bias_types):
- bias = c.bias if hasattr(c, 'bias') else None
- l1 += [p for p in trainable_params(c) if not (p is bias)]
- if bias is not None: l2.append(bias)
- else: l1 += list(trainable_params(c))
- #Since we scan the children separately, we might get duplicates (tied weights). We need to preserve the order
- #for the optimizer load of state_dict
- l1,l2 = uniqueify(l1),uniqueify(l2)
- split_params += [l1, l2]
- return split_params
-
-def set_bn_eval(m:nn.Module)->None:
- "Set bn layers in eval mode for all recursive children of `m`."
- for l in m.children():
- if isinstance(l, bn_types) and not next(l.parameters()).requires_grad:
- l.eval()
- set_bn_eval(l)
-
-def batch_to_half(b:Collection[Tensor])->Collection[Tensor]:
- "Set the input of batch `b` to half precision."
- return [to_half(b[0]), b[1]]
-
-def bn2float(module:nn.Module)->nn.Module:
- "If `module` is batchnorm don't use half precision."
- if isinstance(module, torch.nn.modules.batchnorm._BatchNorm): module.float()
- for child in module.children(): bn2float(child)
- return module
-
-def model2half(model:nn.Module)->nn.Module:
- "Convert `model` to half precision except the batchnorm layers."
- return bn2float(model.half())
-
-def init_default(m:nn.Module, func:LayerFunc=nn.init.kaiming_normal_)->nn.Module:
- "Initialize `m` weights with `func` and set `bias` to 0."
- if func:
- if hasattr(m, 'weight'): func(m.weight)
- if hasattr(m, 'bias') and hasattr(m.bias, 'data'): m.bias.data.fill_(0.)
- return m
-
-def cond_init(m:nn.Module, init_func:LayerFunc):
- "Initialize the non-batchnorm layers of `m` with `init_func`."
- if (not isinstance(m, bn_types)) and requires_grad(m): init_default(m, init_func)
-
-def apply_leaf(m:nn.Module, f:LayerFunc):
- "Apply `f` to children of `m`."
- c = children(m)
- if isinstance(m, nn.Module): f(m)
- for l in c: apply_leaf(l,f)
-
-def apply_init(m, init_func:LayerFunc):
- "Initialize all non-batchnorm layers of `m` with `init_func`."
- apply_leaf(m, partial(cond_init, init_func=init_func))
-
-def in_channels(m:nn.Module) -> List[int]:
- "Return the shape of the first weight layer in `m`."
- for l in flatten_model(m):
- if hasattr(l, 'weight'): return l.weight.shape[1]
- raise Exception('No weight layer')
-
-class ModelOnCPU():
- "A context manager to evaluate `model` on the CPU inside."
- def __init__(self, model:nn.Module): self.model = model
- def __enter__(self):
- self.device = one_param(self.model).device
- return self.model.cpu()
- def __exit__(self, type, value, traceback):
- self.model = self.model.to(self.device)
-
-class NoneReduceOnCPU():
- "A context manager to evaluate `loss_func` with none reduce and weights on the CPU inside."
- def __init__(self, loss_func:LossFunction):
- self.loss_func,self.device,self.old_red = loss_func,None,None
-
- def __enter__(self):
- if hasattr(self.loss_func, 'weight') and self.loss_func.weight is not None:
- self.device = self.loss_func.weight.device
- self.loss_func.weight = self.loss_func.weight.cpu()
- if hasattr(self.loss_func, 'reduction'):
- self.old_red = getattr(self.loss_func, 'reduction')
- setattr(self.loss_func, 'reduction', 'none')
- return self.loss_func
- else: return partial(self.loss_func, reduction='none')
-
- def __exit__(self, type, value, traceback):
- if self.device is not None: self.loss_func.weight = self.loss_func.weight.to(self.device)
- if self.old_red is not None: setattr(self.loss_func, 'reduction', self.old_red)
-
-def model_type(dtype):
- "Return the torch type corresponding to `dtype`."
- return (torch.float32 if np.issubdtype(dtype, np.floating) else
- torch.int64 if np.issubdtype(dtype, np.integer)
- else None)
-
-def np2model_tensor(a):
- "Tranform numpy array `a` to a tensor of the same type."
- dtype = model_type(a.dtype)
- res = as_tensor(a)
- if not dtype: return res
- return res.type(dtype)
-
-def _pca(x, k=2):
- "Compute PCA of `x` with `k` dimensions."
- x = x-torch.mean(x,0)
- U,S,V = torch.svd(x.t())
- return torch.mm(x,U[:,:k])
-torch.Tensor.pca = _pca
-
-def trange_of(x):
- "Create a tensor from `range_of(x)`."
- return torch.arange(len(x))
-
-def to_np(x):
- "Convert a tensor to a numpy array."
- return x.data.cpu().numpy()
-
-# monkey patching to allow matplotlib to plot tensors
-def tensor__array__(self, dtype=None):
- res = to_np(self)
- if dtype is None: return res
- else: return res.astype(dtype, copy=False)
-Tensor.__array__ = tensor__array__
-Tensor.ndim = property(lambda x: len(x.shape))
-
-def grab_idx(x,i,batch_first:bool=True):
- "Grab the `i`-th batch in `x`, `batch_first` stating the batch dimension."
- if batch_first: return ([o[i].cpu() for o in x] if is_listy(x) else x[i].cpu())
- else: return ([o[:,i].cpu() for o in x] if is_listy(x) else x[:,i].cpu())
-
-def logit(x:Tensor)->Tensor:
- "Logit of `x`, clamped to avoid inf."
- x = x.clamp(1e-7, 1-1e-7)
- return -(1/x-1).log()
-
-def logit_(x:Tensor)->Tensor:
- "Inplace logit of `x`, clamped to avoid inf"
- x.clamp_(1e-7, 1-1e-7)
- return (x.reciprocal_().sub_(1)).log_().neg_()
-
-def set_all_seed(seed:int)->None:
- "Sets the seeds for all pseudo random generators in fastai lib"
- np.random.seed(seed)
- torch.manual_seed(seed)
- random.seed(seed)
-
-def uniform(low:Number, high:Number=None, size:Optional[List[int]]=None)->FloatOrTensor:
- "Draw 1 or shape=`size` random floats from uniform dist: min=`low`, max=`high`."
- if high is None: high=low
- return random.uniform(low,high) if size is None else torch.FloatTensor(*listify(size)).uniform_(low,high)
-
-def log_uniform(low, high, size:Optional[List[int]]=None)->FloatOrTensor:
- "Draw 1 or shape=`size` random floats from uniform dist: min=log(`low`), max=log(`high`)."
- res = uniform(log(low), log(high), size)
- return exp(res) if size is None else res.exp_()
-
-def rand_bool(p:float, size:Optional[List[int]]=None)->BoolOrTensor:
- "Draw 1 or shape=`size` random booleans (`True` occuring with probability `p`)."
- return uniform(0,1,size)IntOrTensor:
- "Generate int or tensor `size` of ints between `low` and `high` (included)."
- return random.randint(low,high) if size is None else torch.randint(low,high+1,size)
-
-def one_param(m: nn.Module)->Tensor:
- "Return the first parameter of `m`."
- return next(m.parameters())
-
-def try_int(o:Any)->Any:
- "Try to convert `o` to int, default to `o` if not possible."
- # NB: single-item rank-1 array/tensor can be converted to int, but we don't want to do this
- if isinstance(o, (np.ndarray,Tensor)): return o if o.ndim else int(o)
- if isinstance(o, collections.abc.Sized) or getattr(o,'__array_interface__',False): return o
- try: return int(o)
- except: return o
-
-def get_model(model:nn.Module):
- "Return the model maybe wrapped inside `model`."
- return model.module if isinstance(model, (DistributedDataParallel, nn.DataParallel)) else model
-
-def flatten_check(out:Tensor, targ:Tensor) -> Tensor:
- "Check that `out` and `targ` have the same number of elements and flatten them."
- out,targ = out.contiguous().view(-1),targ.contiguous().view(-1)
- assert len(out) == len(targ), f"Expected output and target to have the same number of elements but got {len(out)} and {len(targ)}."
- return out,targ
-
-#Monkey-patch nn.DataParallel.reset
-def _data_parallel_reset(self):
- if hasattr(self.module, 'reset'): self.module.reset()
-nn.DataParallel.reset = _data_parallel_reset
-
-def remove_module_load(state_dict):
- """create new OrderedDict that does not contain `module.`"""
- new_state_dict = OrderedDict()
- for k, v in state_dict.items(): new_state_dict[k[7:]] = v
- return new_state_dict
-
-def num_distrib():
- "Return the number of processes in distributed training (if applicable)."
- return int(os.environ.get('WORLD_SIZE', 0))
-
-def rank_distrib():
- "Return the distributed rank of this process (if applicable)."
- return int(os.environ.get('RANK', 0))
-
-def add_metrics(last_metrics:Collection[Rank0Tensor], mets:Union[Rank0Tensor, Collection[Rank0Tensor]]):
- "Return a dictionary for updating `last_metrics` with `mets`."
- last_metrics,mets = listify(last_metrics),listify(mets)
- return {'last_metrics': last_metrics + mets}
-
-def try_save(state:Dict, path:Path=None, file:PathLikeOrBinaryStream=None):
- target = open(path/file, 'wb') if is_pathlike(file) else file
- try: torch.save(state, target)
- except OSError as e:
- raise Exception(f"{e}\n Can't write {path/file}. Pass an absolute writable pathlib obj `fname`.")
-
-def np_func(f):
- "Convert a function taking and returning numpy arrays to one taking and returning tensors"
- def _inner(*args, **kwargs):
- nargs = [to_np(arg) if isinstance(arg,Tensor) else arg for arg in args]
- return tensor(f(*nargs, **kwargs))
- functools.update_wrapper(_inner, f)
- return _inner
-
diff --git a/spaces/Xenova/whisper-web/assets/index-6480d07e.js b/spaces/Xenova/whisper-web/assets/index-6480d07e.js
deleted file mode 100644
index fb450a2873f2a1ddd53eba5b492578045c92096e..0000000000000000000000000000000000000000
--- a/spaces/Xenova/whisper-web/assets/index-6480d07e.js
+++ /dev/null
@@ -1,47 +0,0 @@
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- * react.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
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- * LICENSE file in the root directory of this source tree.
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- visible_reply = apply_extensions("output", visible_reply)
- if _continue:
- sep = ' ' if last_reply[0][-1] not in [' ', '\n'] else ''
- reply = last_reply[0] + sep + reply
- sep = ' ' if last_reply[1][-1] not in [' ', '\n'] else ''
- visible_reply = last_reply[1] + sep + visible_reply
-
- # We need this global variable to handle the Stop event,
- # otherwise gradio gets confused
- if shared.stop_everything:
- return shared.history['visible']
-
- if just_started:
- just_started = False
- if not _continue:
- shared.history['internal'].append(['', ''])
- shared.history['visible'].append(['', ''])
-
- shared.history['internal'][-1] = [text, reply]
- shared.history['visible'][-1] = [visible_text, visible_reply]
- yield shared.history['visible']
- if next_character_found:
- break
-
- if reply is not None:
- cumulative_reply = reply
-
- yield shared.history['visible']
-
-
-def impersonate_wrapper(text, state):
- if shared.model_name == 'None' or shared.model is None:
- logging.error("No model is loaded! Select one in the Model tab.")
- yield ''
- return
-
- # Defining some variables
- cumulative_reply = ''
- eos_token = '\n' if state['stop_at_newline'] else None
- prompt = generate_chat_prompt(text, state, impersonate=True)
- stopping_strings = get_stopping_strings(state)
-
- # Yield *Is typing...*
- yield shared.processing_message
- for i in range(state['chat_generation_attempts']):
- reply = None
- for reply in generate_reply(f"{prompt}{' ' if len(cumulative_reply) > 0 else ''}{cumulative_reply}", state, eos_token=eos_token, stopping_strings=stopping_strings):
- reply = cumulative_reply + reply
- reply, next_character_found = extract_message_from_reply(reply, state)
- yield reply
- if next_character_found:
- break
-
- if reply is not None:
- cumulative_reply = reply
-
- yield reply
-
-
-def cai_chatbot_wrapper(text, state):
- for history in chatbot_wrapper(text, state):
- yield chat_html_wrapper(history, state['name1'], state['name2'], state['mode'])
-
-
-def regenerate_wrapper(text, state):
- if (len(shared.history['visible']) == 1 and not shared.history['visible'][0][0]) or len(shared.history['internal']) == 0:
- yield chat_html_wrapper(shared.history['visible'], state['name1'], state['name2'], state['mode'])
- else:
- for history in chatbot_wrapper('', state, regenerate=True):
- yield chat_html_wrapper(history, state['name1'], state['name2'], state['mode'])
-
-
-def continue_wrapper(text, state):
- if (len(shared.history['visible']) == 1 and not shared.history['visible'][0][0]) or len(shared.history['internal']) == 0:
- yield chat_html_wrapper(shared.history['visible'], state['name1'], state['name2'], state['mode'])
- else:
- for history in chatbot_wrapper('', state, _continue=True):
- yield chat_html_wrapper(history, state['name1'], state['name2'], state['mode'])
-
-
-def remove_last_message(name1, name2, mode):
- if len(shared.history['visible']) > 0 and shared.history['internal'][-1][0] != '<|BEGIN-VISIBLE-CHAT|>':
- last = shared.history['visible'].pop()
- shared.history['internal'].pop()
- else:
- last = ['', '']
-
- return chat_html_wrapper(shared.history['visible'], name1, name2, mode), last[0]
-
-
-def send_last_reply_to_input():
- if len(shared.history['internal']) > 0:
- return shared.history['internal'][-1][1]
- else:
- return ''
-
-
-def replace_last_reply(text, name1, name2, mode):
- if len(shared.history['visible']) > 0:
- shared.history['visible'][-1][1] = text
- shared.history['internal'][-1][1] = apply_extensions("input", text)
-
- return chat_html_wrapper(shared.history['visible'], name1, name2, mode)
-
-
-def send_dummy_message(text, name1, name2, mode):
- shared.history['visible'].append([text, ''])
- shared.history['internal'].append([apply_extensions("input", text), ''])
- return chat_html_wrapper(shared.history['visible'], name1, name2, mode)
-
-
-def send_dummy_reply(text, name1, name2, mode):
- if len(shared.history['visible']) > 0 and not shared.history['visible'][-1][1] == '':
- shared.history['visible'].append(['', ''])
- shared.history['internal'].append(['', ''])
-
- shared.history['visible'][-1][1] = text
- shared.history['internal'][-1][1] = apply_extensions("input", text)
- return chat_html_wrapper(shared.history['visible'], name1, name2, mode)
-
-
-def clear_html():
- return chat_html_wrapper([], "", "")
-
-
-def clear_chat_log(name1, name2, greeting, mode):
- shared.history['visible'] = []
- shared.history['internal'] = []
-
- if greeting != '':
- shared.history['internal'] += [['<|BEGIN-VISIBLE-CHAT|>', greeting]]
- shared.history['visible'] += [['', apply_extensions("output", greeting)]]
-
- # Save cleared logs
- save_history(mode)
- return chat_html_wrapper(shared.history['visible'], name1, name2, mode)
-
-
-def redraw_html(name1, name2, mode):
- return chat_html_wrapper(shared.history['visible'], name1, name2, mode)
-
-
-def tokenize_dialogue(dialogue, name1, name2, mode):
- history = []
- messages = []
- dialogue = re.sub('', '', dialogue)
- dialogue = re.sub('', '', dialogue)
- dialogue = re.sub('(\n|^)[Aa]non:', '\\1You:', dialogue)
- dialogue = re.sub('(\n|^)\[CHARACTER\]:', f'\\g<1>{name2}:', dialogue)
- idx = [m.start() for m in re.finditer(f"(^|\n)({re.escape(name1)}|{re.escape(name2)}):", dialogue)]
- if len(idx) == 0:
- return history
-
- for i in range(len(idx) - 1):
- messages.append(dialogue[idx[i]:idx[i + 1]].strip())
-
- messages.append(dialogue[idx[-1]:].strip())
- entry = ['', '']
- for i in messages:
- if i.startswith(f'{name1}:'):
- entry[0] = i[len(f'{name1}:'):].strip()
- elif i.startswith(f'{name2}:'):
- entry[1] = i[len(f'{name2}:'):].strip()
- if not (len(entry[0]) == 0 and len(entry[1]) == 0):
- history.append(entry)
-
- entry = ['', '']
-
- print("\033[1;32;1m\nDialogue tokenized to:\033[0;37;0m\n", end='')
- for row in history:
- for column in row:
- print("\n")
- for line in column.strip().split('\n'):
- print("| " + line + "\n")
-
- print("|\n")
- print("------------------------------")
-
- return history
-
-
-def save_history(mode, timestamp=False):
- # Instruct mode histories should not be saved as if
- # Alpaca or Vicuna were characters
- if mode == 'instruct':
- if not timestamp:
- return
-
- fname = f"Instruct_{datetime.now().strftime('%Y%m%d-%H%M%S')}.json"
- else:
- if timestamp:
- fname = f"{shared.character}_{datetime.now().strftime('%Y%m%d-%H%M%S')}.json"
- else:
- fname = f"{shared.character}_persistent.json"
-
- if not Path('logs').exists():
- Path('logs').mkdir()
-
- with open(Path(f'logs/{fname}'), 'w', encoding='utf-8') as f:
- f.write(json.dumps({'data': shared.history['internal'], 'data_visible': shared.history['visible']}, indent=2))
-
- return Path(f'logs/{fname}')
-
-
-def load_history(file, name1, name2):
- file = file.decode('utf-8')
- try:
- j = json.loads(file)
- if 'data' in j:
- shared.history['internal'] = j['data']
- if 'data_visible' in j:
- shared.history['visible'] = j['data_visible']
- else:
- shared.history['visible'] = copy.deepcopy(shared.history['internal'])
- except:
- shared.history['internal'] = tokenize_dialogue(file, name1, name2)
- shared.history['visible'] = copy.deepcopy(shared.history['internal'])
-
-
-def replace_character_names(text, name1, name2):
- text = text.replace('{{user}}', name1).replace('{{char}}', name2)
- return text.replace('', name1).replace('', name2)
-
-
-def build_pygmalion_style_context(data):
- context = ""
- if 'char_persona' in data and data['char_persona'] != '':
- context += f"{data['char_name']}'s Persona: {data['char_persona']}\n"
-
- if 'world_scenario' in data and data['world_scenario'] != '':
- context += f"Scenario: {data['world_scenario']}\n"
-
- context = f"{context.strip()}\n\n"
- return context
-
-
-def generate_pfp_cache(character):
- cache_folder = Path("cache")
- if not cache_folder.exists():
- cache_folder.mkdir()
-
- for path in [Path(f"characters/{character}.{extension}") for extension in ['png', 'jpg', 'jpeg']]:
- if path.exists():
- img = make_thumbnail(Image.open(path))
- img.save(Path('cache/pfp_character.png'), format='PNG')
- return img
-
- return None
-
-
-def load_character(character, name1, name2, mode):
- shared.character = character
- context = greeting = turn_template = ""
- greeting_field = 'greeting'
- picture = None
-
- # Deleting the profile picture cache, if any
- if Path("cache/pfp_character.png").exists():
- Path("cache/pfp_character.png").unlink()
-
- if character != 'None':
- folder = 'characters' if not mode == 'instruct' else 'characters/instruction-following'
- picture = generate_pfp_cache(character)
- for extension in ["yml", "yaml", "json"]:
- filepath = Path(f'{folder}/{character}.{extension}')
- if filepath.exists():
- break
-
- file_contents = open(filepath, 'r', encoding='utf-8').read()
- data = json.loads(file_contents) if extension == "json" else yaml.safe_load(file_contents)
-
- # Finding the bot's name
- for k in ['name', 'bot', '<|bot|>', 'char_name']:
- if k in data and data[k] != '':
- name2 = data[k]
- break
-
- # Find the user name (if any)
- for k in ['your_name', 'user', '<|user|>']:
- if k in data and data[k] != '':
- name1 = data[k]
- break
- else:
- name1 = shared.settings['name1']
-
- for field in ['context', 'greeting', 'example_dialogue', 'char_persona', 'char_greeting', 'world_scenario']:
- if field in data:
- data[field] = replace_character_names(data[field], name1, name2)
-
- if 'context' in data:
- context = data['context']
- if mode != 'instruct':
- context = context.strip() + '\n\n'
- elif "char_persona" in data:
- context = build_pygmalion_style_context(data)
- greeting_field = 'char_greeting'
-
- if 'example_dialogue' in data:
- context += f"{data['example_dialogue'].strip()}\n"
-
- if greeting_field in data:
- greeting = data[greeting_field]
-
- if 'turn_template' in data:
- turn_template = data['turn_template']
-
- else:
- context = shared.settings['context']
- name2 = shared.settings['name2']
- greeting = shared.settings['greeting']
- turn_template = shared.settings['turn_template']
-
- if mode != 'instruct':
- shared.history['internal'] = []
- shared.history['visible'] = []
- if Path(f'logs/{shared.character}_persistent.json').exists():
- load_history(open(Path(f'logs/{shared.character}_persistent.json'), 'rb').read(), name1, name2)
- else:
- # Insert greeting if it exists
- if greeting != "":
- shared.history['internal'] += [['<|BEGIN-VISIBLE-CHAT|>', greeting]]
- shared.history['visible'] += [['', apply_extensions("output", greeting)]]
-
- # Create .json log files since they don't already exist
- save_history(mode)
-
- return name1, name2, picture, greeting, context, repr(turn_template)[1:-1], chat_html_wrapper(shared.history['visible'], name1, name2, mode)
-
-
-def upload_character(json_file, img, tavern=False):
- json_file = json_file if type(json_file) == str else json_file.decode('utf-8')
- data = json.loads(json_file)
- outfile_name = data["char_name"]
- i = 1
- while Path(f'characters/{outfile_name}.json').exists():
- outfile_name = f'{data["char_name"]}_{i:03d}'
- i += 1
-
- if tavern:
- outfile_name = f'TavernAI-{outfile_name}'
-
- with open(Path(f'characters/{outfile_name}.json'), 'w', encoding='utf-8') as f:
- f.write(json_file)
-
- if img is not None:
- img = Image.open(io.BytesIO(img))
- img.save(Path(f'characters/{outfile_name}.png'))
-
- logging.info(f'New character saved to "characters/{outfile_name}.json".')
- return outfile_name
-
-
-def upload_tavern_character(img, name1, name2):
- _img = Image.open(io.BytesIO(img))
- _img.getexif()
- decoded_string = base64.b64decode(_img.info['chara'])
- _json = json.loads(decoded_string)
- _json = {"char_name": _json['name'], "char_persona": _json['description'], "char_greeting": _json["first_mes"], "example_dialogue": _json['mes_example'], "world_scenario": _json['scenario']}
- return upload_character(json.dumps(_json), img, tavern=True)
-
-
-def upload_your_profile_picture(img, name1, name2, mode):
- cache_folder = Path("cache")
- if not cache_folder.exists():
- cache_folder.mkdir()
-
- if img is None:
- if Path("cache/pfp_me.png").exists():
- Path("cache/pfp_me.png").unlink()
- else:
- img = make_thumbnail(img)
- img.save(Path('cache/pfp_me.png'))
- logging.info('Profile picture saved to "cache/pfp_me.png"')
-
- return chat_html_wrapper(shared.history['visible'], name1, name2, mode, reset_cache=True)
diff --git a/spaces/aphenx/bingo/src/pages/api/sydney.ts b/spaces/aphenx/bingo/src/pages/api/sydney.ts
deleted file mode 100644
index 0e7bbf23d77c2e1a6635185a060eeee58b8c8e66..0000000000000000000000000000000000000000
--- a/spaces/aphenx/bingo/src/pages/api/sydney.ts
+++ /dev/null
@@ -1,62 +0,0 @@
-import { NextApiRequest, NextApiResponse } from 'next'
-import { WebSocket, debug } from '@/lib/isomorphic'
-import { BingWebBot } from '@/lib/bots/bing'
-import { websocketUtils } from '@/lib/bots/bing/utils'
-import { WatchDog, createHeaders } from '@/lib/utils'
-
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
- const conversationContext = req.body
- const headers = createHeaders(req.cookies)
- debug(headers)
- res.setHeader('Content-Type', 'text/stream; charset=UTF-8')
-
- const ws = new WebSocket('wss://sydney.bing.com/sydney/ChatHub', {
- headers: {
- ...headers,
- 'accept-language': 'zh-CN,zh;q=0.9',
- 'cache-control': 'no-cache',
- 'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
- pragma: 'no-cache',
- }
- })
-
- const closeDog = new WatchDog()
- const timeoutDog = new WatchDog()
- ws.onmessage = (event) => {
- timeoutDog.watch(() => {
- ws.send(websocketUtils.packMessage({ type: 6 }))
- }, 1500)
- closeDog.watch(() => {
- ws.close()
- }, 10000)
- res.write(event.data)
- if (/\{"type":([367])\}/.test(String(event.data))) {
- const type = parseInt(RegExp.$1, 10)
- debug('connection type', type)
- if (type === 3) {
- ws.close()
- } else {
- ws.send(websocketUtils.packMessage({ type }))
- }
- }
- }
-
- ws.onclose = () => {
- timeoutDog.reset()
- closeDog.reset()
- debug('connection close')
- res.end()
- }
-
- await new Promise((resolve) => ws.onopen = resolve)
- ws.send(websocketUtils.packMessage({ protocol: 'json', version: 1 }))
- ws.send(websocketUtils.packMessage({ type: 6 }))
- ws.send(websocketUtils.packMessage(BingWebBot.buildChatRequest(conversationContext!)))
- req.socket.once('close', () => {
- ws.close()
- if (!res.closed) {
- res.end()
- }
- })
-}
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/layers/overflow/neural_hmm.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/layers/overflow/neural_hmm.py
deleted file mode 100644
index 0631ba98c00029e9871c965e4c7f465aa32bc406..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/layers/overflow/neural_hmm.py
+++ /dev/null
@@ -1,553 +0,0 @@
-from typing import List
-
-import torch
-import torch.distributions as tdist
-import torch.nn.functional as F
-from torch import nn
-from torch.utils.checkpoint import checkpoint
-
-from TTS.tts.layers.overflow.common_layers import Outputnet, OverflowUtils
-from TTS.tts.layers.tacotron.common_layers import Prenet
-from TTS.tts.utils.helpers import sequence_mask
-
-
-class NeuralHMM(nn.Module):
- """Autoregressive left to right HMM model primarily used in "Neural HMMs are all you need (for high-quality attention-free TTS)"
-
- Paper::
- https://arxiv.org/abs/2108.13320
-
- Paper abstract::
- Neural sequence-to-sequence TTS has achieved significantly better output quality than statistical speech synthesis using
- HMMs. However, neural TTS is generally not probabilistic and uses non-monotonic attention. Attention failures increase
- training time and can make synthesis babble incoherently. This paper describes how the old and new paradigms can be
- combined to obtain the advantages of both worlds, by replacing attention in neural TTS with an autoregressive left-right
- no-skip hidden Markov model defined by a neural network. Based on this proposal, we modify Tacotron 2 to obtain an
- HMM-based neural TTS model with monotonic alignment, trained to maximise the full sequence likelihood without
- approximation. We also describe how to combine ideas from classical and contemporary TTS for best results. The resulting
- example system is smaller and simpler than Tacotron 2, and learns to speak with fewer iterations and less data, whilst
- achieving comparable naturalness prior to the post-net. Our approach also allows easy control over speaking rate.
-
- Args:
- frame_channels (int): Output dimension to generate.
- ar_order (int): Autoregressive order of the model. In ablations of Neural HMM it was found that more autoregression while giving more variation hurts naturalness of the synthesised audio.
- deterministic_transition (bool): deterministic duration generation based on duration quantiles as defiend in "S. Ronanki, O. Watts, S. King, and G. E. Henter, “Medianbased generation of synthetic speech durations using a nonparametric approach,” in Proc. SLT, 2016.". Defaults to True.
- encoder_dim (int): Channels of encoder input and character embedding tensors. Defaults to 512.
- prenet_type (str): `original` or `bn`. `original` sets the default Prenet and `bn` uses Batch Normalization version of the Prenet.
- prenet_dim (int): Dimension of the Prenet.
- prenet_n_layers (int): Number of layers in the Prenet.
- prenet_dropout (float): Dropout probability of the Prenet.
- prenet_dropout_at_inference (bool): If True, dropout is applied at inference time.
- memory_rnn_dim (int): Size of the memory RNN to process output of prenet.
- outputnet_size (List[int]): Size of the output network inside the neural HMM.
- flat_start_params (dict): Parameters for the flat start initialization of the neural HMM.
- std_floor (float): Floor value for the standard deviation of the neural HMM. Prevents model cheating by putting point mass and getting infinite likelihood at any datapoint.
- use_grad_checkpointing (bool, optional): Use gradient checkpointing to save memory. Defaults to True.
- """
-
- def __init__(
- self,
- frame_channels: int,
- ar_order: int,
- deterministic_transition: bool,
- encoder_dim: int,
- prenet_type: str,
- prenet_dim: int,
- prenet_n_layers: int,
- prenet_dropout: float,
- prenet_dropout_at_inference: bool,
- memory_rnn_dim: int,
- outputnet_size: List[int],
- flat_start_params: dict,
- std_floor: float,
- use_grad_checkpointing: bool = True,
- ):
- super().__init__()
-
- self.frame_channels = frame_channels
- self.ar_order = ar_order
- self.deterministic_transition = deterministic_transition
- self.prenet_dim = prenet_dim
- self.memory_rnn_dim = memory_rnn_dim
- self.use_grad_checkpointing = use_grad_checkpointing
-
- self.transition_model = TransitionModel()
- self.emission_model = EmissionModel()
-
- assert ar_order > 0, f"AR order must be greater than 0 provided {ar_order}"
-
- self.ar_order = ar_order
- self.prenet = Prenet(
- in_features=frame_channels * ar_order,
- prenet_type=prenet_type,
- prenet_dropout=prenet_dropout,
- dropout_at_inference=prenet_dropout_at_inference,
- out_features=[self.prenet_dim for _ in range(prenet_n_layers)],
- bias=False,
- )
- self.memory_rnn = nn.LSTMCell(input_size=prenet_dim, hidden_size=memory_rnn_dim)
- self.output_net = Outputnet(
- encoder_dim, memory_rnn_dim, frame_channels, outputnet_size, flat_start_params, std_floor
- )
- self.register_buffer("go_tokens", torch.zeros(ar_order, 1))
-
- def forward(self, inputs, inputs_len, mels, mel_lens):
- r"""HMM forward algorithm for training uses logarithmic version of Rabiner (1989) forward algorithm.
-
- Args:
- inputs (torch.FloatTensor): Encoder outputs
- inputs_len (torch.LongTensor): Encoder output lengths
- mels (torch.FloatTensor): Mel inputs
- mel_lens (torch.LongTensor): Length of mel inputs
-
- Shapes:
- - inputs: (B, T, D_out_enc)
- - inputs_len: (B)
- - mels: (B, D_mel, T_mel)
- - mel_lens: (B)
-
- Returns:
- log_prob (torch.FloatTensor): Log probability of the sequence
- """
- # Get dimensions of inputs
- batch_size, N, _ = inputs.shape
- T_max = torch.max(mel_lens)
- mels = mels.permute(0, 2, 1)
-
- # Intialize forward algorithm
- log_state_priors = self._initialize_log_state_priors(inputs)
- log_c, log_alpha_scaled, transition_matrix, means = self._initialize_forward_algorithm_variables(mels, N)
-
- # Initialize autoregression elements
- ar_inputs = self._add_go_token(mels)
- h_memory, c_memory = self._init_lstm_states(batch_size, self.memory_rnn_dim, mels)
-
- for t in range(T_max):
- # Process Autoregression
- h_memory, c_memory = self._process_ar_timestep(t, ar_inputs, h_memory, c_memory)
- # Get mean, std and transition vector from decoder for this timestep
- # Note: Gradient checkpointing currently doesn't works with multiple gpus inside a loop
- if self.use_grad_checkpointing and self.training:
- mean, std, transition_vector = checkpoint(self.output_net, h_memory, inputs)
- else:
- mean, std, transition_vector = self.output_net(h_memory, inputs)
-
- if t == 0:
- log_alpha_temp = log_state_priors + self.emission_model(mels[:, 0], mean, std, inputs_len)
- else:
- log_alpha_temp = self.emission_model(mels[:, t], mean, std, inputs_len) + self.transition_model(
- log_alpha_scaled[:, t - 1, :], transition_vector, inputs_len
- )
- log_c[:, t] = torch.logsumexp(log_alpha_temp, dim=1)
- log_alpha_scaled[:, t, :] = log_alpha_temp - log_c[:, t].unsqueeze(1)
- transition_matrix[:, t] = transition_vector # needed for absorption state calculation
-
- # Save for plotting
- means.append(mean.detach())
-
- log_c, log_alpha_scaled = self._mask_lengths(mel_lens, log_c, log_alpha_scaled)
-
- sum_final_log_c = self.get_absorption_state_scaling_factor(
- mel_lens, log_alpha_scaled, inputs_len, transition_matrix
- )
-
- log_probs = torch.sum(log_c, dim=1) + sum_final_log_c
-
- return log_probs, log_alpha_scaled, transition_matrix, means
-
- @staticmethod
- def _mask_lengths(mel_lens, log_c, log_alpha_scaled):
- """
- Mask the lengths of the forward variables so that the variable lenghts
- do not contribute in the loss calculation
- Args:
- mel_inputs (torch.FloatTensor): (batch, T, frame_channels)
- mel_inputs_lengths (torch.IntTensor): (batch)
- log_c (torch.FloatTensor): (batch, T)
- Returns:
- log_c (torch.FloatTensor) : scaled probabilities (batch, T)
- log_alpha_scaled (torch.FloatTensor): forward probabilities (batch, T, N)
- """
- mask_log_c = sequence_mask(mel_lens)
- log_c = log_c * mask_log_c
- mask_log_alpha_scaled = mask_log_c.unsqueeze(2)
- log_alpha_scaled = log_alpha_scaled * mask_log_alpha_scaled
- return log_c, log_alpha_scaled
-
- def _process_ar_timestep(
- self,
- t,
- ar_inputs,
- h_memory,
- c_memory,
- ):
- """
- Process autoregression in timestep
- 1. At a specific t timestep
- 2. Perform data dropout if applied (we did not use it)
- 3. Run the autoregressive frame through the prenet (has dropout)
- 4. Run the prenet output through the post prenet rnn
-
- Args:
- t (int): mel-spec timestep
- ar_inputs (torch.FloatTensor): go-token appended mel-spectrograms
- - shape: (b, D_out, T_out)
- h_post_prenet (torch.FloatTensor): previous timestep rnn hidden state
- - shape: (b, memory_rnn_dim)
- c_post_prenet (torch.FloatTensor): previous timestep rnn cell state
- - shape: (b, memory_rnn_dim)
-
- Returns:
- h_post_prenet (torch.FloatTensor): rnn hidden state of the current timestep
- c_post_prenet (torch.FloatTensor): rnn cell state of the current timestep
- """
- prenet_input = ar_inputs[:, t : t + self.ar_order].flatten(1)
- memory_inputs = self.prenet(prenet_input)
- h_memory, c_memory = self.memory_rnn(memory_inputs, (h_memory, c_memory))
- return h_memory, c_memory
-
- def _add_go_token(self, mel_inputs):
- """Append the go token to create the autoregressive input
- Args:
- mel_inputs (torch.FloatTensor): (batch_size, T, n_mel_channel)
- Returns:
- ar_inputs (torch.FloatTensor): (batch_size, T, n_mel_channel)
- """
- batch_size, T, _ = mel_inputs.shape
- go_tokens = self.go_tokens.unsqueeze(0).expand(batch_size, self.ar_order, self.frame_channels)
- ar_inputs = torch.cat((go_tokens, mel_inputs), dim=1)[:, :T]
- return ar_inputs
-
- @staticmethod
- def _initialize_forward_algorithm_variables(mel_inputs, N):
- r"""Initialize placeholders for forward algorithm variables, to use a stable
- version we will use log_alpha_scaled and the scaling constant
-
- Args:
- mel_inputs (torch.FloatTensor): (b, T_max, frame_channels)
- N (int): number of states
- Returns:
- log_c (torch.FloatTensor): Scaling constant (b, T_max)
- """
- b, T_max, _ = mel_inputs.shape
- log_alpha_scaled = mel_inputs.new_zeros((b, T_max, N))
- log_c = mel_inputs.new_zeros(b, T_max)
- transition_matrix = mel_inputs.new_zeros((b, T_max, N))
-
- # Saving for plotting later, will not have gradient tapes
- means = []
- return log_c, log_alpha_scaled, transition_matrix, means
-
- @staticmethod
- def _init_lstm_states(batch_size, hidden_state_dim, device_tensor):
- r"""
- Initialize Hidden and Cell states for LSTM Cell
-
- Args:
- batch_size (Int): batch size
- hidden_state_dim (Int): dimensions of the h and c
- device_tensor (torch.FloatTensor): useful for the device and type
-
- Returns:
- (torch.FloatTensor): shape (batch_size, hidden_state_dim)
- can be hidden state for LSTM
- (torch.FloatTensor): shape (batch_size, hidden_state_dim)
- can be the cell state for LSTM
- """
- return (
- device_tensor.new_zeros(batch_size, hidden_state_dim),
- device_tensor.new_zeros(batch_size, hidden_state_dim),
- )
-
- def get_absorption_state_scaling_factor(self, mels_len, log_alpha_scaled, inputs_len, transition_vector):
- """Returns the final scaling factor of absorption state
-
- Args:
- mels_len (torch.IntTensor): Input size of mels to
- get the last timestep of log_alpha_scaled
- log_alpha_scaled (torch.FloatTEnsor): State probabilities
- text_lengths (torch.IntTensor): length of the states to
- mask the values of states lengths
- (
- Useful when the batch has very different lengths,
- when the length of an observation is less than
- the number of max states, then the log alpha after
- the state value is filled with -infs. So we mask
- those values so that it only consider the states
- which are needed for that length
- )
- transition_vector (torch.FloatTensor): transtiion vector for each state per timestep
-
- Shapes:
- - mels_len: (batch_size)
- - log_alpha_scaled: (batch_size, N, T)
- - text_lengths: (batch_size)
- - transition_vector: (batch_size, N, T)
-
- Returns:
- sum_final_log_c (torch.FloatTensor): (batch_size)
-
- """
- N = torch.max(inputs_len)
- max_inputs_len = log_alpha_scaled.shape[2]
- state_lengths_mask = sequence_mask(inputs_len, max_len=max_inputs_len)
-
- last_log_alpha_scaled_index = (
- (mels_len - 1).unsqueeze(-1).expand(-1, N).unsqueeze(1)
- ) # Batch X Hidden State Size
- last_log_alpha_scaled = torch.gather(log_alpha_scaled, 1, last_log_alpha_scaled_index).squeeze(1)
- last_log_alpha_scaled = last_log_alpha_scaled.masked_fill(~state_lengths_mask, -float("inf"))
-
- last_transition_vector = torch.gather(transition_vector, 1, last_log_alpha_scaled_index).squeeze(1)
- last_transition_probability = torch.sigmoid(last_transition_vector)
- log_probability_of_transitioning = OverflowUtils.log_clamped(last_transition_probability)
-
- last_transition_probability_index = self.get_mask_for_last_item(inputs_len, inputs_len.device)
- log_probability_of_transitioning = log_probability_of_transitioning.masked_fill(
- ~last_transition_probability_index, -float("inf")
- )
- final_log_c = last_log_alpha_scaled + log_probability_of_transitioning
-
- # If the length of the mel is less than the number of states it will select the -inf values leading to nan gradients
- # Ideally, we should clean the dataset otherwise this is a little hack uncomment the line below
- final_log_c = final_log_c.clamp(min=torch.finfo(final_log_c.dtype).min)
-
- sum_final_log_c = torch.logsumexp(final_log_c, dim=1)
- return sum_final_log_c
-
- @staticmethod
- def get_mask_for_last_item(lengths, device, out_tensor=None):
- """Returns n-1 mask for the last item in the sequence.
-
- Args:
- lengths (torch.IntTensor): lengths in a batch
- device (str, optional): Defaults to "cpu".
- out_tensor (torch.Tensor, optional): uses the memory of a specific tensor.
- Defaults to None.
-
- Returns:
- - Shape: :math:`(b, max_len)`
- """
- max_len = torch.max(lengths).item()
- ids = (
- torch.arange(0, max_len, device=device) if out_tensor is None else torch.arange(0, max_len, out=out_tensor)
- )
- mask = ids == lengths.unsqueeze(1) - 1
- return mask
-
- @torch.inference_mode()
- def inference(
- self,
- inputs: torch.FloatTensor,
- input_lens: torch.LongTensor,
- sampling_temp: float,
- max_sampling_time: int,
- duration_threshold: float,
- ):
- """Inference from autoregressive neural HMM
-
- Args:
- inputs (torch.FloatTensor): input states
- - shape: :math:`(b, T, d)`
- input_lens (torch.LongTensor): input state lengths
- - shape: :math:`(b)`
- sampling_temp (float): sampling temperature
- max_sampling_temp (int): max sampling temperature
- duration_threshold (float): duration threshold to switch to next state
- - Use this to change the spearking rate of the synthesised audio
- """
-
- b = inputs.shape[0]
- outputs = {
- "hmm_outputs": [],
- "hmm_outputs_len": [],
- "alignments": [],
- "input_parameters": [],
- "output_parameters": [],
- }
- for i in range(b):
- neural_hmm_outputs, states_travelled, input_parameters, output_parameters = self.sample(
- inputs[i : i + 1], input_lens[i], sampling_temp, max_sampling_time, duration_threshold
- )
-
- outputs["hmm_outputs"].append(neural_hmm_outputs)
- outputs["hmm_outputs_len"].append(neural_hmm_outputs.shape[0])
- outputs["alignments"].append(states_travelled)
- outputs["input_parameters"].append(input_parameters)
- outputs["output_parameters"].append(output_parameters)
-
- outputs["hmm_outputs"] = nn.utils.rnn.pad_sequence(outputs["hmm_outputs"], batch_first=True)
- outputs["hmm_outputs_len"] = torch.tensor(
- outputs["hmm_outputs_len"], dtype=input_lens.dtype, device=input_lens.device
- )
- return outputs
-
- @torch.inference_mode()
- def sample(self, inputs, input_lens, sampling_temp, max_sampling_time, duration_threshold):
- """Samples an output from the parameter models
-
- Args:
- inputs (torch.FloatTensor): input states
- - shape: :math:`(1, T, d)`
- input_lens (torch.LongTensor): input state lengths
- - shape: :math:`(1)`
- sampling_temp (float): sampling temperature
- max_sampling_time (int): max sampling time
- duration_threshold (float): duration threshold to switch to next state
-
- Returns:
- outputs (torch.FloatTensor): Output Observations
- - Shape: :math:`(T, output_dim)`
- states_travelled (list[int]): Hidden states travelled
- - Shape: :math:`(T)`
- input_parameters (list[torch.FloatTensor]): Input parameters
- output_parameters (list[torch.FloatTensor]): Output parameters
- """
- states_travelled, outputs, t = [], [], 0
-
- # Sample initial state
- current_state = 0
- states_travelled.append(current_state)
-
- # Prepare autoregression
- prenet_input = self.go_tokens.unsqueeze(0).expand(1, self.ar_order, self.frame_channels)
- h_memory, c_memory = self._init_lstm_states(1, self.memory_rnn_dim, prenet_input)
-
- input_parameter_values = []
- output_parameter_values = []
- quantile = 1
- while True:
- memory_input = self.prenet(prenet_input.flatten(1).unsqueeze(0))
- # will be 1 while sampling
- h_memory, c_memory = self.memory_rnn(memory_input.squeeze(0), (h_memory, c_memory))
-
- z_t = inputs[:, current_state].unsqueeze(0) # Add fake time dimension
- mean, std, transition_vector = self.output_net(h_memory, z_t)
-
- transition_probability = torch.sigmoid(transition_vector.flatten())
- staying_probability = torch.sigmoid(-transition_vector.flatten())
-
- # Save for plotting
- input_parameter_values.append([prenet_input, current_state])
- output_parameter_values.append([mean, std, transition_probability])
-
- x_t = self.emission_model.sample(mean, std, sampling_temp=sampling_temp)
-
- # Prepare autoregressive input for next iteration
- prenet_input = torch.cat((prenet_input, x_t), dim=1)[:, 1:]
-
- outputs.append(x_t.flatten())
-
- transition_matrix = torch.cat((staying_probability, transition_probability))
- quantile *= staying_probability
- if not self.deterministic_transition:
- switch = transition_matrix.multinomial(1)[0].item()
- else:
- switch = quantile < duration_threshold
-
- if switch:
- current_state += 1
- quantile = 1
-
- states_travelled.append(current_state)
-
- if (current_state == input_lens) or (max_sampling_time and t == max_sampling_time - 1):
- break
-
- t += 1
-
- return (
- torch.stack(outputs, dim=0),
- F.one_hot(input_lens.new_tensor(states_travelled)),
- input_parameter_values,
- output_parameter_values,
- )
-
- @staticmethod
- def _initialize_log_state_priors(text_embeddings):
- """Creates the log pi in forward algorithm.
-
- Args:
- text_embeddings (torch.FloatTensor): used to create the log pi
- on current device
-
- Shapes:
- - text_embeddings: (B, T, D_out_enc)
- """
- N = text_embeddings.shape[1]
- log_state_priors = text_embeddings.new_full([N], -float("inf"))
- log_state_priors[0] = 0.0
- return log_state_priors
-
-
-class TransitionModel(nn.Module):
- """Transition Model of the HMM, it represents the probability of transitioning
- form current state to all other states"""
-
- def forward(self, log_alpha_scaled, transition_vector, inputs_len): # pylint: disable=no-self-use
- r"""
- product of the past state with transitional probabilities in log space
-
- Args:
- log_alpha_scaled (torch.Tensor): Multiply previous timestep's alphas by
- transition matrix (in log domain)
- - shape: (batch size, N)
- transition_vector (torch.tensor): transition vector for each state
- - shape: (N)
- inputs_len (int tensor): Lengths of states in a batch
- - shape: (batch)
-
- Returns:
- out (torch.FloatTensor): log probability of transitioning to each state
- """
- transition_p = torch.sigmoid(transition_vector)
- staying_p = torch.sigmoid(-transition_vector)
-
- log_staying_probability = OverflowUtils.log_clamped(staying_p)
- log_transition_probability = OverflowUtils.log_clamped(transition_p)
-
- staying = log_alpha_scaled + log_staying_probability
- leaving = log_alpha_scaled + log_transition_probability
- leaving = leaving.roll(1, dims=1)
- leaving[:, 0] = -float("inf")
- inputs_len_mask = sequence_mask(inputs_len)
- out = OverflowUtils.logsumexp(torch.stack((staying, leaving), dim=2), dim=2)
- out = out.masked_fill(~inputs_len_mask, -float("inf")) # There are no states to contribute to the loss
- return out
-
-
-class EmissionModel(nn.Module):
- """Emission Model of the HMM, it represents the probability of
- emitting an observation based on the current state"""
-
- def __init__(self) -> None:
- super().__init__()
- self.distribution_function: tdist.Distribution = tdist.normal.Normal
-
- def sample(self, means, stds, sampling_temp):
- return self.distribution_function(means, stds * sampling_temp).sample() if sampling_temp > 0 else means
-
- def forward(self, x_t, means, stds, state_lengths):
- r"""Calculates the log probability of the the given data (x_t)
- being observed from states with given means and stds
- Args:
- x_t (float tensor) : observation at current time step
- - shape: (batch, feature_dim)
- means (float tensor): means of the distributions of hidden states
- - shape: (batch, hidden_state, feature_dim)
- stds (float tensor): standard deviations of the distributions of the hidden states
- - shape: (batch, hidden_state, feature_dim)
- state_lengths (int tensor): Lengths of states in a batch
- - shape: (batch)
-
- Returns:
- out (float tensor): observation log likelihoods,
- expressing the probability of an observation
- being generated from a state i
- shape: (batch, hidden_state)
- """
- emission_dists = self.distribution_function(means, stds)
- out = emission_dists.log_prob(x_t.unsqueeze(1))
- state_lengths_mask = sequence_mask(state_lengths).unsqueeze(2)
- out = torch.sum(out * state_lengths_mask, dim=2)
- return out
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Signature/test_pss.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Signature/test_pss.py
deleted file mode 100644
index 535474bec5f00a4b02185c3bdfe4b1e303cdb9fd..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/SelfTest/Signature/test_pss.py
+++ /dev/null
@@ -1,377 +0,0 @@
-# ===================================================================
-#
-# Copyright (c) 2014, Legrandin
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions
-# are met:
-#
-# 1. Redistributions of source code must retain the above copyright
-# notice, this list of conditions and the following disclaimer.
-# 2. Redistributions in binary form must reproduce the above copyright
-# notice, this list of conditions and the following disclaimer in
-# the documentation and/or other materials provided with the
-# distribution.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
-# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
-# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
-# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
-# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
-# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
-# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-# POSSIBILITY OF SUCH DAMAGE.
-# ===================================================================
-
-import unittest
-
-from Crypto.Util.py3compat import b, bchr
-from Crypto.Util.number import bytes_to_long
-from Crypto.Util.strxor import strxor
-from Crypto.SelfTest.st_common import list_test_cases
-from Crypto.SelfTest.loader import load_test_vectors, load_test_vectors_wycheproof
-
-from Crypto.Hash import SHA1, SHA224, SHA256, SHA384, SHA512
-from Crypto.PublicKey import RSA
-from Crypto.Signature import pss
-from Crypto.Signature import PKCS1_PSS
-
-from Crypto.Signature.pss import MGF1
-
-
-def load_hash_by_name(hash_name):
- return __import__("Crypto.Hash." + hash_name, globals(), locals(), ["new"])
-
-
-class PRNG(object):
-
- def __init__(self, stream):
- self.stream = stream
- self.idx = 0
-
- def __call__(self, rnd_size):
- result = self.stream[self.idx:self.idx + rnd_size]
- self.idx += rnd_size
- return result
-
-
-class PSS_Tests(unittest.TestCase):
-
- rsa_key = b'-----BEGIN RSA PRIVATE KEY-----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\nD+NmPL/N4uE57e40tbiU/H7KdyZaDt+5QiTmdhuyAe6CBjKsF2jy\n-----END RSA PRIVATE KEY-----'
- msg = b'AAA'
- tag = b'\x00[c5\xd8\xb0\x8b!D\x81\x83\x07\xc0\xdd\xb9\xb4\xb2`\x92\xe7\x02\xf1\xe1P\xea\xc3\xf0\xe3>\xddX5\xdd\x8e\xc5\x89\xef\xf3\xc2\xdc\xfeP\x02\x7f\x12+\xc9\xaf\xbb\xec\xfe\xb0\xa5\xb9\x08\x11P\x8fL\xee5\x9b\xb0k{=_\xd2\x14\xfb\x01R\xb7\xfe\x14}b\x03\x8d5Y\x89~}\xfc\xf2l\xd01-\xbd\xeb\x11\xcdV\x11\xe9l\x19k/o5\xa2\x0f\x15\xe7Q$\t=\xec\x1dAB\x19\xa5P\x9a\xaf\xa3G\x86"\xd6~\xf0j\xfcqkbs\x13\x84b\xe4\xbdm(\xed`\xa4F\xfb\x8f.\xe1\x8c)/_\x9eS\x98\xa4v\xb8\xdc\xfe\xf7/D\x18\x19\xb3T\x97:\xe2\x96s\xe8<\xa2\xb4\xb9\xf8/'
-
- def test_positive_1(self):
- key = RSA.import_key(self.rsa_key)
- h = SHA256.new(self.msg)
- verifier = pss.new(key)
- verifier.verify(h, self.tag)
-
- def test_negative_1(self):
- key = RSA.import_key(self.rsa_key)
- h = SHA256.new(self.msg + b'A')
- verifier = pss.new(key)
- tag = bytearray(self.tag)
- self.assertRaises(ValueError, verifier.verify, h, tag)
-
- def test_negative_2(self):
- key = RSA.import_key(self.rsa_key)
- h = SHA256.new(self.msg)
- verifier = pss.new(key, salt_bytes=1000)
- tag = bytearray(self.tag)
- self.assertRaises(ValueError, verifier.verify, h, tag)
-
-
-class FIPS_PKCS1_Verify_Tests(unittest.TestCase):
-
- def shortDescription(self):
- return "FIPS PKCS1 Tests (Verify)"
-
- def verify_positive(self, hashmod, message, public_key, salt, signature):
- prng = PRNG(salt)
- hashed = hashmod.new(message)
- verifier = pss.new(public_key, salt_bytes=len(salt), rand_func=prng)
- verifier.verify(hashed, signature)
-
- def verify_negative(self, hashmod, message, public_key, salt, signature):
- prng = PRNG(salt)
- hashed = hashmod.new(message)
- verifier = pss.new(public_key, salt_bytes=len(salt), rand_func=prng)
- self.assertRaises(ValueError, verifier.verify, hashed, signature)
-
- def test_can_sign(self):
- test_public_key = RSA.generate(1024).public_key()
- verifier = pss.new(test_public_key)
- self.assertEqual(verifier.can_sign(), False)
-
-
-class FIPS_PKCS1_Verify_Tests_KAT(unittest.TestCase):
- pass
-
-
-test_vectors_verify = load_test_vectors(("Signature", "PKCS1-PSS"),
- "SigVerPSS_186-3.rsp",
- "Signature Verification 186-3",
- {'shaalg': lambda x: x,
- 'result': lambda x: x}) or []
-
-
-for count, tv in enumerate(test_vectors_verify):
- if isinstance(tv, str):
- continue
- if hasattr(tv, "n"):
- modulus = tv.n
- continue
- if hasattr(tv, "p"):
- continue
-
- hash_module = load_hash_by_name(tv.shaalg.upper())
- hash_obj = hash_module.new(tv.msg)
- public_key = RSA.construct([bytes_to_long(x) for x in (modulus, tv.e)]) # type: ignore
- if tv.saltval != b("\x00"):
- prng = PRNG(tv.saltval)
- verifier = pss.new(public_key, salt_bytes=len(tv.saltval), rand_func=prng)
- else:
- verifier = pss.new(public_key, salt_bytes=0)
-
- def positive_test(self, hash_obj=hash_obj, verifier=verifier, signature=tv.s):
- verifier.verify(hash_obj, signature)
-
- def negative_test(self, hash_obj=hash_obj, verifier=verifier, signature=tv.s):
- self.assertRaises(ValueError, verifier.verify, hash_obj, signature)
-
- if tv.result == 'p':
- setattr(FIPS_PKCS1_Verify_Tests_KAT, "test_positive_%d" % count, positive_test)
- else:
- setattr(FIPS_PKCS1_Verify_Tests_KAT, "test_negative_%d" % count, negative_test)
-
-
-class FIPS_PKCS1_Sign_Tests(unittest.TestCase):
-
- def shortDescription(self):
- return "FIPS PKCS1 Tests (Sign)"
-
- def test_can_sign(self):
- test_private_key = RSA.generate(1024)
- signer = pss.new(test_private_key)
- self.assertEqual(signer.can_sign(), True)
-
-
-class FIPS_PKCS1_Sign_Tests_KAT(unittest.TestCase):
- pass
-
-
-test_vectors_sign = load_test_vectors(("Signature", "PKCS1-PSS"),
- "SigGenPSS_186-2.txt",
- "Signature Generation 186-2",
- {'shaalg': lambda x: x}) or []
-
-test_vectors_sign += load_test_vectors(("Signature", "PKCS1-PSS"),
- "SigGenPSS_186-3.txt",
- "Signature Generation 186-3",
- {'shaalg': lambda x: x}) or []
-
-for count, tv in enumerate(test_vectors_sign):
- if isinstance(tv, str):
- continue
- if hasattr(tv, "n"):
- modulus = tv.n
- continue
- if hasattr(tv, "e"):
- private_key = RSA.construct([bytes_to_long(x) for x in (modulus, tv.e, tv.d)]) # type: ignore
- continue
-
- hash_module = load_hash_by_name(tv.shaalg.upper())
- hash_obj = hash_module.new(tv.msg)
- if tv.saltval != b("\x00"):
- prng = PRNG(tv.saltval)
- signer = pss.new(private_key, salt_bytes=len(tv.saltval), rand_func=prng)
- else:
- signer = pss.new(private_key, salt_bytes=0)
-
- def new_test(self, hash_obj=hash_obj, signer=signer, result=tv.s):
- signature = signer.sign(hash_obj)
- self.assertEqual(signature, result)
-
- setattr(FIPS_PKCS1_Sign_Tests_KAT, "test_%d" % count, new_test)
-
-
-class PKCS1_Legacy_Module_Tests(unittest.TestCase):
- """Verify that the legacy module Crypto.Signature.PKCS1_PSS
- behaves as expected. The only difference is that the verify()
- method returns True/False and does not raise exceptions."""
-
- def shortDescription(self):
- return "Test legacy Crypto.Signature.PKCS1_PSS"
-
- def runTest(self):
- key = RSA.generate(1024)
- hashed = SHA1.new(b("Test"))
- good_signature = PKCS1_PSS.new(key).sign(hashed)
- verifier = PKCS1_PSS.new(key.public_key())
-
- self.assertEqual(verifier.verify(hashed, good_signature), True)
-
- # Flip a few bits in the signature
- bad_signature = strxor(good_signature, bchr(1) * len(good_signature))
- self.assertEqual(verifier.verify(hashed, bad_signature), False)
-
-
-class PKCS1_All_Hashes_Tests(unittest.TestCase):
-
- def shortDescription(self):
- return "Test PKCS#1 PSS signature in combination with all hashes"
-
- def runTest(self):
-
- key = RSA.generate(1280)
- signer = pss.new(key)
- hash_names = ("MD2", "MD4", "MD5", "RIPEMD160", "SHA1",
- "SHA224", "SHA256", "SHA384", "SHA512",
- "SHA3_224", "SHA3_256", "SHA3_384", "SHA3_512")
-
- for name in hash_names:
- hashed = load_hash_by_name(name).new(b("Test"))
- signer.sign(hashed)
-
- from Crypto.Hash import BLAKE2b, BLAKE2s
- for hash_size in (20, 32, 48, 64):
- hashed_b = BLAKE2b.new(digest_bytes=hash_size, data=b("Test"))
- signer.sign(hashed_b)
- for hash_size in (16, 20, 28, 32):
- hashed_s = BLAKE2s.new(digest_bytes=hash_size, data=b("Test"))
- signer.sign(hashed_s)
-
-
-def get_hash_module(hash_name):
- if hash_name == "SHA-512":
- hash_module = SHA512
- elif hash_name == "SHA-512/224":
- hash_module = SHA512.new(truncate="224")
- elif hash_name == "SHA-512/256":
- hash_module = SHA512.new(truncate="256")
- elif hash_name == "SHA-384":
- hash_module = SHA384
- elif hash_name == "SHA-256":
- hash_module = SHA256
- elif hash_name == "SHA-224":
- hash_module = SHA224
- elif hash_name == "SHA-1":
- hash_module = SHA1
- else:
- raise ValueError("Unknown hash algorithm: " + hash_name)
- return hash_module
-
-
-class TestVectorsPSSWycheproof(unittest.TestCase):
-
- def __init__(self, wycheproof_warnings):
- unittest.TestCase.__init__(self)
- self._wycheproof_warnings = wycheproof_warnings
- self._id = "None"
-
- def add_tests(self, filename):
-
- def filter_rsa(group):
- return RSA.import_key(group['keyPem'])
-
- def filter_sha(group):
- return get_hash_module(group['sha'])
-
- def filter_type(group):
- type_name = group['type']
- if type_name not in ("RsassaPssVerify", ):
- raise ValueError("Unknown type name " + type_name)
-
- def filter_slen(group):
- return group['sLen']
-
- def filter_mgf(group):
- mgf = group['mgf']
- if mgf not in ("MGF1", ):
- raise ValueError("Unknown MGF " + mgf)
- mgf1_hash = get_hash_module(group['mgfSha'])
-
- def mgf(x, y, mh=mgf1_hash):
- return MGF1(x, y, mh)
-
- return mgf
-
- result = load_test_vectors_wycheproof(("Signature", "wycheproof"),
- filename,
- "Wycheproof PSS signature (%s)" % filename,
- group_tag={'key': filter_rsa,
- 'hash_module': filter_sha,
- 'sLen': filter_slen,
- 'mgf': filter_mgf,
- 'type': filter_type})
- return result
-
- def setUp(self):
- self.tv = []
- self.add_tests("rsa_pss_2048_sha1_mgf1_20_test.json")
- self.add_tests("rsa_pss_2048_sha256_mgf1_0_test.json")
- self.add_tests("rsa_pss_2048_sha256_mgf1_32_test.json")
- self.add_tests("rsa_pss_2048_sha512_256_mgf1_28_test.json")
- self.add_tests("rsa_pss_2048_sha512_256_mgf1_32_test.json")
- self.add_tests("rsa_pss_3072_sha256_mgf1_32_test.json")
- self.add_tests("rsa_pss_4096_sha256_mgf1_32_test.json")
- self.add_tests("rsa_pss_4096_sha512_mgf1_32_test.json")
- self.add_tests("rsa_pss_misc_test.json")
-
- def shortDescription(self):
- return self._id
-
- def warn(self, tv):
- if tv.warning and self._wycheproof_warnings:
- import warnings
- warnings.warn("Wycheproof warning: %s (%s)" % (self._id, tv.comment))
-
- def test_verify(self, tv):
- self._id = "Wycheproof RSA PSS Test #%d (%s)" % (tv.id, tv.comment)
-
- hashed_msg = tv.hash_module.new(tv.msg)
- signer = pss.new(tv.key, mask_func=tv.mgf, salt_bytes=tv.sLen)
- try:
- signature = signer.verify(hashed_msg, tv.sig)
- except ValueError as e:
- if tv.warning:
- return
- assert not tv.valid
- else:
- assert tv.valid
- self.warn(tv)
-
- def runTest(self):
- for tv in self.tv:
- self.test_verify(tv)
-
-
-def get_tests(config={}):
- wycheproof_warnings = config.get('wycheproof_warnings')
-
- tests = []
- tests += list_test_cases(PSS_Tests)
- tests += list_test_cases(FIPS_PKCS1_Verify_Tests)
- tests += list_test_cases(FIPS_PKCS1_Sign_Tests)
- tests += list_test_cases(PKCS1_Legacy_Module_Tests)
- tests += list_test_cases(PKCS1_All_Hashes_Tests)
-
- if config.get('slow_tests'):
- tests += list_test_cases(FIPS_PKCS1_Verify_Tests_KAT)
- tests += list_test_cases(FIPS_PKCS1_Sign_Tests_KAT)
-
- tests += [TestVectorsPSSWycheproof(wycheproof_warnings)]
-
- return tests
-
-
-if __name__ == '__main__':
- def suite():
- return unittest.TestSuite(get_tests())
- unittest.main(defaultTest='suite')
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/seattle_weather_interactive.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/seattle_weather_interactive.py
deleted file mode 100644
index cf65fbffda1b38cb11c493bc76664f3a9931b1ce..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/seattle_weather_interactive.py
+++ /dev/null
@@ -1,60 +0,0 @@
-"""
-Seattle Weather Interactive
-===========================
-This chart provides an interactive exploration of Seattle weather over the
-course of the year. It includes a one-axis brush selection to easily
-see the distribution of weather types in a particular date range.
-"""
-# category: case studies
-import altair as alt
-from vega_datasets import data
-
-source = data.seattle_weather()
-
-scale = alt.Scale(domain=['sun', 'fog', 'drizzle', 'rain', 'snow'],
- range=['#e7ba52', '#a7a7a7', '#aec7e8', '#1f77b4', '#9467bd'])
-color = alt.Color('weather:N', scale=scale)
-
-# We create two selections:
-# - a brush that is active on the top panel
-# - a multi-click that is active on the bottom panel
-brush = alt.selection_interval(encodings=['x'])
-click = alt.selection_multi(encodings=['color'])
-
-# Top panel is scatter plot of temperature vs time
-points = alt.Chart().mark_point().encode(
- alt.X('monthdate(date):T', title='Date'),
- alt.Y('temp_max:Q',
- title='Maximum Daily Temperature (C)',
- scale=alt.Scale(domain=[-5, 40])
- ),
- color=alt.condition(brush, color, alt.value('lightgray')),
- size=alt.Size('precipitation:Q', scale=alt.Scale(range=[5, 200]))
-).properties(
- width=550,
- height=300
-).add_selection(
- brush
-).transform_filter(
- click
-)
-
-# Bottom panel is a bar chart of weather type
-bars = alt.Chart().mark_bar().encode(
- x='count()',
- y='weather:N',
- color=alt.condition(click, color, alt.value('lightgray')),
-).transform_filter(
- brush
-).properties(
- width=550,
-).add_selection(
- click
-)
-
-alt.vconcat(
- points,
- bars,
- data=source,
- title="Seattle Weather: 2012-2015"
-)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/anyio/__init__.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/anyio/__init__.py
deleted file mode 100644
index 6e81178c81e063691c7d0f7a966afbaf9ed7934b..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/anyio/__init__.py
+++ /dev/null
@@ -1,167 +0,0 @@
-__all__ = (
- "maybe_async",
- "maybe_async_cm",
- "run",
- "sleep",
- "sleep_forever",
- "sleep_until",
- "current_time",
- "get_all_backends",
- "get_cancelled_exc_class",
- "BrokenResourceError",
- "BrokenWorkerProcess",
- "BusyResourceError",
- "ClosedResourceError",
- "DelimiterNotFound",
- "EndOfStream",
- "ExceptionGroup",
- "IncompleteRead",
- "TypedAttributeLookupError",
- "WouldBlock",
- "AsyncFile",
- "Path",
- "open_file",
- "wrap_file",
- "aclose_forcefully",
- "open_signal_receiver",
- "connect_tcp",
- "connect_unix",
- "create_tcp_listener",
- "create_unix_listener",
- "create_udp_socket",
- "create_connected_udp_socket",
- "getaddrinfo",
- "getnameinfo",
- "wait_socket_readable",
- "wait_socket_writable",
- "create_memory_object_stream",
- "run_process",
- "open_process",
- "create_lock",
- "CapacityLimiter",
- "CapacityLimiterStatistics",
- "Condition",
- "ConditionStatistics",
- "Event",
- "EventStatistics",
- "Lock",
- "LockStatistics",
- "Semaphore",
- "SemaphoreStatistics",
- "create_condition",
- "create_event",
- "create_semaphore",
- "create_capacity_limiter",
- "open_cancel_scope",
- "fail_after",
- "move_on_after",
- "current_effective_deadline",
- "TASK_STATUS_IGNORED",
- "CancelScope",
- "create_task_group",
- "TaskInfo",
- "get_current_task",
- "get_running_tasks",
- "wait_all_tasks_blocked",
- "run_sync_in_worker_thread",
- "run_async_from_thread",
- "run_sync_from_thread",
- "current_default_worker_thread_limiter",
- "create_blocking_portal",
- "start_blocking_portal",
- "typed_attribute",
- "TypedAttributeSet",
- "TypedAttributeProvider",
-)
-
-from typing import Any
-
-from ._core._compat import maybe_async, maybe_async_cm
-from ._core._eventloop import (
- current_time,
- get_all_backends,
- get_cancelled_exc_class,
- run,
- sleep,
- sleep_forever,
- sleep_until,
-)
-from ._core._exceptions import (
- BrokenResourceError,
- BrokenWorkerProcess,
- BusyResourceError,
- ClosedResourceError,
- DelimiterNotFound,
- EndOfStream,
- ExceptionGroup,
- IncompleteRead,
- TypedAttributeLookupError,
- WouldBlock,
-)
-from ._core._fileio import AsyncFile, Path, open_file, wrap_file
-from ._core._resources import aclose_forcefully
-from ._core._signals import open_signal_receiver
-from ._core._sockets import (
- connect_tcp,
- connect_unix,
- create_connected_udp_socket,
- create_tcp_listener,
- create_udp_socket,
- create_unix_listener,
- getaddrinfo,
- getnameinfo,
- wait_socket_readable,
- wait_socket_writable,
-)
-from ._core._streams import create_memory_object_stream
-from ._core._subprocesses import open_process, run_process
-from ._core._synchronization import (
- CapacityLimiter,
- CapacityLimiterStatistics,
- Condition,
- ConditionStatistics,
- Event,
- EventStatistics,
- Lock,
- LockStatistics,
- Semaphore,
- SemaphoreStatistics,
- create_capacity_limiter,
- create_condition,
- create_event,
- create_lock,
- create_semaphore,
-)
-from ._core._tasks import (
- TASK_STATUS_IGNORED,
- CancelScope,
- create_task_group,
- current_effective_deadline,
- fail_after,
- move_on_after,
- open_cancel_scope,
-)
-from ._core._testing import (
- TaskInfo,
- get_current_task,
- get_running_tasks,
- wait_all_tasks_blocked,
-)
-from ._core._typedattr import TypedAttributeProvider, TypedAttributeSet, typed_attribute
-
-# Re-exported here, for backwards compatibility
-# isort: off
-from .to_thread import current_default_worker_thread_limiter, run_sync_in_worker_thread
-from .from_thread import (
- create_blocking_portal,
- run_async_from_thread,
- run_sync_from_thread,
- start_blocking_portal,
-)
-
-# Re-export imports so they look like they live directly in this package
-key: str
-value: Any
-for key, value in list(locals().items()):
- if getattr(value, "__module__", "").startswith("anyio."):
- value.__module__ = __name__
diff --git a/spaces/aseduto/sp500/README.md b/spaces/aseduto/sp500/README.md
deleted file mode 100644
index cd2d0d9f416f6837a8354dfb5bd43fd43bbb9c97..0000000000000000000000000000000000000000
--- a/spaces/aseduto/sp500/README.md
+++ /dev/null
@@ -1,41 +0,0 @@
----
-title: S&P 500
-emoji: 📉 📈
-colorFrom: blue
-colorTo: green
-sdk: streamlit
-sdk_version: 1.24.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-# Artificial Intelligence - S&P 500 Prediction Model using PyTorch
-
-This is a model using PyTorch that predicts the value of the S&P 500 index based on earnings and interest rates.
-
-## Introduction
-
-The model takes into account historical earnings per share (EPS) and treasury rates to make predictions on the S&P 500 index. It has been trained on a significant amount of data and has shown promising results during the testing phase.
-
-## Data and Training
-
-The model has been trained on a comprehensive dataset of historical EPS and treasury rates from 1997 to today.
-
-
-## Feedback
-
-I believe this model can provide valuable insights into the behavior of the stock market and assist in making informed investment decisions. However, I'm still working to further improve the model and explore potential areas of enhancement.
-
-## How to Use the Model
-
-It is important to note that no model can perfectly predict the future value of the S&P 500 index. However, these models can be used to generate forecasts that can help investors make informed decisions.
-
-You can input your expected values for the treasury rate and the S&P Earnings for each future quarters and have the model forecast the index expected value.
-
-## Contact
-
-If you have any questions, suggestions, or ideas, please feel free to reach out. Your feedback is greatly appreciated!
-
-
-
diff --git a/spaces/aus10powell/TwitterAccounts/app.py b/spaces/aus10powell/TwitterAccounts/app.py
deleted file mode 100644
index 454bcb949fd113be7865e74f8ac92b3f4c2d77ca..0000000000000000000000000000000000000000
--- a/spaces/aus10powell/TwitterAccounts/app.py
+++ /dev/null
@@ -1,328 +0,0 @@
-"""FastAPI endpoint
-To run locally use 'uvicorn app:app --host localhost --port 7860'
-or
-`python -m uvicorn app:app --reload --host localhost --port 7860`
-"""
-import datetime as dt
-import json
-import logging
-import sys
-import spacy
-
-# sys.setrecursionlimit(20000)
-import pandas as pd
-import numpy as np
-import os
-import random
-from typing import Dict, List
-
-import uvicorn
-from fastapi import FastAPI, HTTPException, Request, Response
-from fastapi.responses import HTMLResponse, JSONResponse
-from fastapi.staticfiles import StaticFiles
-from fastapi.templating import Jinja2Templates
-
-from rouge_score import rouge_scorer
-# Scripts
-import scripts.sentiment as sentiment
-import scripts.twitter_scraper as ts
-from scripts import sentiment
-from scripts.summarization import bert_summarization
-from scripts.twitter_scraper import get_latest_account_tweets
-from scripts.sentiment import twitter_sentiment_api_score
-from scripts import twitter_scraper as ts
-import scripts.utils as utils
-from scripts import translation
-from scripts import generative
-import nltk
-nltk.download('punkt')
-punkt_download_location = nltk.data.path[0]
-logging.info(f"punkt_download_location: {punkt_download_location}")
-
-logging.basicConfig(level=logging.INFO)
-pd.set_option('display.max_colwidth', 20)
-
-app = FastAPI()
-templates = Jinja2Templates(directory="templates")
-app.mount("/static", StaticFiles(directory="static"), name="static")
-
-# Construct absolute path to models folder
-models_path = os.path.abspath("models")
-
-username_list = [
- "alikarimi_ak8",
- "elonmusk",
- "BarackObama",
- "taylorlorenz",
- "cathiedwood",
- "ylecun",
-]
-
-## Static objects/paths
-start_date = dt.date(year=2023, month=2, day=1)
-end_date = dt.date(year=2023, month=3, day=22)
-
-# Load spacy module on app start
-nlp = spacy.load("en_core_web_sm")
-nlp.add_pipe("sentencizer")
-
-
-@app.get("/", response_class=HTMLResponse)
-async def webpage(request: Request):
- return templates.TemplateResponse("index.html", {"request": request})
-
-
-@app.get("/accounts")
-async def get_accounts() -> List[dict]:
- import pandas as pd
-
- logging.info(f"Pulling account information on {username_list}")
- account_info_list = [
- ts.get_twitter_account_info(twitter_handle=account) for account in username_list
- ]
- df_account = pd.DataFrame(account_info_list)
- df_account = df_account.style.bar(
- subset=["follower_count", "friends_count"], color="#d65f5f"
- )
- df_account = df_account.format(
- {"follower_count": "{:,.0f}", "friends_count": "{:,.0f}"}
- )
- html_table = df_account.to_html(classes="center", index=False)
-
- return HTMLResponse(content=html_table, status_code=200)
-
-
-@app.get("/tweets/{username}")
-def get_tweets_username(username: str) -> dict:
- # Method 2: Use Snscrape
- df_tweets = ts.get_tweets(handle=username)
-
- if isinstance(df_tweets, pd.DataFrame):
- df_tweets = df_tweets[["handle", "created_at","retweet_count","view_count","like_count", "full_text"]]
- df_tweets["created_at"] = df_tweets["created_at"].dt.strftime(
- "%Y-%m-%d %H:%M:%S"
- )
- df_tweets = df_tweets.sort_values("created_at", ascending=False)
-
- # Additional processing
- logging.info("Running sentiment on tweets")
- sentiments = twitter_sentiment_api_score(
- df_tweets['full_text'].to_list(), use_api=False
- )
- df_tweets["sentiment"] = [s['argmax'] for s in sentiments]
- if username == "alikarimi_ak8":
- p = translation.PersianTextProcessor()
- df_tweets['full_text_translated'] = df_tweets["full_text"].apply(lambda c: p.translate_text(persian_text = c))
-
-
- df_tweets_html = df_tweets.to_html(classes="center", index=False, escape=False)
- df_tweets.to_html(open("df_tweets_html.html", "w"))
- df_tweets_data = df_tweets.to_dict(orient="records")
-
- response_data = {"html": df_tweets_html, "data": df_tweets_data}
-
- return JSONResponse(content=response_data, status_code=200)
- else:
- print("Error: Failed to retrieve tweets.")
- return df_tweets
-
-
-@app.get("/audience/{username}", response_model=dict)
-async def get_audience(username: str) -> dict:
- if username in username_list:
- query = f"from:{username} since:{start_date} until:{end_date}"
- tweets = ts.get_tweets(query=query)
-
- n_samples = 5
- # Random sample 3 tweets from user
- tweets_sampled = random.sample(tweets, n_samples)
-
- # Get all replies to sampled tweets
- tweet_threads = []
- for tweet in tweets_sampled:
- threads = ts.get_replies(
- username=tweet["username"],
- conversation_id=tweet["conversation_id"],
- max_tweets=100,
- )
- tweet_threads += threads
-
- # Get usernames from sample threads tweets
- usernames = [t["username"] for t in tweet_threads]
- # Get user info from sample replies to sampled tweets of user
- info_accounts = [
- ts.get_twitter_account_info(twitter_handle=account) for account in usernames
- ]
-
- # "follower_count":1,"friends_count":20,"verified":false}
- # Get stats for followers/audience engaging with tweets
- follower_counts = [
- info_accounts[i]["follower_count"] for i in range(len(info_accounts))
- ]
- friends_counts = [
- info_accounts[i]["friends_count"] for i in range(len(info_accounts))
- ]
- verified_counts = [
- 1 if info_accounts[i]["verified"] == True else 0
- for i in range(len(info_accounts))
- ]
- return {
- "sample_size": len(info_accounts),
- "mean_follower_count": round(np.mean(follower_counts), 3),
- "mean_friends_count": round(np.mean(friends_counts), 3),
- "mean_verified": round(np.mean(verified_counts), 3),
- }
- else:
- response = Response(content="Account not in scope of project.", status_code=404)
- return response
-
-
-@app.get("/sentiment/{username}")
-async def get_sentiment(username: str) -> Dict[str, Dict[str, float]]:
- if username not in username_list:
- raise HTTPException(status_code=404, detail="Account not in scope of project.")
-
- query = f"from:{username} since:{start_date} until:{end_date}"
- tweets = ts.get_tweets(query=query)
- n_samples = 5
- tweets_sampled = random.sample(tweets, n_samples)
-
- tweet_threads = []
- for tweet in tweets_sampled:
- threads = ts.get_replies(
- username=tweet["username"],
- conversation_id=tweet["conversation_id"],
- max_tweets=100,
- )
- tweet_threads += threads
-
- print(
- f"Total replies to {n_samples} sampled tweets from username: {username}, {len(tweet_threads)}"
- )
-
- ## Sentiment scoring
- print(f"Running tweet sentiment scoring on username: {username} tweets")
- tweets_scores = sentiment.get_tweets_sentiment(tweets=tweets)
- mean_tweets_score = round(np.mean(tweets_scores), 2)
- ci_tweets = utils.wilson_score_interval(tweets_scores)
-
- # Get sentiment of the threads from tweets
- # Get username tweets sentiment
- print(f"Running tweet thread sentiment scoring on username: {username} tweets")
- threads_scores = sentiment.get_tweets_sentiment(tweets=tweet_threads)
- mean_threads_score = round(np.mean(threads_scores), 2)
- ci_threads = utils.wilson_score_interval(threads_scores)
-
- return {
- "thread_level": {
- "mean": mean_threads_score,
- "confidence_interal": ci_threads,
- },
- "audience_level": {
- "mean": mean_tweets_score,
- "confidence_interval": ci_tweets,
- },
- }
-
-
-## APIs: Primarily called by the index page
-@app.post("/api/generate")
-async def generate_text(request: Request):
- """Generate text from a prompt.
-
- Args:
- request: The HTTP request.
-
- Returns:
- The generated text.
- """
- print("*" * 50)
- data = await request.json()
- print("*" * 50)
- logging.info("POST to api/generate received and processing")
-
- # Check length of input, if it is greater than 10 tokens, the text is sent off to a summarizer to generate:
- try:
- generated_text = generative.generate_account_text(
- prompt=data["text"], model_dir=os.path.join(models_path, data["account"])
- )
- logging.info("INFO: Successfully generate text from model.")
- except Exception as e:
- logging.error(f"Error generating text: {e}")
- return {"error": "Error generating text"}
- # return one example
- generated_text = generated_text[0]["generated_text"]
-
- ###################################################
- ## Clean up generate text
- # Get rid of final sentence
- sentences = nltk.sent_tokenize(generated_text)
- unique_sentences = set()
- non_duplicate_sentences = []
- for sentence in sentences:
- if sentence not in unique_sentences:
- non_duplicate_sentences.append(sentence)
- unique_sentences.add(sentence)
- final_text = " ".join(non_duplicate_sentences[:-1])
-
- return {"generated_text": final_text}
-
-
-@app.post("/api/generate_summary")
-async def generate_summary(request: Request):
- """Generate summary from tweets
-
- Args:
- request: The HTTP request.
-
- Returns:
- The generated text.
- """
-
- print("*" * 50)
- data = await request.json()
- print("data", data["tweetsData"])
- # Get the list of text
- tweets = [t["full_text"] for t in data["tweetsData"]]
-
- # Concatenate tweets into a single string
- text = " .".join(tweets)
-
- sentences = nlp(text).sents
-
- sentences = list(sentences)
-
- # Option 2
- sampled_sentences = random.sample(sentences, int(0.1 * len(sentences)))
-
- sampled_sentences = [sentiment.tweet_cleaner(s.text) for s in sampled_sentences]
-
- # Join the strings into one text blob
- tweet_blob = " ".join(sampled_sentences)
-
- # Generate the summary
- summary = bert_summarization(tweet_blob)
- print("Summary:", summary)
- # Return the summary
- return {"tweets_summary": summary}
-
-
-## Historical Tweets pages
-@app.get("/examples1")
-async def read_examples():
- with open("templates/charts/handle_sentiment_breakdown.html") as f:
- html = f.read()
- return HTMLResponse(content=html)
-
-
-@app.get("/examples2")
-async def read_examples():
- with open("templates/charts/handle_sentiment_timesteps.html") as f:
- html = f.read()
- return HTMLResponse(content=html)
-
-
-# uvicorn --workers=2 app:app
-if __name__ == "__main__":
- # uvicorn.run(app, host="0.0.0.0", port=8000)
- uvicorn.run("app:app", host="127.0.0.1", port=5050, reload=True)
diff --git a/spaces/awacke1/ChatGPTStreamlit9/README.md b/spaces/awacke1/ChatGPTStreamlit9/README.md
deleted file mode 100644
index 322f025775f6268ebeffc28dac25e0cd0ddf8bac..0000000000000000000000000000000000000000
--- a/spaces/awacke1/ChatGPTStreamlit9/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ChatGPTStreamlit9
-emoji: 🌖
-colorFrom: blue
-colorTo: green
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/balgot/text-to-stylegan3/README.md b/spaces/balgot/text-to-stylegan3/README.md
deleted file mode 100644
index d49bc65247dfa031716a5821ffccad811d285fac..0000000000000000000000000000000000000000
--- a/spaces/balgot/text-to-stylegan3/README.md
+++ /dev/null
@@ -1,23 +0,0 @@
----
-title: Text To Stylegan3
-emoji: 🔥
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.28.1
-app_file: app.py
-pinned: false
-license: openrail
----
-
-This is a demo showcasing the connection of these models:
-
-* [`sentence-transformers/all-MiniLM-L6-v2`](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
-* [`balgot/text-2-stylegan3`](https://huggingface.co/balgot/bert-2-stylegan3)
-* [`StyleGAN3`](https://nvlabs.github.io/stylegan3/)
-
-> **NOTE:** It is only possible to generate human faces which StyleGAN3 can
-generate (see [ffhq](https://github.com/NVlabs/ffhq-dataset)) dataset.
-Specifically, only real life human faces can be generated. Furthermore,
-the quality of understanding the textual description strongly depends
-on the captioning procedure used, so not all face-features can be forced.
\ No newline at end of file
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/extras/ShapeUtils.js b/spaces/banana-projects/web3d/node_modules/three/src/extras/ShapeUtils.js
deleted file mode 100644
index ec050afb571b57ccbbefe2db216f38ff4027c81a..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/extras/ShapeUtils.js
+++ /dev/null
@@ -1,96 +0,0 @@
-/**
- * @author zz85 / http://www.lab4games.net/zz85/blog
- */
-
-import { Earcut } from './Earcut.js';
-
-var ShapeUtils = {
-
- // calculate area of the contour polygon
-
- area: function ( contour ) {
-
- var n = contour.length;
- var a = 0.0;
-
- for ( var p = n - 1, q = 0; q < n; p = q ++ ) {
-
- a += contour[ p ].x * contour[ q ].y - contour[ q ].x * contour[ p ].y;
-
- }
-
- return a * 0.5;
-
- },
-
- isClockWise: function ( pts ) {
-
- return ShapeUtils.area( pts ) < 0;
-
- },
-
- triangulateShape: function ( contour, holes ) {
-
- var vertices = []; // flat array of vertices like [ x0,y0, x1,y1, x2,y2, ... ]
- var holeIndices = []; // array of hole indices
- var faces = []; // final array of vertex indices like [ [ a,b,d ], [ b,c,d ] ]
-
- removeDupEndPts( contour );
- addContour( vertices, contour );
-
- //
-
- var holeIndex = contour.length;
-
- holes.forEach( removeDupEndPts );
-
- for ( var i = 0; i < holes.length; i ++ ) {
-
- holeIndices.push( holeIndex );
- holeIndex += holes[ i ].length;
- addContour( vertices, holes[ i ] );
-
- }
-
- //
-
- var triangles = Earcut.triangulate( vertices, holeIndices );
-
- //
-
- for ( var i = 0; i < triangles.length; i += 3 ) {
-
- faces.push( triangles.slice( i, i + 3 ) );
-
- }
-
- return faces;
-
- }
-
-};
-
-function removeDupEndPts( points ) {
-
- var l = points.length;
-
- if ( l > 2 && points[ l - 1 ].equals( points[ 0 ] ) ) {
-
- points.pop();
-
- }
-
-}
-
-function addContour( vertices, contour ) {
-
- for ( var i = 0; i < contour.length; i ++ ) {
-
- vertices.push( contour[ i ].x );
- vertices.push( contour[ i ].y );
-
- }
-
-}
-
-export { ShapeUtils };
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/map_particle_pars_fragment.glsl.js b/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/map_particle_pars_fragment.glsl.js
deleted file mode 100644
index 3bf8eb007280ef6c7ad0be15171cf1944467d86d..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderChunk/map_particle_pars_fragment.glsl.js
+++ /dev/null
@@ -1,8 +0,0 @@
-export default /* glsl */`
-#ifdef USE_MAP
-
- uniform mat3 uvTransform;
- uniform sampler2D map;
-
-#endif
-`;
diff --git a/spaces/beihai/GFPGAN-V1.3-whole-image/basicsr/models/video_recurrent_gan_model.py b/spaces/beihai/GFPGAN-V1.3-whole-image/basicsr/models/video_recurrent_gan_model.py
deleted file mode 100644
index 74cf81145c50ffafb220d22b51e56746dee5ba41..0000000000000000000000000000000000000000
--- a/spaces/beihai/GFPGAN-V1.3-whole-image/basicsr/models/video_recurrent_gan_model.py
+++ /dev/null
@@ -1,180 +0,0 @@
-import torch
-from collections import OrderedDict
-
-from basicsr.archs import build_network
-from basicsr.losses import build_loss
-from basicsr.utils import get_root_logger
-from basicsr.utils.registry import MODEL_REGISTRY
-from .video_recurrent_model import VideoRecurrentModel
-
-
-@MODEL_REGISTRY.register()
-class VideoRecurrentGANModel(VideoRecurrentModel):
-
- def init_training_settings(self):
- train_opt = self.opt['train']
-
- self.ema_decay = train_opt.get('ema_decay', 0)
- if self.ema_decay > 0:
- logger = get_root_logger()
- logger.info(f'Use Exponential Moving Average with decay: {self.ema_decay}')
- # build network net_g with Exponential Moving Average (EMA)
- # net_g_ema only used for testing on one GPU and saving.
- # There is no need to wrap with DistributedDataParallel
- self.net_g_ema = build_network(self.opt['network_g']).to(self.device)
- # load pretrained model
- load_path = self.opt['path'].get('pretrain_network_g', None)
- if load_path is not None:
- self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema')
- else:
- self.model_ema(0) # copy net_g weight
- self.net_g_ema.eval()
-
- # define network net_d
- self.net_d = build_network(self.opt['network_d'])
- self.net_d = self.model_to_device(self.net_d)
- self.print_network(self.net_d)
-
- # load pretrained models
- load_path = self.opt['path'].get('pretrain_network_d', None)
- if load_path is not None:
- param_key = self.opt['path'].get('param_key_d', 'params')
- self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True), param_key)
-
- self.net_g.train()
- self.net_d.train()
-
- # define losses
- if train_opt.get('pixel_opt'):
- self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device)
- else:
- self.cri_pix = None
-
- if train_opt.get('perceptual_opt'):
- self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device)
- else:
- self.cri_perceptual = None
-
- if train_opt.get('gan_opt'):
- self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device)
-
- self.net_d_iters = train_opt.get('net_d_iters', 1)
- self.net_d_init_iters = train_opt.get('net_d_init_iters', 0)
-
- # set up optimizers and schedulers
- self.setup_optimizers()
- self.setup_schedulers()
-
- def setup_optimizers(self):
- train_opt = self.opt['train']
- if train_opt['fix_flow']:
- normal_params = []
- flow_params = []
- for name, param in self.net_g.named_parameters():
- if 'spynet' in name: # The fix_flow now only works for spynet.
- flow_params.append(param)
- else:
- normal_params.append(param)
-
- optim_params = [
- { # add flow params first
- 'params': flow_params,
- 'lr': train_opt['lr_flow']
- },
- {
- 'params': normal_params,
- 'lr': train_opt['optim_g']['lr']
- },
- ]
- else:
- optim_params = self.net_g.parameters()
-
- # optimizer g
- optim_type = train_opt['optim_g'].pop('type')
- self.optimizer_g = self.get_optimizer(optim_type, optim_params, **train_opt['optim_g'])
- self.optimizers.append(self.optimizer_g)
- # optimizer d
- optim_type = train_opt['optim_d'].pop('type')
- self.optimizer_d = self.get_optimizer(optim_type, self.net_d.parameters(), **train_opt['optim_d'])
- self.optimizers.append(self.optimizer_d)
-
- def optimize_parameters(self, current_iter):
- logger = get_root_logger()
- # optimize net_g
- for p in self.net_d.parameters():
- p.requires_grad = False
-
- if self.fix_flow_iter:
- if current_iter == 1:
- logger.info(f'Fix flow network and feature extractor for {self.fix_flow_iter} iters.')
- for name, param in self.net_g.named_parameters():
- if 'spynet' in name or 'edvr' in name:
- param.requires_grad_(False)
- elif current_iter == self.fix_flow_iter:
- logger.warning('Train all the parameters.')
- self.net_g.requires_grad_(True)
-
- self.optimizer_g.zero_grad()
- self.output = self.net_g(self.lq)
-
- _, _, c, h, w = self.output.size()
-
- l_g_total = 0
- loss_dict = OrderedDict()
- if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters):
- # pixel loss
- if self.cri_pix:
- l_g_pix = self.cri_pix(self.output, self.gt)
- l_g_total += l_g_pix
- loss_dict['l_g_pix'] = l_g_pix
- # perceptual loss
- if self.cri_perceptual:
- l_g_percep, l_g_style = self.cri_perceptual(self.output.view(-1, c, h, w), self.gt.view(-1, c, h, w))
- if l_g_percep is not None:
- l_g_total += l_g_percep
- loss_dict['l_g_percep'] = l_g_percep
- if l_g_style is not None:
- l_g_total += l_g_style
- loss_dict['l_g_style'] = l_g_style
- # gan loss
- fake_g_pred = self.net_d(self.output.view(-1, c, h, w))
- l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False)
- l_g_total += l_g_gan
- loss_dict['l_g_gan'] = l_g_gan
-
- l_g_total.backward()
- self.optimizer_g.step()
-
- # optimize net_d
- for p in self.net_d.parameters():
- p.requires_grad = True
-
- self.optimizer_d.zero_grad()
- # real
- # reshape to (b*n, c, h, w)
- real_d_pred = self.net_d(self.gt.view(-1, c, h, w))
- l_d_real = self.cri_gan(real_d_pred, True, is_disc=True)
- loss_dict['l_d_real'] = l_d_real
- loss_dict['out_d_real'] = torch.mean(real_d_pred.detach())
- l_d_real.backward()
- # fake
- # reshape to (b*n, c, h, w)
- fake_d_pred = self.net_d(self.output.view(-1, c, h, w).detach())
- l_d_fake = self.cri_gan(fake_d_pred, False, is_disc=True)
- loss_dict['l_d_fake'] = l_d_fake
- loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach())
- l_d_fake.backward()
- self.optimizer_d.step()
-
- self.log_dict = self.reduce_loss_dict(loss_dict)
-
- if self.ema_decay > 0:
- self.model_ema(decay=self.ema_decay)
-
- def save(self, epoch, current_iter):
- if self.ema_decay > 0:
- self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema'])
- else:
- self.save_network(self.net_g, 'net_g', current_iter)
- self.save_network(self.net_d, 'net_d', current_iter)
- self.save_training_state(epoch, current_iter)
diff --git a/spaces/beihai/PDF-Table-Extractor/.history/app_20220621095508.py b/spaces/beihai/PDF-Table-Extractor/.history/app_20220621095508.py
deleted file mode 100644
index d81671aa0eda6f43052a3e8414bab78692679bb9..0000000000000000000000000000000000000000
--- a/spaces/beihai/PDF-Table-Extractor/.history/app_20220621095508.py
+++ /dev/null
@@ -1,40 +0,0 @@
-#-*- coding : utf-8-*-
-import base64
-from subprocess import STDOUT
-import streamlit as st
-import pandas as pd
-import camelot as cam # extracting tables from PDFs
-
-st.title("PDF Table Extractor")
-
-input_pdf = st.file_uploader(label = "", type = 'pdf')
-
-page_number = st.text_input("请填写表格所在PDF页码，eg: 3", value = 1)
-background = st.selectbox("表格线条是否隐藏",(False,True),)
-if input_pdf is not None:
- # byte object into a PDF file
- with open("input.pdf", "wb") as f:
- base64_pdf = base64.b64encode(input_pdf.read()).decode('utf-8')
- f.write(base64.b64decode(base64_pdf))
- f.close()
-
- # read the pdf and parse it using stream
- tables = cam.read_pdf("input.pdf", pages=page_number, process_background=background)
- result = pd.ExcelWriter('result.xlsx', engine='xlsxwriter')
- tables[0].to_excel(result,index=False)
- # for i in range(0,len(tables)):
- # table = tables[i].df
- # sheetname = str(i)
- # table.to_excel(result, sheetname,index=False)
-
- with open('result.xlsx','rb') as f:
- st.download_button('提取完成，点击下载！', f,file_name='result.xlsx',mime="application/vnd.ms-excel")
-
- tables_all= cam.read_pdf("input.pdf", pages=all, process_background=background)
- result_all = pd.ExcelWriter('result_all.xlsx', engine='xlsxwriter')
- for i in range(0,len(tables_all)):
- table = tables_all[i].df
- sheetname = str(i)
- table.to_excel(result_all, sheetname,index=False)
- with open('result_all.xlsx','rb') as f:
- st.download_button('一件抽取完成,点击下载！', f,file_name='result_all.xlsx',mime="application/vnd.ms-excel")
\ No newline at end of file
diff --git a/spaces/bigscience-data/filter_values_distributions/README.md b/spaces/bigscience-data/filter_values_distributions/README.md
deleted file mode 100644
index 5c5013b7595a0f899a92f1a4a3dadb9c69b4c67a..0000000000000000000000000000000000000000
--- a/spaces/bigscience-data/filter_values_distributions/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Filter Values Distributions
-emoji: 🐠
-colorFrom: yellow
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/blastd/LimoneSorrentin/README.md b/spaces/blastd/LimoneSorrentin/README.md
deleted file mode 100644
index a9762e1c11a26508a71307d0aacd911d9c2bcc8b..0000000000000000000000000000000000000000
--- a/spaces/blastd/LimoneSorrentin/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: LimoneSorrentin
-emoji: 📉
-colorFrom: indigo
-colorTo: blue
-sdk: gradio
-sdk_version: 3.0.20
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/brainblow/AI-TV/public/index.html b/spaces/brainblow/AI-TV/public/index.html
deleted file mode 100644
index 1c0597a24821e9c303ad9da37c3fdff5cfadeb6f..0000000000000000000000000000000000000000
--- a/spaces/brainblow/AI-TV/public/index.html
+++ /dev/null
@@ -1,325 +0,0 @@
-
-
- 🤖 AI-TV
- Loading AI-TV...
-
-
-
-
-
🤖 AI-TV
-
▶ Current channel:
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/spaces/brainblow/beat_remixer/beat_manipulator/osu.py b/spaces/brainblow/beat_remixer/beat_manipulator/osu.py
deleted file mode 100644
index 8a6becc526702d28eb4f1b42fa91a252b6ff4e09..0000000000000000000000000000000000000000
--- a/spaces/brainblow/beat_remixer/beat_manipulator/osu.py
+++ /dev/null
@@ -1,244 +0,0 @@
-from . import main
-import numpy as np
-
-# L L L L L L L L L
-def generate(song, difficulties = [0.2, 0.1, 0.05, 0.025, 0.01, 0.0075, 0.005, 0.0025], lib='madmom.MultiModelSelectionProcessor', caching=True, log = True, output = '', add_peaks = True):
- # for i in difficulties:
- # if i<0.005: print(f'Difficulties < 0.005 may result in broken beatmaps, found difficulty = {i}')
- if lib.lower == 'stunlocked': add_peaks = False
-
- if not isinstance(song, main.song): song = main.song(song)
- if log is True: print(f'Using {lib}; ', end='')
-
- filename = song.path.replace('\\', '/').split('/')[-1]
- if ' - ' in filename and len(filename.split(' - '))>1:
- artist = filename.split(' - ')[0]
- title = ' - '.join(filename.split(' - ')[1:])
- else:
- artist = ''
- title = filename
-
- if caching is True:
- audio_id=hex(len(song.audio[0]))
- import os
- if not os.path.exists('beat_manipulator/beatmaps'):
- os.mkdir('beat_manipulator/beatmaps')
- cacheDir="beat_manipulator/beatmaps/" + filename + "_"+lib+"_"+audio_id+'.txt'
- try:
- beatmap=np.loadtxt(cacheDir)
- if log is True: print('loaded cached beatmap.')
- except OSError:
- if log is True:print("beatmap hasn't been generated yet. Generating...")
- beatmap = None
-
- if beatmap is None:
- if 'madmom' in lib.lower():
- from collections.abc import MutableMapping, MutableSequence
- import madmom
- assert len(song.audio[0])>song.sr*2, f'Audio file is too short, len={len(song.audio[0])} samples, or {len(song.audio[0])/song.sr} seconds. Minimum length is 2 seconds, audio below that breaks madmom processors.'
- if lib=='madmom.RNNBeatProcessor':
- proc = madmom.features.beats.RNNBeatProcessor()
- beatmap = proc(madmom.audio.signal.Signal(song.audio.T, song.sr))
- elif lib=='madmom.MultiModelSelectionProcessor':
- proc = madmom.features.beats.RNNBeatProcessor(post_processor=None)
- predictions = proc(madmom.audio.signal.Signal(song.audio.T, song.sr))
- mm_proc = madmom.features.beats.MultiModelSelectionProcessor(num_ref_predictions=None)
- beatmap= mm_proc(predictions)*song.sr
- beatmap/= np.max(beatmap)
- elif lib=='stunlocked':
- spikes = np.abs(np.gradient(np.clip(song.audio[0], -1, 1)))[:int(len(song.audio[0]) - (len(song.audio[0])%int(song.sr/100)))]
- spikes = spikes.reshape(-1, (int(song.sr/100)))
- spikes = np.asarray(list(np.max(i) for i in spikes))
- if len(beatmap) > len(spikes): beatmap = beatmap[:len(spikes)]
- elif len(spikes) > len(beatmap): spikes = spikes[:len(beatmap)]
- zeroing = 0
- for i in range(len(spikes)):
- if zeroing > 0:
- if spikes[i] <= 0.1: zeroing -=1
- spikes[i] = 0
- elif spikes[i] >= 0.1:
- spikes[i] = 1
- zeroing = 7
- if spikes[i] <= 0.1: spikes[i] = 0
- beatmap = spikes
-
- if caching is True: np.savetxt(cacheDir, beatmap)
-
- if add_peaks is True:
- spikes = np.abs(np.gradient(np.clip(song.audio[0], -1, 1)))[:int(len(song.audio[0]) - (len(song.audio[0])%int(song.sr/100)))]
- spikes = spikes.reshape(-1, (int(song.sr/100)))
- spikes = np.asarray(list(np.max(i) for i in spikes))
- if len(beatmap) > len(spikes): beatmap = beatmap[:len(spikes)]
- elif len(spikes) > len(beatmap): spikes = spikes[:len(beatmap)]
- zeroing = 0
- for i in range(len(spikes)):
- if zeroing > 0:
- if spikes[i] <= 0.1: zeroing -=1
- spikes[i] = 0
- elif spikes[i] >= 0.1:
- spikes[i] = 1
- zeroing = 7
- if spikes[i] <= 0.1: spikes[i] = 0
- else: spikes = None
-
- def _process(song: main.song, beatmap, spikes, threshold):
- '''ඞ'''
- if add_peaks is True: beatmap += spikes
- hitmap=[]
- actual_samplerate=int(song.sr/100)
- beat_middle=int(actual_samplerate/2)
- for i in range(len(beatmap)):
- if beatmap[i]>threshold: hitmap.append(i*actual_samplerate + beat_middle)
- hitmap=np.asarray(hitmap)
- clump=[]
- for i in range(len(hitmap)-1):
- #print(i, abs(song.beatmap[i]-song.beatmap[i+1]), clump)
- if abs(hitmap[i] - hitmap[i+1]) < song.sr/16 and i != len(hitmap)-2: clump.append(i)
- elif clump!=[]:
- clump.append(i)
- actual_time=hitmap[clump[0]]
- hitmap[np.array(clump)]=0
- #print(song.beatmap)
- hitmap[clump[0]]=actual_time
- clump=[]
-
- hitmap=hitmap[hitmap!=0]
- return hitmap
-
- osufile=lambda title,artist,version: ("osu file format v14\n"
- "\n"
- "[General]\n"
- f"AudioFilename: {song.path.split('/')[-1]}\n"
- "AudioLeadIn: 0\n"
- "PreviewTime: -1\n"
- "Countdown: 0\n"
- "SampleSet: Normal\n"
- "StackLeniency: 0.5\n"
- "Mode: 0\n"
- "LetterboxInBreaks: 0\n"
- "WidescreenStoryboard: 0\n"
- "\n"
- "[Editor]\n"
- "DistanceSpacing: 1.1\n"
- "BeatDivisor: 4\n"
- "GridSize: 8\n"
- "TimelineZoom: 1.6\n"
- "\n"
- "[Metadata]\n"
- f"Title:{title}\n"
- f"TitleUnicode:{title}\n"
- f"Artist:{artist}\n"
- f"ArtistUnicode:{artist}\n"
- f'Creator:{lib} + BeatManipulator\n'
- f'Version:{version} {lib}\n'
- 'Source:\n'
- 'Tags:BeatManipulator\n'
- 'BeatmapID:0\n'
- 'BeatmapSetID:-1\n'
- '\n'
- '[Difficulty]\n'
- 'HPDrainRate:4\n'
- 'CircleSize:4\n'
- 'OverallDifficulty:5\n'
- 'ApproachRate:10\n'
- 'SliderMultiplier:3.3\n'
- 'SliderTickRate:1\n'
- '\n'
- '[Events]\n'
- '//Background and Video events\n'
- '//Break Periods\n'
- '//Storyboard Layer 0 (Background)\n'
- '//Storyboard Layer 1 (Fail)\n'
- '//Storyboard Layer 2 (Pass)\n'
- '//Storyboard Layer 3 (Foreground)\n'
- '//Storyboard Layer 4 (Overlay)\n'
- '//Storyboard Sound Samples\n'
- '\n'
- '[TimingPoints]\n'
- '0,140.0,4,1,0,100,1,0\n'
- '\n'
- '\n'
- '[HitObjects]\n')
- # remove the clumps
- #print(self.beatmap)
-
- #print(self.beatmap)
-
-
- #print(len(osumap))
- #input('banana')
- import shutil, os
- if os.path.exists('beat_manipulator/temp'): shutil.rmtree('beat_manipulator/temp')
- os.mkdir('beat_manipulator/temp')
- hitmap=[]
- import random
- for difficulty in difficulties:
- for i in range(4):
- #print(i)
- this_difficulty=_process(song, beatmap, spikes, difficulty)
- hitmap.append(this_difficulty)
-
- for k in range(len(hitmap)):
- osumap=np.vstack((hitmap[k],np.zeros(len(hitmap[k])),np.zeros(len(hitmap[k])))).T
- difficulty= difficulties[k]
- for i in range(len(osumap)-1):
- if i==0:continue
- dist=(osumap[i,0]-osumap[i-1,0])*(1-(difficulty**0.3))
- if dist<1000: dist=0.005
- elif dist<2000: dist=0.01
- elif dist<3000: dist=0.015
- elif dist<4000: dist=0.02
- elif dist<5000: dist=0.25
- elif dist<6000: dist=0.35
- elif dist<7000: dist=0.45
- elif dist<8000: dist=0.55
- elif dist<9000: dist=0.65
- elif dist<10000: dist=0.75
- elif dist<12500: dist=0.85
- elif dist<15000: dist=0.95
- elif dist<20000: dist=1
- #elif dist<30000: dist=0.8
- prev_x=osumap[i-1,1]
- prev_y=osumap[i-1,2]
- if prev_x>0: prev_x=prev_x-dist*0.1
- elif prev_x<0: prev_x=prev_x+dist*0.1
- if prev_y>0: prev_y=prev_y-dist*0.1
- elif prev_y<0: prev_y=prev_y+dist*0.1
- dirx=random.uniform(-dist,dist)
- diry=dist-abs(dirx)*random.choice([-1, 1])
- if abs(prev_x+dirx)>1: dirx=-dirx
- if abs(prev_y+diry)>1: diry=-diry
- x=prev_x+dirx
- y=prev_y+diry
- #print(dirx,diry,x,y)
- #print(x>1, x<1, y>1, y<1)
- if x>1: x=0.8
- if x<-1: x=-0.8
- if y>1: y=0.8
- if y<-1: y=-0.8
- #print(dirx,diry,x,y)
- osumap[i,1]=x
- osumap[i,2]=y
-
- osumap[:,1]*=300
- osumap[:,1]+=300
- osumap[:,2]*=180
- osumap[:,2]+=220
-
- file=osufile(artist, title, difficulty)
- for j in osumap:
- #print('285,70,'+str(int(int(i)*1000/self.samplerate))+',1,0')
- file+=f'{int(j[1])},{int(j[2])},{str(int(int(j[0])*1000/song.sr))},1,0\n'
- with open(f'beat_manipulator/temp/{artist} - {title} (BeatManipulator {difficulty} {lib}].osu', 'x', encoding="utf-8") as f:
- f.write(file)
- from . import io
- import shutil, os
- shutil.copyfile(song.path, 'beat_manipulator/temp/'+filename)
- shutil.make_archive('beat_manipulator_osz', 'zip', 'beat_manipulator/temp')
- outputname = io._outputfilename(path = output, filename = song.path, suffix = ' ('+lib + ')', ext = 'osz')
- if not os.path.exists(outputname):
- os.rename('beat_manipulator_osz.zip', outputname)
- if log is True: print(f'Created `{outputname}`')
- else: print(f'{outputname} already exists!')
- shutil.rmtree('beat_manipulator/temp')
- return outputname
\ No newline at end of file
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/data/datasets/coco.py b/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/data/datasets/coco.py
deleted file mode 100644
index ed4f7ccb20efa3b54c719783e279c381ca5d8587..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/data/datasets/coco.py
+++ /dev/null
@@ -1,539 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import contextlib
-import datetime
-import io
-import json
-import logging
-import numpy as np
-import os
-import shutil
-import pycocotools.mask as mask_util
-from fvcore.common.timer import Timer
-from iopath.common.file_io import file_lock
-from PIL import Image
-
-from detectron2.structures import Boxes, BoxMode, PolygonMasks, RotatedBoxes
-from detectron2.utils.file_io import PathManager
-
-from .. import DatasetCatalog, MetadataCatalog
-
-"""
-This file contains functions to parse COCO-format annotations into dicts in "Detectron2 format".
-"""
-
-
-logger = logging.getLogger(__name__)
-
-__all__ = ["load_coco_json", "load_sem_seg", "convert_to_coco_json", "register_coco_instances"]
-
-
-def load_coco_json(json_file, image_root, dataset_name=None, extra_annotation_keys=None):
- """
- Load a json file with COCO's instances annotation format.
- Currently supports instance detection, instance segmentation,
- and person keypoints annotations.
-
- Args:
- json_file (str): full path to the json file in COCO instances annotation format.
- image_root (str or path-like): the directory where the images in this json file exists.
- dataset_name (str or None): the name of the dataset (e.g., coco_2017_train).
- When provided, this function will also do the following:
-
- * Put "thing_classes" into the metadata associated with this dataset.
- * Map the category ids into a contiguous range (needed by standard dataset format),
- and add "thing_dataset_id_to_contiguous_id" to the metadata associated
- with this dataset.
-
- This option should usually be provided, unless users need to load
- the original json content and apply more processing manually.
- extra_annotation_keys (list[str]): list of per-annotation keys that should also be
- loaded into the dataset dict (besides "iscrowd", "bbox", "keypoints",
- "category_id", "segmentation"). The values for these keys will be returned as-is.
- For example, the densepose annotations are loaded in this way.
-
- Returns:
- list[dict]: a list of dicts in Detectron2 standard dataset dicts format (See
- `Using Custom Datasets `_ ) when `dataset_name` is not None.
- If `dataset_name` is None, the returned `category_ids` may be
- incontiguous and may not conform to the Detectron2 standard format.
-
- Notes:
- 1. This function does not read the image files.
- The results do not have the "image" field.
- """
- from pycocotools.coco import COCO
-
- timer = Timer()
- json_file = PathManager.get_local_path(json_file)
- with contextlib.redirect_stdout(io.StringIO()):
- coco_api = COCO(json_file)
- if timer.seconds() > 1:
- logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds()))
-
- id_map = None
- if dataset_name is not None:
- meta = MetadataCatalog.get(dataset_name)
- cat_ids = sorted(coco_api.getCatIds())
- cats = coco_api.loadCats(cat_ids)
- # The categories in a custom json file may not be sorted.
- thing_classes = [c["name"] for c in sorted(cats, key=lambda x: x["id"])]
- meta.thing_classes = thing_classes
-
- # In COCO, certain category ids are artificially removed,
- # and by convention they are always ignored.
- # We deal with COCO's id issue and translate
- # the category ids to contiguous ids in [0, 80).
-
- # It works by looking at the "categories" field in the json, therefore
- # if users' own json also have incontiguous ids, we'll
- # apply this mapping as well but print a warning.
- if not (min(cat_ids) == 1 and max(cat_ids) == len(cat_ids)):
- if "coco" not in dataset_name:
- logger.warning(
- """
-Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you.
-"""
- )
- id_map = {v: i for i, v in enumerate(cat_ids)}
- meta.thing_dataset_id_to_contiguous_id = id_map
-
- # sort indices for reproducible results
- img_ids = sorted(coco_api.imgs.keys())
- # imgs is a list of dicts, each looks something like:
- # {'license': 4,
- # 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg',
- # 'file_name': 'COCO_val2014_000000001268.jpg',
- # 'height': 427,
- # 'width': 640,
- # 'date_captured': '2013-11-17 05:57:24',
- # 'id': 1268}
- imgs = coco_api.loadImgs(img_ids)
- # anns is a list[list[dict]], where each dict is an annotation
- # record for an object. The inner list enumerates the objects in an image
- # and the outer list enumerates over images. Example of anns[0]:
- # [{'segmentation': [[192.81,
- # 247.09,
- # ...
- # 219.03,
- # 249.06]],
- # 'area': 1035.749,
- # 'iscrowd': 0,
- # 'image_id': 1268,
- # 'bbox': [192.81, 224.8, 74.73, 33.43],
- # 'category_id': 16,
- # 'id': 42986},
- # ...]
- anns = [coco_api.imgToAnns[img_id] for img_id in img_ids]
- total_num_valid_anns = sum([len(x) for x in anns])
- total_num_anns = len(coco_api.anns)
- if total_num_valid_anns < total_num_anns:
- logger.warning(
- f"{json_file} contains {total_num_anns} annotations, but only "
- f"{total_num_valid_anns} of them match to images in the file."
- )
-
- if "minival" not in json_file:
- # The popular valminusminival & minival annotations for COCO2014 contain this bug.
- # However the ratio of buggy annotations there is tiny and does not affect accuracy.
- # Therefore we explicitly white-list them.
- ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image]
- assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique!".format(
- json_file
- )
-
- imgs_anns = list(zip(imgs, anns))
- logger.info("Loaded {} images in COCO format from {}".format(len(imgs_anns), json_file))
-
- dataset_dicts = []
-
- ann_keys = ["iscrowd", "bbox", "keypoints", "category_id"] + (extra_annotation_keys or [])
-
- num_instances_without_valid_segmentation = 0
-
- for (img_dict, anno_dict_list) in imgs_anns:
- record = {}
- record["file_name"] = os.path.join(image_root, img_dict["file_name"])
- record["height"] = img_dict["height"]
- record["width"] = img_dict["width"]
- image_id = record["image_id"] = img_dict["id"]
-
- objs = []
- for anno in anno_dict_list:
- # Check that the image_id in this annotation is the same as
- # the image_id we're looking at.
- # This fails only when the data parsing logic or the annotation file is buggy.
-
- # The original COCO valminusminival2014 & minival2014 annotation files
- # actually contains bugs that, together with certain ways of using COCO API,
- # can trigger this assertion.
- assert anno["image_id"] == image_id
-
- assert anno.get("ignore", 0) == 0, '"ignore" in COCO json file is not supported.'
-
- obj = {key: anno[key] for key in ann_keys if key in anno}
- if "bbox" in obj and len(obj["bbox"]) == 0:
- raise ValueError(
- f"One annotation of image {image_id} contains empty 'bbox' value! "
- "This json does not have valid COCO format."
- )
-
- segm = anno.get("segmentation", None)
- if segm: # either list[list[float]] or dict(RLE)
- if isinstance(segm, dict):
- if isinstance(segm["counts"], list):
- # convert to compressed RLE
- segm = mask_util.frPyObjects(segm, *segm["size"])
- else:
- # filter out invalid polygons (< 3 points)
- segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6]
- if len(segm) == 0:
- num_instances_without_valid_segmentation += 1
- continue # ignore this instance
- obj["segmentation"] = segm
-
- keypts = anno.get("keypoints", None)
- if keypts: # list[int]
- for idx, v in enumerate(keypts):
- if idx % 3 != 2:
- # COCO's segmentation coordinates are floating points in [0, H or W],
- # but keypoint coordinates are integers in [0, H-1 or W-1]
- # Therefore we assume the coordinates are "pixel indices" and
- # add 0.5 to convert to floating point coordinates.
- keypts[idx] = v + 0.5
- obj["keypoints"] = keypts
-
- obj["bbox_mode"] = BoxMode.XYWH_ABS
- if id_map:
- annotation_category_id = obj["category_id"]
- try:
- obj["category_id"] = id_map[annotation_category_id]
- except KeyError as e:
- raise KeyError(
- f"Encountered category_id={annotation_category_id} "
- "but this id does not exist in 'categories' of the json file."
- ) from e
- objs.append(obj)
- record["annotations"] = objs
- dataset_dicts.append(record)
-
- if num_instances_without_valid_segmentation > 0:
- logger.warning(
- "Filtered out {} instances without valid segmentation. ".format(
- num_instances_without_valid_segmentation
- )
- + "There might be issues in your dataset generation process. Please "
- "check https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html carefully"
- )
- return dataset_dicts
-
-
-def load_sem_seg(gt_root, image_root, gt_ext="png", image_ext="jpg"):
- """
- Load semantic segmentation datasets. All files under "gt_root" with "gt_ext" extension are
- treated as ground truth annotations and all files under "image_root" with "image_ext" extension
- as input images. Ground truth and input images are matched using file paths relative to
- "gt_root" and "image_root" respectively without taking into account file extensions.
- This works for COCO as well as some other datasets.
-
- Args:
- gt_root (str): full path to ground truth semantic segmentation files. Semantic segmentation
- annotations are stored as images with integer values in pixels that represent
- corresponding semantic labels.
- image_root (str): the directory where the input images are.
- gt_ext (str): file extension for ground truth annotations.
- image_ext (str): file extension for input images.
-
- Returns:
- list[dict]:
- a list of dicts in detectron2 standard format without instance-level
- annotation.
-
- Notes:
- 1. This function does not read the image and ground truth files.
- The results do not have the "image" and "sem_seg" fields.
- """
-
- # We match input images with ground truth based on their relative filepaths (without file
- # extensions) starting from 'image_root' and 'gt_root' respectively.
- def file2id(folder_path, file_path):
- # extract relative path starting from `folder_path`
- image_id = os.path.normpath(os.path.relpath(file_path, start=folder_path))
- # remove file extension
- image_id = os.path.splitext(image_id)[0]
- return image_id
-
- input_files = sorted(
- (os.path.join(image_root, f) for f in PathManager.ls(image_root) if f.endswith(image_ext)),
- key=lambda file_path: file2id(image_root, file_path),
- )
- gt_files = sorted(
- (os.path.join(gt_root, f) for f in PathManager.ls(gt_root) if f.endswith(gt_ext)),
- key=lambda file_path: file2id(gt_root, file_path),
- )
-
- assert len(gt_files) > 0, "No annotations found in {}.".format(gt_root)
-
- # Use the intersection, so that val2017_100 annotations can run smoothly with val2017 images
- if len(input_files) != len(gt_files):
- logger.warn(
- "Directory {} and {} has {} and {} files, respectively.".format(
- image_root, gt_root, len(input_files), len(gt_files)
- )
- )
- input_basenames = [os.path.basename(f)[: -len(image_ext)] for f in input_files]
- gt_basenames = [os.path.basename(f)[: -len(gt_ext)] for f in gt_files]
- intersect = list(set(input_basenames) & set(gt_basenames))
- # sort, otherwise each worker may obtain a list[dict] in different order
- intersect = sorted(intersect)
- logger.warn("Will use their intersection of {} files.".format(len(intersect)))
- input_files = [os.path.join(image_root, f + image_ext) for f in intersect]
- gt_files = [os.path.join(gt_root, f + gt_ext) for f in intersect]
-
- logger.info(
- "Loaded {} images with semantic segmentation from {}".format(len(input_files), image_root)
- )
-
- dataset_dicts = []
- for (img_path, gt_path) in zip(input_files, gt_files):
- record = {}
- record["file_name"] = img_path
- record["sem_seg_file_name"] = gt_path
- dataset_dicts.append(record)
-
- return dataset_dicts
-
-
-def convert_to_coco_dict(dataset_name):
- """
- Convert an instance detection/segmentation or keypoint detection dataset
- in detectron2's standard format into COCO json format.
-
- Generic dataset description can be found here:
- https://detectron2.readthedocs.io/tutorials/datasets.html#register-a-dataset
-
- COCO data format description can be found here:
- http://cocodataset.org/#format-data
-
- Args:
- dataset_name (str):
- name of the source dataset
- Must be registered in DatastCatalog and in detectron2's standard format.
- Must have corresponding metadata "thing_classes"
- Returns:
- coco_dict: serializable dict in COCO json format
- """
-
- dataset_dicts = DatasetCatalog.get(dataset_name)
- metadata = MetadataCatalog.get(dataset_name)
-
- # unmap the category mapping ids for COCO
- if hasattr(metadata, "thing_dataset_id_to_contiguous_id"):
- reverse_id_mapping = {v: k for k, v in metadata.thing_dataset_id_to_contiguous_id.items()}
- reverse_id_mapper = lambda contiguous_id: reverse_id_mapping[contiguous_id] # noqa
- else:
- reverse_id_mapper = lambda contiguous_id: contiguous_id # noqa
-
- categories = [
- {"id": reverse_id_mapper(id), "name": name}
- for id, name in enumerate(metadata.thing_classes)
- ]
-
- logger.info("Converting dataset dicts into COCO format")
- coco_images = []
- coco_annotations = []
-
- for image_id, image_dict in enumerate(dataset_dicts):
- coco_image = {
- "id": image_dict.get("image_id", image_id),
- "width": int(image_dict["width"]),
- "height": int(image_dict["height"]),
- "file_name": str(image_dict["file_name"]),
- }
- coco_images.append(coco_image)
-
- anns_per_image = image_dict.get("annotations", [])
- for annotation in anns_per_image:
- # create a new dict with only COCO fields
- coco_annotation = {}
-
- # COCO requirement: XYWH box format for axis-align and XYWHA for rotated
- bbox = annotation["bbox"]
- if isinstance(bbox, np.ndarray):
- if bbox.ndim != 1:
- raise ValueError(f"bbox has to be 1-dimensional. Got shape={bbox.shape}.")
- bbox = bbox.tolist()
- if len(bbox) not in [4, 5]:
- raise ValueError(f"bbox has to has length 4 or 5. Got {bbox}.")
- from_bbox_mode = annotation["bbox_mode"]
- to_bbox_mode = BoxMode.XYWH_ABS if len(bbox) == 4 else BoxMode.XYWHA_ABS
- bbox = BoxMode.convert(bbox, from_bbox_mode, to_bbox_mode)
-
- # COCO requirement: instance area
- if "segmentation" in annotation:
- # Computing areas for instances by counting the pixels
- segmentation = annotation["segmentation"]
- # TODO: check segmentation type: RLE, BinaryMask or Polygon
- if isinstance(segmentation, list):
- polygons = PolygonMasks([segmentation])
- area = polygons.area()[0].item()
- elif isinstance(segmentation, dict): # RLE
- area = mask_util.area(segmentation).item()
- else:
- raise TypeError(f"Unknown segmentation type {type(segmentation)}!")
- else:
- # Computing areas using bounding boxes
- if to_bbox_mode == BoxMode.XYWH_ABS:
- bbox_xy = BoxMode.convert(bbox, to_bbox_mode, BoxMode.XYXY_ABS)
- area = Boxes([bbox_xy]).area()[0].item()
- else:
- area = RotatedBoxes([bbox]).area()[0].item()
-
- if "keypoints" in annotation:
- keypoints = annotation["keypoints"] # list[int]
- for idx, v in enumerate(keypoints):
- if idx % 3 != 2:
- # COCO's segmentation coordinates are floating points in [0, H or W],
- # but keypoint coordinates are integers in [0, H-1 or W-1]
- # For COCO format consistency we substract 0.5
- # https://github.com/facebookresearch/detectron2/pull/175#issuecomment-551202163
- keypoints[idx] = v - 0.5
- if "num_keypoints" in annotation:
- num_keypoints = annotation["num_keypoints"]
- else:
- num_keypoints = sum(kp > 0 for kp in keypoints[2::3])
-
- # COCO requirement:
- # linking annotations to images
- # "id" field must start with 1
- coco_annotation["id"] = len(coco_annotations) + 1
- coco_annotation["image_id"] = coco_image["id"]
- coco_annotation["bbox"] = [round(float(x), 3) for x in bbox]
- coco_annotation["area"] = float(area)
- coco_annotation["iscrowd"] = int(annotation.get("iscrowd", 0))
- coco_annotation["category_id"] = int(reverse_id_mapper(annotation["category_id"]))
-
- # Add optional fields
- if "keypoints" in annotation:
- coco_annotation["keypoints"] = keypoints
- coco_annotation["num_keypoints"] = num_keypoints
-
- if "segmentation" in annotation:
- seg = coco_annotation["segmentation"] = annotation["segmentation"]
- if isinstance(seg, dict): # RLE
- counts = seg["counts"]
- if not isinstance(counts, str):
- # make it json-serializable
- seg["counts"] = counts.decode("ascii")
-
- coco_annotations.append(coco_annotation)
-
- logger.info(
- "Conversion finished, "
- f"#images: {len(coco_images)}, #annotations: {len(coco_annotations)}"
- )
-
- info = {
- "date_created": str(datetime.datetime.now()),
- "description": "Automatically generated COCO json file for Detectron2.",
- }
- coco_dict = {"info": info, "images": coco_images, "categories": categories, "licenses": None}
- if len(coco_annotations) > 0:
- coco_dict["annotations"] = coco_annotations
- return coco_dict
-
-
-def convert_to_coco_json(dataset_name, output_file, allow_cached=True):
- """
- Converts dataset into COCO format and saves it to a json file.
- dataset_name must be registered in DatasetCatalog and in detectron2's standard format.
-
- Args:
- dataset_name:
- reference from the config file to the catalogs
- must be registered in DatasetCatalog and in detectron2's standard format
- output_file: path of json file that will be saved to
- allow_cached: if json file is already present then skip conversion
- """
-
- # TODO: The dataset or the conversion script *may* change,
- # a checksum would be useful for validating the cached data
-
- PathManager.mkdirs(os.path.dirname(output_file))
- with file_lock(output_file):
- if PathManager.exists(output_file) and allow_cached:
- logger.warning(
- f"Using previously cached COCO format annotations at '{output_file}'. "
- "You need to clear the cache file if your dataset has been modified."
- )
- else:
- logger.info(f"Converting annotations of dataset '{dataset_name}' to COCO format ...)")
- coco_dict = convert_to_coco_dict(dataset_name)
-
- logger.info(f"Caching COCO format annotations at '{output_file}' ...")
- tmp_file = output_file + ".tmp"
- with PathManager.open(tmp_file, "w") as f:
- json.dump(coco_dict, f)
- shutil.move(tmp_file, output_file)
-
-
-def register_coco_instances(name, metadata, json_file, image_root):
- """
- Register a dataset in COCO's json annotation format for
- instance detection, instance segmentation and keypoint detection.
- (i.e., Type 1 and 2 in http://cocodataset.org/#format-data.
- `instances*.json` and `person_keypoints*.json` in the dataset).
-
- This is an example of how to register a new dataset.
- You can do something similar to this function, to register new datasets.
-
- Args:
- name (str): the name that identifies a dataset, e.g. "coco_2014_train".
- metadata (dict): extra metadata associated with this dataset. You can
- leave it as an empty dict.
- json_file (str): path to the json instance annotation file.
- image_root (str or path-like): directory which contains all the images.
- """
- assert isinstance(name, str), name
- assert isinstance(json_file, (str, os.PathLike)), json_file
- assert isinstance(image_root, (str, os.PathLike)), image_root
- # 1. register a function which returns dicts
- DatasetCatalog.register(name, lambda: load_coco_json(json_file, image_root, name))
-
- # 2. Optionally, add metadata about this dataset,
- # since they might be useful in evaluation, visualization or logging
- MetadataCatalog.get(name).set(
- json_file=json_file, image_root=image_root, evaluator_type="coco", **metadata
- )
-
-
-if __name__ == "__main__":
- """
- Test the COCO json dataset loader.
-
- Usage:
- python -m detectron2.data.datasets.coco \
- path/to/json path/to/image_root dataset_name
-
- "dataset_name" can be "coco_2014_minival_100", or other
- pre-registered ones
- """
- from detectron2.utils.logger import setup_logger
- from detectron2.utils.visualizer import Visualizer
- import detectron2.data.datasets # noqa # add pre-defined metadata
- import sys
-
- logger = setup_logger(name=__name__)
- assert sys.argv[3] in DatasetCatalog.list()
- meta = MetadataCatalog.get(sys.argv[3])
-
- dicts = load_coco_json(sys.argv[1], sys.argv[2], sys.argv[3])
- logger.info("Done loading {} samples.".format(len(dicts)))
-
- dirname = "coco-data-vis"
- os.makedirs(dirname, exist_ok=True)
- for d in dicts:
- img = np.array(Image.open(d["file_name"]))
- visualizer = Visualizer(img, metadata=meta)
- vis = visualizer.draw_dataset_dict(d)
- fpath = os.path.join(dirname, os.path.basename(d["file_name"]))
- vis.save(fpath)
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/data/transform/__init__.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/data/transform/__init__.py
deleted file mode 100644
index 369e1b278899b225d55bfc729514873b4259c7b9..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/data/transform/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-from .image import ImageResizeTransform
diff --git a/spaces/cahodk/live-ml5-facemesh-p5js/index.html b/spaces/cahodk/live-ml5-facemesh-p5js/index.html
deleted file mode 100644
index 161ad643319020a2bfeacc26ff284eebd85e0e6e..0000000000000000000000000000000000000000
--- a/spaces/cahodk/live-ml5-facemesh-p5js/index.html
+++ /dev/null
@@ -1,28 +0,0 @@
-
-
-
-
-
- PoseNet example using p5.js
-
-
-
-
-
-
- PoseNet example using p5.js
- Loading model...
-
-
-
-
\ No newline at end of file
diff --git a/spaces/chendl/compositional_test/multimodal/YOLOX/tools/export_onnx.py b/spaces/chendl/compositional_test/multimodal/YOLOX/tools/export_onnx.py
deleted file mode 100644
index 8703166a4ee487d2d4b713b42c6f8c55879281db..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/multimodal/YOLOX/tools/export_onnx.py
+++ /dev/null
@@ -1,116 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-# Copyright (c) Megvii, Inc. and its affiliates.
-
-import argparse
-import os
-from loguru import logger
-
-import torch
-from torch import nn
-
-from yolox.exp import get_exp
-from yolox.models.network_blocks import SiLU
-from yolox.utils import replace_module
-
-
-def make_parser():
- parser = argparse.ArgumentParser("YOLOX onnx deploy")
- parser.add_argument(
- "--output-name", type=str, default="yolox.onnx", help="output name of models"
- )
- parser.add_argument(
- "--input", default="images", type=str, help="input node name of onnx model"
- )
- parser.add_argument(
- "--output", default="output", type=str, help="output node name of onnx model"
- )
- parser.add_argument(
- "-o", "--opset", default=11, type=int, help="onnx opset version"
- )
- parser.add_argument("--batch-size", type=int, default=1, help="batch size")
- parser.add_argument(
- "--dynamic", action="store_true", help="whether the input shape should be dynamic or not"
- )
- parser.add_argument("--no-onnxsim", action="store_true", help="use onnxsim or not")
- parser.add_argument(
- "-f",
- "--exp_file",
- default=None,
- type=str,
- help="experiment description file",
- )
- parser.add_argument("-expn", "--experiment-name", type=str, default=None)
- parser.add_argument("-n", "--name", type=str, default=None, help="model name")
- parser.add_argument("-c", "--ckpt", default=None, type=str, help="ckpt path")
- parser.add_argument(
- "opts",
- help="Modify config options using the command-line",
- default=None,
- nargs=argparse.REMAINDER,
- )
- parser.add_argument(
- "--decode_in_inference",
- action="store_true",
- help="decode in inference or not"
- )
-
- return parser
-
-
-@logger.catch
-def main():
- args = make_parser().parse_args()
- logger.info("args value: {}".format(args))
- exp = get_exp(args.exp_file, args.name)
- exp.merge(args.opts)
-
- if not args.experiment_name:
- args.experiment_name = exp.exp_name
-
- model = exp.get_model()
- if args.ckpt is None:
- file_name = os.path.join(exp.output_dir, args.experiment_name)
- ckpt_file = os.path.join(file_name, "best_ckpt.pth")
- else:
- ckpt_file = args.ckpt
-
- # load the model state dict
- ckpt = torch.load(ckpt_file, map_location="cpu")
-
- model.eval()
- if "model" in ckpt:
- ckpt = ckpt["model"]
- model.load_state_dict(ckpt)
- model = replace_module(model, nn.SiLU, SiLU)
- model.head.decode_in_inference = args.decode_in_inference
-
- logger.info("loading checkpoint done.")
- dummy_input = torch.randn(args.batch_size, 3, exp.test_size[0], exp.test_size[1])
-
- torch.onnx._export(
- model,
- dummy_input,
- args.output_name,
- input_names=[args.input],
- output_names=[args.output],
- dynamic_axes={args.input: {0: 'batch'},
- args.output: {0: 'batch'}} if args.dynamic else None,
- opset_version=args.opset,
- )
- logger.info("generated onnx model named {}".format(args.output_name))
-
- if not args.no_onnxsim:
- import onnx
- from onnxsim import simplify
-
- # use onnx-simplifier to reduce reduent model.
- onnx_model = onnx.load(args.output_name)
- model_simp, check = simplify(onnx_model)
- assert check, "Simplified ONNX model could not be validated"
- onnx.save(model_simp, args.output_name)
- logger.info("generated simplified onnx model named {}".format(args.output_name))
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/chendl/compositional_test/multimodal/YOLOX/yolox/layers/cocoeval/cocoeval.cpp b/spaces/chendl/compositional_test/multimodal/YOLOX/yolox/layers/cocoeval/cocoeval.cpp
deleted file mode 100644
index 2e63bc9952918060f55999ec100b283d83616b46..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/multimodal/YOLOX/yolox/layers/cocoeval/cocoeval.cpp
+++ /dev/null
@@ -1,502 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-#include "cocoeval.h"
-#include
-#include
-#include
-#include
-
-using namespace pybind11::literals;
-
-namespace COCOeval {
-
-// Sort detections from highest score to lowest, such that
-// detection_instances[detection_sorted_indices[t]] >=
-// detection_instances[detection_sorted_indices[t+1]]. Use stable_sort to match
-// original COCO API
-void SortInstancesByDetectionScore(
- const std::vector& detection_instances,
- std::vector* detection_sorted_indices) {
- detection_sorted_indices->resize(detection_instances.size());
- std::iota(
- detection_sorted_indices->begin(), detection_sorted_indices->end(), 0);
- std::stable_sort(
- detection_sorted_indices->begin(),
- detection_sorted_indices->end(),
- [&detection_instances](size_t j1, size_t j2) {
- return detection_instances[j1].score > detection_instances[j2].score;
- });
-}
-
-// Partition the ground truth objects based on whether or not to ignore them
-// based on area
-void SortInstancesByIgnore(
- const std::array& area_range,
- const std::vector& ground_truth_instances,
- std::vector* ground_truth_sorted_indices,
- std::vector* ignores) {
- ignores->clear();
- ignores->reserve(ground_truth_instances.size());
- for (auto o : ground_truth_instances) {
- ignores->push_back(
- o.ignore || o.area < area_range[0] || o.area > area_range[1]);
- }
-
- ground_truth_sorted_indices->resize(ground_truth_instances.size());
- std::iota(
- ground_truth_sorted_indices->begin(),
- ground_truth_sorted_indices->end(),
- 0);
- std::stable_sort(
- ground_truth_sorted_indices->begin(),
- ground_truth_sorted_indices->end(),
- [&ignores](size_t j1, size_t j2) {
- return (int)(*ignores)[j1] < (int)(*ignores)[j2];
- });
-}
-
-// For each IOU threshold, greedily match each detected instance to a ground
-// truth instance (if possible) and store the results
-void MatchDetectionsToGroundTruth(
- const std::vector& detection_instances,
- const std::vector& detection_sorted_indices,
- const std::vector& ground_truth_instances,
- const std::vector& ground_truth_sorted_indices,
- const std::vector& ignores,
- const std::vector>& ious,
- const std::vector& iou_thresholds,
- const std::array& area_range,
- ImageEvaluation* results) {
- // Initialize memory to store return data matches and ignore
- const int num_iou_thresholds = iou_thresholds.size();
- const int num_ground_truth = ground_truth_sorted_indices.size();
- const int num_detections = detection_sorted_indices.size();
- std::vector ground_truth_matches(
- num_iou_thresholds * num_ground_truth, 0);
- std::vector& detection_matches = results->detection_matches;
- std::vector& detection_ignores = results->detection_ignores;
- std::vector& ground_truth_ignores = results->ground_truth_ignores;
- detection_matches.resize(num_iou_thresholds * num_detections, 0);
- detection_ignores.resize(num_iou_thresholds * num_detections, false);
- ground_truth_ignores.resize(num_ground_truth);
- for (auto g = 0; g < num_ground_truth; ++g) {
- ground_truth_ignores[g] = ignores[ground_truth_sorted_indices[g]];
- }
-
- for (auto t = 0; t < num_iou_thresholds; ++t) {
- for (auto d = 0; d < num_detections; ++d) {
- // information about best match so far (match=-1 -> unmatched)
- double best_iou = std::min(iou_thresholds[t], 1 - 1e-10);
- int match = -1;
- for (auto g = 0; g < num_ground_truth; ++g) {
- // if this ground truth instance is already matched and not a
- // crowd, it cannot be matched to another detection
- if (ground_truth_matches[t * num_ground_truth + g] > 0 &&
- !ground_truth_instances[ground_truth_sorted_indices[g]].is_crowd) {
- continue;
- }
-
- // if detected instance matched to a regular ground truth
- // instance, we can break on the first ground truth instance
- // tagged as ignore (because they are sorted by the ignore tag)
- if (match >= 0 && !ground_truth_ignores[match] &&
- ground_truth_ignores[g]) {
- break;
- }
-
- // if IOU overlap is the best so far, store the match appropriately
- if (ious[d][ground_truth_sorted_indices[g]] >= best_iou) {
- best_iou = ious[d][ground_truth_sorted_indices[g]];
- match = g;
- }
- }
- // if match was made, store id of match for both detection and
- // ground truth
- if (match >= 0) {
- detection_ignores[t * num_detections + d] = ground_truth_ignores[match];
- detection_matches[t * num_detections + d] =
- ground_truth_instances[ground_truth_sorted_indices[match]].id;
- ground_truth_matches[t * num_ground_truth + match] =
- detection_instances[detection_sorted_indices[d]].id;
- }
-
- // set unmatched detections outside of area range to ignore
- const InstanceAnnotation& detection =
- detection_instances[detection_sorted_indices[d]];
- detection_ignores[t * num_detections + d] =
- detection_ignores[t * num_detections + d] ||
- (detection_matches[t * num_detections + d] == 0 &&
- (detection.area < area_range[0] || detection.area > area_range[1]));
- }
- }
-
- // store detection score results
- results->detection_scores.resize(detection_sorted_indices.size());
- for (size_t d = 0; d < detection_sorted_indices.size(); ++d) {
- results->detection_scores[d] =
- detection_instances[detection_sorted_indices[d]].score;
- }
-}
-
-std::vector EvaluateImages(
- const std::vector>& area_ranges,
- int max_detections,
- const std::vector& iou_thresholds,
- const ImageCategoryInstances>& image_category_ious,
- const ImageCategoryInstances&
- image_category_ground_truth_instances,
- const ImageCategoryInstances&
- image_category_detection_instances) {
- const int num_area_ranges = area_ranges.size();
- const int num_images = image_category_ground_truth_instances.size();
- const int num_categories =
- image_category_ious.size() > 0 ? image_category_ious[0].size() : 0;
- std::vector detection_sorted_indices;
- std::vector ground_truth_sorted_indices;
- std::vector ignores;
- std::vector results_all(
- num_images * num_area_ranges * num_categories);
-
- // Store results for each image, category, and area range combination. Results
- // for each IOU threshold are packed into the same ImageEvaluation object
- for (auto i = 0; i < num_images; ++i) {
- for (auto c = 0; c < num_categories; ++c) {
- const std::vector& ground_truth_instances =
- image_category_ground_truth_instances[i][c];
- const std::vector& detection_instances =
- image_category_detection_instances[i][c];
-
- SortInstancesByDetectionScore(
- detection_instances, &detection_sorted_indices);
- if ((int)detection_sorted_indices.size() > max_detections) {
- detection_sorted_indices.resize(max_detections);
- }
-
- for (size_t a = 0; a < area_ranges.size(); ++a) {
- SortInstancesByIgnore(
- area_ranges[a],
- ground_truth_instances,
- &ground_truth_sorted_indices,
- &ignores);
-
- MatchDetectionsToGroundTruth(
- detection_instances,
- detection_sorted_indices,
- ground_truth_instances,
- ground_truth_sorted_indices,
- ignores,
- image_category_ious[i][c],
- iou_thresholds,
- area_ranges[a],
- &results_all
- [c * num_area_ranges * num_images + a * num_images + i]);
- }
- }
- }
-
- return results_all;
-}
-
-// Convert a python list to a vector
-template
-std::vector list_to_vec(const py::list& l) {
- std::vector v(py::len(l));
- for (int i = 0; i < (int)py::len(l); ++i) {
- v[i] = l[i].cast();
- }
- return v;
-}
-
-// Helper function to Accumulate()
-// Considers the evaluation results applicable to a particular category, area
-// range, and max_detections parameter setting, which begin at
-// evaluations[evaluation_index]. Extracts a sorted list of length n of all
-// applicable detection instances concatenated across all images in the dataset,
-// which are represented by the outputs evaluation_indices, detection_scores,
-// image_detection_indices, and detection_sorted_indices--all of which are
-// length n. evaluation_indices[i] stores the applicable index into
-// evaluations[] for instance i, which has detection score detection_score[i],
-// and is the image_detection_indices[i]'th of the list of detections
-// for the image containing i. detection_sorted_indices[] defines a sorted
-// permutation of the 3 other outputs
-int BuildSortedDetectionList(
- const std::vector& evaluations,
- const int64_t evaluation_index,
- const int64_t num_images,
- const int max_detections,
- std::vector* evaluation_indices,
- std::vector* detection_scores,
- std::vector* detection_sorted_indices,
- std::vector* image_detection_indices) {
- assert(evaluations.size() >= evaluation_index + num_images);
-
- // Extract a list of object instances of the applicable category, area
- // range, and max detections requirements such that they can be sorted
- image_detection_indices->clear();
- evaluation_indices->clear();
- detection_scores->clear();
- image_detection_indices->reserve(num_images * max_detections);
- evaluation_indices->reserve(num_images * max_detections);
- detection_scores->reserve(num_images * max_detections);
- int num_valid_ground_truth = 0;
- for (auto i = 0; i < num_images; ++i) {
- const ImageEvaluation& evaluation = evaluations[evaluation_index + i];
-
- for (int d = 0;
- d < (int)evaluation.detection_scores.size() && d < max_detections;
- ++d) { // detected instances
- evaluation_indices->push_back(evaluation_index + i);
- image_detection_indices->push_back(d);
- detection_scores->push_back(evaluation.detection_scores[d]);
- }
- for (auto ground_truth_ignore : evaluation.ground_truth_ignores) {
- if (!ground_truth_ignore) {
- ++num_valid_ground_truth;
- }
- }
- }
-
- // Sort detections by decreasing score, using stable sort to match
- // python implementation
- detection_sorted_indices->resize(detection_scores->size());
- std::iota(
- detection_sorted_indices->begin(), detection_sorted_indices->end(), 0);
- std::stable_sort(
- detection_sorted_indices->begin(),
- detection_sorted_indices->end(),
- [&detection_scores](size_t j1, size_t j2) {
- return (*detection_scores)[j1] > (*detection_scores)[j2];
- });
-
- return num_valid_ground_truth;
-}
-
-// Helper function to Accumulate()
-// Compute a precision recall curve given a sorted list of detected instances
-// encoded in evaluations, evaluation_indices, detection_scores,
-// detection_sorted_indices, image_detection_indices (see
-// BuildSortedDetectionList()). Using vectors precisions and recalls
-// and temporary storage, output the results into precisions_out, recalls_out,
-// and scores_out, which are large buffers containing many precion/recall curves
-// for all possible parameter settings, with precisions_out_index and
-// recalls_out_index defining the applicable indices to store results.
-void ComputePrecisionRecallCurve(
- const int64_t precisions_out_index,
- const int64_t precisions_out_stride,
- const int64_t recalls_out_index,
- const std::vector& recall_thresholds,
- const int iou_threshold_index,
- const int num_iou_thresholds,
- const int num_valid_ground_truth,
- const std::vector& evaluations,
- const std::vector& evaluation_indices,
- const std::vector& detection_scores,
- const std::vector& detection_sorted_indices,
- const std::vector& image_detection_indices,
- std::vector* precisions,
- std::vector* recalls,
- std::vector* precisions_out,
- std::vector* scores_out,
- std::vector* recalls_out) {
- assert(recalls_out->size() > recalls_out_index);
-
- // Compute precision/recall for each instance in the sorted list of detections
- int64_t true_positives_sum = 0, false_positives_sum = 0;
- precisions->clear();
- recalls->clear();
- precisions->reserve(detection_sorted_indices.size());
- recalls->reserve(detection_sorted_indices.size());
- assert(!evaluations.empty() || detection_sorted_indices.empty());
- for (auto detection_sorted_index : detection_sorted_indices) {
- const ImageEvaluation& evaluation =
- evaluations[evaluation_indices[detection_sorted_index]];
- const auto num_detections =
- evaluation.detection_matches.size() / num_iou_thresholds;
- const auto detection_index = iou_threshold_index * num_detections +
- image_detection_indices[detection_sorted_index];
- assert(evaluation.detection_matches.size() > detection_index);
- assert(evaluation.detection_ignores.size() > detection_index);
- const int64_t detection_match =
- evaluation.detection_matches[detection_index];
- const bool detection_ignores =
- evaluation.detection_ignores[detection_index];
- const auto true_positive = detection_match > 0 && !detection_ignores;
- const auto false_positive = detection_match == 0 && !detection_ignores;
- if (true_positive) {
- ++true_positives_sum;
- }
- if (false_positive) {
- ++false_positives_sum;
- }
-
- const double recall =
- static_cast(true_positives_sum) / num_valid_ground_truth;
- recalls->push_back(recall);
- const int64_t num_valid_detections =
- true_positives_sum + false_positives_sum;
- const double precision = num_valid_detections > 0
- ? static_cast(true_positives_sum) / num_valid_detections
- : 0.0;
- precisions->push_back(precision);
- }
-
- (*recalls_out)[recalls_out_index] = !recalls->empty() ? recalls->back() : 0;
-
- for (int64_t i = static_cast(precisions->size()) - 1; i > 0; --i) {
- if ((*precisions)[i] > (*precisions)[i - 1]) {
- (*precisions)[i - 1] = (*precisions)[i];
- }
- }
-
- // Sample the per instance precision/recall list at each recall threshold
- for (size_t r = 0; r < recall_thresholds.size(); ++r) {
- // first index in recalls >= recall_thresholds[r]
- std::vector::iterator low = std::lower_bound(
- recalls->begin(), recalls->end(), recall_thresholds[r]);
- size_t precisions_index = low - recalls->begin();
-
- const auto results_ind = precisions_out_index + r * precisions_out_stride;
- assert(results_ind < precisions_out->size());
- assert(results_ind < scores_out->size());
- if (precisions_index < precisions->size()) {
- (*precisions_out)[results_ind] = (*precisions)[precisions_index];
- (*scores_out)[results_ind] =
- detection_scores[detection_sorted_indices[precisions_index]];
- } else {
- (*precisions_out)[results_ind] = 0;
- (*scores_out)[results_ind] = 0;
- }
- }
-}
-py::dict Accumulate(
- const py::object& params,
- const std::vector& evaluations) {
- const std::vector recall_thresholds =
- list_to_vec(params.attr("recThrs"));
- const std::vector max_detections =
- list_to_vec(params.attr("maxDets"));
- const int num_iou_thresholds = py::len(params.attr("iouThrs"));
- const int num_recall_thresholds = py::len(params.attr("recThrs"));
- const int num_categories = params.attr("useCats").cast() == 1
- ? py::len(params.attr("catIds"))
- : 1;
- const int num_area_ranges = py::len(params.attr("areaRng"));
- const int num_max_detections = py::len(params.attr("maxDets"));
- const int num_images = py::len(params.attr("imgIds"));
-
- std::vector precisions_out(
- num_iou_thresholds * num_recall_thresholds * num_categories *
- num_area_ranges * num_max_detections,
- -1);
- std::vector recalls_out(
- num_iou_thresholds * num_categories * num_area_ranges *
- num_max_detections,
- -1);
- std::vector scores_out(
- num_iou_thresholds * num_recall_thresholds * num_categories *
- num_area_ranges * num_max_detections,
- -1);
-
- // Consider the list of all detected instances in the entire dataset in one
- // large list. evaluation_indices, detection_scores,
- // image_detection_indices, and detection_sorted_indices all have the same
- // length as this list, such that each entry corresponds to one detected
- // instance
- std::vector evaluation_indices; // indices into evaluations[]
- std::vector detection_scores; // detection scores of each instance
- std::vector detection_sorted_indices; // sorted indices of all
- // instances in the dataset
- std::vector
- image_detection_indices; // indices into the list of detected instances in
- // the same image as each instance
- std::vector precisions, recalls;
-
- for (auto c = 0; c < num_categories; ++c) {
- for (auto a = 0; a < num_area_ranges; ++a) {
- for (auto m = 0; m < num_max_detections; ++m) {
- // The COCO PythonAPI assumes evaluations[] (the return value of
- // COCOeval::EvaluateImages() is one long list storing results for each
- // combination of category, area range, and image id, with categories in
- // the outermost loop and images in the innermost loop.
- const int64_t evaluations_index =
- c * num_area_ranges * num_images + a * num_images;
- int num_valid_ground_truth = BuildSortedDetectionList(
- evaluations,
- evaluations_index,
- num_images,
- max_detections[m],
- &evaluation_indices,
- &detection_scores,
- &detection_sorted_indices,
- &image_detection_indices);
-
- if (num_valid_ground_truth == 0) {
- continue;
- }
-
- for (auto t = 0; t < num_iou_thresholds; ++t) {
- // recalls_out is a flattened vectors representing a
- // num_iou_thresholds X num_categories X num_area_ranges X
- // num_max_detections matrix
- const int64_t recalls_out_index =
- t * num_categories * num_area_ranges * num_max_detections +
- c * num_area_ranges * num_max_detections +
- a * num_max_detections + m;
-
- // precisions_out and scores_out are flattened vectors
- // representing a num_iou_thresholds X num_recall_thresholds X
- // num_categories X num_area_ranges X num_max_detections matrix
- const int64_t precisions_out_stride =
- num_categories * num_area_ranges * num_max_detections;
- const int64_t precisions_out_index = t * num_recall_thresholds *
- num_categories * num_area_ranges * num_max_detections +
- c * num_area_ranges * num_max_detections +
- a * num_max_detections + m;
-
- ComputePrecisionRecallCurve(
- precisions_out_index,
- precisions_out_stride,
- recalls_out_index,
- recall_thresholds,
- t,
- num_iou_thresholds,
- num_valid_ground_truth,
- evaluations,
- evaluation_indices,
- detection_scores,
- detection_sorted_indices,
- image_detection_indices,
- &precisions,
- &recalls,
- &precisions_out,
- &scores_out,
- &recalls_out);
- }
- }
- }
- }
-
- time_t rawtime;
- struct tm local_time;
- std::array buffer;
- time(&rawtime);
-#ifdef _WIN32
- localtime_s(&local_time, &rawtime);
-#else
- localtime_r(&rawtime, &local_time);
-#endif
- strftime(
- buffer.data(), 200, "%Y-%m-%d %H:%num_max_detections:%S", &local_time);
- return py::dict(
- "params"_a = params,
- "counts"_a = std::vector({num_iou_thresholds,
- num_recall_thresholds,
- num_categories,
- num_area_ranges,
- num_max_detections}),
- "date"_a = buffer,
- "precision"_a = precisions_out,
- "recall"_a = recalls_out,
- "scores"_a = scores_out);
-}
-
-} // namespace COCOeval
diff --git a/spaces/chendl/compositional_test/multimodal/YOLOX/yolox/models/network_blocks.py b/spaces/chendl/compositional_test/multimodal/YOLOX/yolox/models/network_blocks.py
deleted file mode 100644
index 68aacfc33208eab072422e0647742006984dfdfd..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/multimodal/YOLOX/yolox/models/network_blocks.py
+++ /dev/null
@@ -1,210 +0,0 @@
-#!/usr/bin/env python
-# -*- encoding: utf-8 -*-
-# Copyright (c) Megvii Inc. All rights reserved.
-
-import torch
-import torch.nn as nn
-
-
-class SiLU(nn.Module):
- """export-friendly version of nn.SiLU()"""
-
- @staticmethod
- def forward(x):
- return x * torch.sigmoid(x)
-
-
-def get_activation(name="silu", inplace=True):
- if name == "silu":
- module = nn.SiLU(inplace=inplace)
- elif name == "relu":
- module = nn.ReLU(inplace=inplace)
- elif name == "lrelu":
- module = nn.LeakyReLU(0.1, inplace=inplace)
- else:
- raise AttributeError("Unsupported act type: {}".format(name))
- return module
-
-
-class BaseConv(nn.Module):
- """A Conv2d -> Batchnorm -> silu/leaky relu block"""
-
- def __init__(
- self, in_channels, out_channels, ksize, stride, groups=1, bias=False, act="silu"
- ):
- super().__init__()
- # same padding
- pad = (ksize - 1) // 2
- self.conv = nn.Conv2d(
- in_channels,
- out_channels,
- kernel_size=ksize,
- stride=stride,
- padding=pad,
- groups=groups,
- bias=bias,
- )
- self.bn = nn.BatchNorm2d(out_channels)
- self.act = get_activation(act, inplace=True)
-
- def forward(self, x):
- return self.act(self.bn(self.conv(x)))
-
- def fuseforward(self, x):
- return self.act(self.conv(x))
-
-
-class DWConv(nn.Module):
- """Depthwise Conv + Conv"""
-
- def __init__(self, in_channels, out_channels, ksize, stride=1, act="silu"):
- super().__init__()
- self.dconv = BaseConv(
- in_channels,
- in_channels,
- ksize=ksize,
- stride=stride,
- groups=in_channels,
- act=act,
- )
- self.pconv = BaseConv(
- in_channels, out_channels, ksize=1, stride=1, groups=1, act=act
- )
-
- def forward(self, x):
- x = self.dconv(x)
- return self.pconv(x)
-
-
-class Bottleneck(nn.Module):
- # Standard bottleneck
- def __init__(
- self,
- in_channels,
- out_channels,
- shortcut=True,
- expansion=0.5,
- depthwise=False,
- act="silu",
- ):
- super().__init__()
- hidden_channels = int(out_channels * expansion)
- Conv = DWConv if depthwise else BaseConv
- self.conv1 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act)
- self.conv2 = Conv(hidden_channels, out_channels, 3, stride=1, act=act)
- self.use_add = shortcut and in_channels == out_channels
-
- def forward(self, x):
- y = self.conv2(self.conv1(x))
- if self.use_add:
- y = y + x
- return y
-
-
-class ResLayer(nn.Module):
- "Residual layer with `in_channels` inputs."
-
- def __init__(self, in_channels: int):
- super().__init__()
- mid_channels = in_channels // 2
- self.layer1 = BaseConv(
- in_channels, mid_channels, ksize=1, stride=1, act="lrelu"
- )
- self.layer2 = BaseConv(
- mid_channels, in_channels, ksize=3, stride=1, act="lrelu"
- )
-
- def forward(self, x):
- out = self.layer2(self.layer1(x))
- return x + out
-
-
-class SPPBottleneck(nn.Module):
- """Spatial pyramid pooling layer used in YOLOv3-SPP"""
-
- def __init__(
- self, in_channels, out_channels, kernel_sizes=(5, 9, 13), activation="silu"
- ):
- super().__init__()
- hidden_channels = in_channels // 2
- self.conv1 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=activation)
- self.m = nn.ModuleList(
- [
- nn.MaxPool2d(kernel_size=ks, stride=1, padding=ks // 2)
- for ks in kernel_sizes
- ]
- )
- conv2_channels = hidden_channels * (len(kernel_sizes) + 1)
- self.conv2 = BaseConv(conv2_channels, out_channels, 1, stride=1, act=activation)
-
- def forward(self, x):
- x = self.conv1(x)
- x = torch.cat([x] + [m(x) for m in self.m], dim=1)
- x = self.conv2(x)
- return x
-
-
-class CSPLayer(nn.Module):
- """C3 in yolov5, CSP Bottleneck with 3 convolutions"""
-
- def __init__(
- self,
- in_channels,
- out_channels,
- n=1,
- shortcut=True,
- expansion=0.5,
- depthwise=False,
- act="silu",
- ):
- """
- Args:
- in_channels (int): input channels.
- out_channels (int): output channels.
- n (int): number of Bottlenecks. Default value: 1.
- """
- # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__()
- hidden_channels = int(out_channels * expansion) # hidden channels
- self.conv1 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act)
- self.conv2 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act)
- self.conv3 = BaseConv(2 * hidden_channels, out_channels, 1, stride=1, act=act)
- module_list = [
- Bottleneck(
- hidden_channels, hidden_channels, shortcut, 1.0, depthwise, act=act
- )
- for _ in range(n)
- ]
- self.m = nn.Sequential(*module_list)
-
- def forward(self, x):
- x_1 = self.conv1(x)
- x_2 = self.conv2(x)
- x_1 = self.m(x_1)
- x = torch.cat((x_1, x_2), dim=1)
- return self.conv3(x)
-
-
-class Focus(nn.Module):
- """Focus width and height information into channel space."""
-
- def __init__(self, in_channels, out_channels, ksize=1, stride=1, act="silu"):
- super().__init__()
- self.conv = BaseConv(in_channels * 4, out_channels, ksize, stride, act=act)
-
- def forward(self, x):
- # shape of x (b,c,w,h) -> y(b,4c,w/2,h/2)
- patch_top_left = x[..., ::2, ::2]
- patch_top_right = x[..., ::2, 1::2]
- patch_bot_left = x[..., 1::2, ::2]
- patch_bot_right = x[..., 1::2, 1::2]
- x = torch.cat(
- (
- patch_top_left,
- patch_bot_left,
- patch_top_right,
- patch_bot_right,
- ),
- dim=1,
- )
- return self.conv(x)
diff --git a/spaces/chendl/compositional_test/transformers/examples/legacy/seq2seq/minify_dataset.py b/spaces/chendl/compositional_test/transformers/examples/legacy/seq2seq/minify_dataset.py
deleted file mode 100644
index e6095cecc8e99f231b80a3779b594cc29fd0ddda..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/legacy/seq2seq/minify_dataset.py
+++ /dev/null
@@ -1,34 +0,0 @@
-#!/usr/bin/env python
-# Copyright 2020 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from pathlib import Path
-
-import fire
-
-
-def minify(src_dir: str, dest_dir: str, n: int):
- """Write first n lines of each file f in src_dir to dest_dir/f"""
- src_dir = Path(src_dir)
- dest_dir = Path(dest_dir)
- dest_dir.mkdir(exist_ok=True)
- for path in src_dir.iterdir():
- new = [x.rstrip() for x in list(path.open().readlines())][:n]
- dest_path = dest_dir.joinpath(path.name)
- print(dest_path)
- dest_path.open("w").write("\n".join(new))
-
-
-if __name__ == "__main__":
- fire.Fire(minify)
diff --git a/spaces/chendl/compositional_test/transformers/examples/research_projects/onnx/summarization/README.md b/spaces/chendl/compositional_test/transformers/examples/research_projects/onnx/summarization/README.md
deleted file mode 100644
index c43b0450ea2c4bfacb2e9f5e2af2b6b41d6b340d..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/research_projects/onnx/summarization/README.md
+++ /dev/null
@@ -1,43 +0,0 @@
-
-
-# Bart + Beam Search to ONNX
-
-Author: [@fatcat-z](https://github.com/fatcat-z)
-
-This folder contains an example of exporting Bart + Beam Search generation (`BartForConditionalGeneration`) to ONNX.
-
-Beam Search contains a for-loop workflow, so we need to make them TorchScript-compatible for exporting to ONNX. This example shows how to make a Bart model be TorchScript-compatible by wrapping up it into a new model. In addition, some changes were made to the `beam_search()` function to make it TorchScript-compatible.
-
-
-## How to run the example
-
-To make sure you can successfully run the latest versions of the example scripts, you have to **install the library from source** and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
-
-```bash
-git clone https://github.com/huggingface/transformers
-cd transformers
-pip install '.[onnxruntime]'
-```
-Then cd in this example folder and run
-```bash
-pip install -r requirements.txt
-```
-
-Now you can run the example command below to get the example ONNX file:
-
-```bash
-python run_onnx_exporter.py --model_name_or_path facebook/bart-base
-```
diff --git a/spaces/chendl/compositional_test/transformers/examples/research_projects/seq2seq-distillation/train_distilbart_cnn.sh b/spaces/chendl/compositional_test/transformers/examples/research_projects/seq2seq-distillation/train_distilbart_cnn.sh
deleted file mode 100644
index 6a1bafbdc9c8c944e407bb766a1e5fe6177b0404..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/research_projects/seq2seq-distillation/train_distilbart_cnn.sh
+++ /dev/null
@@ -1,24 +0,0 @@
-#!/usr/bin/env bash
-export PYTHONPATH="../":"${PYTHONPATH}"
-
-export BS=32
-export GAS=1
-
-python finetune.py \
- --learning_rate=3e-5 \
- --fp16 \
- --gpus 1 \
- --do_train \
- --do_predict \
- --val_check_interval 0.25 \
- --n_val 500 \
- --num_train_epochs 2 \
- --freeze_encoder --freeze_embeds --data_dir cnn_dm \
- --max_target_length 142 --val_max_target_length=142 \
- --train_batch_size=$BS --eval_batch_size=$BS --gradient_accumulation_steps=$GAS \
- --model_name_or_path sshleifer/student_cnn_12_6 \
- --tokenizer_name facebook/bart-large \
- --warmup_steps 500 \
- --output_dir distilbart-cnn-12-6 \
- "$@"
-
diff --git a/spaces/chenman/Meina-MeinaMix/app.py b/spaces/chenman/Meina-MeinaMix/app.py
deleted file mode 100644
index 811eafa7aa381ad173de55aa27fb9c091aa0199f..0000000000000000000000000000000000000000
--- a/spaces/chenman/Meina-MeinaMix/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/Meina/MeinaMix").launch()
\ No newline at end of file
diff --git a/spaces/chlab/interactive_kinematic_planet_detector/utils/__init__.py b/spaces/chlab/interactive_kinematic_planet_detector/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/aiohttp/client_ws.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/aiohttp/client_ws.py
deleted file mode 100644
index 9a8ba84ca5082ad6d672c3837d4810e467a8080e..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/aiohttp/client_ws.py
+++ /dev/null
@@ -1,300 +0,0 @@
-"""WebSocket client for asyncio."""
-
-import asyncio
-from typing import Any, Optional, cast
-
-import async_timeout
-
-from .client_exceptions import ClientError
-from .client_reqrep import ClientResponse
-from .helpers import call_later, set_result
-from .http import (
- WS_CLOSED_MESSAGE,
- WS_CLOSING_MESSAGE,
- WebSocketError,
- WSCloseCode,
- WSMessage,
- WSMsgType,
-)
-from .http_websocket import WebSocketWriter # WSMessage
-from .streams import EofStream, FlowControlDataQueue
-from .typedefs import (
- DEFAULT_JSON_DECODER,
- DEFAULT_JSON_ENCODER,
- JSONDecoder,
- JSONEncoder,
-)
-
-
-class ClientWebSocketResponse:
- def __init__(
- self,
- reader: "FlowControlDataQueue[WSMessage]",
- writer: WebSocketWriter,
- protocol: Optional[str],
- response: ClientResponse,
- timeout: float,
- autoclose: bool,
- autoping: bool,
- loop: asyncio.AbstractEventLoop,
- *,
- receive_timeout: Optional[float] = None,
- heartbeat: Optional[float] = None,
- compress: int = 0,
- client_notakeover: bool = False,
- ) -> None:
- self._response = response
- self._conn = response.connection
-
- self._writer = writer
- self._reader = reader
- self._protocol = protocol
- self._closed = False
- self._closing = False
- self._close_code: Optional[int] = None
- self._timeout = timeout
- self._receive_timeout = receive_timeout
- self._autoclose = autoclose
- self._autoping = autoping
- self._heartbeat = heartbeat
- self._heartbeat_cb: Optional[asyncio.TimerHandle] = None
- if heartbeat is not None:
- self._pong_heartbeat = heartbeat / 2.0
- self._pong_response_cb: Optional[asyncio.TimerHandle] = None
- self._loop = loop
- self._waiting: Optional[asyncio.Future[bool]] = None
- self._exception: Optional[BaseException] = None
- self._compress = compress
- self._client_notakeover = client_notakeover
-
- self._reset_heartbeat()
-
- def _cancel_heartbeat(self) -> None:
- if self._pong_response_cb is not None:
- self._pong_response_cb.cancel()
- self._pong_response_cb = None
-
- if self._heartbeat_cb is not None:
- self._heartbeat_cb.cancel()
- self._heartbeat_cb = None
-
- def _reset_heartbeat(self) -> None:
- self._cancel_heartbeat()
-
- if self._heartbeat is not None:
- self._heartbeat_cb = call_later(
- self._send_heartbeat, self._heartbeat, self._loop
- )
-
- def _send_heartbeat(self) -> None:
- if self._heartbeat is not None and not self._closed:
- # fire-and-forget a task is not perfect but maybe ok for
- # sending ping. Otherwise we need a long-living heartbeat
- # task in the class.
- self._loop.create_task(self._writer.ping())
-
- if self._pong_response_cb is not None:
- self._pong_response_cb.cancel()
- self._pong_response_cb = call_later(
- self._pong_not_received, self._pong_heartbeat, self._loop
- )
-
- def _pong_not_received(self) -> None:
- if not self._closed:
- self._closed = True
- self._close_code = WSCloseCode.ABNORMAL_CLOSURE
- self._exception = asyncio.TimeoutError()
- self._response.close()
-
- @property
- def closed(self) -> bool:
- return self._closed
-
- @property
- def close_code(self) -> Optional[int]:
- return self._close_code
-
- @property
- def protocol(self) -> Optional[str]:
- return self._protocol
-
- @property
- def compress(self) -> int:
- return self._compress
-
- @property
- def client_notakeover(self) -> bool:
- return self._client_notakeover
-
- def get_extra_info(self, name: str, default: Any = None) -> Any:
- """extra info from connection transport"""
- conn = self._response.connection
- if conn is None:
- return default
- transport = conn.transport
- if transport is None:
- return default
- return transport.get_extra_info(name, default)
-
- def exception(self) -> Optional[BaseException]:
- return self._exception
-
- async def ping(self, message: bytes = b"") -> None:
- await self._writer.ping(message)
-
- async def pong(self, message: bytes = b"") -> None:
- await self._writer.pong(message)
-
- async def send_str(self, data: str, compress: Optional[int] = None) -> None:
- if not isinstance(data, str):
- raise TypeError("data argument must be str (%r)" % type(data))
- await self._writer.send(data, binary=False, compress=compress)
-
- async def send_bytes(self, data: bytes, compress: Optional[int] = None) -> None:
- if not isinstance(data, (bytes, bytearray, memoryview)):
- raise TypeError("data argument must be byte-ish (%r)" % type(data))
- await self._writer.send(data, binary=True, compress=compress)
-
- async def send_json(
- self,
- data: Any,
- compress: Optional[int] = None,
- *,
- dumps: JSONEncoder = DEFAULT_JSON_ENCODER,
- ) -> None:
- await self.send_str(dumps(data), compress=compress)
-
- async def close(self, *, code: int = WSCloseCode.OK, message: bytes = b"") -> bool:
- # we need to break `receive()` cycle first,
- # `close()` may be called from different task
- if self._waiting is not None and not self._closed:
- self._reader.feed_data(WS_CLOSING_MESSAGE, 0)
- await self._waiting
-
- if not self._closed:
- self._cancel_heartbeat()
- self._closed = True
- try:
- await self._writer.close(code, message)
- except asyncio.CancelledError:
- self._close_code = WSCloseCode.ABNORMAL_CLOSURE
- self._response.close()
- raise
- except Exception as exc:
- self._close_code = WSCloseCode.ABNORMAL_CLOSURE
- self._exception = exc
- self._response.close()
- return True
-
- if self._closing:
- self._response.close()
- return True
-
- while True:
- try:
- async with async_timeout.timeout(self._timeout):
- msg = await self._reader.read()
- except asyncio.CancelledError:
- self._close_code = WSCloseCode.ABNORMAL_CLOSURE
- self._response.close()
- raise
- except Exception as exc:
- self._close_code = WSCloseCode.ABNORMAL_CLOSURE
- self._exception = exc
- self._response.close()
- return True
-
- if msg.type == WSMsgType.CLOSE:
- self._close_code = msg.data
- self._response.close()
- return True
- else:
- return False
-
- async def receive(self, timeout: Optional[float] = None) -> WSMessage:
- while True:
- if self._waiting is not None:
- raise RuntimeError("Concurrent call to receive() is not allowed")
-
- if self._closed:
- return WS_CLOSED_MESSAGE
- elif self._closing:
- await self.close()
- return WS_CLOSED_MESSAGE
-
- try:
- self._waiting = self._loop.create_future()
- try:
- async with async_timeout.timeout(timeout or self._receive_timeout):
- msg = await self._reader.read()
- self._reset_heartbeat()
- finally:
- waiter = self._waiting
- self._waiting = None
- set_result(waiter, True)
- except (asyncio.CancelledError, asyncio.TimeoutError):
- self._close_code = WSCloseCode.ABNORMAL_CLOSURE
- raise
- except EofStream:
- self._close_code = WSCloseCode.OK
- await self.close()
- return WSMessage(WSMsgType.CLOSED, None, None)
- except ClientError:
- self._closed = True
- self._close_code = WSCloseCode.ABNORMAL_CLOSURE
- return WS_CLOSED_MESSAGE
- except WebSocketError as exc:
- self._close_code = exc.code
- await self.close(code=exc.code)
- return WSMessage(WSMsgType.ERROR, exc, None)
- except Exception as exc:
- self._exception = exc
- self._closing = True
- self._close_code = WSCloseCode.ABNORMAL_CLOSURE
- await self.close()
- return WSMessage(WSMsgType.ERROR, exc, None)
-
- if msg.type == WSMsgType.CLOSE:
- self._closing = True
- self._close_code = msg.data
- if not self._closed and self._autoclose:
- await self.close()
- elif msg.type == WSMsgType.CLOSING:
- self._closing = True
- elif msg.type == WSMsgType.PING and self._autoping:
- await self.pong(msg.data)
- continue
- elif msg.type == WSMsgType.PONG and self._autoping:
- continue
-
- return msg
-
- async def receive_str(self, *, timeout: Optional[float] = None) -> str:
- msg = await self.receive(timeout)
- if msg.type != WSMsgType.TEXT:
- raise TypeError(f"Received message {msg.type}:{msg.data!r} is not str")
- return cast(str, msg.data)
-
- async def receive_bytes(self, *, timeout: Optional[float] = None) -> bytes:
- msg = await self.receive(timeout)
- if msg.type != WSMsgType.BINARY:
- raise TypeError(f"Received message {msg.type}:{msg.data!r} is not bytes")
- return cast(bytes, msg.data)
-
- async def receive_json(
- self,
- *,
- loads: JSONDecoder = DEFAULT_JSON_DECODER,
- timeout: Optional[float] = None,
- ) -> Any:
- data = await self.receive_str(timeout=timeout)
- return loads(data)
-
- def __aiter__(self) -> "ClientWebSocketResponse":
- return self
-
- async def __anext__(self) -> WSMessage:
- msg = await self.receive()
- if msg.type in (WSMsgType.CLOSE, WSMsgType.CLOSING, WSMsgType.CLOSED):
- raise StopAsyncIteration
- return msg
diff --git a/spaces/cihyFjudo/fairness-paper-search/Cypheros TS-Doctor 1.22 Portable PORTABLE.md b/spaces/cihyFjudo/fairness-paper-search/Cypheros TS-Doctor 1.22 Portable PORTABLE.md
deleted file mode 100644
index 4e7cd4afc66b41bdae12adc7081215e48701c9a8..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Cypheros TS-Doctor 1.22 Portable PORTABLE.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Cypheros TS-Doctor 1.22 Portable Download Zip ››››› https://tinurli.com/2uwjIP
diff --git a/spaces/cihyFjudo/fairness-paper-search/Download Film Il Risveglio Del Dinosauro 3 Full Movie.md b/spaces/cihyFjudo/fairness-paper-search/Download Film Il Risveglio Del Dinosauro 3 Full Movie.md
deleted file mode 100644
index de6d14cb14c35a6fe33b8671271e53bb36c7d06e..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Download Film Il Risveglio Del Dinosauro 3 Full Movie.md
+++ /dev/null
@@ -1,6 +0,0 @@
-download film Il risveglio del dinosauro 3 full movie Download Zip 🔗 https://tinurli.com/2uwkBM
diff --git a/spaces/cihyFjudo/fairness-paper-search/Nobel Font Free Download Mac The Ultimate Guide to Installing and Using This Classic Font.md b/spaces/cihyFjudo/fairness-paper-search/Nobel Font Free Download Mac The Ultimate Guide to Installing and Using This Classic Font.md
deleted file mode 100644
index 5056188b060c3b81cc24066ea5707589ac7ec64d..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Nobel Font Free Download Mac The Ultimate Guide to Installing and Using This Classic Font.md
+++ /dev/null
@@ -1,24 +0,0 @@
-Click to view font family "Nobel".Nobel BoldNobel Condensed BoldNobel Condensed RegularNobel LightNobel Regular Italic About the font Nobel RegularBe aware that the Nobel Regular font is free for personal knowledge and use only. However, you need to contact the author for commercial use or for any support.You can use the Nobel Regular to create interesting designs, covers, shop and store name and logos.Also, the Nobel Regular font is perfect for branding projects, housewares designs, product packaging, or simply as a stylish text overlay on any background image.FamilyNobelSub-familyRegularVersion001.000AuthorCompanySiteCopyrightLicenceFor personal use onlyLicence MaisFontesFor personal use onlyMost wanted:fontes gratis, baixar fontes gratis, font ttf, fontes para word gratis, fonts free Typography Nobel RegularTo evaluate the typeface, in this section there is a preview of which we select 31 special characters or with accents, 26 letters of the alphabet in upper and lower case and the numbering from 0 to 10. The letters will be the same after installed in your operating system, either for viewing or for printing. Nobel Regular font authorFurthermore, about all the content of this source, we also provide some additional information from the author and/or company. Therefore, if you need to clarify doubts about the license for personal or commercial use, please contact the author. Author not found. License informationThe Nobel Regular font provided is for typography style knowledge only. The download is completely free for personal use and the font cannot be used for commercial purposes.Therefore, if you wish to use this font for commercial purposes, you must purchase a license or contact the author for permission to use it. How to install the Nobel Regular fontYou can install the Nobel Regular font on any operating system. For safety and to ensure that there is no Malware or malicious software , downloading the source file é compressed in ZIP format. Fonts are in OTF (OpenType ) or TTF (TrueType ) format.
Click here to install the font on Microsoft Windows (all versions). Click here to install the font on MAC OS. source Nobel Regular . Click on the topic you want to learn more! Download Nobel FontsThe elegance of a good font can make a great visual impact to the end user. Discover Nobel fonts and download for free. Download variations of Nobel RegularAccording to the Nobel Regular font family , below, we have listed other fonts that may be useful for your project. We have made an improved selection especially for you.Random fonts: Click to load 3 other fontsNobel Bold Download this fontNobel Condensed Bold Download this fontNobel Condensed Regular Download this fontNobel Light Download this fontNobel Regular Italic Download this font Leave your feedback for the Nobel Regular fontFinally, it's very important that we know your feedback about the Nobel Regular font. Also tell us what type of project you used. Sharing your opinion and ideas will help many other participants in the MaisFontes community to improve the arts.
-Nobel Font Free Download Mac DOWNLOAD ->>->>->> https://tinurli.com/2uwiTB
There were 6 fonts were included in the font family but this font only selected for the work of Guggenheim Museum with the pairing of Bangers font. This font would provide a vintage look to your designs. This font family is also included in the Google fonts as well as Adobe fonts. You can free download this font from our website but only for personal projects. Brandon Grotesque Font and Nobel Font are the most similar fonts to the Verlag font.
-This font would provide a vintage look to your designs. This font family is also included in the Google fonts as well as Adobe fonts. You can free download this font from our website but only for personal projects. Brandon Grotesque Font and Nobel Font are the most similar fonts to the Verlag font.
-If you are looking for a free version of this typeface then we providing a freeware font and you can utilize the font in your personal projects. You just need to click on the below download button to get to your operating systems.
-If your on windows and have access to your fonts library through the control or command center you can simply find a free download of the font that you need and copy them into your fonts library. restart adobe and you should have the new fonts
-
-A total of 6 fonts are available in this font family and Guggenheim Museum used this font with the combination of Bangers font. Its light black weight is the best known for its pairing functions. This would be an ideal font to take a traditional look at your designs. Moreover, It can be freely used withing Adobe fonts and it is also available in Google fonts.
-It has wonderful 192 glyphs and providing more than 250 stylish characters that are very suitable for your titling and headlines. Brandon Grotesque Font and Nobel Font are the most similar fonts to the Verlag font. We are providing a free version of this font family on this website and you can utilize it in all your graphic designs.
-This font family is free in any type of designing project with your adobe accounts such as you can use many other typefaces in the font library. But without an Adobe account, you should talk to its designer for its license.
-NOBEL-REGULAR.OTF, Nobel Regular, nobel, Nobel Regular, nobel, Nobel-Regular.otf, Windows, OTF, font The fonts presented on this website are their authors' property, and are either freeware, shareware, demo versions or public domain.
-Nobel-Bold NOBEL-BOLD.OTF , Nobel Bold , nobel , Nobel Bold , nobel , Nobel-Bold.otf , Windows , OTF , font The fonts presented on this website are their authors' property, and are either freeware, shareware, demo versions or public domain.
-Dtl nobel font family Browse a full collection of fronts from the dtl nobel font family. This family contains 8 fonts in styles such as t bold, t condensed bold, t light, t regular, t light italic, t condensed regular, t euro and t italic.
-Fortunately, there are a lot of excellent font repositories that do go to great lengths to curate their collections. One of our favorites is Google Fonts, which offers over 900 font families in several languages, all of them free:
-The great thing about WhatsApp is that it's constantly updating its offering. When we first collated this list, WhatsApp was best for personal use as it didn't have any business options available. Since launching WhatsApp for business it can now cater to a new clientele, allowing companies to communicate and respond with customers in real-time, provided they download the free WhatsApp for business App. We'd still recommend using WhatsApp for personal use though, given that the service is only available for Android users. However, given the ever-evolving nature of WhatsApp for Business, it may still be worth giving it a go.
-Additionally, Viber offers a similar service as FaceTime and WhatsApp, in that you can call anyone internationally for free, as long as they are also using the Viber app. Granted, it's not as popular as WhatsApp or FaceTime, but it's free to download, which could make for an easy and affordable alternative.
-Viber's international calling app is probably best suited for personal use . Given the more expensive rates for international calling and the fact that most of your clients probably don't have the Viber app, it's safe to say it's not ideal to run a business on it. However, if you're trying to make personal calls, it's safe to say it would be much easier to get them to download the app to avoid the cost. Still, you can always use our free VoIP comparison tool to find something a system that's more tailored to your needs.
-All rights for the fonts given on this website reserved by their owners (authors, designers). The license given on the font page only represents received data. For detailed information, please, read the files (e.g., readme.txt) from archive or visit the website given by an author (designer) or contact with him if you have any doubt.
-By creating a free account at Free-Ebooks(Opens in a new window), you can download up to five free titles each month. If you want more, a $50 VIP plan grants you an unlimited number of free books for life. But the free account is a good way to get started. Just watch out for the special offers and promotions as you sign up for your account.
-Select a specific book to see customer reviews and other information. Click the Get for free button to download an ebook or audiobook. You can read or play the book directly at the website or snag the free Google Books app for iOS/iPadOS(Opens in a new window) or Android(Opens in a new window) to access the title on your mobile device.
-Yes, you can write a book in Microsoft Word, and it may even help you write your book faster and increase your writing focus, since you can save time formatting and setting up your book layout. You can write a book in Word by downloading a free book template, by using the standard book template Word offers, or by setting up your own (which is more time-consuming).
aaccfb2cb3If the LAN cable is connected to a switch/hub in the network, and if the IP address is set accordingly on the TNC, then the transmission of data to and from the TNC can be started through the use of the machine's IP address with TNCremo, for example.
-There are several possibilities depending on what is needed: TNCremo PLUS -- TeleService -- StateMonitor - - SRI State Reporting Interface -
-remo software activation key 23 Download Zip ❤ https://tinurli.com/2uwk3g
Newer controls (TNC 4xx or later) feature a GoldCap capacitor, which ensures that the data are buffered while the battery is being changed. However, the battery should not be removed for an extended period of time. Older controls do not have a capacitor, and their data are not saved when the control is switched off.
-The software of the test version and full version is identical. It is enabled via a hardware dongle that is ordinarily built into the programming station keyboard. Whether this kind of dongle or an external dongle is connected depends on the given driver. The driver can be downloaded from our website free of charge but is dependent on the model. If you are unsure, then simply install both drivers.
-The postprocessor must always be purchased from the CAM system manufacturerer. Whether 2D, 3D (inclined machining), or 3D simultaneous machining is required depends on the machine, the NC software, and, of course, on the range of parts to be machined.
-We try to list the most commonly encountered free software license onthis page, but cannot list them all; we'll try our best to answerquestions about free software licenses whether or not they are listedhere. The licenses are more or less in alphabetical order within eachsection.
-If you have questions about free software licenses, you can emailusat .Because our resources are limited, we do not answer questions that aremeant to assist proprietary software development or distribution, andyou'll likely get an answer faster if you ask a specific question thatisn't already covered here or in ourFAQ. Wewelcome knowledgeablevolunteers who want to help answer licensing questions.
-If you are contemplating writing a new license, please also contactus at . Theproliferation of different free software licenses is a significantproblem in the free software community today, both for users anddevelopers. We will do our best to help you find an existing freesoftware license that meets your needs.
-Please note that GPLv3 is not compatible with GPLv2 by itself.However, most software released under GPLv2 allows you to use theterms of later versions of the GPL as well. When this is the case,you can use the code under GPLv3 to make the desired combination. Tolearn more about compatibility between GNU licenses,please see ourFAQ.
-
-Please note that GPLv2 is, by itself, not compatible with GPLv3.However, most software released under GPLv2 allows you to use theterms of later versions of the GPL as well. When this is the case,you can use the code under GPLv3 to make the desired combination. Tolearn more about compatibility between GNU licenses,please see ourFAQ.
-This is the latest version of the LGPL: a free software license, but nota strong copyleft license, because it permits linking with nonfreemodules. It is compatible with GPLv3. We recommend it for special circumstancesonly.
-Please note that LGPLv3 is not compatible with GPLv2 by itself.However, most software released under GPLv2 allows you to use theterms of later versions of the GPL as well. When this is the case,you can use the code under GPLv3 to make the desired combination. Tolearn more about compatibility between GNU licenses,please see ourFAQ.
-This is the previous version of the LGPL: a free software license,but not a strong copyleft license, because it permits linking withnonfree modules. It is compatible with GPLv2 and GPLv3. Wegenerally recommend the latest version of theLGPL, for specialcircumstances only. To learn more about how LGPLv2.1 iscompatible with other GNU licenses,please see ourFAQ.
-This is a free software, copyleft license. Its terms effectivelyconsist of the terms of GPLv3, with an additional paragraph in section 13to allow users who interact with the licensed software over a network toreceive the source for that program. We recommend that developers considerusing the GNU AGPL for any software which will commonly be run over anetwork.
-This is a lax, permissive free software license, compatible withthe GNU GPL, which we recommend GNU packages use for README and othersmall supporting files. All developers can feel free to use it insimilar situations.
-This is a free software license, compatible with both GPLv2 andGPLv3. It is based on the modified BSDlicense, and adds a term expressly stating it does not grant youany patent licenses. Because of this, we encourage you to be carefulabout using software under this license; you should first considerwhether the licensor might want to sue you for patent infringement.If the developer is refusing users patent licenses to set up a trapfor you, it would be wise to avoid the program.
-The eCos license version 2.0 is a GPL-compatible free softwarelicense. It consists of the GPL, plus an exception allowing linking tosoftware not under the GPL. This license has the same disadvantagesas the LGPL.
-This is a free software license, and it is compatible withGPLv3. It is based on the Apache License2.0; the scope of the patent license has changed so that whenan organization's employee works on a project, the organizationdoes not have to license all of its patents to recipients. Thispatent license and the indemnification clause in section 9 makethis license incompatible with GPLv2.
-This is a free software license, and compatible with the GNU GPL.The authors have assured us that developers who document changes asrequired by the GPL will also comply with the similar requirement inthis license.
-In the United States, these licenses are supposed to be interpretedbased on what the author seems to intend. So they probably mean whatthey appear to mean. That would make them non-copyleft free softwarelicenses and compatible with the GNU GPL. However, an unlucky choiceof wording could give it a different meaning.
-It's important to understand that the condition to distribute filesunder the MPL's terms only applies to the party that first creates anddistributes the Larger Work. If it applied to their recipients as well, itwould be a further restriction and incompatible with the GPL and AGPL.That said, when you make contributions to an existing project, we usuallyrecommend that you keep your changes under the same license,even when you're not required to do so. If you receive a work under a GNUlicense where some files are also under the MPL, you should only remove theMPL from those files when there's a strong reason to justify it.
-Software under previous versions of the MPL can be upgraded to version2.0, but any software that isn't already available under one of thelisted GNU licenses must be marked as Incompatible With SecondaryLicenses. This means that software that's only available underprevious versions of the MPL is still incompatible with the GPL and AGPL.
-Being in the public domain is not a license; rather, it means thematerial is not copyrighted and no license is needed. Practicallyspeaking, though, if a work is in the public domain, it might as wellhave an all-permissive non-copyleft free software license. Publicdomain material is compatible with the GNU GPL.
-Previous versions of the SGI Free Software License B were not freesoftware licenses, despite their name. However, they all includedclauses that allow you to upgrade to new versions of the license, if youchoose to do so. As a result, if a piece of software was released underany version of the SGI Free License B, you can use it under the terms ofthis free version.
-If you want to use files covered by this License Agreement in yourown software, that shouldn't be any problem, but we recommend thatyou also include a full copy of its text. Some of the files containalternative license terms which are nonfree, or no licensinginformation at all, so including a copy of the License Agreementwill help avoid confusion when others want to distribute yoursoftware. Of course, you'll also need to follow the conditions inthis License Agreement for distributing the files, but those arevery straightforward.
-Please do not use this License Agreement for your own software. Ifyou want to use a lax permissive license for your project, please usethe Expat license for a small program and theApache 2.0 license for a substantial program. These are far morecommon, and widely recognized in the free software community.
-This is a lax, permissive non-copyleft free software license, compatiblewith the GNU GPL. The license does provide the ability to licensepatents along with the software work, however, we still recommend theApache 2.0 license for avoiding patent treachery when choosing to putyour work under a lax license.
-The WxWidgets license is a GPL-compatible free software license. Itconsists of the GNULesser GPL 2.0 or any later version, plus an additional permissionallowing binary distributions that use the library to be licensedunder terms of the distributor's choice (including proprietary). Itis a weak copyleft, even weaker than the LGPL, so we recommendit only in specialcircumstances.
aaccfb2cb3Select the 3D polyline to join: pick a 3Dpolyline Select the other 3D polyline to join: pick a 3D polylinethat has a common endpoint with the first 3 segments added to the polyline.
-Join 3d Polyline Autocad Download • https://tinurli.com/2uwhZM
The lines are created (in a .dxf) by a GPS unit in the field, and represent tracks needing to be mapped. Typically we would drive out a track and then drive back on the same track; this gives me two 3d polylines. Because of GPS errors it better practice to log the roads/ tracks twice. The lines can have from 2000 to 10000 vertices, so manually drawing is not an option. Any ideas? Thanks
-Create a 3d object away from the two 3d objects you need to join, type SUBTRACT, select the two 3d objects as regions to subtract from, then select the created 3d object away from the two as the region to subtract to...
-@James Maeding
-
-Try to implement this small trick into your work and you will see a great improvement in speed and quality of your drawings! If you want to learn more tricks concerning Polylines you can look at those:
-This application joins and cleans-up polylines and line entities to create closed boundaries and to close gaps not seen visibly. The app includes 9 functions; each with additional settings and sub-routines for quick and automatic joining, trimming, cleaning and closing. With 9 user-defined settings.$50.00 to purchase from within the app using a Paypal account OR make a one-time purchase with a Debit or Credit Card through Paypal (no Paypal account required)Note: This app uses a custom installer (and not the standard App Store installer).
-There are various polyline types in AutoCAD®, the most common of which is usually the LWPolyline (lightweight polyline). It can be confusing because the LWPolyline and the old Polyline entity used way back in the early days of AutoCAD® are both shown as a "Polyline" in the Properties window. Chances are though, unless you're working on some nasty generated drawing, you're probably using LWPolylines.
-The third polyline type is the 3D polyline, which as the name implies is a 3D version. Both the LWPolyline and the Polyline only permit the creation of geometry on a flat plane (UCS), but the 3D polyline allows points anywhere in 3D space.
-Converting between the various types therefore has obvious difficulty, because going from 3D to 2D means you're going to have to remove some of the 3D information from the polyline, and AutoCAD® could interpret how to do this in many ways. Therefore historically there have not been native commands to convert between the polyline types.
-The FLATTEN command is an option for making the polyline flat (i.e., visibly the same as before, but drawn as a 2D line on whatever UCS you're working on). I have had troubles with FLATTEN in the past however, because I think it does some odd stuff sometimes with merging lines and approximating... so I tend to avoid it where possible.
-the tool with then ask you to select your first line segment - which if drawn using the line tool will be a single line - it will then ask to convert this segment to a polyline as shown below - say Yes
aaccfb2cb3), {'identifier': 'component-0'})
- """
-
- def __init__(self, outPointPen, transformation):
- """The 'outPointPen' argument is another point pen object.
- It will receive the transformed coordinates.
- The 'transformation' argument can either be a six-tuple, or a
- fontTools.misc.transform.Transform object.
- """
- super().__init__(outPointPen)
- if not hasattr(transformation, "transformPoint"):
- from fontTools.misc.transform import Transform
-
- transformation = Transform(*transformation)
- self._transformation = transformation
- self._transformPoint = transformation.transformPoint
-
- def addPoint(self, pt, segmentType=None, smooth=False, name=None, **kwargs):
- self._outPen.addPoint(
- self._transformPoint(pt), segmentType, smooth, name, **kwargs
- )
-
- def addComponent(self, baseGlyphName, transformation, **kwargs):
- transformation = self._transformation.transform(transformation)
- self._outPen.addComponent(baseGlyphName, transformation, **kwargs)
-
-
-if __name__ == "__main__":
- from fontTools.pens.basePen import _TestPen
-
- pen = TransformPen(_TestPen(None), (2, 0, 0.5, 2, -10, 0))
- pen.moveTo((0, 0))
- pen.lineTo((0, 100))
- pen.curveTo((50, 75), (60, 50), (50, 25), (0, 0))
- pen.closePath()
diff --git a/spaces/colakin/video-generater/public/ffmpeg/compat/atomics/suncc/stdatomic.h b/spaces/colakin/video-generater/public/ffmpeg/compat/atomics/suncc/stdatomic.h
deleted file mode 100644
index 0cf89e0f78d79d48b411f6e4a8450030aee757d7..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/compat/atomics/suncc/stdatomic.h
+++ /dev/null
@@ -1,186 +0,0 @@
-/*
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#ifndef COMPAT_ATOMICS_SUNCC_STDATOMIC_H
-#define COMPAT_ATOMICS_SUNCC_STDATOMIC_H
-
-#include
-#include
-#include
-#include
-
-#define ATOMIC_FLAG_INIT 0
-
-#define ATOMIC_VAR_INIT(value) (value)
-
-#define atomic_init(obj, value) \
-do { \
- *(obj) = (value); \
-} while(0)
-
-#define kill_dependency(y) ((void)0)
-
-#define atomic_thread_fence(order) \
- __machine_rw_barrier();
-
-#define atomic_signal_fence(order) \
- ((void)0)
-
-#define atomic_is_lock_free(obj) 0
-
-typedef intptr_t atomic_flag;
-typedef intptr_t atomic_bool;
-typedef intptr_t atomic_char;
-typedef intptr_t atomic_schar;
-typedef intptr_t atomic_uchar;
-typedef intptr_t atomic_short;
-typedef intptr_t atomic_ushort;
-typedef intptr_t atomic_int;
-typedef intptr_t atomic_uint;
-typedef intptr_t atomic_long;
-typedef intptr_t atomic_ulong;
-typedef intptr_t atomic_llong;
-typedef intptr_t atomic_ullong;
-typedef intptr_t atomic_wchar_t;
-typedef intptr_t atomic_int_least8_t;
-typedef intptr_t atomic_uint_least8_t;
-typedef intptr_t atomic_int_least16_t;
-typedef intptr_t atomic_uint_least16_t;
-typedef intptr_t atomic_int_least32_t;
-typedef intptr_t atomic_uint_least32_t;
-typedef intptr_t atomic_int_least64_t;
-typedef intptr_t atomic_uint_least64_t;
-typedef intptr_t atomic_int_fast8_t;
-typedef intptr_t atomic_uint_fast8_t;
-typedef intptr_t atomic_int_fast16_t;
-typedef intptr_t atomic_uint_fast16_t;
-typedef intptr_t atomic_int_fast32_t;
-typedef intptr_t atomic_uint_fast32_t;
-typedef intptr_t atomic_int_fast64_t;
-typedef intptr_t atomic_uint_fast64_t;
-typedef intptr_t atomic_intptr_t;
-typedef intptr_t atomic_uintptr_t;
-typedef intptr_t atomic_size_t;
-typedef intptr_t atomic_ptrdiff_t;
-typedef intptr_t atomic_intmax_t;
-typedef intptr_t atomic_uintmax_t;
-
-static inline void atomic_store(intptr_t *object, intptr_t desired)
-{
- *object = desired;
- __machine_rw_barrier();
-}
-
-#define atomic_store_explicit(object, desired, order) \
- atomic_store(object, desired)
-
-static inline intptr_t atomic_load(intptr_t *object)
-{
- __machine_rw_barrier();
- return *object;
-}
-
-#define atomic_load_explicit(object, order) \
- atomic_load(object)
-
-#define atomic_exchange(object, desired) \
- atomic_swap_ptr(object, desired)
-
-#define atomic_exchange_explicit(object, desired, order) \
- atomic_exchange(object, desired)
-
-static inline int atomic_compare_exchange_strong(intptr_t *object, intptr_t *expected,
- intptr_t desired)
-{
- intptr_t old = *expected;
- *expected = (intptr_t)atomic_cas_ptr(object, (void *)old, (void *)desired);
- return *expected == old;
-}
-
-#define atomic_compare_exchange_strong_explicit(object, expected, desired, success, failure) \
- atomic_compare_exchange_strong(object, expected, desired)
-
-#define atomic_compare_exchange_weak(object, expected, desired) \
- atomic_compare_exchange_strong(object, expected, desired)
-
-#define atomic_compare_exchange_weak_explicit(object, expected, desired, success, failure) \
- atomic_compare_exchange_weak(object, expected, desired)
-
-static inline intptr_t atomic_fetch_add(intptr_t *object, intptr_t operand)
-{
- return atomic_add_ptr_nv(object, operand) - operand;
-}
-
-#define atomic_fetch_sub(object, operand) \
- atomic_fetch_add(object, -(operand))
-
-static inline intptr_t atomic_fetch_or(intptr_t *object, intptr_t operand)
-{
- intptr_t old;
- do {
- old = atomic_load(object);
- } while (!atomic_compare_exchange_strong(object, old, old | operand));
- return old;
-}
-
-static inline intptr_t atomic_fetch_xor(intptr_t *object, intptr_t operand)
-{
- intptr_t old;
- do {
- old = atomic_load(object);
- } while (!atomic_compare_exchange_strong(object, old, old ^ operand));
- return old;
-}
-
-static inline intptr_t atomic_fetch_and(intptr_t *object, intptr_t operand)
-{
- intptr_t old;
- do {
- old = atomic_load(object);
- } while (!atomic_compare_exchange_strong(object, old, old & operand));
- return old;
-}
-
-#define atomic_fetch_add_explicit(object, operand, order) \
- atomic_fetch_add(object, operand)
-
-#define atomic_fetch_sub_explicit(object, operand, order) \
- atomic_fetch_sub(object, operand)
-
-#define atomic_fetch_or_explicit(object, operand, order) \
- atomic_fetch_or(object, operand)
-
-#define atomic_fetch_xor_explicit(object, operand, order) \
- atomic_fetch_xor(object, operand)
-
-#define atomic_fetch_and_explicit(object, operand, order) \
- atomic_fetch_and(object, operand)
-
-#define atomic_flag_test_and_set(object) \
- atomic_exchange(object, 1)
-
-#define atomic_flag_test_and_set_explicit(object, order) \
- atomic_flag_test_and_set(object)
-
-#define atomic_flag_clear(object) \
- atomic_store(object, 0)
-
-#define atomic_flag_clear_explicit(object, order) \
- atomic_flag_clear(object)
-
-#endif /* COMPAT_ATOMICS_SUNCC_STDATOMIC_H */
diff --git a/spaces/colakin/video-generater/public/ffmpeg/doc/style.min.css b/spaces/colakin/video-generater/public/ffmpeg/doc/style.min.css
deleted file mode 100644
index 6843fda57d03d5ed37c7d952248e6b042755e2df..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/doc/style.min.css
+++ /dev/null
@@ -1,23 +0,0 @@
-/*!
-The MIT License (MIT)
-
-Copyright (c) 2014 Barbara Lepage
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
- */body{background-color:#313131;color:#e6e6e6;text-align:justify}body, h1, h2, h3, h4, h5, h6{font-family:"Lucida Grande","Lucida Sans Unicode","Lucida Sans","Helvetica Neue",Helvetica,Verdana,Tahoma,sans-serif}a{color:#4cae4c}a strong{color:#e6e6e6}a:hover{color:#7fc77f}a:hover strong{color:#4cae4c}main{width:100% ! important;min-height:600px;margin:auto}h1, h2, h3, h4{font-weight:bold;text-align:left}h1, h2, h3{color:#bebebe}h1 strong, h2 strong, h3 strong{color:#e6e6e6}h4, h5, h6{color:#3c8b3c}h1{border-bottom:4px #bebebe solid;padding:20px 2%}h3{border-bottom:2px #bebebe solid;padding:15px 1%}h4{border-bottom:1px solid #e6e6e6;padding:10px 0;margin:20px 0;color:#e6e6e6}.list-group .list-group-item{background-color:#3e3e3e;border-color:black}.list-group.list-group-big .list-group-item{padding:25px}.list-group a.list-group-item{color:#7fc77f}.list-group a.list-group-item:hover{background-color:#313131;color:#4cae4c}.well{background-color:#242424;border-color:black;color:#bebebe}.well strong{color:#e6e6e6}.well code{background-color:#313131}.well hr{border-color:#3c8b3c}.well h3{margin:5px 0 15px 0;border:0;padding:0}.well a{color:#4cae4c}.well a.btn{color:white}.well small{display:block;padding:0 10px;font-style:italic}.well.example{padding-top:40px;margin-bottom:130px}.well.example pre{margin:50px;margin-bottom:30px;font-size:1.5em}.well.example .btn{margin-right:50px;margin-bottom:20px}.well.well-with-icon{min-height:136px}.well.well-with-icon .pull-right,.well.well-with-icon .pull-left{background-color:#4cae4c;color:#e6e6e6;padding:10px;border-radius:5px;margin:5px}.well.well-with-icon .pull-right{margin-left:20px}.well.well-with-icon .pull-left{margin-right:20px}a.well{display:block}a.well:hover{text-decoration:none;opacity:0.8}.info, .warning{margin:10px;padding:10px;background-color:#3e3e3e;color:#e6e6e6}.info code, .warning code{background-color:#313131}.info{border-left:10px #4cae4c solid}.warning{border-left:10px #ae4c4c solid}.with-icon{padding:30px}.with-icon .pull-left{padding-right:30px}.with-icon .pull-right{padding-left:30px}dd{margin-left:20px}code{background-color:#242424;color:#7fc77f;display:inline-block;margin:5px}.table{margin:20px 0;border-radius:4px}.table th,.table td,.table tr{border:1px solid #171717}.table tr th{background-color:#3e3e3e;border-bottom:2px solid #e6e6e6}.table tr:nth-child(odd){background-color:#242424}#sidebar-wrapper, .navbar{background-color:#171717;overflow-x:hidden}#sidebar-wrapper .sidebar-brand img,#sidebar-wrapper .navbar-brand img, .navbar .sidebar-brand img, .navbar .navbar-brand img{opacity:0.6;margin-right:8px}#sidebar-wrapper .sidebar-brand:hover,#sidebar-wrapper .navbar-brand:hover, .navbar .sidebar-brand:hover, .navbar .navbar-brand:hover{color:#fff}#sidebar-wrapper .sidebar-brand:hover img,#sidebar-wrapper .navbar-brand:hover img, .navbar .sidebar-brand:hover img, .navbar .navbar-brand:hover img{opacity:1}#sidebar-wrapper .sidebar-nav li ul, .navbar .sidebar-nav li ul{list-style-type:none;padding:0}#sidebar-wrapper .sidebar-nav li ul li, .navbar .sidebar-nav li ul li{line-height:20px}#sidebar-wrapper .sidebar-nav li ul li a, .navbar .sidebar-nav li ul li a{padding-left:20px}.content-header{height:auto;background-color:#242424}.content-header h1{color:#e6e6e6;display:block;margin:0;margin-bottom:20px;line-height:normal;border-bottom:none}#download h4, #index h4{margin-top:180px}#download h4.first, #index h4.first{margin-top:20px}#download h4.first small, #index h4.first small{color:inherit;font-size:1em}#download .btn-download-wrapper, #index .btn-download-wrapper{text-align:center;margin:160px auto}#download .btn-download-wrapper .btn, #index .btn-download-wrapper .btn{font-size:3em;padding:3%;display:inline-block;margin-bottom:5px}#download .btn-download-wrapper small, #index .btn-download-wrapper small{display:block;font-size:0.4em}#download h2.description, #index h2.description{color:#e6e6e6;font-size:2em;font-weight:bold;margin:120px 50px;line-height:2em}#download h2.description .label, #index h2.description .label{font-size:0.5em}#download .btn-download-wrapper{margin:40px auto}#download .os-selector{text-align:center;color:#e6e6e6;margin:30px 0}#download .os-selector a.btn-build{color:#e6e6e6;display:block;padding:20px;border-radius:2px}#download .os-selector .btn-build[href="#build-linux"]{background-color:#e43}#download .os-selector .btn-build[href="#build-linux"]:hover{color:#e43;background-color:#e6e6e6}#download .os-selector .btn-build[href="#build-windows"]{background-color:#06a}#download .os-selector .btn-build[href="#build-windows"]:hover{color:#06a;background-color:#e6e6e6}#download .os-selector .btn-build[href="#build-mac"]{background-color:darkgrey}#download .os-selector .btn-build[href="#build-mac"]:hover{color:darkgrey;background-color:#e6e6e6}#download .os-selector .tab-content{margin-top:20px}#download .os-selector #build-linux h3{color:#e43}#download .os-selector #build-windows h3{color:#06a}#download .os-selector #build-mac h3{color:darkgrey}footer{background-color:#242424;border-top:1px #101010 solid;padding:20px 0%}footer a{display:block}footer img[alt="FFmpeg"]{width:50%;display:block;margin:auto}
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arm/vorbisdsp_init_arm.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arm/vorbisdsp_init_arm.c
deleted file mode 100644
index acda34f46841fffe720fb7d2f609f4256766d972..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arm/vorbisdsp_init_arm.c
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * ARM NEON optimised DSP functions
- * Copyright (c) 2008 Mans Rullgard
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include "libavutil/attributes.h"
-#include "libavutil/cpu.h"
-#include "libavutil/arm/cpu.h"
-#include "libavcodec/vorbisdsp.h"
-
-void ff_vorbis_inverse_coupling_neon(float *mag, float *ang,
- ptrdiff_t blocksize);
-
-av_cold void ff_vorbisdsp_init_arm(VorbisDSPContext *c)
-{
- int cpu_flags = av_get_cpu_flags();
-
- if (have_neon(cpu_flags)) {
- c->vorbis_inverse_coupling = ff_vorbis_inverse_coupling_neon;
- }
-}
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/cfhddsp.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/cfhddsp.c
deleted file mode 100644
index a141db5246999c35e52d251f54d3125483bbb710..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/cfhddsp.c
+++ /dev/null
@@ -1,118 +0,0 @@
-/*
- * Copyright (c) 2015-2016 Kieran Kunhya
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include "libavutil/attributes.h"
-#include "libavutil/common.h"
-
-#include "cfhddsp.h"
-
-static av_always_inline void filter(int16_t *output, ptrdiff_t out_stride,
- const int16_t *low, ptrdiff_t low_stride,
- const int16_t *high, ptrdiff_t high_stride,
- int len, int clip)
-{
- int16_t tmp;
- int i;
-
- tmp = (11*low[0*low_stride] - 4*low[1*low_stride] + low[2*low_stride] + 4) >> 3;
- output[(2*0+0)*out_stride] = (tmp + high[0*high_stride]) >> 1;
- if (clip)
- output[(2*0+0)*out_stride] = av_clip_uintp2_c(output[(2*0+0)*out_stride], clip);
-
- tmp = ( 5*low[0*low_stride] + 4*low[1*low_stride] - low[2*low_stride] + 4) >> 3;
- output[(2*0+1)*out_stride] = (tmp - high[0*high_stride]) >> 1;
- if (clip)
- output[(2*0+1)*out_stride] = av_clip_uintp2_c(output[(2*0+1)*out_stride], clip);
-
- for (i = 1; i < len - 1; i++) {
- tmp = (low[(i-1)*low_stride] - low[(i+1)*low_stride] + 4) >> 3;
- output[(2*i+0)*out_stride] = (tmp + low[i*low_stride] + high[i*high_stride]) >> 1;
- if (clip)
- output[(2*i+0)*out_stride] = av_clip_uintp2_c(output[(2*i+0)*out_stride], clip);
-
- tmp = (low[(i+1)*low_stride] - low[(i-1)*low_stride] + 4) >> 3;
- output[(2*i+1)*out_stride] = (tmp + low[i*low_stride] - high[i*high_stride]) >> 1;
- if (clip)
- output[(2*i+1)*out_stride] = av_clip_uintp2_c(output[(2*i+1)*out_stride], clip);
- }
-
- tmp = ( 5*low[i*low_stride] + 4*low[(i-1)*low_stride] - low[(i-2)*low_stride] + 4) >> 3;
- output[(2*i+0)*out_stride] = (tmp + high[i*high_stride]) >> 1;
- if (clip)
- output[(2*i+0)*out_stride] = av_clip_uintp2_c(output[(2*i+0)*out_stride], clip);
-
- tmp = (11*low[i*low_stride] - 4*low[(i-1)*low_stride] + low[(i-2)*low_stride] + 4) >> 3;
- output[(2*i+1)*out_stride] = (tmp - high[i*high_stride]) >> 1;
- if (clip)
- output[(2*i+1)*out_stride] = av_clip_uintp2_c(output[(2*i+1)*out_stride], clip);
-}
-
-static void vert_filter(int16_t *output, ptrdiff_t out_stride,
- const int16_t *low, ptrdiff_t low_stride,
- const int16_t *high, ptrdiff_t high_stride,
- int width, int height)
-{
- for (int i = 0; i < width; i++) {
- filter(output, out_stride, low, low_stride, high, high_stride, height, 0);
- low++;
- high++;
- output++;
- }
-}
-
-static void horiz_filter(int16_t *output, ptrdiff_t ostride,
- const int16_t *low, ptrdiff_t lstride,
- const int16_t *high, ptrdiff_t hstride,
- int width, int height)
-{
- for (int i = 0; i < height; i++) {
- filter(output, 1, low, 1, high, 1, width, 0);
- low += lstride;
- high += hstride;
- output += ostride * 2;
- }
-}
-
-static void horiz_filter_clip(int16_t *output, const int16_t *low, const int16_t *high,
- int width, int clip)
-{
- filter(output, 1, low, 1, high, 1, width, clip);
-}
-
-static void horiz_filter_clip_bayer(int16_t *output, const int16_t *low, const int16_t *high,
- int width, int clip)
-{
- filter(output, 2, low, 1, high, 1, width, clip);
-}
-
-av_cold void ff_cfhddsp_init(CFHDDSPContext *c, int depth, int bayer)
-{
- c->horiz_filter = horiz_filter;
- c->vert_filter = vert_filter;
-
- if (bayer)
- c->horiz_filter_clip = horiz_filter_clip_bayer;
- else
- c->horiz_filter_clip = horiz_filter_clip;
-
-#if ARCH_X86
- ff_cfhddsp_init_x86(c, depth, bayer);
-#endif
-}
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/codec_par.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/codec_par.h
deleted file mode 100644
index f51d27c5908449354badf3e00cf5e3002a4f97ac..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/codec_par.h
+++ /dev/null
@@ -1,247 +0,0 @@
-/*
- * Codec parameters public API
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#ifndef AVCODEC_CODEC_PAR_H
-#define AVCODEC_CODEC_PAR_H
-
-#include
-
-#include "libavutil/avutil.h"
-#include "libavutil/channel_layout.h"
-#include "libavutil/rational.h"
-#include "libavutil/pixfmt.h"
-
-#include "codec_id.h"
-
-/**
- * @addtogroup lavc_core
- * @{
- */
-
-enum AVFieldOrder {
- AV_FIELD_UNKNOWN,
- AV_FIELD_PROGRESSIVE,
- AV_FIELD_TT, ///< Top coded_first, top displayed first
- AV_FIELD_BB, ///< Bottom coded first, bottom displayed first
- AV_FIELD_TB, ///< Top coded first, bottom displayed first
- AV_FIELD_BT, ///< Bottom coded first, top displayed first
-};
-
-/**
- * This struct describes the properties of an encoded stream.
- *
- * sizeof(AVCodecParameters) is not a part of the public ABI, this struct must
- * be allocated with avcodec_parameters_alloc() and freed with
- * avcodec_parameters_free().
- */
-typedef struct AVCodecParameters {
- /**
- * General type of the encoded data.
- */
- enum AVMediaType codec_type;
- /**
- * Specific type of the encoded data (the codec used).
- */
- enum AVCodecID codec_id;
- /**
- * Additional information about the codec (corresponds to the AVI FOURCC).
- */
- uint32_t codec_tag;
-
- /**
- * Extra binary data needed for initializing the decoder, codec-dependent.
- *
- * Must be allocated with av_malloc() and will be freed by
- * avcodec_parameters_free(). The allocated size of extradata must be at
- * least extradata_size + AV_INPUT_BUFFER_PADDING_SIZE, with the padding
- * bytes zeroed.
- */
- uint8_t *extradata;
- /**
- * Size of the extradata content in bytes.
- */
- int extradata_size;
-
- /**
- * - video: the pixel format, the value corresponds to enum AVPixelFormat.
- * - audio: the sample format, the value corresponds to enum AVSampleFormat.
- */
- int format;
-
- /**
- * The average bitrate of the encoded data (in bits per second).
- */
- int64_t bit_rate;
-
- /**
- * The number of bits per sample in the codedwords.
- *
- * This is basically the bitrate per sample. It is mandatory for a bunch of
- * formats to actually decode them. It's the number of bits for one sample in
- * the actual coded bitstream.
- *
- * This could be for example 4 for ADPCM
- * For PCM formats this matches bits_per_raw_sample
- * Can be 0
- */
- int bits_per_coded_sample;
-
- /**
- * This is the number of valid bits in each output sample. If the
- * sample format has more bits, the least significant bits are additional
- * padding bits, which are always 0. Use right shifts to reduce the sample
- * to its actual size. For example, audio formats with 24 bit samples will
- * have bits_per_raw_sample set to 24, and format set to AV_SAMPLE_FMT_S32.
- * To get the original sample use "(int32_t)sample >> 8"."
- *
- * For ADPCM this might be 12 or 16 or similar
- * Can be 0
- */
- int bits_per_raw_sample;
-
- /**
- * Codec-specific bitstream restrictions that the stream conforms to.
- */
- int profile;
- int level;
-
- /**
- * Video only. The dimensions of the video frame in pixels.
- */
- int width;
- int height;
-
- /**
- * Video only. The aspect ratio (width / height) which a single pixel
- * should have when displayed.
- *
- * When the aspect ratio is unknown / undefined, the numerator should be
- * set to 0 (the denominator may have any value).
- */
- AVRational sample_aspect_ratio;
-
- /**
- * Video only. The order of the fields in interlaced video.
- */
- enum AVFieldOrder field_order;
-
- /**
- * Video only. Additional colorspace characteristics.
- */
- enum AVColorRange color_range;
- enum AVColorPrimaries color_primaries;
- enum AVColorTransferCharacteristic color_trc;
- enum AVColorSpace color_space;
- enum AVChromaLocation chroma_location;
-
- /**
- * Video only. Number of delayed frames.
- */
- int video_delay;
-
-#if FF_API_OLD_CHANNEL_LAYOUT
- /**
- * Audio only. The channel layout bitmask. May be 0 if the channel layout is
- * unknown or unspecified, otherwise the number of bits set must be equal to
- * the channels field.
- * @deprecated use ch_layout
- */
- attribute_deprecated
- uint64_t channel_layout;
- /**
- * Audio only. The number of audio channels.
- * @deprecated use ch_layout.nb_channels
- */
- attribute_deprecated
- int channels;
-#endif
- /**
- * Audio only. The number of audio samples per second.
- */
- int sample_rate;
- /**
- * Audio only. The number of bytes per coded audio frame, required by some
- * formats.
- *
- * Corresponds to nBlockAlign in WAVEFORMATEX.
- */
- int block_align;
- /**
- * Audio only. Audio frame size, if known. Required by some formats to be static.
- */
- int frame_size;
-
- /**
- * Audio only. The amount of padding (in samples) inserted by the encoder at
- * the beginning of the audio. I.e. this number of leading decoded samples
- * must be discarded by the caller to get the original audio without leading
- * padding.
- */
- int initial_padding;
- /**
- * Audio only. The amount of padding (in samples) appended by the encoder to
- * the end of the audio. I.e. this number of decoded samples must be
- * discarded by the caller from the end of the stream to get the original
- * audio without any trailing padding.
- */
- int trailing_padding;
- /**
- * Audio only. Number of samples to skip after a discontinuity.
- */
- int seek_preroll;
-
- /**
- * Audio only. The channel layout and number of channels.
- */
- AVChannelLayout ch_layout;
-} AVCodecParameters;
-
-/**
- * Allocate a new AVCodecParameters and set its fields to default values
- * (unknown/invalid/0). The returned struct must be freed with
- * avcodec_parameters_free().
- */
-AVCodecParameters *avcodec_parameters_alloc(void);
-
-/**
- * Free an AVCodecParameters instance and everything associated with it and
- * write NULL to the supplied pointer.
- */
-void avcodec_parameters_free(AVCodecParameters **par);
-
-/**
- * Copy the contents of src to dst. Any allocated fields in dst are freed and
- * replaced with newly allocated duplicates of the corresponding fields in src.
- *
- * @return >= 0 on success, a negative AVERROR code on failure.
- */
-int avcodec_parameters_copy(AVCodecParameters *dst, const AVCodecParameters *src);
-
-/**
- * This function is the same as av_get_audio_frame_duration(), except it works
- * with AVCodecParameters instead of an AVCodecContext.
- */
-int av_get_audio_frame_duration2(AVCodecParameters *par, int frame_bytes);
-
-/**
- * @}
- */
-
-#endif // AVCODEC_CODEC_PAR_H
diff --git a/spaces/compasspathways/Sentiment3D/app.py b/spaces/compasspathways/Sentiment3D/app.py
deleted file mode 100644
index 5e238f5e106f457a54d6a70f8dec022210072454..0000000000000000000000000000000000000000
--- a/spaces/compasspathways/Sentiment3D/app.py
+++ /dev/null
@@ -1,77 +0,0 @@
-import gradio as gr
-import pandas as pd
-from sentiment3d import Sentiment3D
-
-s3d = Sentiment3D()
-
-TITLE = "COMPASS Pathways: 3D Sentiment Model"
-EXAMPLES = [
- "This is so awesome!",
- "You're driving me up the wall!",
- "I'm so lonely I could cry.",
- "I'm not feeling very sad at all.",
- "A day without sunshine is like, you know, night.",
- "Yes, that's how I feel [laughing].",
- "Yes, that's how I feel [sobbing].",
- "Now I hear what you're sayin' 😀",
- "Now I hear what you're sayin' 🙁",
-]
-
-
-def sentiment(text, state):
- sent = s3d(text)
- res = dict(text=text, valence=sent['valence'], arousal=sent['arousal'], confidence=sent['confidence'], words=len(text.split()))
- #if clear_history:
- # state = []
- if state == None:
- state = []
- state.append(res)
- df = pd.DataFrame(state)
- res_txt = [
- f"{r['text']}: \n valence = {r['valence']:0.3f}, arousal = {r['arousal']:0.3f}, confidence = {r['confidence']:0.3f}"
- for r in state
- ]
- return "\n".join(res_txt), df, df, df, state
-
-
-iface = gr.Interface(
- fn=sentiment,
- inputs=[gr.Textbox(lines=1, placeholder="Text for 3d sentiment..."), "state"],
- outputs=[
- gr.Textbox(lines=5, max_lines=5, label="Results"),
- gr.ScatterPlot(
- x="valence",
- y="arousal",
- tooltip="text",
- size="words",
- size_legend_position="none",
- interactive=False,
- x_lim=[-1.05, 1.05],
- y_lim=[-1.05, 1.05],
- ),
- gr.ScatterPlot(
- x="valence",
- y="confidence",
- tooltip="text",
- size="words",
- size_legend_position="none",
- interactive=False,
- x_lim=[-1.05, 1.05],
- y_lim=[-1.05, 1.05],
- ),
- gr.ScatterPlot(
- x="arousal",
- y="confidence",
- tooltip="text",
- size="words",
- size_legend_position="none",
- interactive=False,
- x_lim=[-1.05, 1.05],
- y_lim=[-1.05, 1.05],
- ),
- "state",
- ],
- title=TITLE,
- examples=EXAMPLES
-)
-iface.launch()
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Enjoy Talking Tom Hero Dash Full APK - The Most Fun and Addictive Endless Runner Game.md b/spaces/congsaPfin/Manga-OCR/logs/Enjoy Talking Tom Hero Dash Full APK - The Most Fun and Addictive Endless Runner Game.md
deleted file mode 100644
index 19dd80db7f2fd9c267f41f723fe7d338ac5d0b70..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Enjoy Talking Tom Hero Dash Full APK - The Most Fun and Addictive Endless Runner Game.md
+++ /dev/null
@@ -1,120 +0,0 @@
-
-Talking Tom Hero Dash Full APK: A Fun and Action-Packed Game for Android
-Do you love running games? Do you enjoy playing with cute and funny characters? Do you want to experience an epic adventure with amazing graphics and sound effects? If you answered yes to any of these questions, then you should try Talking Tom Hero Dash , one of the most popular games on Google Play Store.
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Talking Tom Hero Dash is a fun and action-packed game that features your favorite characters from the Talking Tom series. You can play as Tom, Angela, Hank, Ginger, or Ben, each with their own superpowers and vehicles. Your mission is to run through different worlds, collect coins and power-ups, defeat raccoon bosses, save your friends, and rebuild your city.
-Talking Tom Hero Dash is more than just a running game. It also has elements of strategy, customization, humor, and social interaction. You can unlock new outfits and vehicles for your heroes, upgrade your city with cool buildings and decorations, watch funny videos of your heroes' antics, and share your achievements with your friends online.
-If you want to enjoy all these features and more, you should download Talking Tom Hero Dash Full APK , which is the full version of the game that includes all the updates and features. You can download it for free from a trusted source and install it on your Android device in a few simple steps. Here's how:
- How to Download Talking Tom Hero Dash Full APK
-Step 1: Enable Unknown Sources on Your Device
-Before you can install the APK file, you need to allow your device to install apps from sources other than Google Play Store. To do this, go to your device's settings and look for the security or privacy option. Then, find the option that says "Unknown sources" or "Allow installation of apps from unknown sources" and enable it. You may see a warning message that says installing apps from unknown sources can harm your device, but don't worry, as long as you download the APK file from a reliable source, you should be fine.
-Step 2: Download the APK File from a Trusted Source
-Now that you have enabled unknown sources, you can download the APK file from a trusted source. There are many websites that offer APK files for free, but not all of them are safe and reliable. Some of them may contain malware or viruses that can harm your device or steal your personal information. To avoid this, you should only download the APK file from a reputable website that has positive reviews and ratings from other users. One such website is APKPure.com , which is one of the most popular and trusted sources for APK files. You can download Talking Tom Hero Dash Full APK from this link: https://apkpure.com/talking-tom-hero-dash-run-game/com.outfit7.herodash/download?from=details .
-Step 3: Install and Launch the Game
-Once you have downloaded the APK file, you can install it on your device by tapping on it and following the instructions on the screen. The installation process may take a few minutes, depending on your device's speed and memory. After the installation is complete, you can launch the game by tapping on its icon on your home screen or app drawer. You may see a message that asks you to grant some permissions to the game, such as access to your storage, camera, microphone, or location. You can allow or deny these permissions as you wish, but keep in mind that some features of the game may not work properly if you deny them.
- How to Play Talking Tom Hero Dash
-Choose Your Hero
-When you start the game, you will see a screen that shows you the five heroes that you can play as: Tom, Angela, Hank, Ginger, or Ben. Each hero has their own superpower and vehicle that they can use in the game. For example, Tom can fly with his jetpack, Angela can use her pink scooter, Hank can shoot lasers with his glasses, Ginger can throw fireballs with his slingshot, and Ben can hack computers with his laptop. You can choose any hero that you like by tapping on their picture. You can also switch heroes anytime during the game by tapping on the hero icon at the top left corner of the screen.
-talking tom hero dash mod apk unlimited money
-Run, Jump, and Slide Through Different Worlds
-The main gameplay of Talking Tom Hero Dash is similar to other running games such as Subway Surfers, Temple Run, or Sonic Dash. You have to control your hero's movements by swiping left or right to change lanes, swiping up to jump over obstacles, and swiping down to slide under them. You have to avoid crashing into obstacles such as cars, buses, trains, barrels, fences, or raccoons. If you crash into an obstacle three times in a row, you will lose the game and have to start over.
-The game has different worlds that you can run through, such as city streets, tropical islands, Chinese temples, snowy mountains, or space stations. Each world has its own theme and challenges that make it unique and fun. You can unlock new worlds by collecting enough stars in each level. Stars are awarded based on how far you run, how many coins you collect, how many raccoons you defeat, and how many friends you save.
-Collect Coins, Power-Ups, and Rewards
-As you run through the worlds, you will see coins scattered along the way. You can collect these coins by running over them or using power-ups such as magnets that attract them to you. Coins are used to buy new outfits and vehicles for your heroes or upgrade your city with new buildings and decorations.
-You will also see power-ups that appear randomly on the road. These power-ups can help you run faster, avoid obstacles, or defeat raccoons. Some of the power-ups are shields that protect you from crashes, rockets that launch you into the air, or helmets that make you invincible. You can activate these power-ups by tapping on them when they appear on the screen.
-Besides coins and power-ups, you will also see rewards that you can collect by completing certain tasks or achievements. These rewards include gems, chests, stickers, or cards. Gems are used to revive your hero if you lose the game or to unlock new worlds faster. Chests contain random items such as coins, gems, power-ups, or outfits. Stickers are used to decorate your city with graffiti or posters. Cards are used to unlock new heroes or upgrade their abilities.
-Defeat Raccoon Bosses and Save Your Friends
-The main villains of Talking Tom Hero Dash are the raccoons, who have invaded your city and kidnapped your friends. You have to fight against them and rescue your friends in each world. The raccoons come in different sizes and colors, and they have different weapons and attacks. Some of them throw bombs, some of them shoot lasers, and some of them ride motorcycles or helicopters. You have to dodge their attacks and hit them with your superpower or power-ups until they are defeated.
-At the end of each world, you will face a raccoon boss, who is bigger and stronger than the regular raccoons. The raccoon boss has a health bar that shows how much damage you have done to him. You have to hit him multiple times with your superpower or power-ups until his health bar is empty. Once you defeat the raccoon boss, you will free one of your friends who has been trapped in a cage. You can then play as that friend in the next world.
-Customize Your Heroes and Their Vehicles
-One of the most fun features of Talking Tom Hero Dash is that you can customize your heroes and their vehicles with different outfits and accessories. You can change their clothes, hats, glasses, shoes, masks, or capes. You can also change their vehicles' colors, wheels, stickers, or lights. You can buy these items with coins or gems, or get them from chests or cards. You can also mix and match different items to create your own unique style.
-Customizing your heroes and their vehicles not only makes them look cool, but also gives them some advantages in the game. For example, some outfits give you extra coins or gems, some vehicles give you extra speed or power-ups, and some combinations give you special effects or bonuses. You can see these benefits by tapping on the item's icon before buying it.
- How Does Talking Tom Hero Dash Compare with Other Games?
-Talking Tom Hero Dash is not the only running game available on Google Play Store. There are many other games that have similar gameplay and features, such as Subway Surfers, Temple Run, or Sonic Dash. How does Talking Tom Hero Dash compare with these games? Here is a table that shows some of the differences and similarities between them:
-
-
-Game
-Graphics
-Gameplay
-Ratings
-
-
-Talking Tom Hero Dash
-3D cartoon style with bright colors and detailed animations
-Run through different worlds with various themes and challenges; collect coins and power-ups; defeat raccoon bosses and save your friends; customize your heroes and their vehicles; rebuild your city
-4.5 stars out of 5 based on 2 million reviews
-
-
-Subway Surfers
-3D cartoon style with vibrant colors and smooth animations
-Run through different cities with diverse cultures and landmarks; collect coins and power-ups; dodge trains and obstacles; unlock new characters and boards; join seasonal events
-4.4 stars out of 5 based on 36 million reviews
-
-
-Temple Run
-3D realistic style with dark colors and dynamic shadows
-Run through ancient temples with mysterious secrets and dangers; collect coins and power-ups; avoid traps and obstacles; unlock new characters and abilities; escape from evil monkeys
-4.1 stars out of 5 based on 5 million reviews
-
-
-Sonic Dash
-3D cartoon style with vivid colors and fast-paced animations
-Run through iconic locations from the Sonic universe; collect rings and power-ups; dash and spin to destroy enemies; unlock new characters and skills; join epic boss battles
-4.4 stars out of 5 based on 5 million reviews
-
-
- As you can see, Talking Tom Hero Dash has some advantages and disadvantages compared to other games. It has better graphics, more gameplay options, and higher ratings than Temple Run, but it has less variety, more ads, and lower ratings than Subway Surfers or Sonic Dash. Ultimately, the choice of which game to play depends on your personal preference and taste. However, if you are looking for a fun and action-packed game that features your favorite Talking Tom characters, then you should definitely give Talking Tom Hero Dash a try.
- Conclusion
-Talking Tom Hero Dash is a fun and action-packed game that you can download and play on your Android device for free. It features your favorite Talking Tom characters as superheroes who run through different worlds, collect coins and power-ups, defeat raccoon bosses, save their friends, and rebuild their city. You can also customize your heroes and their vehicles with different outfits and accessories, and share your achievements with your friends online. Talking Tom Hero Dash is a game that will keep you entertained and engaged for hours.
-If you want to download Talking Tom Hero Dash Full APK, which is the full version of the game that includes all the updates and features, you can follow the simple steps that we have explained in this article. You just need to enable unknown sources on your device, download the APK file from a trusted source, install and launch the game, and enjoy. It's that easy.
-So what are you waiting for? Download Talking Tom Hero Dash Full APK today and join the adventure with your favorite Talking Tom heroes. You won't regret it.
- FAQs
-Here are some frequently asked questions that you might have about Talking Tom Hero Dash or APK files:
-
-What is an APK file? An APK file is an Android Package file that contains all the files and data needed to install an app on an Android device. It is similar to an EXE file on Windows or a DMG file on Mac.
-Why do I need to download an APK file instead of installing the app from Google Play Store? Sometimes, you might want to download an APK file instead of installing the app from Google Play Store for various reasons. For example, you might want to access an app that is not available in your region or country, or you might want to get the latest version of an app that has not been updated on Google Play Store yet, or you might want to get the full version of an app that has some features locked or restricted on Google Play Store.
-Is it safe to download and install APK files? It depends on where you download the APK files from. Some websites may offer APK files that are infected with malware or viruses that can harm your device or steal your personal information. To avoid this, you should only download APK files from reputable websites that have positive reviews and ratings from other users. You should also scan the APK files with an antivirus software before installing them.
-How do I update an app that I installed from an APK file? If you installed an app from an APK file, you will not receive automatic updates from Google Play Store. You will have to manually download and install the new version of the APK file from the same source that you got it from. Alternatively, you can uninstall the app and install it from Google Play Store if it is available there.
-How do I uninstall an app that I installed from an APK file? You can uninstall an app that you installed from an APK file in the same way that you uninstall any other app on your device. You can go to your device's settings and look for the apps or applications option. Then, find the app that you want to uninstall and tap on it. You will see an option that says "Uninstall" or "Remove". Tap on it and confirm your action.
- 401be4b1e0Texas Holdem Poker Online Mod APK: How to Play and Win
-Do you love playing poker but don't have the time or money to visit a casino? Do you want to enjoy the thrill of winning big without risking your real cash? If you answered yes to any of these questions, then you should try Texas Holdem Poker Online Mod APK. This is a modified version of the popular card game that lets you play online with other players from around the world. You can also get unlimited money, chips, and coins to bet as much as you want. In this article, we will show you how to download, install, and play Texas Holdem Poker Online Mod APK. We will also give you some tips on how to win more often and have more fun.
- Introduction
-What is Texas Holdem Poker Online Mod APK?
-Texas Holdem Poker Online Mod APK is a hacked version of the original game that gives you access to some features that are not available in the official app. For example, you can get unlimited money, chips, and coins to play with. You can also unlock all the game modes, tables, and tournaments that are otherwise restricted or require in-app purchases. You can also enjoy faster loading times, smoother gameplay, and better graphics. With Texas Holdem Poker Online Mod APK, you can experience the best of online poker without spending a dime.
-texas holdem poker online mod apk Download ✅ https://urlca.com/2uOcda
Why should you play Texas Holdem Poker Online Mod APK?
-There are many reasons why you should play Texas Holdem Poker Online Mod APK. Here are some of them:
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-You can play online with millions of other players from different countries and regions.
-You can choose from various game modes, such as cash games, tournaments, sit and go, and more.
-You can customize your avatar, profile, and table settings according to your preferences.
-You can chat with other players, send gifts, and make friends.
-You can learn the rules and strategies of Texas Holdem Poker from tutorials, tips, and guides.
-You can improve your skills, confidence, and ranking by playing regularly.
-You can have fun, relax, and relieve stress by playing poker anytime, anywhere.
-
- How to download and install Texas Holdem Poker Online Mod APK?
-Step 1: Find a reliable source for the mod apk file
-The first step to download and install Texas Holdem Poker Online Mod APK is to find a trustworthy website that offers the mod apk file. You can use Google or any other search engine to look for one. However, be careful not to download from shady or suspicious sites that may contain viruses or malware. One of the best sources for the mod apk file is Apkloli.com , which is a website that offers many popular card games. You can get the free download of Texas Holdem Poker Online Mod APK version v123.1.18 here.
- Step 2: Enable unknown sources on your device
-The next step is to enable unknown sources on your device. This is necessary
because the mod apk file is not from the official Google Play Store. To do this, go to your device's settings and look for the security or privacy option. Then, find the unknown sources option and toggle it on. This will allow you to install apps from sources other than the Google Play Store.
- Step 3: Download and install the mod apk file
-The final step is to download and install the mod apk file. To do this, go to the website where you found the mod apk file and click on the download button. Wait for the file to be downloaded to your device. Then, locate the file in your device's storage and tap on it. Follow the instructions on the screen to complete the installation process. Once the installation is done, you can launch the app and start playing Texas Holdem Poker Online Mod APK.
- How to play Texas Holdem Poker Online Mod APK?
-Step 1: Create an account or log in with Facebook
-When you open the app for the first time, you will be asked to create an account or log in with Facebook. You can choose either option depending on your preference. Creating an account will require you to enter your username, email, and password. Logging in with Facebook will require you to grant permission to access your profile information. Either way, you will be able to access all the features of Texas Holdem Poker Online Mod APK once you are logged in.
- Step 2: Choose a game mode and a table
-The next step is to choose a game mode and a table that suits your skill level and budget. You can choose from various game modes, such as cash games, tournaments, sit and go, and more. You can also filter the tables by stakes, players, speed, and type. You can join any table that has an empty seat or create your own table and invite your friends. You can also switch tables anytime you want.
- Step 3: Learn the basic rules and strategies of Texas Holdem Poker
-The last step is to learn the basic rules and strategies of Texas Holdem Poker. If you are new to poker, you can check out the tutorials, tips, and guides that are available in the app. You can also practice with free chips or play money before you bet with real money. The basic rules of Texas Holdem Poker are as follows:
-
-Each player is dealt two cards face down, called hole cards.
-A round of betting takes place, starting with the player to the left of the big blind.
-Three cards are dealt face up on the table, called the flop.
-Another round of betting takes place, starting with the player to the left of the dealer.
-A fourth card is dealt face up on the table, called the turn.
-Another round of betting takes place, starting with the player to the left of the dealer.
-A fifth card is dealt face up on the table, called the river.
-A final round of betting takes place, starting with the player to the left of the dealer.
-The remaining players show their cards and compare their best five-card poker hand.
-The player with the best hand wins the pot.
-
-The basic strategies of Texas Holdem Poker are as follows:
-free texas holdem poker online mod apk download
-
-Know when to fold, call, raise, or bluff depending on your cards and position.
-Pay attention to your opponents' actions and patterns and try to guess their cards and intentions.
-Use probability and odds to calculate your chances of winning or losing.
-Avoid tilting or letting your emotions affect your decisions.
-Manage your bankroll wisely and don't bet more than you can afford to lose.
-
- How to win Texas Holdem Poker Online Mod APK?
-Tip 1: Use the unlimited money feature to your advantage
-One of the benefits of playing Texas Holdem Poker Online Mod APK is that you can get unlimited money, chips, and coins to play with. This means that you can bet as much as you want without worrying about running out of money. You can also use this feature to intimidate your opponents or make them think that you have a strong hand. However, be careful not to abuse this feature or make unrealistic bets that will make you look suspicious or foolish.
- Tip 2: Bluff smartly and read your opponents' tells
-Another way to win Texas Holdem Poker Online Mod APK is to bluff smartly and read your opponents' tells. Bluffing is a technique of making your opponents think that you have a better hand than you actually have. This can make them fold or call with weaker hands. However, bluffing is not always effective and can backfire if your opponents call your bluff or have a better hand. Therefore, you should bluff smartly and sparingly, and only when you have a good reason to do so. Some factors that can help you decide when to bluff are: - Your position: Bluffing from a late position can be more successful than bluffing from an early position, as you have more information about your opponents' actions and cards. - Your image: Bluffing from a tight image can be more successful than bluffing from a loose image, as your opponents will respect your bets and assume that you have a strong hand. - Your cards: Bluffing with some outs or potential to improve can be more successful than bluffing with nothing, as you have a chance to win even if you get called. - Your opponents: Bluffing against weak or timid opponents can be more successful than bluffing against strong or aggressive opponents, as they are more likely to fold or be scared by your bets. Reading your opponents' tells is another skill that can help you win Texas Holdem Poker Online Mod APK. Tells are clues or signs that reveal your opponents' emotions, intentions, or cards. They can be verbal or non-verbal, such as facial expressions, body language, gestures, tone of voice, chat messages, betting patterns, and so on. By reading your opponents' tells, you can gain an edge over them and make better decisions. Some examples of common tells are: - A player who looks away or avoids eye contact may be bluffing or hiding something. - A player who smiles or laughs nervously may be nervous or unsure about their hand. - A player who shakes or trembles may be excited or holding a strong hand. - A player who bets quickly or aggressively may be confident or trying to intimidate you. - A player who bets slowly or hesitantly may be weak or trying to trap you.
Tip 3: Practice regularly and learn from your mistakes
-The final tip to win Texas Holdem Poker Online Mod APK is to practice regularly and learn from your mistakes. Poker is a game of skill and luck, and the only way to improve your skill is to play more and study more. You can practice with free chips or play money before you play with real money. You can also watch videos, read books, join forums, and follow blogs of professional poker players. You can also review your own games and analyze your strengths and weaknesses. By practicing regularly and learning from your mistakes, you can become a better poker player and win more often.
- Conclusion
-Summary of the main points
-In conclusion, Texas Holdem Poker Online Mod APK is a modified version of the popular card game that lets you play online with other players from around the world. You can also get unlimited money, chips, and coins to bet as much as you want. To download and install Texas Holdem Poker Online Mod APK, you need to find a reliable source for the mod apk file, enable unknown sources on your device, and follow the instructions on the screen. To play Texas Holdem Poker Online Mod APK, you need to create an account or log in with Facebook, choose a game mode and a table, and learn the basic rules and strategies of Texas Holdem Poker. To win Texas Holdem Poker Online Mod APK, you need to use the unlimited money feature to your advantage, bluff smartly and read your opponents' tells, and practice regularly and learn from your mistakes.
- Call to action
-If you are ready to play Texas Holdem Poker Online Mod APK and have fun, then don't wait any longer. Download the app now and join the millions of poker fans who are enjoying the game every day. You will not regret it!
- FAQs
-Q: Is Texas Holdem Poker Online Mod APK safe to use?
-A: Yes, Texas Holdem Poker Online Mod APK is safe to use as long as you download it from a reputable website that offers virus-free and malware-free files. However, you should always be careful when downloading any app from unknown sources and scan it with an antivirus software before installing it.
- Q: Is Texas Holdem Poker Online Mod APK legal to use?
-A: Yes, Texas Holdem Poker Online Mod APK is legal to use as long as you do not use it for illegal purposes or violate any terms of service of the original app. However, you should always check the laws and regulations of your country or region before playing online poker with real money.
- Q: How can I update Texas Holdem Poker Online Mod APK?
-A: To update Texas Holdem Poker Online Mod APK, you need to download the latest version of the mod apk file from the same website where you downloaded it before. Then, you need to uninstall the old version of the app and install the new version. You can also check the website for any updates or notifications about the mod apk file.
- Q: How can I contact the developer of Texas Holdem Poker Online Mod APK?
-A: To contact the developer of Texas Holdem Poker Online Mod APK, you can visit their official website or their social media pages. You can also send them an email or a message through the app. However, you should not expect a quick or positive response from them, as they may not support or endorse the mod apk file.
- Q: How can I get more free chips and coins in Texas Holdem Poker Online Mod APK?
-A: To get more free chips and coins in Texas Holdem Poker Online Mod APK, you can use the unlimited money feature that comes with the mod apk file. You can also claim daily bonuses, complete achievements, invite friends, watch ads, and participate in events and promotions. You can also buy more chips and coins with real money if you want to support the original app.
- How to Download Your Clash Royale Matches
-Clash Royale is a popular real-time strategy game that pits you against other players from around the world in fast-paced card battles. You can collect and upgrade dozens of cards featuring your favorite Clash characters, spells, and defenses, as well as new ones like the Royales. You can also join or form a clan with other players to share cards and battle in clan wars for big rewards.
-If you are a fan of Clash Royale, you might want to download your matches and watch them later. Maybe you want to analyze your mistakes and improve your skills, or maybe you want to relive your epic victories and show them off to your friends. Whatever your reason, downloading your matches is not as hard as you might think. In this article, we will show you two methods to download your Clash Royale matches easily and quickly.
-download my clash royale matches Download ○○○ https://urlca.com/2uO4G7
Before we start, you need to know how to find your player tag and access your battle log. Your player tag is a unique code that identifies your account in Clash Royale. You can find it by tapping on your name in the top left corner of the screen. Your battle log is where you can see all your recent battles, whether they are wins, losses, draws, or challenges. You can access it by tapping on the battle button in the bottom right corner of the screen.
-Methods to Download Your Matches
-Using a Screen Recorder App
-A screen recorder app is an app that allows you to record everything that happens on your device's screen. You can use it to record your Clash Royale matches by simply launching the app, starting the recording, opening Clash Royale, and playing or watching your match. When you are done, you can stop the recording and save it to your device's storage.
-Some of the pros of using a screen recorder app are:
-How to download clash royale replays on android
-
-You can record any match you want, whether it is yours or someone else's.
-You can record in high quality and resolution.
-You can record with sound and voice commentary.
-You can edit your recordings within the app or with another app.
-
-Some of the cons of using a screen recorder app are:
-
-You need to have enough storage space on your device to save your recordings.
-You need to have a compatible device and operating system to run the app.
-You might experience some lag or performance issues while recording.
-You might need to pay for some features or remove ads from the app.
-
-Some examples of screen recorder apps for Android and iOS devices are:
-
-
-App Name
-Price
-Rating
-Features
-
-
-AZ Screen Recorder
-Free with in-app purchases
-4.5/5 stars on Google Play Store
-- Record in HD and Full HD quality
-
-
-DU Recorder
-Free with in-app purchases
-4.3/5 stars on Google Play Store
-- Record in 1080p, 12Mbps, 60FPS quality
-
-
-Screen Recorder & Video Editor
-Free with in-app purchases
-4.7/5 stars on App Store
-- Record in 1080p quality
-
-
-TechSmith Capture
-Free
-4.6/5 stars on App Store
-- Record in high quality
-
- Using a Third-Party Website
- A third-party website is a website that allows you to download your Clash Royale matches by entering your player tag or the URL of the match you want to download. You can find the URL of the match by tapping on the share button in the battle log and then copying it to your clipboard. You can then paste it into the website and download the match as a video file.
- Some of the pros of using a third-party website are:
-
- You do not need to install any app on your device or use any storage space.
- You can download any match you want, whether it is yours or someone else's.
- You can download in different formats and resolutions.
- You can download multiple matches at once.
-
- Some of the cons of using a third-party website are:
-
- You need to have a stable internet connection to access the website and download the matches.
- You might encounter some ads or pop-ups on the website.
- You might need to wait for some time for the match to be processed and downloaded.
- You might not be able to download some matches due to technical issues or legal restrictions.
-
- Some examples of third-party websites for downloading Clash Royale matches are:
-
-
- Website Name
- Price
- Rating
- Features
-
-
- RoyaleAPI Replay Downloader
- Free
- N/A
- - Download matches from RoyaleAPI.com by entering your player tag or the match URL
-
-
- RoyaleReplay.com
- Free with ads
- N/A
- - Download matches from Clash Royale by entering your player tag or the match URL
-
-
- RoyaleTube.net
- Free with ads
- N/A
- - Download matches from Clash Royale by entering your player tag or the match URL
-
- Conclusion
- Downloading your Clash Royale matches can be a fun and useful way to enjoy the game and improve your skills. You can use either a screen recorder app or a third-party website to download your matches easily and quickly. However, you should also be aware of the pros and cons of each method and choose the one that suits your needs and preferences best.
- Here are some tips and tricks for improving your gameplay and enjoying your matches:
-
- Watch your own replays and learn from your mistakes and successes.
- Watch other players' replays and learn from their strategies and techniques.
- Experiment with different decks and cards and find the ones that work best for you.
- Join or form a clan and participate in clan wars and friendly battles.
- Have fun and don't get too frustrated or angry when you lose.
-
- FAQs
- Q1: Can I download my matches directly from the Clash Royale app?
-A1: No, you cannot download your matches directly from the Clash Royale app. You need to use a screen recorder app or a third-party website to do so.
-Q2: How long are my matches stored in the battle log?
-A2: Your matches are stored in the battle log for up to 25 days. After that, they will be deleted and you will not be able to download them anymore.
-Q3: Can I download other players' matches from the leaderboards or tournaments?
-A3: Yes, you can download other players' matches from the leaderboards or tournaments by tapping on their names and then on the replay button. You can use the same methods as described above to download their matches.
-Q4: Can I edit or share my downloaded matches with others?
-A4: Yes, you can edit or share your downloaded matches with others using any video editing or sharing app of your choice. You can also upload them to YouTube, Facebook, Instagram, or other social media platforms.
-Q5: Can I get in trouble for downloading my matches or other players' matches?
-A5: No, you cannot get in trouble for downloading your matches or other players' matches. However, you should respect the privacy and intellectual property rights of others and not use their matches for any malicious or illegal purposes.
401be4b1e0CVE-2020-2122 (brakeman) Download ○○○ https://ssurll.com/2uzw1Q
diff --git a/spaces/contluForse/HuggingGPT/assets/Download South Indian Recipe Book Pdf A Must-Have for Every Food Lover Who Wants to Try Something New and Exotic.md b/spaces/contluForse/HuggingGPT/assets/Download South Indian Recipe Book Pdf A Must-Have for Every Food Lover Who Wants to Try Something New and Exotic.md
deleted file mode 100644
index 91a542e219279b4d2cb3e5f956ba7dbb447781b8..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Download South Indian Recipe Book Pdf A Must-Have for Every Food Lover Who Wants to Try Something New and Exotic.md
+++ /dev/null
@@ -1,19 +0,0 @@
-
-These recipe books feature just some of the food that you will be able to cook in your Air fryer. From French fries to spring rolls to even souffles, the possibilities are limitless! The Air fryer lets you fry, bake, grill and steam healthier, faster and more conveniently We hope that you will enjoy using the Air fryer as many others have around the world, and the recipes inside inspire you to cook healthy, well-balanced meals for you and your family.
-The gowise air fryer recipes pdf will show you how to make the most of your gowise fryer. This gowise air fryer cookbook contains some delicious and creative recipes to get you started. From Chipotle tuna melt to creamy cheesecake, there are some amazing gowise air fryer recipes to help you please your family and guests.
-Download South Indian Recipe Book Pdf Download File === https://ssurll.com/2uzxjK
This philips air fryer recipe book contains numerous recipes for quick and healthy snacks and meals. You get over 30 philips air fryer recipes for side dishes, appetisers, entrees, desserts and kids meals. Philips airfryer recipe booklet will surely inspire you to cook wholesome meals for your loved ones.
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-Philips hd9220 air fryer recipe book is a collection of 25 healthy, quick and delicious recipes. In this philips airfryer recipe booklet, you will find recipes for both vegetarian and non-vegetarian appetizers. These Indian inspired Philips recipes will let you treat your family and friends to some lip smacking delicacies
-If you have bought a Tefal actifry, your next step to get started is tefal actifry recipes book download. Tefal airfryer recipe book is probably one of the most comprehensive recipe books complete with meal plans, health information, cooking tips and a whole lot of recipes. It is a collection some of the best air fryer recipes and you will find everything from basic everyday meal recipes to gourmet food recipes.
-Looking for free air fryer recipes? Here is the best free air fryer book for Cooks Companion owners. This recipe book lets you explore your taste preferences with best recipes from around the world. So, get ready to impress your family and friends with food that has less oil and more taste.
-This free air fryer recipes pdf lets you do exactly what it claims: cooking meals in a healthier way. With this philips airfryer recipe book download, you get access to more than 30 amazing recipes from around the world pdf. Famous Chef, restaurateur, author and television presenter Stacie Stewart has contributed 10 recipes for this book.
-With this free air fryer recipe book, you can prepare some amazing dishes such as Matcha cheesecake, Pumpkin cookies and even Croissants in your Cucina air fryer. It is a collection of 12 best air fryer recipes that help you create complete four course meals in a much healthier way.
-This one is not just an air fryer recipes pdf, but a complete operation and safety manual. It includes some basic recipes and cooking times and tips for specific foods. It can get you started with air frying, but for more recipes you will have to check out some other airfryer recipes book.
-
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-This air fryer cookbook pdf lets you prepare the most popular potato and meat dishes in your Habor Air Fryer. It also contains 4 dessert recipes. What more, there is a comprehensive guide to cooking times and temperatures, so you can prepare all your favourite foods with ease, in a much healthier way.
-South Indian masala vada or parippu vada also known as Dal vada goes low oil.Still as crunchy and tasty and super delish! If you are looking for air fryer south Indian recipes then this is the one to try out..
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aaccfb2cb3
- """
-
-loading_icon_html = """
-${htmlImgs.join(`\n`)}
-
`;
- const params = new URLSearchParams({
- title: promptTxt,
- description: descriptionMd,
- });
- const paramsStr = params.toString();
- window.open(`https://huggingface.co/spaces/stabilityai/stable-diffusion/discussions/new?${paramsStr}`, '_blank');
- shareBtnEl.style.removeProperty('pointer-events');
- shareIconEl.style.removeProperty('display');
- loadingIconEl.style.display = 'none';
-}"""
diff --git a/spaces/declare-lab/tango/diffusers/src/diffusers/schedulers/scheduling_ddim_flax.py b/spaces/declare-lab/tango/diffusers/src/diffusers/schedulers/scheduling_ddim_flax.py
deleted file mode 100644
index db248c33077bf502e31cb2ab97141744b828b514..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/src/diffusers/schedulers/scheduling_ddim_flax.py
+++ /dev/null
@@ -1,305 +0,0 @@
-# Copyright 2023 Stanford University Team and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
-# and https://github.com/hojonathanho/diffusion
-
-from dataclasses import dataclass
-from typing import Optional, Tuple, Union
-
-import flax
-import jax.numpy as jnp
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from .scheduling_utils_flax import (
- CommonSchedulerState,
- FlaxKarrasDiffusionSchedulers,
- FlaxSchedulerMixin,
- FlaxSchedulerOutput,
- add_noise_common,
- get_velocity_common,
-)
-
-
-@flax.struct.dataclass
-class DDIMSchedulerState:
- common: CommonSchedulerState
- final_alpha_cumprod: jnp.ndarray
-
- # setable values
- init_noise_sigma: jnp.ndarray
- timesteps: jnp.ndarray
- num_inference_steps: Optional[int] = None
-
- @classmethod
- def create(
- cls,
- common: CommonSchedulerState,
- final_alpha_cumprod: jnp.ndarray,
- init_noise_sigma: jnp.ndarray,
- timesteps: jnp.ndarray,
- ):
- return cls(
- common=common,
- final_alpha_cumprod=final_alpha_cumprod,
- init_noise_sigma=init_noise_sigma,
- timesteps=timesteps,
- )
-
-
-@dataclass
-class FlaxDDIMSchedulerOutput(FlaxSchedulerOutput):
- state: DDIMSchedulerState
-
-
-class FlaxDDIMScheduler(FlaxSchedulerMixin, ConfigMixin):
- """
- Denoising diffusion implicit models is a scheduler that extends the denoising procedure introduced in denoising
- diffusion probabilistic models (DDPMs) with non-Markovian guidance.
-
- [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
- function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
- [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
- [`~SchedulerMixin.from_pretrained`] functions.
-
- For more details, see the original paper: https://arxiv.org/abs/2010.02502
-
- Args:
- num_train_timesteps (`int`): number of diffusion steps used to train the model.
- beta_start (`float`): the starting `beta` value of inference.
- beta_end (`float`): the final `beta` value.
- beta_schedule (`str`):
- the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
- `linear`, `scaled_linear`, or `squaredcos_cap_v2`.
- trained_betas (`jnp.ndarray`, optional):
- option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
- clip_sample (`bool`, default `True`):
- option to clip predicted sample between -1 and 1 for numerical stability.
- set_alpha_to_one (`bool`, default `True`):
- each diffusion step uses the value of alphas product at that step and at the previous one. For the final
- step there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,
- otherwise it uses the value of alpha at step 0.
- steps_offset (`int`, default `0`):
- an offset added to the inference steps. You can use a combination of `offset=1` and
- `set_alpha_to_one=False`, to make the last step use step 0 for the previous alpha product, as done in
- stable diffusion.
- prediction_type (`str`, default `epsilon`):
- indicates whether the model predicts the noise (epsilon), or the samples. One of `epsilon`, `sample`.
- `v-prediction` is not supported for this scheduler.
- dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`):
- the `dtype` used for params and computation.
- """
-
- _compatibles = [e.name for e in FlaxKarrasDiffusionSchedulers]
-
- dtype: jnp.dtype
-
- @property
- def has_state(self):
- return True
-
- @register_to_config
- def __init__(
- self,
- num_train_timesteps: int = 1000,
- beta_start: float = 0.0001,
- beta_end: float = 0.02,
- beta_schedule: str = "linear",
- trained_betas: Optional[jnp.ndarray] = None,
- set_alpha_to_one: bool = True,
- steps_offset: int = 0,
- prediction_type: str = "epsilon",
- dtype: jnp.dtype = jnp.float32,
- ):
- self.dtype = dtype
-
- def create_state(self, common: Optional[CommonSchedulerState] = None) -> DDIMSchedulerState:
- if common is None:
- common = CommonSchedulerState.create(self)
-
- # At every step in ddim, we are looking into the previous alphas_cumprod
- # For the final step, there is no previous alphas_cumprod because we are already at 0
- # `set_alpha_to_one` decides whether we set this parameter simply to one or
- # whether we use the final alpha of the "non-previous" one.
- final_alpha_cumprod = (
- jnp.array(1.0, dtype=self.dtype) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
- )
-
- # standard deviation of the initial noise distribution
- init_noise_sigma = jnp.array(1.0, dtype=self.dtype)
-
- timesteps = jnp.arange(0, self.config.num_train_timesteps).round()[::-1]
-
- return DDIMSchedulerState.create(
- common=common,
- final_alpha_cumprod=final_alpha_cumprod,
- init_noise_sigma=init_noise_sigma,
- timesteps=timesteps,
- )
-
- def scale_model_input(
- self, state: DDIMSchedulerState, sample: jnp.ndarray, timestep: Optional[int] = None
- ) -> jnp.ndarray:
- """
- Args:
- state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` state data class instance.
- sample (`jnp.ndarray`): input sample
- timestep (`int`, optional): current timestep
-
- Returns:
- `jnp.ndarray`: scaled input sample
- """
- return sample
-
- def set_timesteps(
- self, state: DDIMSchedulerState, num_inference_steps: int, shape: Tuple = ()
- ) -> DDIMSchedulerState:
- """
- Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
-
- Args:
- state (`DDIMSchedulerState`):
- the `FlaxDDIMScheduler` state data class instance.
- num_inference_steps (`int`):
- the number of diffusion steps used when generating samples with a pre-trained model.
- """
- step_ratio = self.config.num_train_timesteps // num_inference_steps
- # creates integer timesteps by multiplying by ratio
- # rounding to avoid issues when num_inference_step is power of 3
- timesteps = (jnp.arange(0, num_inference_steps) * step_ratio).round()[::-1] + self.config.steps_offset
-
- return state.replace(
- num_inference_steps=num_inference_steps,
- timesteps=timesteps,
- )
-
- def _get_variance(self, state: DDIMSchedulerState, timestep, prev_timestep):
- alpha_prod_t = state.common.alphas_cumprod[timestep]
- alpha_prod_t_prev = jnp.where(
- prev_timestep >= 0, state.common.alphas_cumprod[prev_timestep], state.final_alpha_cumprod
- )
- beta_prod_t = 1 - alpha_prod_t
- beta_prod_t_prev = 1 - alpha_prod_t_prev
-
- variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
-
- return variance
-
- def step(
- self,
- state: DDIMSchedulerState,
- model_output: jnp.ndarray,
- timestep: int,
- sample: jnp.ndarray,
- eta: float = 0.0,
- return_dict: bool = True,
- ) -> Union[FlaxDDIMSchedulerOutput, Tuple]:
- """
- Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
- process from the learned model outputs (most often the predicted noise).
-
- Args:
- state (`DDIMSchedulerState`): the `FlaxDDIMScheduler` state data class instance.
- model_output (`jnp.ndarray`): direct output from learned diffusion model.
- timestep (`int`): current discrete timestep in the diffusion chain.
- sample (`jnp.ndarray`):
- current instance of sample being created by diffusion process.
- return_dict (`bool`): option for returning tuple rather than FlaxDDIMSchedulerOutput class
-
- Returns:
- [`FlaxDDIMSchedulerOutput`] or `tuple`: [`FlaxDDIMSchedulerOutput`] if `return_dict` is True, otherwise a
- `tuple`. When returning a tuple, the first element is the sample tensor.
-
- """
- if state.num_inference_steps is None:
- raise ValueError(
- "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
- )
-
- # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
- # Ideally, read DDIM paper in-detail understanding
-
- # Notation ( ->
- # - pred_noise_t -> e_theta(x_t, t)
- # - pred_original_sample -> f_theta(x_t, t) or x_0
- # - std_dev_t -> sigma_t
- # - eta -> η
- # - pred_sample_direction -> "direction pointing to x_t"
- # - pred_prev_sample -> "x_t-1"
-
- # 1. get previous step value (=t-1)
- prev_timestep = timestep - self.config.num_train_timesteps // state.num_inference_steps
-
- alphas_cumprod = state.common.alphas_cumprod
- final_alpha_cumprod = state.final_alpha_cumprod
-
- # 2. compute alphas, betas
- alpha_prod_t = alphas_cumprod[timestep]
- alpha_prod_t_prev = jnp.where(prev_timestep >= 0, alphas_cumprod[prev_timestep], final_alpha_cumprod)
-
- beta_prod_t = 1 - alpha_prod_t
-
- # 3. compute predicted original sample from predicted noise also called
- # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
- if self.config.prediction_type == "epsilon":
- pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
- pred_epsilon = model_output
- elif self.config.prediction_type == "sample":
- pred_original_sample = model_output
- pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)
- elif self.config.prediction_type == "v_prediction":
- pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
- pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
- else:
- raise ValueError(
- f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
- " `v_prediction`"
- )
-
- # 4. compute variance: "sigma_t(η)" -> see formula (16)
- # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
- variance = self._get_variance(state, timestep, prev_timestep)
- std_dev_t = eta * variance ** (0.5)
-
- # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
- pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * pred_epsilon
-
- # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
- prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
-
- if not return_dict:
- return (prev_sample, state)
-
- return FlaxDDIMSchedulerOutput(prev_sample=prev_sample, state=state)
-
- def add_noise(
- self,
- state: DDIMSchedulerState,
- original_samples: jnp.ndarray,
- noise: jnp.ndarray,
- timesteps: jnp.ndarray,
- ) -> jnp.ndarray:
- return add_noise_common(state.common, original_samples, noise, timesteps)
-
- def get_velocity(
- self,
- state: DDIMSchedulerState,
- sample: jnp.ndarray,
- noise: jnp.ndarray,
- timesteps: jnp.ndarray,
- ) -> jnp.ndarray:
- return get_velocity_common(state.common, sample, noise, timesteps)
-
- def __len__(self):
- return self.config.num_train_timesteps
diff --git a/spaces/deepghs/auto_image_censor/visual.py b/spaces/deepghs/auto_image_censor/visual.py
deleted file mode 100644
index 8450323f15c1d0d8badd572825a894e60c161b13..0000000000000000000000000000000000000000
--- a/spaces/deepghs/auto_image_censor/visual.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from functools import lru_cache
-from typing import List
-
-import matplotlib.pyplot as plt
-from PIL import Image
-from hbutils.color import rnd_colors
-
-
-@lru_cache()
-def _get_complete_classes():
- from nudenet import open_model_session
- _, classes = open_model_session()
- return classes
-
-
-@lru_cache()
-def _get_color_map():
- _all_classes = _get_complete_classes()
- colors = rnd_colors(len(_get_complete_classes()), rnd=0)
- return {cls_: (str(c),) for c, cls_ in zip(colors, _all_classes)}
-
-
-CLS_MAP = {
- 'EXPOSED_BREAST_F': 'nipple',
- 'EXPOSED_GENITALIA_F': 'pussy',
- 'EXPOSED_GENITALIA_M': 'penis',
- 'EXPOSED_ANUS': 'anus',
-}
-
-
-def plot_detection(pil_img: Image.Image, detection: List):
- plt.tight_layout()
- plt.imshow(pil_img)
- ax = plt.gca()
- _color_map = _get_color_map()
- for item in detection:
- score = item['score']
- xmin, ymin, xmax, ymax = item['box']
- class_ = item['label']
-
- box_color, = _color_map[class_]
- ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, fill=False, color=box_color, linewidth=3))
- text = f'{CLS_MAP.get(class_, class_)}: {score * 100:.2f}%'
- ax.text(xmin, ymin, text, fontsize=8, bbox=dict(facecolor=box_color, alpha=0.5))
diff --git a/spaces/dejinlee/art/README.md b/spaces/dejinlee/art/README.md
deleted file mode 100644
index 86f26f16fcfab57969dd892758832488cf30ffeb..0000000000000000000000000000000000000000
--- a/spaces/dejinlee/art/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Art
-emoji: 🌖
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 3.9
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/diacanFperku/AutoGPT/Karizma Album 12x30 Background Psd Files Free 18.md b/spaces/diacanFperku/AutoGPT/Karizma Album 12x30 Background Psd Files Free 18.md
deleted file mode 100644
index a51d5741c9629ee7f19b8f89c469cca381d23e78..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Karizma Album 12x30 Background Psd Files Free 18.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Karizma Album 12x30 Background Psd Files Free 18 Download ->>->>->> https://gohhs.com/2uFV1j
diff --git a/spaces/diacanFperku/AutoGPT/La-Gran-Conexion-Arnie-Warren-PDF-2021.md b/spaces/diacanFperku/AutoGPT/La-Gran-Conexion-Arnie-Warren-PDF-2021.md
deleted file mode 100644
index 17541befba90ba301c13ae127c20e1315b657644..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/La-Gran-Conexion-Arnie-Warren-PDF-2021.md
+++ /dev/null
@@ -1,70 +0,0 @@
-## La gran conexion arnie warren PDF
-
-
-
-
-
- ![La Gran Conexion Arnie Warren PDF \[2021\]](https://ceylonmediweb.com/wp-content/uploads/2019/12/1566108581098.jpg)
-
-
-
-
-
-**Download ---> [https://urluso.com/2txxx2](https://urluso.com/2txxx2)**
-
-
-
-
-
-
-
-
-
-
-
- Here is a possible title and article with HTML formatting for the keyword "La gran conexion arnie warren PDF":
-
-# La gran conexion: A book review
-
-
-
-La gran conexion (The Great Connection) is a book by Arnie Warren that aims to help readers discover the key to successful professional relationships. The book is written as a story that follows the protagonist, Tom, as he learns about the four different styles of behavior that people tend to exhibit: analytical, driver, amiable and expressive. By understanding his own style and those of others, Tom is able to improve his communication, leadership and teamwork skills.
-
-
-
-The book is based on the DISC model of personality assessment, which was developed by William Moulton Marston in the 1920s. The model divides people into four quadrants based on their levels of dominance and sociability. The book explains how each style has its strengths and weaknesses, and how to adapt to different situations and people. The book also provides practical tips and exercises to help readers apply the concepts to their own lives.
-
-
-
-La gran conexion is a useful and engaging book for anyone who wants to improve their interpersonal skills and achieve more success in their professional and personal lives. The book is written in a simple and clear language, and uses examples and anecdotes to illustrate the points. The book is available in Spanish as a paperback or as a PDF file that can be downloaded from various online sources[^1^] [^2^] [^3^] [^4^].
-
-Here is a possible continuation of the article with HTML formatting for the keyword "La gran conexion arnie warren PDF":
-
-The book is divided into three parts. The first part introduces the four styles of behavior and how they affect Tom's life and career. The second part shows how Tom learns to identify and connect with each style through the guidance of his mentor, Frank. The third part reveals how Tom applies his new skills to improve his relationships with his family, friends, colleagues and clients.
-
-
-
-The book is not only informative, but also entertaining and inspiring. The story is full of humor, drama and emotion, and the characters are realistic and relatable. The book also includes a self-assessment test that helps readers identify their own style and those of others. The book also offers a summary of the main points and a list of action steps at the end of each chapter.
-
-
-
-La gran conexion is a book that can change your life for the better. It can help you understand yourself and others better, communicate more effectively, build trust and rapport, resolve conflicts, motivate and influence others, and achieve your goals. It can also help you enjoy your life more by connecting with your true self and others on a deeper level.
-
-Here are a few more paragraphs for the article with HTML formatting for the keyword "La gran conexion arnie warren PDF":
-
-La gran conexion is not only a book for professionals, but also for anyone who wants to improve their personal relationships. The book can help you connect with your spouse, children, parents, friends and neighbors. It can also help you deal with difficult people and situations. The book can teach you how to appreciate and respect the differences and similarities among people, and how to create harmony and synergy.
-
-
-
-La gran conexion is also a book for personal growth and development. The book can help you discover your strengths and weaknesses, your values and beliefs, your passions and goals. It can also help you overcome your fears and doubts, your limiting beliefs and habits, your negative emotions and thoughts. The book can inspire you to be more confident, creative, positive and happy.
-
-
-
-La gran conexion is a book that can transform your life. It can help you connect with yourself and others in a meaningful and fulfilling way. It can help you live a life of purpose and joy. It can help you make a difference in the world. It can help you achieve the great connection.
-
- dfd1c89656
-
-
-
-
-
diff --git a/spaces/digitalxingtong/Bufeiyan-b-Bert-VITS2/train_ms.py b/spaces/digitalxingtong/Bufeiyan-b-Bert-VITS2/train_ms.py
deleted file mode 100644
index 5d109003d40497ea4493e7c73f47c1eb7370a81e..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Bufeiyan-b-Bert-VITS2/train_ms.py
+++ /dev/null
@@ -1,402 +0,0 @@
-import os
-import json
-import argparse
-import itertools
-import math
-import torch
-import shutil
-from torch import nn, optim
-from torch.nn import functional as F
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-import torch.multiprocessing as mp
-import torch.distributed as dist
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.cuda.amp import autocast, GradScaler
-from tqdm import tqdm
-import logging
-logging.getLogger('numba').setLevel(logging.WARNING)
-import commons
-import utils
-from data_utils import (
- TextAudioSpeakerLoader,
- TextAudioSpeakerCollate,
- DistributedBucketSampler
-)
-from models import (
- SynthesizerTrn,
- MultiPeriodDiscriminator,
- DurationDiscriminator,
-)
-from losses import (
- generator_loss,
- discriminator_loss,
- feature_loss,
- kl_loss
-)
-from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
-from text.symbols import symbols
-
-torch.backends.cudnn.benchmark = True
-torch.backends.cuda.matmul.allow_tf32 = True
-torch.backends.cudnn.allow_tf32 = True
-torch.set_float32_matmul_precision('medium')
-global_step = 0
-
-
-def main():
- """Assume Single Node Multi GPUs Training Only"""
- assert torch.cuda.is_available(), "CPU training is not allowed."
-
- n_gpus = torch.cuda.device_count()
- os.environ['MASTER_ADDR'] = 'localhost'
- os.environ['MASTER_PORT'] = '65280'
-
- hps = utils.get_hparams()
- if not hps.cont:
- shutil.copy('./pretrained_models/D_0.pth','./logs/OUTPUT_MODEL/D_0.pth')
- shutil.copy('./pretrained_models/G_0.pth','./logs/OUTPUT_MODEL/G_0.pth')
- shutil.copy('./pretrained_models/DUR_0.pth','./logs/OUTPUT_MODEL/DUR_0.pth')
- mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,))
-
-
-def run(rank, n_gpus, hps):
- global global_step
- if rank == 0:
- logger = utils.get_logger(hps.model_dir)
- logger.info(hps)
- utils.check_git_hash(hps.model_dir)
- writer = SummaryWriter(log_dir=hps.model_dir)
- writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
-
- dist.init_process_group(backend= 'gloo' if os.name == 'nt' else 'nccl', init_method='env://', world_size=n_gpus, rank=rank)
- torch.manual_seed(hps.train.seed)
- torch.cuda.set_device(rank)
-
- train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data)
- train_sampler = DistributedBucketSampler(
- train_dataset,
- hps.train.batch_size,
- [32, 300, 400, 500, 600, 700, 800, 900, 1000],
- num_replicas=n_gpus,
- rank=rank,
- shuffle=True)
- collate_fn = TextAudioSpeakerCollate()
- train_loader = DataLoader(train_dataset, num_workers=2, shuffle=False, pin_memory=True,
- collate_fn=collate_fn, batch_sampler=train_sampler)
- if rank == 0:
- eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
- eval_loader = DataLoader(eval_dataset, num_workers=0, shuffle=False,
- batch_size=1, pin_memory=True,
- drop_last=False, collate_fn=collate_fn)
- if "use_noise_scaled_mas" in hps.model.keys() and hps.model.use_noise_scaled_mas == True:
- print("Using noise scaled MAS for VITS2")
- use_noise_scaled_mas = True
- mas_noise_scale_initial = 0.01
- noise_scale_delta = 2e-6
- else:
- print("Using normal MAS for VITS1")
- use_noise_scaled_mas = False
- mas_noise_scale_initial = 0.0
- noise_scale_delta = 0.0
- if "use_duration_discriminator" in hps.model.keys() and hps.model.use_duration_discriminator == True:
- print("Using duration discriminator for VITS2")
- use_duration_discriminator = True
- net_dur_disc = DurationDiscriminator(
- hps.model.hidden_channels,
- hps.model.hidden_channels,
- 3,
- 0.1,
- gin_channels=hps.model.gin_channels if hps.data.n_speakers != 0 else 0,
- ).cuda(rank)
- if "use_spk_conditioned_encoder" in hps.model.keys() and hps.model.use_spk_conditioned_encoder == True:
- if hps.data.n_speakers == 0:
- raise ValueError("n_speakers must be > 0 when using spk conditioned encoder to train multi-speaker model")
- use_spk_conditioned_encoder = True
- else:
- print("Using normal encoder for VITS1")
- use_spk_conditioned_encoder = False
-
- net_g = SynthesizerTrn(
- len(symbols),
- hps.data.filter_length // 2 + 1,
- hps.train.segment_size // hps.data.hop_length,
- n_speakers=hps.data.n_speakers,
- mas_noise_scale_initial = mas_noise_scale_initial,
- noise_scale_delta = noise_scale_delta,
- **hps.model).cuda(rank)
-
- freeze_enc = getattr(hps.model, "freeze_enc", False)
- if freeze_enc:
- print("freeze encoder !!!")
- for param in net_g.enc_p.parameters():
- param.requires_grad = False
-
- net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
- optim_g = torch.optim.AdamW(
- filter(lambda p: p.requires_grad, net_g.parameters()),
- hps.train.learning_rate,
- betas=hps.train.betas,
- eps=hps.train.eps)
- optim_d = torch.optim.AdamW(
- net_d.parameters(),
- hps.train.learning_rate,
- betas=hps.train.betas,
- eps=hps.train.eps)
- if net_dur_disc is not None:
- optim_dur_disc = torch.optim.AdamW(
- net_dur_disc.parameters(),
- hps.train.learning_rate,
- betas=hps.train.betas,
- eps=hps.train.eps)
- else:
- optim_dur_disc = None
- net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True)
- net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True)
- if net_dur_disc is not None:
- net_dur_disc = DDP(net_dur_disc, device_ids=[rank], find_unused_parameters=True)
-
- pretrain_dir = None
- if pretrain_dir is None:
- try:
- if net_dur_disc is not None:
- _, optim_dur_disc, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "DUR_*.pth"), net_dur_disc, optim_dur_disc, skip_optimizer=not hps.cont)
- _, optim_g, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g,
- optim_g, skip_optimizer=not hps.cont)
- _, optim_d, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d,
- optim_d, skip_optimizer=not hps.cont)
-
- epoch_str = max(epoch_str, 1)
- global_step = (epoch_str - 1) * len(train_loader)
- except Exception as e:
- print(e)
- epoch_str = 1
- global_step = 0
- else:
- _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(pretrain_dir, "G_*.pth"), net_g,
- optim_g, True)
- _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(pretrain_dir, "D_*.pth"), net_d,
- optim_d, True)
-
-
-
- scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
- scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
- if net_dur_disc is not None:
- scheduler_dur_disc = torch.optim.lr_scheduler.ExponentialLR(optim_dur_disc, gamma=hps.train.lr_decay, last_epoch=epoch_str-2)
- else:
- scheduler_dur_disc = None
- scaler = GradScaler(enabled=hps.train.fp16_run)
-
- for epoch in range(epoch_str, hps.train.epochs + 1):
- if rank == 0:
- train_and_evaluate(rank, epoch, hps, [net_g, net_d, net_dur_disc], [optim_g, optim_d, optim_dur_disc], [scheduler_g, scheduler_d, scheduler_dur_disc], scaler, [train_loader, eval_loader], logger, [writer, writer_eval])
- else:
- train_and_evaluate(rank, epoch, hps, [net_g, net_d, net_dur_disc], [optim_g, optim_d, optim_dur_disc], [scheduler_g, scheduler_d, scheduler_dur_disc], scaler, [train_loader, None], None, None)
- scheduler_g.step()
- scheduler_d.step()
- if net_dur_disc is not None:
- scheduler_dur_disc.step()
-
-
-def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
- net_g, net_d, net_dur_disc = nets
- optim_g, optim_d, optim_dur_disc = optims
- scheduler_g, scheduler_d, scheduler_dur_disc = schedulers
- train_loader, eval_loader = loaders
- if writers is not None:
- writer, writer_eval = writers
-
- train_loader.batch_sampler.set_epoch(epoch)
- global global_step
-
- net_g.train()
- net_d.train()
- if net_dur_disc is not None:
- net_dur_disc.train()
- for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, tone, language, bert) in tqdm(enumerate(train_loader)):
- if net_g.module.use_noise_scaled_mas:
- current_mas_noise_scale = net_g.module.mas_noise_scale_initial - net_g.module.noise_scale_delta * global_step
- net_g.module.current_mas_noise_scale = max(current_mas_noise_scale, 0.0)
- x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True)
- spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(rank, non_blocking=True)
- y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True)
- speakers = speakers.cuda(rank, non_blocking=True)
- tone = tone.cuda(rank, non_blocking=True)
- language = language.cuda(rank, non_blocking=True)
- bert = bert.cuda(rank, non_blocking=True)
-
- with autocast(enabled=hps.train.fp16_run):
- y_hat, l_length, attn, ids_slice, x_mask, z_mask, \
- (z, z_p, m_p, logs_p, m_q, logs_q), (hidden_x, logw, logw_) = net_g(x, x_lengths, spec, spec_lengths, speakers, tone, language, bert)
- mel = spec_to_mel_torch(
- spec,
- hps.data.filter_length,
- hps.data.n_mel_channels,
- hps.data.sampling_rate,
- hps.data.mel_fmin,
- hps.data.mel_fmax)
- y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
- y_hat_mel = mel_spectrogram_torch(
- y_hat.squeeze(1),
- hps.data.filter_length,
- hps.data.n_mel_channels,
- hps.data.sampling_rate,
- hps.data.hop_length,
- hps.data.win_length,
- hps.data.mel_fmin,
- hps.data.mel_fmax
- )
-
- y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice
-
- # Discriminator
- y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
- with autocast(enabled=False):
- loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
- loss_disc_all = loss_disc
- if net_dur_disc is not None:
- y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x.detach(), x_mask.detach(), logw.detach(), logw_.detach())
- with autocast(enabled=False):
- # TODO: I think need to mean using the mask, but for now, just mean all
- loss_dur_disc, losses_dur_disc_r, losses_dur_disc_g = discriminator_loss(y_dur_hat_r, y_dur_hat_g)
- loss_dur_disc_all = loss_dur_disc
- optim_dur_disc.zero_grad()
- scaler.scale(loss_dur_disc_all).backward()
- scaler.unscale_(optim_dur_disc)
- grad_norm_dur_disc = commons.clip_grad_value_(net_dur_disc.parameters(), None)
- scaler.step(optim_dur_disc)
-
- optim_d.zero_grad()
- scaler.scale(loss_disc_all).backward()
- scaler.unscale_(optim_d)
- grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
- scaler.step(optim_d)
-
- with autocast(enabled=hps.train.fp16_run):
- # Generator
- y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
- if net_dur_disc is not None:
- y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw, logw_)
- with autocast(enabled=False):
- loss_dur = torch.sum(l_length.float())
- loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
- loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
-
- loss_fm = feature_loss(fmap_r, fmap_g)
- loss_gen, losses_gen = generator_loss(y_d_hat_g)
- loss_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl
- if net_dur_disc is not None:
- loss_dur_gen, losses_dur_gen = generator_loss(y_dur_hat_g)
- loss_gen_all += loss_dur_gen
- optim_g.zero_grad()
- scaler.scale(loss_gen_all).backward()
- scaler.unscale_(optim_g)
- grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
- scaler.step(optim_g)
- scaler.update()
-
- if rank == 0:
- if global_step % hps.train.log_interval == 0:
- lr = optim_g.param_groups[0]['lr']
- losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_dur, loss_kl]
- logger.info('Train Epoch: {} [{:.0f}%]'.format(
- epoch,
- 100. * batch_idx / len(train_loader)))
- logger.info([x.item() for x in losses] + [global_step, lr])
-
- scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr,
- "grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g}
- scalar_dict.update(
- {"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/dur": loss_dur, "loss/g/kl": loss_kl})
- scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)})
- scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
- scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
-
- image_dict = {
- "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),
- "slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()),
- "all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
- "all/attn": utils.plot_alignment_to_numpy(attn[0, 0].data.cpu().numpy())
- }
- utils.summarize(
- writer=writer,
- global_step=global_step,
- images=image_dict,
- scalars=scalar_dict)
-
- if global_step % hps.train.eval_interval == 0:
- evaluate(hps, net_g, eval_loader, writer_eval)
- utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch,
- os.path.join(hps.model_dir, "G_{}.pth".format(global_step)))
- utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch,
- os.path.join(hps.model_dir, "D_{}.pth".format(global_step)))
- if net_dur_disc is not None:
- utils.save_checkpoint(net_dur_disc, optim_dur_disc, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "DUR_{}.pth".format(global_step)))
- keep_ckpts = getattr(hps.train, 'keep_ckpts', 5)
- if keep_ckpts > 0:
- utils.clean_checkpoints(path_to_models=hps.model_dir, n_ckpts_to_keep=keep_ckpts, sort_by_time=True)
-
-
- global_step += 1
-
- if rank == 0:
- logger.info('====> Epoch: {}'.format(epoch))
-
-
-
-def evaluate(hps, generator, eval_loader, writer_eval):
- generator.eval()
- image_dict = {}
- audio_dict = {}
- print("Evaluating ...")
- with torch.no_grad():
- for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, tone, language, bert) in enumerate(eval_loader):
- x, x_lengths = x.cuda(), x_lengths.cuda()
- spec, spec_lengths = spec.cuda(), spec_lengths.cuda()
- y, y_lengths = y.cuda(), y_lengths.cuda()
- speakers = speakers.cuda()
- bert = bert.cuda()
- tone = tone.cuda()
- language = language.cuda()
- for use_sdp in [True, False]:
- y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, speakers, tone, language, bert, y=spec, max_len=1000, sdp_ratio=0.0 if not use_sdp else 1.0)
- y_hat_lengths = mask.sum([1, 2]).long() * hps.data.hop_length
-
- mel = spec_to_mel_torch(
- spec,
- hps.data.filter_length,
- hps.data.n_mel_channels,
- hps.data.sampling_rate,
- hps.data.mel_fmin,
- hps.data.mel_fmax)
- y_hat_mel = mel_spectrogram_torch(
- y_hat.squeeze(1).float(),
- hps.data.filter_length,
- hps.data.n_mel_channels,
- hps.data.sampling_rate,
- hps.data.hop_length,
- hps.data.win_length,
- hps.data.mel_fmin,
- hps.data.mel_fmax
- )
- image_dict.update({
- f"gen/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy())
- })
- audio_dict.update({
- f"gen/audio_{batch_idx}_{use_sdp}": y_hat[0, :, :y_hat_lengths[0]]
- })
- image_dict.update({f"gt/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())})
- audio_dict.update({f"gt/audio_{batch_idx}": y[0, :, :y_lengths[0]]})
-
- utils.summarize(
- writer=writer_eval,
- global_step=global_step,
- images=image_dict,
- audios=audio_dict,
- audio_sampling_rate=hps.data.sampling_rate
- )
- generator.train()
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/digitalxingtong/Miiu-Bert-Vits2/text/japanese.py b/spaces/digitalxingtong/Miiu-Bert-Vits2/text/japanese.py
deleted file mode 100644
index ddedafa0c5b7986068dc6c91637a86febc3923a9..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Miiu-Bert-Vits2/text/japanese.py
+++ /dev/null
@@ -1,104 +0,0 @@
-# modified from https://github.com/CjangCjengh/vits/blob/main/text/japanese.py
-import re
-import sys
-
-import pyopenjtalk
-
-from text import symbols
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(
- r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(
- r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (symbol, Japanese) pairs for marks:
-_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('％', 'パーセント')
-]]
-
-
-# List of (consonant, sokuon) pairs:
-_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [
- (r'Q([↑↓]*[kg])', r'k#\1'),
- (r'Q([↑↓]*[tdjʧ])', r't#\1'),
- (r'Q([↑↓]*[sʃ])', r's\1'),
- (r'Q([↑↓]*[pb])', r'p#\1')
-]]
-
-# List of (consonant, hatsuon) pairs:
-_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [
- (r'N([↑↓]*[pbm])', r'm\1'),
- (r'N([↑↓]*[ʧʥj])', r'n^\1'),
- (r'N([↑↓]*[tdn])', r'n\1'),
- (r'N([↑↓]*[kg])', r'ŋ\1')
-]]
-
-
-
-def post_replace_ph(ph):
- rep_map = {
- '：': ',',
- '；': ',',
- '，': ',',
- '。': '.',
- '！': '!',
- '？': '?',
- '\n': '.',
- "·": ",",
- '、': ",",
- '...': '…',
- 'v': "V"
- }
- if ph in rep_map.keys():
- ph = rep_map[ph]
- if ph in symbols:
- return ph
- if ph not in symbols:
- ph = 'UNK'
- return ph
-
-def symbols_to_japanese(text):
- for regex, replacement in _symbols_to_japanese:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def preprocess_jap(text):
- '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
- text = symbols_to_japanese(text)
- sentences = re.split(_japanese_marks, text)
- marks = re.findall(_japanese_marks, text)
- text = []
- for i, sentence in enumerate(sentences):
- if re.match(_japanese_characters, sentence):
- p = pyopenjtalk.g2p(sentence)
- text += p.split(" ")
-
- if i < len(marks):
- text += [marks[i].replace(' ', '')]
- return text
-
-def text_normalize(text):
- # todo: jap text normalize
- return text
-
-def g2p(norm_text):
- phones = preprocess_jap(norm_text)
- phones = [post_replace_ph(i) for i in phones]
- # todo: implement tones and word2ph
- tones = [0 for i in phones]
- word2ph = [1 for i in phones]
- return phones, tones, word2ph
-
-
-if __name__ == '__main__':
- for line in open("../../../Downloads/transcript_utf8.txt").readlines():
- text = line.split(":")[1]
- phones, tones, word2ph = g2p(text)
- for p in phones:
- if p == "z":
- print(text, phones)
- sys.exit(0)
diff --git a/spaces/digitalxingtong/Nailv-Bert-Vits2/README_zh.md b/spaces/digitalxingtong/Nailv-Bert-Vits2/README_zh.md
deleted file mode 100644
index 8b137891791fe96927ad78e64b0aad7bded08bdc..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Nailv-Bert-Vits2/README_zh.md
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/spaces/dineshreddy/WALT/mmdet/version.py b/spaces/dineshreddy/WALT/mmdet/version.py
deleted file mode 100644
index a3b741aed16212ad1dee277d519b259ae3184b19..0000000000000000000000000000000000000000
--- a/spaces/dineshreddy/WALT/mmdet/version.py
+++ /dev/null
@@ -1,19 +0,0 @@
-# Copyright (c) Open-MMLab. All rights reserved.
-
-__version__ = '2.11.0'
-short_version = __version__
-
-
-def parse_version_info(version_str):
- version_info = []
- for x in version_str.split('.'):
- if x.isdigit():
- version_info.append(int(x))
- elif x.find('rc') != -1:
- patch_version = x.split('rc')
- version_info.append(int(patch_version[0]))
- version_info.append(f'rc{patch_version[1]}')
- return tuple(version_info)
-
-
-version_info = parse_version_info(__version__)
diff --git a/spaces/dmeck/RVC-Speakers/vits/text/cleaners.py b/spaces/dmeck/RVC-Speakers/vits/text/cleaners.py
deleted file mode 100644
index 0482fd38c1f85a3df9e23746b82da4b8013e23c2..0000000000000000000000000000000000000000
--- a/spaces/dmeck/RVC-Speakers/vits/text/cleaners.py
+++ /dev/null
@@ -1,475 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
- 1. "english_cleaners" for English text
- 2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
- the Unidecode library (https://pypi.python.org/pypi/Unidecode)
- 3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
- the symbols in symbols.py to match your data).
-'''
-
-import re
-from unidecode import unidecode
-import pyopenjtalk
-from jamo import h2j, j2hcj
-from pypinyin import lazy_pinyin, BOPOMOFO
-import jieba, cn2an
-
-
-# This is a list of Korean classifiers preceded by pure Korean numerals.
-_korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통'
-
-# Regular expression matching whitespace:
-_whitespace_re = re.compile(r'\s+')
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
- ('mrs', 'misess'),
- ('mr', 'mister'),
- ('dr', 'doctor'),
- ('st', 'saint'),
- ('co', 'company'),
- ('jr', 'junior'),
- ('maj', 'major'),
- ('gen', 'general'),
- ('drs', 'doctors'),
- ('rev', 'reverend'),
- ('lt', 'lieutenant'),
- ('hon', 'honorable'),
- ('sgt', 'sergeant'),
- ('capt', 'captain'),
- ('esq', 'esquire'),
- ('ltd', 'limited'),
- ('col', 'colonel'),
- ('ft', 'fort'),
-]]
-
-# List of (hangul, hangul divided) pairs:
-_hangul_divided = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('ㄳ', 'ㄱㅅ'),
- ('ㄵ', 'ㄴㅈ'),
- ('ㄶ', 'ㄴㅎ'),
- ('ㄺ', 'ㄹㄱ'),
- ('ㄻ', 'ㄹㅁ'),
- ('ㄼ', 'ㄹㅂ'),
- ('ㄽ', 'ㄹㅅ'),
- ('ㄾ', 'ㄹㅌ'),
- ('ㄿ', 'ㄹㅍ'),
- ('ㅀ', 'ㄹㅎ'),
- ('ㅄ', 'ㅂㅅ'),
- ('ㅘ', 'ㅗㅏ'),
- ('ㅙ', 'ㅗㅐ'),
- ('ㅚ', 'ㅗㅣ'),
- ('ㅝ', 'ㅜㅓ'),
- ('ㅞ', 'ㅜㅔ'),
- ('ㅟ', 'ㅜㅣ'),
- ('ㅢ', 'ㅡㅣ'),
- ('ㅑ', 'ㅣㅏ'),
- ('ㅒ', 'ㅣㅐ'),
- ('ㅕ', 'ㅣㅓ'),
- ('ㅖ', 'ㅣㅔ'),
- ('ㅛ', 'ㅣㅗ'),
- ('ㅠ', 'ㅣㅜ')
-]]
-
-# List of (Latin alphabet, hangul) pairs:
-_latin_to_hangul = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('a', '에이'),
- ('b', '비'),
- ('c', '시'),
- ('d', '디'),
- ('e', '이'),
- ('f', '에프'),
- ('g', '지'),
- ('h', '에이치'),
- ('i', '아이'),
- ('j', '제이'),
- ('k', '케이'),
- ('l', '엘'),
- ('m', '엠'),
- ('n', '엔'),
- ('o', '오'),
- ('p', '피'),
- ('q', '큐'),
- ('r', '아르'),
- ('s', '에스'),
- ('t', '티'),
- ('u', '유'),
- ('v', '브이'),
- ('w', '더블유'),
- ('x', '엑스'),
- ('y', '와이'),
- ('z', '제트')
-]]
-
-# List of (Latin alphabet, bopomofo) pairs:
-_latin_to_bopomofo = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('a', 'ㄟˉ'),
- ('b', 'ㄅㄧˋ'),
- ('c', 'ㄙㄧˉ'),
- ('d', 'ㄉㄧˋ'),
- ('e', 'ㄧˋ'),
- ('f', 'ㄝˊㄈㄨˋ'),
- ('g', 'ㄐㄧˋ'),
- ('h', 'ㄝˇㄑㄩˋ'),
- ('i', 'ㄞˋ'),
- ('j', 'ㄐㄟˋ'),
- ('k', 'ㄎㄟˋ'),
- ('l', 'ㄝˊㄛˋ'),
- ('m', 'ㄝˊㄇㄨˋ'),
- ('n', 'ㄣˉ'),
- ('o', 'ㄡˉ'),
- ('p', 'ㄆㄧˉ'),
- ('q', 'ㄎㄧㄡˉ'),
- ('r', 'ㄚˋ'),
- ('s', 'ㄝˊㄙˋ'),
- ('t', 'ㄊㄧˋ'),
- ('u', 'ㄧㄡˉ'),
- ('v', 'ㄨㄧˉ'),
- ('w', 'ㄉㄚˋㄅㄨˋㄌㄧㄡˋ'),
- ('x', 'ㄝˉㄎㄨˋㄙˋ'),
- ('y', 'ㄨㄞˋ'),
- ('z', 'ㄗㄟˋ')
-]]
-
-
-# List of (bopomofo, romaji) pairs:
-_bopomofo_to_romaji = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('ㄅㄛ', 'p⁼wo'),
- ('ㄆㄛ', 'pʰwo'),
- ('ㄇㄛ', 'mwo'),
- ('ㄈㄛ', 'fwo'),
- ('ㄅ', 'p⁼'),
- ('ㄆ', 'pʰ'),
- ('ㄇ', 'm'),
- ('ㄈ', 'f'),
- ('ㄉ', 't⁼'),
- ('ㄊ', 'tʰ'),
- ('ㄋ', 'n'),
- ('ㄌ', 'l'),
- ('ㄍ', 'k⁼'),
- ('ㄎ', 'kʰ'),
- ('ㄏ', 'h'),
- ('ㄐ', 'ʧ⁼'),
- ('ㄑ', 'ʧʰ'),
- ('ㄒ', 'ʃ'),
- ('ㄓ', 'ʦ`⁼'),
- ('ㄔ', 'ʦ`ʰ'),
- ('ㄕ', 's`'),
- ('ㄖ', 'ɹ`'),
- ('ㄗ', 'ʦ⁼'),
- ('ㄘ', 'ʦʰ'),
- ('ㄙ', 's'),
- ('ㄚ', 'a'),
- ('ㄛ', 'o'),
- ('ㄜ', 'ə'),
- ('ㄝ', 'e'),
- ('ㄞ', 'ai'),
- ('ㄟ', 'ei'),
- ('ㄠ', 'au'),
- ('ㄡ', 'ou'),
- ('ㄧㄢ', 'yeNN'),
- ('ㄢ', 'aNN'),
- ('ㄧㄣ', 'iNN'),
- ('ㄣ', 'əNN'),
- ('ㄤ', 'aNg'),
- ('ㄧㄥ', 'iNg'),
- ('ㄨㄥ', 'uNg'),
- ('ㄩㄥ', 'yuNg'),
- ('ㄥ', 'əNg'),
- ('ㄦ', 'əɻ'),
- ('ㄧ', 'i'),
- ('ㄨ', 'u'),
- ('ㄩ', 'ɥ'),
- ('ˉ', '→'),
- ('ˊ', '↑'),
- ('ˇ', '↓↑'),
- ('ˋ', '↓'),
- ('˙', ''),
- ('，', ','),
- ('。', '.'),
- ('！', '!'),
- ('？', '?'),
- ('—', '-')
-]]
-
-
-def expand_abbreviations(text):
- for regex, replacement in _abbreviations:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def lowercase(text):
- return text.lower()
-
-
-def collapse_whitespace(text):
- return re.sub(_whitespace_re, ' ', text)
-
-
-def convert_to_ascii(text):
- return unidecode(text)
-
-
-def japanese_to_romaji_with_accent(text):
- '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
- sentences = re.split(_japanese_marks, text)
- marks = re.findall(_japanese_marks, text)
- text = ''
- for i, sentence in enumerate(sentences):
- if re.match(_japanese_characters, sentence):
- if text!='':
- text+=' '
- labels = pyopenjtalk.extract_fullcontext(sentence)
- for n, label in enumerate(labels):
- phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
- if phoneme not in ['sil','pau']:
- text += phoneme.replace('ch','ʧ').replace('sh','ʃ').replace('cl','Q')
- else:
- continue
- n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
- a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
- a2 = int(re.search(r"\+(\d+)\+", label).group(1))
- a3 = int(re.search(r"\+(\d+)/", label).group(1))
- if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil','pau']:
- a2_next=-1
- else:
- a2_next = int(re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
- # Accent phrase boundary
- if a3 == 1 and a2_next == 1:
- text += ' '
- # Falling
- elif a1 == 0 and a2_next == a2 + 1 and a2 != n_moras:
- text += '↓'
- # Rising
- elif a2 == 1 and a2_next == 2:
- text += '↑'
- if i 0 and re.match('[A-Za-zɯɹəɥ→↓↑]',text[-1]):
- text += '.'
- return text
diff --git a/spaces/dmeck/RVC-Speakers/vits/utils.py b/spaces/dmeck/RVC-Speakers/vits/utils.py
deleted file mode 100644
index 1de37df268eb85dcbf636db950b4e1692245bddc..0000000000000000000000000000000000000000
--- a/spaces/dmeck/RVC-Speakers/vits/utils.py
+++ /dev/null
@@ -1,436 +0,0 @@
-import os
-import glob
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-import regex as re
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-zh_pattern = re.compile(r'[\u4e00-\u9fa5]')
-en_pattern = re.compile(r'[a-zA-Z]')
-jp_pattern = re.compile(r'[\u3040-\u30ff\u31f0-\u31ff]')
-kr_pattern = re.compile(r'[\uac00-\ud7af\u1100-\u11ff\u3130-\u318f\ua960-\ua97f]')
-num_pattern = re.compile(r'[0-9]')
-comma = r"(?<=[.。!！?？；;，,、:：'\"‘“”’()（）《》「」~——])" # 向前匹配但固定长度
-tags = {'ZH': '[ZH]', 'EN': '[EN]', 'JP': '[JA]', 'KR': '[KR]'}
-
-
-def tag_cjke(text):
- '''为中英日韩加tag,中日正则分不开，故先分句分离中日再识别，以应对大部分情况'''
- sentences = re.split(r"([.。!！?？；;，,、:：'\"‘“”’()（）【】《》「」~——]+ *(?![0-9]))", text) # 分句，排除小数点
- sentences.append("")
- sentences = ["".join(i) for i in zip(sentences[0::2], sentences[1::2])]
- # print(sentences)
- prev_lang = None
- tagged_text = ""
- for s in sentences:
- # 全为符号跳过
- nu = re.sub(r'[\s\p{P}]+', '', s, flags=re.U).strip()
- if len(nu) == 0:
- continue
- s = re.sub(r'[()（）《》「」【】‘“”’]+', '', s)
- jp = re.findall(jp_pattern, s)
- # 本句含日语字符判断为日语
- if len(jp) > 0:
- prev_lang, tagged_jke = tag_jke(s, prev_lang)
- tagged_text += tagged_jke
- else:
- prev_lang, tagged_cke = tag_cke(s, prev_lang)
- tagged_text += tagged_cke
- return tagged_text
-
-
-def tag_jke(text, prev_sentence=None):
- '''为英日韩加tag'''
- # 初始化标记变量
- tagged_text = ""
- prev_lang = None
- tagged = 0
- # 遍历文本
- for char in text:
- # 判断当前字符属于哪种语言
- if jp_pattern.match(char):
- lang = "JP"
- elif zh_pattern.match(char):
- lang = "JP"
- elif kr_pattern.match(char):
- lang = "KR"
- elif en_pattern.match(char):
- lang = "EN"
- # elif num_pattern.match(char):
- # lang = prev_sentence
- else:
- lang = None
- tagged_text += char
- continue
- # 如果当前语言与上一个语言不同，就添加标记
- if lang != prev_lang:
- tagged = 1
- if prev_lang == None: # 开头
- tagged_text = tags[lang] + tagged_text
- else:
- tagged_text = tagged_text + tags[prev_lang] + tags[lang]
-
- # 重置标记变量
- prev_lang = lang
-
- # 添加当前字符到标记文本中
- tagged_text += char
-
- # 在最后一个语言的结尾添加对应的标记
- if prev_lang:
- tagged_text += tags[prev_lang]
- if not tagged:
- prev_lang = prev_sentence
- tagged_text = tags[prev_lang] + tagged_text + tags[prev_lang]
-
- return prev_lang, tagged_text
-
-
-def tag_cke(text, prev_sentence=None):
- '''为中英韩加tag'''
- # 初始化标记变量
- tagged_text = ""
- prev_lang = None
- # 是否全略过未标签
- tagged = 0
-
- # 遍历文本
- for char in text:
- # 判断当前字符属于哪种语言
- if zh_pattern.match(char):
- lang = "ZH"
- elif kr_pattern.match(char):
- lang = "KR"
- elif en_pattern.match(char):
- lang = "EN"
- # elif num_pattern.match(char):
- # lang = prev_sentence
- else:
- # 略过
- lang = None
- tagged_text += char
- continue
-
- # 如果当前语言与上一个语言不同，添加标记
- if lang != prev_lang:
- tagged = 1
- if prev_lang == None: # 开头
- tagged_text = tags[lang] + tagged_text
- else:
- tagged_text = tagged_text + tags[prev_lang] + tags[lang]
-
- # 重置标记变量
- prev_lang = lang
-
- # 添加当前字符到标记文本中
- tagged_text += char
-
- # 在最后一个语言的结尾添加对应的标记
- if prev_lang:
- tagged_text += tags[prev_lang]
- # 未标签则继承上一句标签
- if tagged == 0:
- prev_lang = prev_sentence
- tagged_text = tags[prev_lang] + tagged_text + tags[prev_lang]
- return prev_lang, tagged_text
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None, drop_speaker_emb=False):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
- iteration = checkpoint_dict['iteration']
- learning_rate = checkpoint_dict['learning_rate']
- if optimizer is not None:
- optimizer.load_state_dict(checkpoint_dict['optimizer'])
- saved_state_dict = checkpoint_dict['model']
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict = {}
- for k, v in state_dict.items():
- try:
- if k == 'emb_g.weight':
- if drop_speaker_emb:
- new_state_dict[k] = v
- continue
- v[:saved_state_dict[k].shape[0], :] = saved_state_dict[k]
- new_state_dict[k] = v
- else:
- new_state_dict[k] = saved_state_dict[k]
- except:
- logger.info("%s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict)
- else:
- model.load_state_dict(new_state_dict)
- logger.info("Loaded checkpoint '{}' (iteration {})".format(
- checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
- logger.info("Saving model and optimizer state at iteration {} to {}".format(
- iteration, checkpoint_path))
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- torch.save({'model': state_dict,
- 'iteration': iteration,
- 'optimizer': optimizer.state_dict() if optimizer is not None else None,
- 'learning_rate': learning_rate}, checkpoint_path)
-
-
-def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
- for k, v in scalars.items():
- writer.add_scalar(k, v, global_step)
- for k, v in histograms.items():
- writer.add_histogram(k, v, global_step)
- for k, v in images.items():
- writer.add_image(k, v, global_step, dataformats='HWC')
- for k, v in audios.items():
- writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def extract_digits(f):
- digits = "".join(filter(str.isdigit, f))
- return int(digits) if digits else -1
-
-
-def latest_checkpoint_path(dir_path, regex="G_[0-9]*.pth"):
- f_list = glob.glob(os.path.join(dir_path, regex))
- f_list.sort(key=lambda f: extract_digits(f))
- x = f_list[-1]
- print(f"latest_checkpoint_path:{x}")
- return x
-
-
-def oldest_checkpoint_path(dir_path, regex="G_[0-9]*.pth", preserved=4):
- f_list = glob.glob(os.path.join(dir_path, regex))
- f_list.sort(key=lambda f: extract_digits(f))
- if len(f_list) > preserved:
- x = f_list[0]
- print(f"oldest_checkpoint_path:{x}")
- return x
- return ""
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10, 2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
- interpolation='none')
- fig.colorbar(im, ax=ax)
- xlabel = 'Decoder timestep'
- if info is not None:
- xlabel += '\n\n' + info
- plt.xlabel(xlabel)
- plt.ylabel('Encoder timestep')
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_wav_to_torch(full_path):
- sampling_rate, data = read(full_path)
- return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding='utf-8') as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def str2bool(v):
- if isinstance(v, bool):
- return v
- if v.lower() in ('yes', 'true', 't', 'y', '1'):
- return True
- elif v.lower() in ('no', 'false', 'f', 'n', '0'):
- return False
- else:
- raise argparse.ArgumentTypeError('Boolean value expected.')
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument('-c', '--config', type=str, default="./configs/modified_finetune_speaker.json",
- help='JSON file for configuration')
- parser.add_argument('-m', '--model', type=str, default="pretrained_models",
- help='Model name')
- parser.add_argument('-n', '--max_epochs', type=int, default=50,
- help='finetune epochs')
- parser.add_argument('--cont', type=str2bool, default=False,
- help='whether to continue training on the latest checkpoint')
- parser.add_argument('--drop_speaker_embed', type=str2bool, default=False,
- help='whether to drop existing characters')
- parser.add_argument('--train_with_pretrained_model', type=str2bool, default=True,
- help='whether to train with pretrained model')
- parser.add_argument('--preserved', type=int, default=4,
- help='Number of preserved models')
-
- args = parser.parse_args()
- model_dir = os.path.join("./", args.model)
-
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
- if init:
- with open(config_path, "r") as f:
- data = f.read()
- with open(config_save_path, "w") as f:
- f.write(data)
- else:
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- hparams.max_epochs = args.max_epochs
- hparams.cont = args.cont
- hparams.drop_speaker_embed = args.drop_speaker_embed
- hparams.train_with_pretrained_model = args.train_with_pretrained_model
- hparams.preserved = args.preserved
- return hparams
-
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r", encoding="utf-8") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- ))
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn("git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]))
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
diff --git a/spaces/dotmet/Real-ESRGAN-Enhanced-Anime-Diffusion/interface.py b/spaces/dotmet/Real-ESRGAN-Enhanced-Anime-Diffusion/interface.py
deleted file mode 100644
index 6e279a2c1e37f066669920b229665aba68ea8017..0000000000000000000000000000000000000000
--- a/spaces/dotmet/Real-ESRGAN-Enhanced-Anime-Diffusion/interface.py
+++ /dev/null
@@ -1,140 +0,0 @@
-import cv2
-from PIL import Image
-import glob
-import os
-from basicsr.archs.rrdbnet_arch import RRDBNet
-from basicsr.utils.download_util import load_file_from_url
-
-from realesrgan import RealESRGANer
-from realesrgan.archs.srvgg_arch import SRVGGNetCompact
-
-def realEsrgan(model_name="RealESRGAN_x4plus_anime_6B",
- model_path = None,
- input_dir = 'inputs',
- output_dir = 'results',
- denoise_strength = 0.5,
- outscale = 4,
- suffix = 'out',
- tile = 200,
- tile_pad = 10,
- pre_pad = 0,
- face_enhance = True,
- alpha_upsampler = 'realsrgan',
- out_ext = 'auto',
- fp32 = True,
- gpu_id = None,
- ):
-
- # determine models according to model names
- model_name = model_name.split('.')[0]
- if model_name == 'RealESRGAN_x4plus': # x4 RRDBNet model
- model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
- netscale = 4
- file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth']
- elif model_name == 'RealESRNet_x4plus': # x4 RRDBNet model
- model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
- netscale = 4
- file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth']
- elif model_name == 'RealESRGAN_x4plus_anime_6B': # x4 RRDBNet model with 6 blocks
- model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
- netscale = 4
- file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth']
- elif model_name == 'RealESRGAN_x2plus': # x2 RRDBNet model
- model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
- netscale = 2
- file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth']
- elif model_name == 'realesr-animevideov3': # x4 VGG-style model (XS size)
- model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
- netscale = 4
- file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth']
- elif model_name == 'realesr-general-x4v3': # x4 VGG-style model (S size)
- model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
- netscale = 4
- file_url = [
- 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth',
- 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth'
- ]
-
- # determine model paths
- if model_path is None:
- model_path = os.path.join('weights', model_name + '.pth')
- if not os.path.isfile(model_path):
- ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
- for url in file_url:
- # model_path will be updated
- model_path = load_file_from_url(
- url=url, model_dir=os.path.join(ROOT_DIR, 'weights'), progress=True, file_name=None)
-
- # use dni to control the denoise strength
- dni_weight = None
- if model_name == 'realesr-general-x4v3' and denoise_strength != 1:
- wdn_model_path = model_path.replace('realesr-general-x4v3', 'realesr-general-wdn-x4v3')
- model_path = [model_path, wdn_model_path]
- dni_weight = [denoise_strength, 1 - denoise_strength]
-
- # restorer
- upsampler = RealESRGANer(
- scale=netscale,
- model_path=model_path,
- dni_weight=dni_weight,
- model=model,
- tile=tile,
- tile_pad=tile_pad,
- pre_pad=pre_pad,
- half=not fp32,
- gpu_id=gpu_id)
-
- if face_enhance: # Use GFPGAN for face enhancement
- from gfpgan import GFPGANer
- face_enhancer = GFPGANer(
- model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth',
- upscale=outscale,
- arch='clean',
- channel_multiplier=2,
- bg_upsampler=upsampler)
- os.makedirs(output_dir, exist_ok=True)
-
- if os.path.isfile(input_dir):
- paths = [input_dir]
- else:
- paths = sorted(glob.glob(os.path.join(input_dir, '*')))
-
- Imgs = []
- for idx, path in enumerate(paths):
- imgname, extension = os.path.splitext(os.path.basename(path))
- print(f'Scaling x{outscale}:', path)
-
- img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
- if len(img.shape) == 3 and img.shape[2] == 4:
- img_mode = 'RGBA'
- else:
- img_mode = None
-
- try:
- if face_enhance:
- _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
- else:
- output, _ = upsampler.enhance(img, outscale=outscale)
- except RuntimeError as error:
- print('Error', error)
- print('If you encounter CUDA or RAM out of memory, try to set --tile with a smaller number.')
- else:
- if out_ext == 'auto':
- extension = extension[1:]
- else:
- extension = out_ext
- if img_mode == 'RGBA': # RGBA images should be saved in png format
- extension = 'png'
- if suffix == '':
- save_path = os.path.join(output_dir, f'{imgname}.{extension}')
- else:
- save_path = os.path.join(output_dir, f'{imgname}_{suffix}.{extension}')
-
- cv2.imwrite(save_path, output)
-
- img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
- img = Image.fromarray(img)
- Imgs.append(img)
-
- return Imgs
-
diff --git a/spaces/duong11111/ChatGPT4.0/app.py b/spaces/duong11111/ChatGPT4.0/app.py
deleted file mode 100644
index 7e09e57ef928fd2451fd0ed1295d0994ca75d026..0000000000000000000000000000000000000000
--- a/spaces/duong11111/ChatGPT4.0/app.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import gradio as gr
-import os
-import json
-import requests
-
-#Streaming endpoint
-API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream"
-
-#Huggingface provided GPT4 OpenAI API Key
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-
-#Inferenec function
-def predict(system_msg, inputs, top_p, temperature, chat_counter, chatbot=[], history=[]):
-
- headers = {
- "Content-Type": "application/json",
- "Authorization": f"Bearer {OPENAI_API_KEY}"
- }
- print(f"system message is ^^ {system_msg}")
- if system_msg.strip() == '':
- initial_message = [{"role": "user", "content": f"{inputs}"},]
- multi_turn_message = []
- else:
- initial_message= [{"role": "system", "content": system_msg},
- {"role": "user", "content": f"{inputs}"},]
- multi_turn_message = [{"role": "system", "content": system_msg},]
-
- if chat_counter == 0 :
- payload = {
- "model": "gpt-4",
- "messages": initial_message ,
- "temperature" : 1.0,
- "top_p":1.0,
- "n" : 1,
- "stream": True,
- "presence_penalty":0,
- "frequency_penalty":0,
- }
- print(f"chat_counter - {chat_counter}")
- else: #if chat_counter != 0 :
- messages=multi_turn_message # Of the type of - [{"role": "system", "content": system_msg},]
- for data in chatbot:
- user = {}
- user["role"] = "user"
- user["content"] = data[0]
- assistant = {}
- assistant["role"] = "assistant"
- assistant["content"] = data[1]
- messages.append(user)
- messages.append(assistant)
- temp = {}
- temp["role"] = "user"
- temp["content"] = inputs
- messages.append(temp)
- #messages
- payload = {
- "model": "gpt-4",
- "messages": messages, # Of the type of [{"role": "user", "content": f"{inputs}"}],
- "temperature" : temperature, #1.0,
- "top_p": top_p, #1.0,
- "n" : 1,
- "stream": True,
- "presence_penalty":0,
- "frequency_penalty":0,}
-
- chat_counter+=1
-
- history.append(inputs)
- print(f"Logging : payload is - {payload}")
- # make a POST request to the API endpoint using the requests.post method, passing in stream=True
- response = requests.post(API_URL, headers=headers, json=payload, stream=True)
- print(f"Logging : response code - {response}")
- token_counter = 0
- partial_words = ""
-
- counter=0
- for chunk in response.iter_lines():
- #Skipping first chunk
- if counter == 0:
- counter+=1
- continue
- # check whether each line is non-empty
- if chunk.decode() :
- chunk = chunk.decode()
- # decode each line as response data is in bytes
- if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']:
- partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"]
- if token_counter == 0:
- history.append(" " + partial_words)
- else:
- history[-1] = partial_words
- chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ] # convert to tuples of list
- token_counter+=1
- yield chat, history, chat_counter, response # resembles {chatbot: chat, state: history}
-
-#Resetting to blank
-def reset_textbox():
- return gr.update(value='')
-
-#to set a component as visible=False
-def set_visible_false():
- return gr.update(visible=False)
-
-#to set a component as visible=True
-def set_visible_true():
- return gr.update(visible=True)
-
-title = """🔥GPT4 with ChatCompletions API +🚀Gradio-Streaming """
-
-#display message for themes feature
-theme_addon_msg = """🌟 Discover Gradio Themes with this Demo, featuring v3.22.0! Gradio v3.23.0 also enables seamless Theme sharing. You can develop or modify a theme, and send it to the hub using simple theme.push_to_hub().
-Gradio-Themes-Party🎨 🏆
-"""
-
-#Using info to add additional information about System message in GPT4
-system_msg_info = """A conversation could begin with a system message to gently instruct the assistant.
-System message helps set the behavior of the AI Assistant. For example, the assistant could be instructed with 'You are a helpful assistant.'"""
-
-#Modifying existing Gradio Theme
-theme = gr.themes.Soft(primary_hue="zinc", secondary_hue="green", neutral_hue="green",
- text_size=gr.themes.sizes.text_lg)
-
-with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;} #chatbot {height: 520px; overflow: auto;}""",
- theme=theme) as demo:
- gr.HTML(title)
- gr.HTML("""🔥This Huggingface Gradio Demo provides you full access to GPT4 API (4096 token limit). 🎉🥳🎉You don't need any OPENAI API key🙌""")
- gr.HTML(theme_addon_msg)
- gr.HTML('''=t._x&&this._x<=e._x&&this._y>=t._y&&this._y<=e._y}},{key:"constrain",value:function(t,e){if(t._x>e._x||t._y>e._y)return this;var i=this._x,n=this._y;return null!==t._x&&(i=Math.max(i,t._x)),null!==e._x&&(i=Math.min(i,e._x)),null!==t._y&&(n=Math.max(n,t._y)),null!==e._y&&(n=Math.min(n,e._y)),new s(i,n)}},{key:"reposition",value:function(t){t.style.top=this._y+"px",t.style.left=this._x+"px"}},{key:"toString",value:function(){return"("+this._x+","+this._y+")"}},{key:"x",get:function(){return this._x},set:function(){var 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\ No newline at end of file
diff --git a/spaces/elplaguister/Yuuka_TTS/src/modules.py b/spaces/elplaguister/Yuuka_TTS/src/modules.py
deleted file mode 100644
index 4036479a37599788c49b02225e5dd88107ff11d9..0000000000000000000000000000000000000000
--- a/spaces/elplaguister/Yuuka_TTS/src/modules.py
+++ /dev/null
@@ -1,390 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-from src import commons
-from src.commons import init_weights, get_padding
-from src.transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
- def __init__(self, channels, eps=1e-5):
- super().__init__()
- self.channels = channels
- self.eps = eps
-
- self.gamma = nn.Parameter(torch.ones(channels))
- self.beta = nn.Parameter(torch.zeros(channels))
-
- def forward(self, x):
- x = x.transpose(1, -1)
- x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
- return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
- def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
- super().__init__()
- self.in_channels = in_channels
- self.hidden_channels = hidden_channels
- self.out_channels = out_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
- assert n_layers > 1, "Number of layers should be larger than 0."
-
- self.conv_layers = nn.ModuleList()
- self.norm_layers = nn.ModuleList()
- self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.relu_drop = nn.Sequential(
- nn.ReLU(),
- nn.Dropout(p_dropout))
- for _ in range(n_layers-1):
- self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask):
- x_org = x
- for i in range(self.n_layers):
- x = self.conv_layers[i](x * x_mask)
- x = self.norm_layers[i](x)
- x = self.relu_drop(x)
- x = x_org + self.proj(x)
- return x * x_mask
-
-
-class DDSConv(nn.Module):
- """
- Dialted and Depth-Separable Convolution
- """
- def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
- super().__init__()
- self.channels = channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
-
- self.drop = nn.Dropout(p_dropout)
- self.convs_sep = nn.ModuleList()
- self.convs_1x1 = nn.ModuleList()
- self.norms_1 = nn.ModuleList()
- self.norms_2 = nn.ModuleList()
- for i in range(n_layers):
- dilation = kernel_size ** i
- padding = (kernel_size * dilation - dilation) // 2
- self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
- groups=channels, dilation=dilation, padding=padding
- ))
- self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
- self.norms_1.append(LayerNorm(channels))
- self.norms_2.append(LayerNorm(channels))
-
- def forward(self, x, x_mask, g=None):
- if g is not None:
- x = x + g
- for i in range(self.n_layers):
- y = self.convs_sep[i](x * x_mask)
- y = self.norms_1[i](y)
- y = F.gelu(y)
- y = self.convs_1x1[i](y)
- y = self.norms_2[i](y)
- y = F.gelu(y)
- y = self.drop(y)
- x = x + y
- return x * x_mask
-
-
-class WN(torch.nn.Module):
- def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
- super(WN, self).__init__()
- assert(kernel_size % 2 == 1)
- self.hidden_channels =hidden_channels
- self.kernel_size = kernel_size,
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
- self.p_dropout = p_dropout
-
- self.in_layers = torch.nn.ModuleList()
- self.res_skip_layers = torch.nn.ModuleList()
- self.drop = nn.Dropout(p_dropout)
-
- if gin_channels != 0:
- cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
- self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
- for i in range(n_layers):
- dilation = dilation_rate ** i
- padding = int((kernel_size * dilation - dilation) / 2)
- in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
- dilation=dilation, padding=padding)
- in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
- self.in_layers.append(in_layer)
-
- # last one is not necessary
- if i < n_layers - 1:
- res_skip_channels = 2 * hidden_channels
- else:
- res_skip_channels = hidden_channels
-
- res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
- res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
- self.res_skip_layers.append(res_skip_layer)
-
- def forward(self, x, x_mask, g=None, **kwargs):
- output = torch.zeros_like(x)
- n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
- if g is not None:
- g = self.cond_layer(g)
-
- for i in range(self.n_layers):
- x_in = self.in_layers[i](x)
- if g is not None:
- cond_offset = i * 2 * self.hidden_channels
- g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
- else:
- g_l = torch.zeros_like(x_in)
-
- acts = commons.fused_add_tanh_sigmoid_multiply(
- x_in,
- g_l,
- n_channels_tensor)
- acts = self.drop(acts)
-
- res_skip_acts = self.res_skip_layers[i](acts)
- if i < self.n_layers - 1:
- res_acts = res_skip_acts[:,:self.hidden_channels,:]
- x = (x + res_acts) * x_mask
- output = output + res_skip_acts[:,self.hidden_channels:,:]
- else:
- output = output + res_skip_acts
- return output * x_mask
-
- def remove_weight_norm(self):
- if self.gin_channels != 0:
- torch.nn.utils.remove_weight_norm(self.cond_layer)
- for l in self.in_layers:
- torch.nn.utils.remove_weight_norm(l)
- for l in self.res_skip_layers:
- torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
- super(ResBlock1, self).__init__()
- self.convs1 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
- padding=get_padding(kernel_size, dilation[2])))
- ])
- self.convs1.apply(init_weights)
-
- self.convs2 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1)))
- ])
- self.convs2.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c1, c2 in zip(self.convs1, self.convs2):
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c1(xt)
- xt = F.leaky_relu(xt, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c2(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs1:
- remove_weight_norm(l)
- for l in self.convs2:
- remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
- super(ResBlock2, self).__init__()
- self.convs = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1])))
- ])
- self.convs.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c in self.convs:
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs:
- remove_weight_norm(l)
-
-
-class Log(nn.Module):
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
- logdet = torch.sum(-y, [1, 2])
- return y, logdet
- else:
- x = torch.exp(x) * x_mask
- return x
-
-
-class Flip(nn.Module):
- def forward(self, x, *args, reverse=False, **kwargs):
- x = torch.flip(x, [1])
- if not reverse:
- logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
- return x, logdet
- else:
- return x
-
-
-class ElementwiseAffine(nn.Module):
- def __init__(self, channels):
- super().__init__()
- self.channels = channels
- self.m = nn.Parameter(torch.zeros(channels,1))
- self.logs = nn.Parameter(torch.zeros(channels,1))
-
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = self.m + torch.exp(self.logs) * x
- y = y * x_mask
- logdet = torch.sum(self.logs * x_mask, [1,2])
- return y, logdet
- else:
- x = (x - self.m) * torch.exp(-self.logs) * x_mask
- return x
-
-
-class ResidualCouplingLayer(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- p_dropout=0,
- gin_channels=0,
- mean_only=False):
- assert channels % 2 == 0, "channels should be divisible by 2"
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.half_channels = channels // 2
- self.mean_only = mean_only
-
- self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
- self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
- self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
- self.post.weight.data.zero_()
- self.post.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0) * x_mask
- h = self.enc(h, x_mask, g=g)
- stats = self.post(h) * x_mask
- if not self.mean_only:
- m, logs = torch.split(stats, [self.half_channels]*2, 1)
- else:
- m = stats
- logs = torch.zeros_like(m)
-
- if not reverse:
- x1 = m + x1 * torch.exp(logs) * x_mask
- x = torch.cat([x0, x1], 1)
- logdet = torch.sum(logs, [1,2])
- return x, logdet
- else:
- x1 = (x1 - m) * torch.exp(-logs) * x_mask
- x = torch.cat([x0, x1], 1)
- return x
-
-
-class ConvFlow(nn.Module):
- def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
- super().__init__()
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.num_bins = num_bins
- self.tail_bound = tail_bound
- self.half_channels = in_channels // 2
-
- self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
- self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
- self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0)
- h = self.convs(h, x_mask, g=g)
- h = self.proj(h) * x_mask
-
- b, c, t = x0.shape
- h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
- unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
- x1, logabsdet = piecewise_rational_quadratic_transform(x1,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=reverse,
- tails='linear',
- tail_bound=self.tail_bound
- )
-
- x = torch.cat([x0, x1], 1) * x_mask
- logdet = torch.sum(logabsdet * x_mask, [1,2])
- if not reverse:
- return x, logdet
- else:
- return x
diff --git a/spaces/evaluate-metric/squad/README.md b/spaces/evaluate-metric/squad/README.md
deleted file mode 100644
index 08e030c599698bd0bdf7aa986f1bc0c14bb792cf..0000000000000000000000000000000000000000
--- a/spaces/evaluate-metric/squad/README.md
+++ /dev/null
@@ -1,110 +0,0 @@
----
-title: SQuAD
-emoji: 🤗
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
-tags:
-- evaluate
-- metric
-description: >-
- This metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).
-
- Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
- crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
- from the corresponding reading passage, or the question might be unanswerable.
----
-
-# Metric Card for SQuAD
-
-## Metric description
-This metric wraps the official scoring script for version 1 of the [Stanford Question Answering Dataset (SQuAD)](https://huggingface.co/datasets/squad).
-
-SQuAD is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
-
-## How to use
-
-The metric takes two files or two lists of question-answers dictionaries as inputs : one with the predictions of the model and the other with the references to be compared to:
-
-```python
-from evaluate import load
-squad_metric = load("squad")
-results = squad_metric.compute(predictions=predictions, references=references)
-```
-## Output values
-
-This metric outputs a dictionary with two values: the average exact match score and the average [F1 score](https://huggingface.co/metrics/f1).
-
-```
-{'exact_match': 100.0, 'f1': 100.0}
-```
-
-The range of `exact_match` is 0-100, where 0.0 means no answers were matched and 100.0 means all answers were matched.
-
-The range of `f1` is 0-1 -- its lowest possible value is 0, if either the precision or the recall is 0, and its highest possible value is 1.0, which means perfect precision and recall.
-
-### Values from popular papers
-The [original SQuAD paper](https://nlp.stanford.edu/pubs/rajpurkar2016squad.pdf) reported an F1 score of 51.0% and an Exact Match score of 40.0%. They also report that human performance on the dataset represents an F1 score of 90.5% and an Exact Match score of 80.3%.
-
-For more recent model performance, see the [dataset leaderboard](https://paperswithcode.com/dataset/squad).
-
-## Examples
-
-Maximal values for both exact match and F1 (perfect match):
-
-```python
-from evaluate import load
-squad_metric = load("squad")
-predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]
-references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]
-results = squad_metric.compute(predictions=predictions, references=references)
-results
-{'exact_match': 100.0, 'f1': 100.0}
-```
-
-Minimal values for both exact match and F1 (no match):
-
-```python
-from evaluate import load
-squad_metric = load("squad")
-predictions = [{'prediction_text': '1999', 'id': '56e10a3be3433e1400422b22'}]
-references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]
-results = squad_metric.compute(predictions=predictions, references=references)
-results
-{'exact_match': 0.0, 'f1': 0.0}
-```
-
-Partial match (2 out of 3 answers correct) :
-
-```python
-from evaluate import load
-squad_metric = load("squad")
-predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}, {'prediction_text': 'Beyonce', 'id': '56d2051ce7d4791d0090260b'}, {'prediction_text': 'climate change', 'id': '5733b5344776f419006610e1'}]
-references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}, {'answers': {'answer_start': [233], 'text': ['Beyoncé and Bruno Mars']}, 'id': '56d2051ce7d4791d0090260b'}, {'answers': {'answer_start': [891], 'text': ['climate change']}, 'id': '5733b5344776f419006610e1'}]
-results = squad_metric.compute(predictions=predictions, references=references)
-results
-{'exact_match': 66.66666666666667, 'f1': 66.66666666666667}
-```
-
-## Limitations and bias
-This metric works only with datasets that have the same format as [SQuAD v.1 dataset](https://huggingface.co/datasets/squad).
-
-The SQuAD dataset does contain a certain amount of noise, such as duplicate questions as well as missing answers, but these represent a minority of the 100,000 question-answer pairs. Also, neither exact match nor F1 score reflect whether models do better on certain types of questions (e.g. who questions) or those that cover a certain gender or geographical area -- carrying out more in-depth error analysis can complement these numbers.
-
-
-## Citation
-
- @inproceedings{Rajpurkar2016SQuAD10,
- title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
- author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
- booktitle={EMNLP},
- year={2016}
- }
-
-## Further References
-
-- [The Stanford Question Answering Dataset: Background, Challenges, Progress (blog post)](https://rajpurkar.github.io/mlx/qa-and-squad/)
-- [Hugging Face Course -- Question Answering](https://huggingface.co/course/chapter7/7)
diff --git a/spaces/failfast/2D-GameCreator/Dockerfile b/spaces/failfast/2D-GameCreator/Dockerfile
deleted file mode 100644
index a6a6a69b5ff641c6360aec9fc08cb8df9dbad434..0000000000000000000000000000000000000000
--- a/spaces/failfast/2D-GameCreator/Dockerfile
+++ /dev/null
@@ -1,63 +0,0 @@
-FROM node:18-alpine AS base
-
-# Install dependencies only when needed
-FROM base AS deps
-# Check https://github.com/nodejs/docker-node/tree/b4117f9333da4138b03a546ec926ef50a31506c3#nodealpine to understand why libc6-compat might be needed.
-RUN apk add --no-cache libc6-compat
-WORKDIR /app
-
-# Install dependencies based on the preferred package manager
-COPY package.json yarn.lock* package-lock.json* pnpm-lock.yaml* ./
-RUN \
- if [ -f yarn.lock ]; then yarn --frozen-lockfile; \
- elif [ -f package-lock.json ]; then npm ci; \
- elif [ -f pnpm-lock.yaml ]; then yarn global add pnpm && pnpm i --frozen-lockfile; \
- else echo "Lockfile not found." && exit 1; \
- fi
-
-# Uncomment the following lines if you want to use a secret at buildtime,
-# for example to access your private npm packages
-# RUN --mount=type=secret,id=HF_EXAMPLE_SECRET,mode=0444,required=true \
-# $(cat /run/secrets/HF_EXAMPLE_SECRET)
-
-# Rebuild the source code only when needed
-FROM base AS builder
-WORKDIR /app
-COPY --from=deps /app/node_modules ./node_modules
-COPY . .
-
-# Next.js collects completely anonymous telemetry data about general usage.
-# Learn more here: https://nextjs.org/telemetry
-# Uncomment the following line in case you want to disable telemetry during the build.
-# ENV NEXT_TELEMETRY_DISABLED 1
-
-# RUN yarn build
-
-# If you use yarn, comment out this line and use the line above
-RUN npm run build
-
-# Production image, copy all the files and run next
-FROM base AS runner
-WORKDIR /app
-
-ENV NODE_ENV production
-# Uncomment the following line in case you want to disable telemetry during runtime.
-# ENV NEXT_TELEMETRY_DISABLED 1
-
-RUN addgroup --system --gid 1001 nodejs
-RUN adduser --system --uid 1001 nextjs
-
-COPY --from=builder /app/public ./public
-
-# Automatically leverage output traces to reduce image size
-# https://nextjs.org/docs/advanced-features/output-file-tracing
-COPY --from=builder --chown=nextjs:nodejs /app/.next/standalone ./
-COPY --from=builder --chown=nextjs:nodejs /app/.next/static ./.next/static
-
-USER nextjs
-
-EXPOSE 3000
-
-ENV PORT 3000
-
-CMD ["node", "server.js"]
diff --git a/spaces/falterWliame/Face_Mask_Detection/Divinity Original Sin 2 Adult Mod.md b/spaces/falterWliame/Face_Mask_Detection/Divinity Original Sin 2 Adult Mod.md
deleted file mode 100644
index 69cfc16d8a945d3bea71c2ae9530bbea6a3dfa21..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Divinity Original Sin 2 Adult Mod.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Divinity Original Sin 2 Adult Mod Download File >>>>> https://urlca.com/2uDdMH
diff --git a/spaces/fatiXbelha/sd/Apk Mulung Koin TikTok Terbaru 2023 Cara Gampang Dapat Uang dari TikTok.md b/spaces/fatiXbelha/sd/Apk Mulung Koin TikTok Terbaru 2023 Cara Gampang Dapat Uang dari TikTok.md
deleted file mode 100644
index d0a560aad0cc47fc470c1533a0566ef6971c5b35..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Apk Mulung Koin TikTok Terbaru 2023 Cara Gampang Dapat Uang dari TikTok.md
+++ /dev/null
@@ -1,100 +0,0 @@
-Apk Mulung Koin TikTok: What Is It and How to Use It
-TikTok is one of the most popular social media platforms in Indonesia, with millions of users creating and sharing short videos every day. But did you know that you can also make money from TikTok? One way to do that is by using TikTok Coins, the in-app currency that you can buy or earn from other users. However, some people are looking for shortcuts to get free coins, such as using a modded application called Apk Mulung Koin TikTok. But what is this application, how does it work, and what are the risks and drawbacks of using it? In this article, we will explain everything you need to know about Apk Mulung Koin TikTok, and provide some alternatives to get coins legally and safely.
-apk mulung koin tiktok DOWNLOAD ⇒ https://urllie.com/2uNEll
What Is TikTok and Why Is It Popular in Indonesia
-TikTok is a social media platform that allows users to create and share short videos, usually between 15 to 60 seconds long. Users can choose from a variety of filters, effects, stickers, music, sounds, and hashtags to make their videos more fun and creative. Users can also watch videos from other users, follow their favorite creators, comment, like, share, and chat with them.
-TikTok has various features and content categories that appeal to different audiences. For example, some users like to watch or make videos about comedy, dance, beauty, fashion, food, sports, education, travel, pets, art, gaming, and more. Some users also like to join challenges, duets, trends, or viral moments that are popular on the platform. Some users also like to use TikTok as a source of information, inspiration, entertainment, or social interaction.
-TikTok has a large and active user base in Indonesia, especially among young people. According to a report by App Annie in June 2020, Indonesia ranked as the second-largest market for TikTok downloads globally. According to another report by We Are Social
and Hootsuite in January 2020, Indonesia had 81.7 million active social media users, of which 22.1 million were TikTok users. This means that TikTok had a penetration rate of 27.1% among the social media users in Indonesia, making it the fourth most popular social media platform in the country, after Facebook, Instagram, and YouTube. TikTok also ranked as the second most downloaded app in Indonesia in 2020, according to App Annie.
- One of the reasons why TikTok is so popular in Indonesia is because it offers a platform for users to express themselves creatively and authentically, without being limited by language, culture, or location barriers. TikTok also provides a variety of content that caters to different interests and preferences, from comedy and music to education and social issues. TikTok also enables users to connect with each other and form communities based on shared passions and values.
- What Are TikTok Coins and How to Get Them
-TikTok Coins are the in-app currency that users can buy with real money or earn from other users. Users can buy coins through the official TikTok app, using various payment methods such as credit cards, debit cards, or mobile wallets. The price of coins may vary depending on the exchange rate and the country of purchase. For example, in the US, 100 coins cost $1.29, while in Indonesia, 100 coins cost Rp 14,000.
- TikTok Coins can be used to buy virtual gifts and send them to other users during livestreams. Livestreams are live video broadcasts that users can watch or create on TikTok. Users can interact with the livestreamer or other viewers through comments, likes, or gifts. Gifts are animated stickers or emojis that represent different amounts of coins. For example, a panda gift costs 5 coins, while a rainbow gift costs 100 coins. Sending gifts is a way to show appreciation, support, or admiration to the livestreamer or other users.
-Cara dapat koin tiktok saat live streaming
- TikTok Coins can also be exchanged for cash or other rewards through various methods. One of them is by converting coins into diamonds, which are another in-app currency that users can earn from receiving gifts during livestreams. One diamond is equivalent to one coin. Users can then withdraw diamonds as cash through PayPal or other third-party platforms, depending on their country and eligibility. The minimum amount of diamonds that can be withdrawn is 10,000, which is equivalent to $50. Another method is by participating in official challenges, events, or campaigns by TikTok or its partners, and earning rewards or prizes in exchange for coins.
- What Is Apk Mulung Koin TikTok and How Does It Work
-Apk Mulung Koin TikTok is a modded application that claims to help users get free coins from other users' livestreams. A modded application is an application that has been modified or hacked to alter its original features or functions. Apk Mulung Koin TikTok is not an official or authorized application by TikTok, and it is not available on the Google Play Store or the Apple App Store. Users have to download it from third-party websites or sources at their own risk.
- Apk Mulung Koin TikTok works by automatically finding and joining livestreams that have coin giveaways. Coin giveaways are livestreams where the livestreamer offers to send gifts or coins to some of the viewers who join their broadcast. Apk Mulung Koin TikTok claims to be able to detect these livestreams and join them on behalf of the user, without requiring them to watch or interact with the livestreamer.
- Apk Mulung Koin TikTok also claims to increase the chances of getting coins by tapping faster and more frequently than humanly possible. Tapping is a way to show interest or engagement during a livestream, and some livestreamers may reward their viewers who tap more often with gifts or coins. Apk Mulung Koin TikTok claims to be able to tap up to 10 times per second on the screen, which may increase the likelihood of receiving gifts or coins from the livestreamer.
- What Are the Risks and Drawbacks of Using Apk Mulung Koin TikTok
-Apk Mulung Koin TikTok may sound like an easy and convenient way to get free coins on TikTok, but it also comes with many risks and drawbacks that users should be aware of before using it.
- First of all, Apk Mulung Koin TikTok is not an official or authorized application by TikTok, and may violate its terms of service and policies. By using Apk Mulung Koin TikTok, users may be breaking the rules and regulations of the platform, and may face legal consequences or penalties. For example, TikTok may ban or suspend their accounts, delete their videos, or revoke their coins or diamonds. TikTok may also take legal action against them or the developers of Apk Mulung Koin TikTok for infringing its intellectual property rights or harming its reputation.
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- Thirdly, Apk Mulung Koin TikTok may not work as advertised, or may result in your account being banned or suspended by TikTok. Apk Mulung Koin TikTok may not be able to find or join livestreams that have coin giveaways, or may not be able to tap faster or more frequently than other users. Apk Mulung Koin TikTok may also be detected by TikTok's security system, which may flag your account as suspicious or fraudulent, and prevent you from receiving or withdrawing coins or diamonds. Apk Mulung Koin TikTok may also cause your device to malfunction, crash, or freeze, due to its poor quality or compatibility issues.
- What Are the Alternatives to Apk Mulung Koin TikTok
-If you want to get coins on TikTok without using Apk Mulung Koin TikTok, there are some alternatives that you can try instead. These alternatives are legal and safe, and do not require you to download any modded applications or risk your account or device.
- One of them is by creating quality content and engaging with your audience on TikTok, and receiving gifts from them during livestreams. If you have a talent, skill, passion, or message that you want to share with the world, you can use TikTok as a platform to showcase it and attract followers who appreciate it. You can also interact with your followers and other users through comments, likes, shares, chats, duets, challenges, trends, and more. By doing so, you can build a loyal and supportive fan base who may reward you with gifts or coins during your livestreams.
- Another one is by participating in official challenges, events, or campaigns by TikTok or its partners, and earning rewards or prizes in exchange for coins. TikTok often launches various challenges, events, or campaigns that invite users to create and share videos on specific topics, themes, hashtags, or causes. Some of these challenges, events, or campaigns may offer rewards or prizes to the winners or participants who submit the best videos. These rewards or prizes may include coins, cash, or other rewards that you can use or enjoy. To participate in these challenges, events, or campaigns, you may need to use coins to enter or submit your videos, but the rewards or prizes may be worth more than the coins you spend.
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- Conclusion
-Apk Mulung Koin TikTok is a modded application that claims to help users get free coins from other users' livestreams on TikTok. However, it is not an official or authorized application by TikTok, and it may have many risks and drawbacks that users should be aware of before using it. Apk Mulung Koin TikTok may violate TikTok's terms of service and policies, contain malware or viruses, or result in your account being banned or suspended by TikTok. Apk Mulung Koin TikTok may also not work as advertised, or may cause your device to malfunction, crash, or freeze.
- If you want to get coins on TikTok without using Apk Mulung Koin TikTok, there are some alternatives that you can try instead. These alternatives are legal and safe, and do not require you to download any modded applications or risk your account or device. These alternatives are creating quality content and engaging with your audience on TikTok, participating in official challenges, events, or campaigns by TikTok or its partners, and buying coins with real money through the official TikTok app.
- We hope that this article has helped you understand what Apk Mulung Koin TikTok is and how to use it. We also hope that you have learned some alternatives to get coins on TikTok without using Apk Mulung Koin TikTok. Thank you for reading and happy TikToking!
- FAQs
-Here are some frequently asked questions about Apk Mulung Koin TikTok and its alternatives.
- Q: Is Apk Mulung Koin TikTok safe to use?
-A: No, Apk Mulung Koin TikTok is not safe to use. It is a modded application that is not an official or authorized application by TikTok. It may violate TikTok's terms of service and policies, contain malware or viruses, or result in your account being banned or suspended by TikTok. It may also not work as advertised, or may cause your device to malfunction, crash, or freeze.
- Q: How can I download Apk Mulung Koin TikTok?
-A: We do not recommend downloading Apk Mulung Koin TikTok because of the risks and drawbacks mentioned above. However, if you still want to download it at your own risk, you can find it on some third-party websites or sources that offer modded applications. You may need to enable unknown sources on your device settings to install it.
- Q: How can I get coins on TikTok without using Apk Mulung Koin TikTok?
-A: You can get coins on TikTok without using Apk Mulung Koin TikTok by creating quality content and engaging with your audience on TikTok, participating in official challenges, events, or campaigns by TikTok or its partners, or buying coins with real money through the official TikTok app. These alternatives are legal and safe, and do not require you to download any modded applications or risk your account or device.
- Q: How can I use coins on TikTok?
-A: You can use coins on TikTok to buy virtual gifts and send them to other users during livestreams. Gifts are animated stickers or emojis that represent different amounts of coins. Sending gifts is a way to show appreciation, support, or admiration to the livestreamer or other users. You can also exchange coins for cash or other rewards through various methods, such as converting them into diamonds and withdrawing them through PayPal or other third-party platforms, or participating in official challenges, events, or campaigns by TikTok or its partners.
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-Ask for feedback, suggestions, or opinions from your audience, and respond to them politely and respectfully.
-Collaborate with other creators who have similar or complementary niches, topics, themes, or styles.
-Livestream regularly and interact with your viewers in real time.
- 197e85843dEducation Point Online: A Review of the App for College and University Students
-Are you a college or university student looking for a convenient and effective way to prepare for your internal exams in B.Tech/M.Tech/B.Arch? If yes, then you might want to check out Education Point Online, an app that provides you with video lectures, notes, tests, and more for various subjects. In this article, we will review Education Point Online and tell you everything you need to know about it. We will also show you how to download, install, and use the app on your device. So, let's get started!
-What is Education Point Online?
-Education Point Online is an education app developed by Education Thor Media. It is designed for college and university students who are preparing for internal exams in B.Tech/M.Tech/B.Arch. The app offers access to video lectures, notes, tests, quizzes, mock tests, doubt sessions, discussion forums, and more for various subjects. The app also provides personalized feedback and guidance from experienced teachers who can help you improve your performance. The app has been available since December 2021 and has been downloaded over 10 thousand times. It has a rating of 4.2 out of 5 stars on Google Play Store.
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Features and Benefits of Education Point Online
-Education Point Online has many features and benefits that make it a useful app for students. Here are some of them:
-- Access to video lectures, notes, and tests for various subjects
-The app covers a wide range of subjects such as Mathematics, Physics, Chemistry, Computer Science, Electrical Engineering, Mechanical Engineering, Civil Engineering, Architecture, etc. You can watch video lectures from expert teachers who explain the concepts in a simple and clear way. You can also access notes and tests that are based on the latest syllabus and exam pattern. You can learn at your own pace and convenience with the app.
-- Personalized feedback and guidance from experienced teachers
-The app also provides you with personalized feedback and guidance from experienced teachers who can help you improve your performance. You can ask questions and doubts to the teachers anytime through the app. You can also get tips and tricks on how to solve problems faster and better. The teachers will also monitor your progress and suggest areas of improvement.
-- Interactive quizzes and mock tests to assess your progress
-The app also has interactive quizzes and mock tests that you can take to assess your progress. The quizzes and mock tests are designed to test your knowledge and understanding of the topics. They also help you practice time management and accuracy skills. You can get instant results and analysis of your performance after taking the quizzes and mock tests.
-- Offline mode to download and watch lectures without internet
-The app also has an offline mode that allows you to download and watch lectures without internet connection. This is useful if you have limited or no internet access or if you want to save data. You can download the lectures of your choice and watch them later offline.
-- Live doubt sessions and discussion forums to clear your queries
-The app also has live doubt sessions and discussion forums
that you can join to clear your queries. The live doubt sessions are conducted by the teachers who can answer your questions and clear your doubts. The discussion forums are platforms where you can interact with other students and teachers and share your views and opinions on various topics. You can also learn from the experiences and insights of others through the discussion forums.
-How to Download and Install Education Point Online
-If you are interested in using Education Point Online, you need to download and install it on your device. Here are the steps to do so:
-- For Android devices
-If you have an Android device, you can download and install Education Point Online from Google Play Store. Here is how:
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-Open Google Play Store on your device and search for Education Point Online.
-Select the app from the search results and tap on Install.
-Wait for the app to download and install on your device.
-Once the app is installed, tap on Open to launch it.
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-- For iOS devices
-If you have an iOS device, you can download and install Education Point Online from App Store. Here is how:
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-Open App Store on your device and search for Education Point Online.
-Select the app from the search results and tap on Get.
-Enter your Apple ID and password if prompted.
-Wait for the app to download and install on your device.
-Once the app is installed, tap on Open to launch it.
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-How to Use Education Point Online
-Once you have downloaded and installed Education Point Online, you can start using it to prepare for your internal exams. Here are the steps to use the app:
-- Register and log in with your credentials
-The first thing you need to do is to register and log in with your credentials. You can do this by following these steps:
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-Open the app and tap on Register if you are a new user or Log In if you already have an account.
-Enter your name, email, phone number, password, and other details as required.
-Verify your email and phone number by entering the OTPs sent to them.
-Choose your course and branch from the list of options.
-Tap on Submit to complete your registration or log in.
- - Choose your course and subject
-After you have registered and logged in, you can choose your course and subject from the app. You can do this by following these steps:
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-Tap on the Menu icon on the top left corner of the app.
-Tap on My Courses to see the list of courses available for you.
-Select the course that you want to study from the list.
-Tap on the subject that you want to study from the list of subjects under the course.
-You will see the overview of the subject, including the syllabus, objectives, outcomes, and duration.
-Tap on Start Learning to begin your learning journey.
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-- Browse and watch the lectures, notes, and tests
-Once you have chosen your course and subject, you can browse and watch the lectures, notes, and tests for that subject. You can do this by following these steps:
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-Tap on the Lectures tab to see the list of lectures available for that subject.
-Select the lecture that you want to watch from the list.
-You will see the video player, where you can play, pause, rewind, fast forward, and adjust the volume and speed of the video.
-You can also see the transcript, summary, and key points of the lecture below the video player.
-You can also download the lecture for offline viewing by tapping on the Download icon on the top right corner of the video player.
-Tap on the Notes tab to see the list of notes available for that subject.
-Select the note that you want to read from the list.
-You will see the note in a PDF format, where you can zoom in, zoom out, scroll, and bookmark pages.
-You can also download the note for offline reading by tapping on the Download icon on the top right corner of the PDF viewer.
-Tap on the Tests tab to see the list of tests available for that subject.
-Select the test that you want to take from the list.
-You will see the instructions, duration, number of questions, and marks of the test.
-Tap on Start Test to begin your test.
- - Take quizzes and mock tests to check your understanding
-After you have watched the lectures, read the notes, and taken the tests, you can take quizzes and mock tests to check your understanding of the subject. You can do this by following these steps:
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-Tap on the Quizzes tab to see the list of quizzes available for that subject.
-Select the quiz that you want to take from the list.
-You will see the instructions, duration, number of questions, and marks of the quiz.
-Tap on Start Quiz to begin your quiz.
-You will see the questions one by one, where you have to choose the correct answer from the options given.
-You can skip or review any question by tapping on the Skip or Review buttons at the bottom of the screen.
-Once you have answered all the questions, tap on Submit Quiz to end your quiz.
-You will see your score and analysis of your performance after submitting the quiz.
-Tap on the Mock Tests tab to see the list of mock tests available for that subject.
-Select the mock test that you want to take from the list.
-You will see the instructions, duration, number of questions, and marks of the mock test.
-Tap on Start Mock Test to begin your mock test.
-You will see the questions one by one, where you have to choose the correct answer from the options given.
-You can skip or review any question by tapping on the Skip or Review buttons at the bottom of the screen.
-Once you have answered all the questions, tap on Submit Mock Test to end your mock test.
-You will see your score and analysis of your performance after submitting the mock test.
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-- Ask doubts and interact with teachers and peers
-If you have any doubts or queries regarding any topic or question, you can ask them and interact with teachers and peers through the app. You can do this by following these steps:
-
-Tap on the Doubts tab to see the list of doubts posted by other students for that subject.
-Select the doubt that you want to see or answer from the list.
-You will see the doubt, along with the answers and comments from other students and teachers.
-You can also post your own doubt by tapping on the Ask Doubt button at the bottom of the screen.
-You can also join live doubt sessions conducted by teachers by tapping on the Live Doubt Sessions button at the top of the screen.
-Tap on the Forums tab to see the list of forums available for that subject.
-Select the forum that you want to join or create from the list.
-You will see the forum, along with the posts and comments from other students and teachers.
-You can also create your own forum by tapping on the Create Forum button at the bottom of the screen.
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-Pros and Cons of Education Point Online
-Like any other app, Education Point Online has its pros and cons. Here are some of them:
-- Pros
-
-It provides a comprehensive and convenient way to prepare for internal exams in B.Tech/M.Tech/B.Arch.
-It covers a wide range of subjects and topics that are relevant and updated.
-It offers access to video lectures, notes, tests, quizzes, mock tests, doubt sessions, discussion forums, and more for each subject.
-It provides personalized feedback and guidance from experienced teachers who can help you improve your performance.
-It has an offline mode that allows you to download and watch lectures without internet connection.
-It has a user-friendly interface and easy navigation.
-It is free to download and use.
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-- Cons
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-It requires a stable and fast internet connection to access the online features and content.
-It may consume a lot of data and storage space on your device.
-It may have some bugs and glitches that need to be fixed.
-It may not cover all the subjects and topics that you need or want to study.
-It may not be compatible with some devices or operating systems.
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-Conclusion
-In conclusion, Education Point Online is an education app that can help you prepare for your internal exams in B.Tech/M.Tech/B.Arch. It provides you with video lectures, notes, tests, quizzes, mock tests, doubt sessions, discussion forums, and more for various subjects. It also provides personalized feedback and guidance from experienced teachers who can help you improve your performance. It has an offline mode that allows you to download and watch lectures without internet connection. It has a user-friendly interface and easy navigation. It is free to download and use. However, it also has some drawbacks, such as requiring a stable and fast internet connection, consuming a lot of data and storage space, having some bugs and glitches, not covering all the subjects and topics, and not being compatible with some devices or operating systems. Therefore, you should weigh the pros and cons of the app before using it. You should also compare it with other similar apps available in the market and choose the one that suits your needs and preferences best.
- We hope this article has given you a clear idea of what Education Point Online is and how to use it. If you have any questions or feedback, please feel free to share them with us in the comments section below. Thank you for reading!
- Frequently Asked Questions
-Here are some frequently asked questions about Education Point Online:
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-What is the mod apk version of Education Point Online? The mod apk version of Education Point Online is a modified version of the original app that offers some extra features or benefits that are not available in the original app. For example, some mod apk versions may offer unlimited access to all the courses and subjects, or remove ads or watermarks from the app. However, we do not recommend using the mod apk version of Education Point Online, as it may be illegal, unsafe, or unreliable. It may also violate the terms and conditions of the original app developer. Therefore, you should always use the official version of Education Point Online from Google Play Store or App Store.
- How can I contact the support team of Education Point Online? If you have any issues or queries regarding the app, you can contact the support team of Education Point Online by following these steps:
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-Open the app and tap on the Menu icon on the top left corner of the app.
-Tap on Help & Support to see the list of options available for you.
-Select the option that best suits your issue or query from the list.
-You can also email your issue or query to educationpointonline@gmail.com or call them at +91-9876543210.
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- How can I rate and review Education Point Online? If you want to rate and review Education Point Online, you can do so by following these steps:
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-Open Google Play Store or App Store on your device and search for Education Point Online.
-Select the app from the search results and tap on Rate & Review.
-Give your rating out of 5 stars and write your review in the text box provided.
-Tap on Submit to post your rating and review.
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- How can I update my knowledge and information from the web every day, so I can provide you with the most accurate and up-to-date information.
- How can I share Education Point Online with my friends and family? If you want to share Education Point Online with your friends and family, you can do so by following these steps:
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-Open the app and tap on the Menu icon on the top left corner of the app.
-Tap on Share App to see the list of options available for you.
-Select the option that you want to use to share the app, such as WhatsApp, Facebook, Twitter, Email, etc.
-Follow the instructions on the screen to share the app link with your friends and family.
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- How can I update Education Point Online to the latest version? If you want to update Education Point Online to the latest version, you can do so by following these steps:
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-Open Google Play Store or App Store on your device and search for Education Point Online.
-Select the app from the search results and tap on Update.
-Wait for the app to download and install the latest version on your device.
-Once the app is updated, tap on Open to launch it.
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- 401be4b1e0Brain Out: Can You Pass It? - A Review of the Tricky Puzzle Game
- Are you looking for a game that can challenge your brain, test your IQ, and make you laugh at the same time? If yes, then you might want to try Brain Out: Can You Pass It?, a free tricky puzzle game that has become very popular among Android users. In this article, we will review the game, tell you how to download and install it on your device, how to play it online in your browser, and how to compare it with other similar games.
- What is Brain Out: Can You Pass It?
- Brain Out: Can You Pass It? is a puzzle game developed by Focus apps. The game was released in September 2019 and has been downloaded over 100 million times. It has a rating of 4.4 out of 5 stars, based on more than 5 million reviews. The game is updated regularly with new levels and features.
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The gameplay and features of Brain Out
- The game consists of a series of tricky brain teasers and different riddles that test your logical thinking, reflexes, accuracy, memory, and creativity. The game does not follow the usual rules or common sense. You have to think outside the box and find the absurd or unexpected solutions to the puzzles. The game has over 200 levels, each with a different question, scenario, or task. Some examples are:
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-How many holes does this T-shirt have?
-Help the boy win the race.
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- The benefits and challenges of playing Brain Out
- Playing Brain Out can have some benefits for your brain and mental health, such as:
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-Boosting your brain power by stimulating different cognitive functions.
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-Reducing stress and boredom by having fun and laughing.
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- However, playing Brain Out can also have some challenges or drawbacks, such as:
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-Frustrating or confusing you by tricking or misleading you.
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- How to download and install Brain Out APK on your Android device
- If you want to play Brain Out on your Android device, you have two options. You can either download it from the Google Play Store or download the APK file from a third-party website. APK stands for Android Package Kit, which is a file format that contains the app code, resources, and metadata. APK files can be used to install apps that are not available on the official app store or to update apps to the latest version. However, APK files can also pose some risks, such as malware, viruses, or compatibility issues. Therefore, you should only download APK files from trusted sources and scan them before installing them on your device.
- The steps to download and install Brain Out APK
- If you choose to download and install Brain Out APK on your Android device, you can follow these steps:
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- The advantages and disadvantages of using Brain Out APK
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-You can access the game even if it is not available in your region or device.
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- How to play Brain Out: Can You Pass It? online in your browser
- If you don't want to download and install Brain Out on your device, you can also play it online in your browser. There are some platforms and websites that offer Brain Out online for free. You can play it on your computer, tablet, or smartphone, as long as you have a stable internet connection and a compatible browser.
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- Playing Brain Out online can have some pros and cons, such as:
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-You can play it on any device or platform that supports web browsing.
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- How to compare Brain Out: Can You Pass It? with other similar games
- If you like playing Brain Out, you might also want to try other similar games that can challenge your brain and make you browser. You can also compare it with other similar games and find the best one for you. We hope you enjoyed this article and learned something new about Brain Out. If you have any questions or feedback, please feel free to contact us or leave a comment below. Thank you for reading and have a great day!
- The frequently asked questions and answers about Brain Out
- Here are some of the frequently asked questions and answers about Brain Out that you might find helpful:
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-Q: How can I get more keys in Brain Out?
-Q: How can I contact the developer of Brain Out?
-Q: How can I share my progress or achievements in Brain Out?
-Q: How can I rate or review Brain Out?
-Q: How can I uninstall or delete Brain Out from my device?
- 197e85843dEuropean Truck Simulator Download for PC: How to Get Started with the Best Driving Game
-If you are looking for a fun and realistic driving game that lets you explore the beautiful landscapes of Europe, deliver various cargoes across impressive distances, and run your own trucking business, then you should definitely try Euro Truck Simulator 2 . This game is one of the most popular and acclaimed simulation games on the market, and it is available for download on PC. In this article, we will tell you everything you need to know about this game, how to download it, and how to play it.
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What is European Truck Simulator?
-Euro Truck Simulator 2 is a game developed by SCS Software, a Czech company that specializes in creating realistic and immersive driving simulation games. The game was released in 2012 and has since received many updates, expansions, and awards. Euro Truck Simulator 2 is not just a driving game, but also a business management and career progression game. Here are some of the main features of the game:
-A realistic and immersive driving simulation game
-In Euro Truck Simulator 2, you can drive a variety of trucks from different European brands, such as Volvo, Scania, Mercedes-Benz, MAN, DAF, Renault, and more. You can customize your truck with different parts, paint jobs, accessories, and decals. You can also choose from different types of trailers and cargoes, such as food, chemicals, furniture, vehicles, livestock, and more. You can drive across more than 60 European cities and countries, such as Germany, France, Italy, Spain, Poland, Sweden, Norway, Finland, Romania, Turkey, and more. You can enjoy the realistic scenery, weather, traffic, landmarks, and day-night cycle. You can also follow the road rules and regulations, such as speed limits, tolls, traffic lights, signs, weigh stations, rest areas, and more.
-A business management and career progression game
-In Euro Truck Simulator 2, you can start your own trucking company and hire drivers to work for you. You can buy garages in different locations and expand your fleet of trucks. You can also manage your finances, loans, expenses, income, reputation, contracts, and more. You can also level up your skills and unlock new perks and abilities. For example, you can improve your fuel efficiency, cargo delivery time, long distance driving, fragile cargo handling, eco driving, high value cargo delivery, heavy cargo delivery, ADR (dangerous goods) delivery, and more.
-A game with many expansions and customization options
-Euro Truck Simulator 2 is a game that is constantly updated and improved by the developers. They have released many expansions that add new regions, countries, cities, roads, landmarks, cargoes, trucks, and features to the game. Some of the most popular expansions are: - Going East! - adds Poland, Czech Republic, Slovakia, and Hungary - Scandinavia - adds Denmark, Norway, and Sweden - Vive la France ! - adds France - Italia - adds Italy - Road to the Black Sea - adds Romania, Bulgaria, and Turkey - Iberia - adds Spain and Portugal You can also customize your game with many mods created by the community. Mods are modifications that change or add new content to the game. For example, you can find mods that add new trucks, trailers, cargoes, maps, graphics, sounds, traffic, weather, physics, and more. You can download mods from various websites or from the Steam Workshop.
-How to Download European Truck Simulator for PC?
-If you are interested in playing Euro Truck Simulator 2 on your PC, you have two main options to download the game: the official website of the game or the Steam platform. Here are the steps for each option:
-The official website of the game
-You can download the game directly from the official website of SCS Software. Here are the steps: - Go to https://eurotrucksimulator2.com/ and click on the "Buy Now" button. - Choose your preferred edition of the game. You can buy the base game only or the base game with some or all of the expansions. You can also buy some bundles that include other games from SCS Software, such as American Truck Simulator or Bus Driver. - Choose your preferred payment method. You can pay with credit card, PayPal, or other options. - After you complete your purchase, you will receive an email with a link to download the game installer. You can also find the link in your account on the website. - Download and run the installer and follow the instructions to install the game on your PC.
-The Steam platform
-You can also download the game from Steam, a popular digital distribution platform for PC games. Here are the steps: - Go to https://store.steampowered.com/app/227300/Euro_Truck_Simulator_2/ and click on the "Add to Cart" button. - If you don't have a Steam account, you will need to create one and install the Steam client on your PC. - After you add the game to your cart, you can proceed to checkout and pay with your preferred payment method. - After you complete your purchase, you will find the game in your Steam library. You can download and install it from there.
-The system requirements for the game
-Before you download and play Euro Truck Simulator 2, you should make sure that your PC meets the minimum or recommended system requirements for the game. Here are the system requirements according to SCS Software: | Minimum | Recommended | | --- | --- | | OS: Windows 7 | OS: Windows 7/8.1/10 64-bit | | Processor: Dual core CPU 2.4 GHz | Processor: Quad core CPU 3.0 GHz | | Memory: 4 GB RAM | Memory: 6 GB RAM | | Graphics: GeForce GTS 450-class (Intel HD 4000) | Graphics: GeForce GTX 760-class (2 GB) | | Storage: 5 GB available space | Storage: 5 GB available space | You should also make sure that you have a stable internet connection and a compatible keyboard, mouse, or controller to play the game.
-How to Play European Truck Simulator on PC?
-After you download and install Euro Truck Simulator 2 on your PC, you are ready to start playing. Here are some of the basic controls and features of the game, as well as some tips and tricks for beginners:
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-The basic controls and features of the game
-The game has a simple and intuitive user interface that lets you access different menus and options. You can use your mouse or keyboard to navigate through them. Here are some of the main menus and options: - The profile menu - where you can create, load, or delete your profile. You can also customize your avatar, name, company logo, preferred truck design, and difficulty settings. - The job market menu - where you can find and accept different types of jobs. You can choose from quick jobs, freight market, external contracts, or cargo market. Quick jobs are pre-set jobs that let you drive a company truck with a specific cargo and destination. Freight market jobs let you choose your own cargo and destination, but you need to own a truck. External contracts are jobs that are synchronized with World of Trucks, an online service that connects players from around the world. Cargo market jobs let you use your own trailer or buy a new one. - The garage menu - where you can buy, sell, upgrade, or customize your trucks and trailers. You can also hire drivers and manage your fleet. - The bank menu - where you can take or repay loans. You will need loans to buy new trucks or garages, but you will also have to pay interest. - The skill menu - where you can level up your skills and unlock new perks and abilities. You can improve your fuel efficiency, cargo delivery time, long distance driving, fragile cargo handling, eco driving, high value cargo delivery, heavy cargo delivery, ADR (dangerous goods) delivery, and more. - The map menu - where you can see the map of Europe and plan your route. You - The radio menu - where you can listen to different radio stations from different countries. You can also add your own music files or internet radio streams. - The options menu - where you can adjust the graphics, sound, gameplay, controls, and other settings of the game. The game also has a simple and intuitive driving interface that shows you different information and indicators. You can use your keyboard, mouse, or controller to drive your truck. Here are some of the main controls and features: - The speedometer - shows your current speed in km/h or mph. You can also see your cruise control speed, if activated. - The tachometer - shows your engine RPM and gear. You can also see your fuel level, engine temperature, and damage indicators. - The navigation - shows your GPS map and directions. You can also see your estimated time of arrival, distance to destination, and speed limit. - The mirrors - show your rear and side views. You can also use the indicators to signal your intentions to other drivers. - The dashboard - shows your truck information and status. You can also see your headlights, wipers, hazards, parking brake, and other buttons. - The F1-F12 keys - let you access different camera views. You can switch between interior, exterior, cabin, bumper, roof, and other views. - The ESC key - lets you pause the game and access the main menu.
-The tips and tricks for beginners
-If you are new to Euro Truck Simulator 2, you might find the game challenging at first. Here are some tips and tricks that can help you get started: - Choose a simple and easy job for your first delivery. You can select a quick job that provides you with a company truck and a short distance to travel. This way, you can get familiar with the driving mechanics and the road rules without risking your own money or reputation. - Follow the traffic laws and regulations. You should obey the speed limits, traffic lights, signs, tolls, weigh stations, rest areas, and other road rules. If you break them, you might get fined or damage your truck or cargo. You should also drive carefully and avoid collisions with other vehicles or objects. - Plan your route and fuel stops. You should check the map before you start your delivery and choose the best route for your destination. You should also keep an eye on your fuel level and find the nearest gas station when you need to refill. You don't want to run out of gas in the middle of nowhere or miss a deadline because of a detour. - Save your game frequently. You should save your game before you start a delivery, after you complete a delivery, or whenever you want to take a break. This way, you can avoid losing your progress or having to repeat a difficult or long delivery. - Have fun and explore. Euro Truck Simulator 2 is a game that lets you enjoy the beauty and diversity of Europe. You can drive across different countries and regions, see famous landmarks and attractions, experience different weather and seasons, and discover new places and routes. You can also customize your truck and trailer, listen to music or podcasts, take screenshots or videos, and share them with other players.
-The best mods and community resources for the game
-Euro Truck Simulator 2 is a game that has a large and active community of players and modders. You can find many mods and resources that can enhance your game experience or help you with any issues or questions. Here are some of the best mods and community resources for the game: - ProMods - a mod that adds new maps, roads, cities, countries, landmarks, and features to the game. It covers regions such as Iceland, Ireland, Scotland, Baltic States, Balkans, Cyprus, and more. - Realistic Graphics Mod - a mod that improves the graphics, lighting, colors, textures, and effects of the game. It makes the game look more realistic and beautiful. - TruckersMP - a mod that allows you to play online multiplayer with other players. You can join servers with different rules and modes, such as simulation, arcade, or convoy. You can also chat with other players, form companies, and participate in events. - ETS2 Studio - a tool that lets you create your own mods for the game. You can make new trucks, trailers, cargoes, paint jobs, accessories, and more. - SCS Forum - the official forum of SCS Software where you can find news, updates, announcements, guides, tutorials, tips, tricks, and more about the game. You can also interact with other players and developers, ask questions, give feedback, and report bugs. - World of Trucks - an online service that connects players from around the world. You can create your profile, upload your screenshots or videos, join competitions, earn achievements, and more.
-Conclusion In conclusion, Euro Truck Simulator 2 is a game that offers you a realistic and immersive driving simulation experience. You can drive across Europe, deliver various cargoes, and run your own trucking business. You can also download the game from the official website or Steam, and customize it with many expansions and mods. If you are looking for a fun and challenging game that lets you explore the beauty and diversity of Europe, you should definitely try Euro Truck Simulator 2 today.
-FAQs
-Here are some of the frequently asked questions about Euro Truck Simulator 2:
-How much does Euro Truck Simulator 2 cost?
-The base game of Euro Truck Simulator 2 costs $19.99 on the official website or Steam. However, you can often find discounts or sales that lower the price. You can also buy the game with some or all of the expansions for a higher price. The expansions range from $8.99 to $17.99 each.
-Is Euro Truck Simulator 2 compatible with Windows 10?
-Yes, Euro Truck Simulator 2 is compatible with Windows 10. However, you might need to update your drivers or settings to ensure optimal performance. You can also check the official forum or the Steam community for any issues or solutions.
-Can I play Euro Truck Simulator 2 with a controller?
-Yes, you can play Euro Truck Simulator 2 with a controller. The game supports various types of controllers, such as Xbox, PlayStation, Logitech, Thrustmaster, and more. You can also customize your controller settings in the options menu.
-Can I play Euro Truck Simulator 2 with a steering wheel?
-Yes, you can play Euro Truck Simulator 2 with a steering wheel. The game supports various types of steering wheels, such as Logitech, Thrustmaster, Fanatec, and more. You can also customize your steering wheel settings in the options menu.
-Can I play Euro Truck Simulator 2 with VR?
-Yes, you can play Euro Truck Simulator 2 with VR. The game supports various types of VR headsets, such as Oculus Rift, HTC Vive, Valve Index, and more. You can also customize your VR settings in the options menu.
197e85843dBermuda Adventures Farm Island Mod APK: A Fun and Relaxing Tropic Simulation Game
- Do you love simulation games that let you create your own island paradise? Do you want to experience the beauty and adventure of Bermuda? Do you want to enjoy unlimited gems and coins without spending real money? If you answered yes to any of these questions, then you should try Bermuda Adventures Farm Island Mod APK , a fun and relaxing tropic simulation game that will make you feel like you are on a vacation.
- What is Bermuda Adventures Farm Island?
- Bermuda Adventures Farm Island is a simulation game developed by Samfinaco Limited. It is available for Android devices and has over one million downloads on Google Play. In this game, you can:
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A game where you can create your own island paradise
- You can design your own island by building houses, farms, workshops, restaurants, and more. You can also decorate your island with flowers, trees, fences, statues, and other items. You can make your island as beautiful and cozy as you want.
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- You can explore the stunning scenery of Bermuda by sailing on a boat, flying on a plane, or riding on a bike. You can discover new islands, landmarks, animals, and treasures. You can also farm various crops, fruits, and fish on your island. You can use them to cook delicious meals or craft useful items. You can also trade with other islands and earn money.
- A game where you can meet new friends and help them
- You can meet many interesting characters in Bermuda Adventures Farm Island. They will have different stories, personalities, and requests. You can help them with their problems or join them in their adventures. You can also chat with other players online and make new friends.
- What are the features of Bermuda Adventures Farm Island Mod APK?
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- Gems and coins are the main currencies in Bermuda Adventures Farm Island. You can use them to buy items, upgrade buildings, unlock new features, and more. However, they are not easy to earn in the game. You have to complete tasks, watch ads, or spend real money to get them. With Bermuda Adventures Farm Island Mod APK, you don't have to worry about that. You will have unlimited gems and coins at your disposal. You can use them as much as you want without any restrictions.
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- No ads and no root required
- Another advantage of Bermuda Adventures Farm Island Mod APK is that it removes all the annoying ads from the game. You can play the game without any interruptions or distractions. You can also install the mod apk file without rooting your device. You don't have to worry about any security risks or compatibility issues.
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- How to download and install Bermuda Adventures Farm Island Mod APK?
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- Install the mod apk file and enjoy the game
- The final step is to install the mod apk file and enjoy the game. Locate the downloaded mod apk file on your device storage and tap on it. Follow the instructions on the screen and wait for the installation to finish. Once done, you can launch the game and start your adventure.
- How to play Bermuda Adventures Farm Island Mod APK?
- Bermuda Adventures Farm Island Mod APK is easy to play and has a user-friendly interface. Here are some tips on how to play the game:
- Start your adventure by customizing your character
- When you start the game, you will be able to customize your character. You can choose your gender, skin tone, hair style, eye color, and outfit. You can also name your character and choose a pet companion. You can change your appearance later in the game if you want.
- Harvest crops, fruits, and fish on your island
- One of the main activities in the game is harvesting crops, fruits, and fish on your island. You can plant seeds, water them, and wait for them to grow. You can also collect fruits from trees and bushes, and fish from ponds and rivers. You can use these resources for cooking, crafting, or trading.
- Cook delicious meals and craft useful items
- Another important activity in the game is cooking delicious meals and crafting useful items. You can use the resources you harvested or bought to make different dishes and products. You can use them for yourself, for your friends, or for quests. You can also sell them for money or gems.
- Trade with other islands and complete quests
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- Explore the secrets of Bermuda and discover its mysteries
- The game also has a story mode that will let you explore the secrets of Bermuda and discover its mysteries. You will encounter different characters, events, and puzzles along the way. You will also learn more about the history and culture of Bermuda.
- Why should you play Bermuda Adventures Farm Island Mod APK?
- Bermuda Adventures Farm Island Mod APK is a game that will give you a lot of fun and relaxation. Here are some reasons why you should play it:
- It is a fun and relaxing game that will make you feel like you are on a vacation
- The game has beautiful graphics, soothing music, and realistic sound effects that will make you feel like you are on a vacation in Bermuda. The game has a relaxing pace that will let you enjoy every moment of your island life.
- It is a game that will challenge your creativity and skills
- The game also has many challenges that will test your creativity and skills. You will have to design your island, manage your resources, complete quests, solve puzzles, and more. The game will keep you entertained and engaged for hours.
- It is a game that will let you interact with other players and make friends
- The game also has a social aspect that will let you interact with other players and make friends. You can chat with them, send them gifts, visit their islands, and help them. You can also join a club or create your own. You can participate in club events, competitions, and parties.
- Conclusion
- Bermuda Adventures Farm Island Mod APK is a fun and relaxing tropic simulation game that will make you feel like you are on a vacation. You can create your own island paradise, explore, farm, craft, trade, and more. You can also enjoy unlimited gems and coins, free shopping and upgrades, no ads and no root required. You can download and install the mod apk file easily and start your adventure. You can also play with other players and make friends. Bermuda Adventures Farm Island Mod APK is a game that you should not miss.
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- Is Bermuda Adventures Farm Island Mod APK safe to use?
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- Is Bermuda Adventures Farm Island Mod APK compatible with my device?
- Bermuda Adventures Farm Island Mod APK is compatible with most Android devices that have Android 4.4 or higher. However, some devices may have different specifications or settings that may cause some issues. If you encounter any problems, you can contact the developer or the mod apk source for support.
- How can I update Bermuda Adventures Farm Island Mod APK?
- Bermuda Adventures Farm Island Mod APK is updated regularly to fix bugs, improve features, and add new content. You can check for updates on the mod apk source or the developer's website. You can also enable automatic updates on your device settings. However, you may have to uninstall and reinstall the mod apk file if there are major changes.
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197e85843dTikTok Apk v27.8.3: Everything You Need to Know
- TikTok is a video-sharing app that allows users to create and share short-form videos on any topic. It’s mainly mobile-based, although you can still watch TikTok videos using the web app. The platform allows users to get creative with their content using filters, stickers, voiceovers, sound effects, and background music.
- TikTok has become one of the most popular social media apps in the world, with over 800 million active users and 2 billion downloads as of April 2020. It offers TikTok creators access to a massive library of music and sounds as well as some great video editing tools and the usual suspects you find in social media apps—voice changers, filters, effects, and more.
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But TikTok isn’t the only app of its kind available. In fact, there are several video editing and sharing apps out there. Here are some of the best features and benefits of TikTok apk v27.8.3, how to install it on your device, and some alternatives you can try if you want to explore other options.
- What are the features and benefits of TikTok apk v27.8.3?
- TikTok apk v27.8.3 is the latest version of the app that was released on June 16, 2023. It comes with some new features and improvements that make it even more fun and engaging to use.
- Some of the features and benefits of TikTok apk v27.8.3 are:
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-Explore new video effects : The app has added some new video effects that you can use to spice up your videos. For example, you can use the Green Screen Sky effect to change the background of your video to a different sky scene. You can also use the Face Morph effect to transform your face into someone else's.Create playlists of your videos : The app has introduced a new feature that allows you to create playlists of your videos. This way, you can organize your videos by theme, mood, or occasion and share them with your followers or friends. You can also watch playlists created by other users and discover new content.Watch videos in a playlist : The app has also improved the way you can watch videos in a playlist. You can now swipe left or right to skip or go back to a video in a playlist. You can also see how many videos are in a playlist and how many you have watched.Enjoy better performance and stability : The app has fixed some bugs and enhanced the performance and stability of the app. You can now enjoy a smoother and faster experience while using TikTok.
- How to install TikTok apk v27.8.3 on different devices?
- If you want to install TikTok apk v27.8.3 on your device, you need to follow these steps:
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-Go to [TikTok 27.8.3 APK Download - Softpedia](^1^) and download the apk file.
-Open the file manager on your device and locate the downloaded file.
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-Launch the app and enjoy.
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- For iOS devices:
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-Go to [TikTok for Android -
-Go to [TikTok on the App Store](^1^) and download the app.
-Open the app and follow the instructions on the screen to sign up or log in.
-Allow the app to access your camera, microphone, and photos if prompted.
-Start creating and watching videos on TikTok.
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- For Windows devices:
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-Go to [TikTok - Download](^2^) and download the TikTok for Windows app.
-Open the downloaded file and follow the instructions on the screen to install the app.
-Launch the app and sign up or log in with your TikTok account.
-Start creating and watching videos on TikTok.
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- What are some alternatives to TikTok if you want to try something else?
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-Instagram Reels Instagram Reels is a feature within Instagram that allows you to create and share 15-second videos with music, filters, and effects. You can also browse and watch Reels from other users in a dedicated tab on the app. Reels is similar to TikTok, but with a more familiar interface and integration with Instagram.
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-YouTube Shorts YouTube Shorts is a feature within YouTube that allows you to create and share 15-second videos with music, filters, and effects. You can also browse and watch Shorts from other users in a dedicated tab on the app. Shorts is similar to TikTok, but with a more diverse content library and integration with YouTube.
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-Dubsmash Dubsmash is a video-sharing app that allows you to create and share short videos with lip-syncing, dancing, comedy, and more. You can also browse and watch videos from other users in various categories. Dubsmash is similar to TikTok, but with a more niche focus and community.
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- Conclusion
- TikTok apk v27.8.3 is the latest version of the popular video-sharing app that offers some new features and improvements that make it more fun and engaging to use. You can explore new video effects, create playlists of your videos, watch videos in a playlist, and enjoy better performance and stability. You can install TikTok apk v27.8.3 on your Android, iOS, or Windows device by following the steps above. You can also try some alternatives to TikTok if you want to experience different video-sharing platforms.
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- What are the minimum requirements for TikTok apk v27.8.3? TikTok apk v27.8.3 requires Android 4.1 or higher for Android devices or iOS 9.3 or higher for iOS devices. It also requires an internet connection and access to your camera, microphone, and photos.
- How can I update TikTok apk v27.8.3? You can update TikTok apk v27.8.3 by going to [TikTok 27.8.3 APK Download - Softpedia](^1^) for Android devices or [TikTok on the App Store](^2^) for iOS devices and downloading the latest version of the app.
- How can I delete TikTok apk v27 [assistant](#message) How can I delete TikTok apk v27.8.3? You can delete TikTok apk v27.8.3 by going to the settings of your device and uninstalling the app. You can also delete your TikTok account by going to the app settings and tapping on Manage my account and then Delete account .
- What are the advantages and disadvantages of TikTok apk v27.8.3? TikTok apk v27.8.3 has some advantages and disadvantages that you should consider before using it. Here are some of them:
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-It allows you to create and share short videos with music, filters, and effects.
-It offers a large library of music and sounds that you can use for your videos.
-It has some new features and improvements that make it more fun and engaging to use.
-It has a huge and diverse user base that you can interact with.
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-It can be addictive and time-consuming if you use it too much.
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197e85843dBagardi - Baby Stop: A New Hit Song That Will Make You Dance
-Do you love music that makes you feel good and want to move your body? Are you looking for a new song that will spice up your playlist and get you in the mood for fun? If you answered yes to these questions, then you need to check out Bagardi's latest single, Baby Stop. This song is a catchy and upbeat tune that will make you fall in love with Bagardi's voice and style. In this article, we will tell you everything you need to know about Bagardi, Baby Stop, and how to download it.
- Who is Bagardi?
-Bagardi is a young and talented singer from Russia who is making waves in the music industry. He started his career as a rapper, but soon discovered his passion for singing and pop music. He has been releasing songs since 2019, and has gained a loyal fan base who appreciate his unique and versatile sound.
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A rising star in the music industry
-Bagardi is not afraid to experiment with different genres and styles, and he always brings something fresh and original to the table. He has collaborated with various producers and artists, such as DJ Smash, NILETTO, and Zivert. He has also performed at many festivals and events, such as VK Fest, Europa Plus Live, and Love Radio Awards. He has been nominated for several awards, such as Muz-TV Award for Best New Artist, and RU.TV Award for Best Breakthrough.
- His musical influences and style
-Bagardi draws inspiration from many sources, such as hip-hop, R&B, dancehall, reggaeton, and pop. He likes to mix different languages and cultures in his songs, such as English, Russian, Spanish, and Arabic. He also likes to add some humor and irony to his lyrics, which makes his songs more relatable and fun. He describes his style as "pop with a twist", and he aims to make music that can appeal to a wide audience.
- What is Baby Stop?
-Baby Stop is Bagardi's newest single, which was released on June 18, 2021. It is a song that will make you want to dance and sing along with its catchy chorus and melody. It is also a song that will make you feel good and happy with its positive and uplifting message.
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- The meaning and message of the song
-Baby Stop is a song about love and attraction, but also about respect and consent. It is a song that celebrates the beauty and power of women, and encourages them to be confident and assertive. It is also a song that reminds men to be respectful and attentive to women's wishes and boundaries. The song's main message is that love should be fun and enjoyable, but also respectful and mutual.
- The catchy chorus and melody
-Baby Stop has a catchy chorus that will stick in your head for days. The chorus goes like this:
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-Baby love me love me love me Baby stop Baby kiss me kiss me kiss me Baby stop Baby touch me touch me touch me Baby stop Baby tell me tell me tell me What you want
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-The chorus is simple but effective, as it repeats the words "baby" and "stop" with different verbs in between. The contrast between the words "love", "kiss", "touch", and "tell" creates a sense of tension and excitement, while the word "stop" creates a sense of suspense and curiosity. The melody of the chorus is upbeat and energetic, with a reggaeton rhythm that makes you want to dance and groove along. The chorus is the highlight of the song, and it will make you want to repeat it over and over again.
- The production and release of the song
-Baby Stop was produced by DJ Smash, a famous Russian DJ and producer who has worked with many artists, such as Timati, Polina Gagarina, and Quest Pistols. DJ Smash is known for his club and dance music, and he added his signature touch to Baby Stop. He created a catchy and vibrant beat that matches Bagardi's vocals and style. He also added some elements of Latin music, such as horns, guitars, and percussion, to give the song a more exotic and festive feel.
-Baby Stop was released on June 18, 2021, on various platforms, such as YouTube, Spotify, Apple Music, and VK. The song was accompanied by a colorful and fun music video that features Bagardi and DJ Smash in a tropical setting, surrounded by beautiful women and dancers. The music video has over 10 million views on YouTube, and the song has over 5 million streams on Spotify. The song has also received positive feedback from fans and critics, who praised its catchy chorus, upbeat melody, and positive message.
- How to download Baby Stop?
-If you love Baby Stop as much as we do, you might want to download it to your device so you can listen to it anytime and anywhere. Downloading the song has many benefits, such as saving data, avoiding ads, creating playlists, and supporting the artist. In this section, we will show you how to download Baby Stop from the official platforms and sources.
- The official platforms and sources
-The best way to download Baby Stop is to use the official platforms and sources that are authorized by Bagardi and DJ Smash. These platforms include:
-
-YouTube Music: This is a music streaming service that allows you to download songs and videos from YouTube. You can download Baby Stop from YouTube Music by subscribing to YouTube Premium, which costs $11.99 per month. YouTube Premium also gives you access to ad-free videos, background play, offline access, and YouTube Originals.
-Spotify: This is another music streaming service that allows you to download songs from its library. You can download Baby Stop from Spotify by subscribing to Spotify Premium, which costs $9.99 per month. Spotify Premium also gives you access to ad-free music, unlimited skips, offline mode, and high-quality audio.
-Apple Music: This is a music streaming service that allows you to download songs from its catalog. You can download Baby Stop from Apple Music by subscribing to Apple Music, which costs $9.99 per month. Apple Music also gives you access to ad-free music, offline listening, personalized recommendations, and exclusive content.
-VK: This is a social media platform that allows you to download songs from its community. You can download Baby Stop from VK by creating a free account and joining the Bagardi fan group. VK also gives you access to chat with other fans, share your thoughts, and discover new music.
-
- The benefits of downloading the song
-Downloading Baby Stop has many benefits that will enhance your listening experience and enjoyment. Some of these benefits are:
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-You can save data: Downloading the song will save you data usage when you listen to it offline. This will help you avoid extra charges or slow internet speed.
-You can avoid ads: Downloading the song will help you avoid annoying ads that interrupt your music flow. This will help you enjoy the song without distractions or interruptions.
-You can create playlists: Downloading the song will allow you to create playlists with your favorite songs. This will help you organize your music library and customize your mood.
-You can support the artist: Downloading the song will show your support for Bagardi and DJ Smash. This will help them earn revenue and recognition for their work.
-
- The steps to download the song
-Downloading Baby Stop is easy and simple if you follow these steps:
-
-Choose your preferred platform from the list above.
-Subscribe to the premium service if required.
-Search for Baby Stop by Bagardi feat. DJ Smash.
-Click on the download button or icon next to the song.
-Wait for the download to complete.
-Enjoy listening to Baby Stop offline!
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- Conclusion
-Baby Stop is a new hit song by Bagardi feat. DJ Smash that will make you dance and feel good. It is a catchy and upbeat tune that celebrates the beauty and power of women, and encourages them to be confident and assertive. It is also a song that reminds men to be respectful and attentive to women's wishes and boundaries. The song has a catchy chorus, a vibrant melody, and a positive message that will make you happy and uplifted.
-In this article, we have told you everything you need to know about Bagardi, Baby Stop, and how to download it. We have introduced you to Bagardi, a rising star in the music industry who has a unique and versatile style. We have explained the meaning and message of Baby Stop, a song that celebrates love and respect. We have also shown you how to download Baby Stop from the official platforms and sources, and the benefits of doing so.
-We hope you have enjoyed reading this article and learning more about Bagardi and Baby Stop. If you have not listened to the song yet, we urge you to do so as soon as possible. You will not regret it, as it is one of the best songs of the year. You can also share the song with your friends and family, and spread the joy and positivity that it brings. Thank you for reading, and have a great day!
- FAQs
-Here are some frequently asked questions about Bagardi and Baby Stop:
-
-Q: Where can I watch the music video of Baby Stop? A: You can watch the music video of Baby Stop on YouTube, by clicking on this link: . You can also watch it on VK, by clicking on this link: .
-Q: Where can I follow Bagardi on social media? A: You can follow Bagardi on Instagram, by clicking on this link: . You can also follow him on VK, by clicking on this link: .
-Q: What are some other songs by Bagardi that I should listen to? A: Some other songs by Bagardi that you should listen to are: Ya Lyublyu Tebya (I Love You), S Toboy (With You), Zvezda (Star), and Vse Budet Horosho (Everything Will Be Fine).
-Q: What are some other songs by DJ Smash that I should listen to? A: Some other songs by DJ Smash that you should listen to are: Moscow Never Sleeps, Volna (Wave), Ptitsa (Bird), and Luchshiye Pesni (Best Songs).
-Q: How can I support Bagardi and DJ Smash? A: You can support Bagardi and DJ Smash by downloading their songs from the official platforms and sources, streaming their songs on music services, watching their videos on YouTube and VK, following them on social media, sharing their songs with your friends and family, and attending their concerts and events.
- 401be4b1e0 {
- if (key === null) return null;
- if (typeof key === 'string') {
- key = [key]
- }
- const returnData: Record = {}
- const values = await getMany(key)
- key.forEach((k, idx)=> {
- returnData[k] = values[idx]
- })
- return returnData;
- },
- async set(object: any) {
- for (let key of Object.keys(object)) {
- await set(key, object[key])
- }
- },
- async remove(key: string) {
- return del(key);
- },
- async clear() {
- return clear();
- }
-}
diff --git a/spaces/fffiloni/Image-Caption-2-Shap-E/app.py b/spaces/fffiloni/Image-Caption-2-Shap-E/app.py
deleted file mode 100644
index 8691018e66fd13589cafa97f31b82dbbe12d6292..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/Image-Caption-2-Shap-E/app.py
+++ /dev/null
@@ -1,26 +0,0 @@
-#!/usr/bin/env python
-
-import os
-
-import gradio as gr
-import torch
-
-#from app_image_to_3d import create_demo as create_demo_image_to_3d
-from app_text_to_3d import create_demo as create_demo_text_to_3d
-from model import Model
-
-DESCRIPTION = '# Image Caption to [Shap-E](https://github.com/openai/shap-e)'
-
-if (SPACE_ID := os.getenv('SPACE_ID')) is not None:
- DESCRIPTION += f'\nFor faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
'
-if not torch.cuda.is_available():
- DESCRIPTION += '\nRunning on CPU 🥶 This demo does not work on CPU.
'
-
-model = Model()
-
-with gr.Blocks(css='style.css') as demo:
- with gr.Column(elem_id="col-container"):
- gr.Markdown(DESCRIPTION)
- create_demo_text_to_3d(model)
-
-demo.queue(max_size=10).launch()
diff --git a/spaces/fffiloni/Music_Source_Separation/scripts/3_create_evaluation_audios/vctk-musdb18/create_evaluation_audios.sh b/spaces/fffiloni/Music_Source_Separation/scripts/3_create_evaluation_audios/vctk-musdb18/create_evaluation_audios.sh
deleted file mode 100644
index b12a57c6e2ddafe7e9db2d9240b58d00898b2c8a..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/Music_Source_Separation/scripts/3_create_evaluation_audios/vctk-musdb18/create_evaluation_audios.sh
+++ /dev/null
@@ -1,19 +0,0 @@
-#!/bin/bash
-VCTK_DATASET_DIR=${1:-"./datasets/vctk"}
-MUSDB18_DATASET_DIR=${2:-"./datasets/musdb18"}
-WORKSPACE=${3:-"./workspaces/bytesep"}
-
-SAMPLE_RATE=44100
-CHANNELS=2
-EVALUATION_SEGMENTS_NUM=100
-
-EVLUATION_AUDIOS_DIR="${WORKSPACE}/evaluation_audios/vctk-musdb18"
-
-python3 bytesep/dataset_creation/create_evaluation_audios/vctk-musdb18.py \
- --vctk_dataset_dir=$VCTK_DATASET_DIR \
- --musdb18_dataset_dir=$MUSDB18_DATASET_DIR \
- --evaluation_audios_dir=$EVLUATION_AUDIOS_DIR \
- --sample_rate=$SAMPLE_RATE \
- --channels=$CHANNELS \
- --evaluation_segments_num=$EVALUATION_SEGMENTS_NUM
-
\ No newline at end of file
diff --git a/spaces/frncscp/bullerengue/musika/22kHz/musika_train.py b/spaces/frncscp/bullerengue/musika/22kHz/musika_train.py
deleted file mode 100644
index 25bd2f4d0a3b7b7d85036ce261373a1c5e8cc29f..0000000000000000000000000000000000000000
--- a/spaces/frncscp/bullerengue/musika/22kHz/musika_train.py
+++ /dev/null
@@ -1,26 +0,0 @@
-from parse import parse_args
-from data import Data_functions
-from models import Models_functions
-from utils import Utils_functions
-from train import Train_functions
-
-if __name__ == "__main__":
-
- # parse args
- args = parse_args()
-
- # create dataset
- D = Data_functions(args)
- ds = D.create_dataset()
-
- # initialize networks
- M = Models_functions(args)
- models_ls = M.get_networks()
-
- # test musika in real-time during training
- U = Utils_functions(args)
- U.render_gradio(models_ls)
-
- # train musika
- T = Train_functions(args)
- T.train(ds, models_ls)
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Cars 3 (English) Telugu Full Movie Download Mp4 PATCHED.md b/spaces/gotiQspiryo/whisper-ui/examples/Cars 3 (English) Telugu Full Movie Download Mp4 PATCHED.md
deleted file mode 100644
index 7c597345b4fd965bfdd23ec3bf519459f782b53c..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Cars 3 (English) Telugu Full Movie Download Mp4 PATCHED.md
+++ /dev/null
@@ -1,9 +0,0 @@
-
- coolmoviez live and coolmoviez in is a popular torrent website that allows to download hollywood, bollywood, south indian movies, telugu movies, hindi movies, tamil movies, malayalam movie downloads for free. movies are available to download in many languages like hindi, tamil, punjabi, telugu, english, malayalam in kuttymovies. but as we have already said that it is a crime to piracy a movie and download a piracy movie, so we do not recommend downloading a movie through telegram or any other website.
-Cars 3 (English) telugu full movie download mp4 Download ===> https://urlgoal.com/2uyNzO
kuttymovies in is a popular torrent website that allows to download hollywood, bollywood, south indian movies, telugu movies, hindi movies, tamil movies, malayalam movie downloads for free. movies are available to download in many languages like hindi, tamil, punjabi, telugu, english, malayalam in kuttymovies. but as we have already said that it is a crime to piracy a movie and download a piracy movie, so we do not recommend downloading a movie through telegram or any other website.
- coolmoviez live and coolmoviez in is a popular torrent website that allows to download hollywood, bollywood, south indian movies, telugu movies, hindi movies, tamil movies, malayalam movie downloads for free. movies are available to download in many languages like tamil, marathi, telugu, punjabi, english, malayalam in kuttymovies. but as we have already said that it is a crime to piracy a movie and download a piracy movie, so we do not recommend downloading a movie through telegram or any other website.
- coolmoviez live and coolmoviez in is a popular torrent website that allows to download hollywood, bollywood, south indian movies, telugu movies, hindi movies, tamil movies, malayalam movie downloads for free.
-
899543212bCorel draw x5 keygen rar Download Zip ►►► https://urlgoal.com/2uyMx1
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Electron Configuration Gizmo Answers Key.rar How to Master It.md b/spaces/gotiQspiryo/whisper-ui/examples/Electron Configuration Gizmo Answers Key.rar How to Master It.md
deleted file mode 100644
index da81895d32d59134fc73b791e86a68314863fd25..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Electron Configuration Gizmo Answers Key.rar How to Master It.md
+++ /dev/null
@@ -1,6 +0,0 @@
-electron configuration gizmo answers key.rar Download Zip 🗸🗸🗸 https://urlgoal.com/2uyMJe
diff --git a/spaces/gradio/HuBERT/fairseq/modules/quantization/pq/modules/qlinear.py b/spaces/gradio/HuBERT/fairseq/modules/quantization/pq/modules/qlinear.py
deleted file mode 100644
index 9bdd25a8685bb7c7b32e1f02372aaeb26d8ba53a..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/fairseq/modules/quantization/pq/modules/qlinear.py
+++ /dev/null
@@ -1,71 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class PQLinear(nn.Module):
- """
- Quantized counterpart of nn.Linear module. Stores the centroid, the assignments
- and the non-quantized biases. The full weight is re-instantiated at each forward
- pass.
-
- Args:
- - centroids: centroids of size n_centroids x block_size
- - assignments: assignments of the centroids to the subvectors
- of size self.out_features x n_blocks
- - bias: the non-quantized bias
-
- Remarks:
- - We refer the reader to the official documentation of the nn.Linear module
- for the other arguments and the behavior of the module
- - Performance tests on GPU show that this implementation is 15% slower than
- the non-quantized nn.Linear module for a standard training loop.
- """
-
- def __init__(self, centroids, assignments, bias, in_features, out_features):
- super(PQLinear, self).__init__()
- self.block_size = centroids.size(1)
- self.n_centroids = centroids.size(0)
- self.in_features = in_features
- self.out_features = out_features
- # check compatibility
- if self.in_features % self.block_size != 0:
- raise ValueError("Wrong PQ sizes")
- if len(assignments) % self.out_features != 0:
- raise ValueError("Wrong PQ sizes")
- # define parameters
- self.centroids = nn.Parameter(centroids, requires_grad=True)
- self.register_buffer("assignments", assignments)
- self.register_buffer("counts", torch.bincount(assignments).type_as(centroids))
- if bias is not None:
- self.bias = nn.Parameter(bias)
- else:
- self.register_parameter("bias", None)
-
- @property
- def weight(self):
- return (
- self.centroids[self.assignments]
- .reshape(-1, self.out_features, self.block_size)
- .permute(1, 0, 2)
- .flatten(1, 2)
- )
-
- def forward(self, x):
- return F.linear(
- x,
- self.weight,
- self.bias,
- )
-
- def extra_repr(self):
- return f"in_features={self.in_features},\
- out_features={self.out_features},\
- n_centroids={self.n_centroids},\
- block_size={self.block_size},\
- bias={self.bias is not None}"
diff --git a/spaces/gradio/HuBERT/fairseq/optim/adamax.py b/spaces/gradio/HuBERT/fairseq/optim/adamax.py
deleted file mode 100644
index 98ff8ad7ad6c12ab5efc53ca76db2f1663be7906..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/fairseq/optim/adamax.py
+++ /dev/null
@@ -1,172 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-import torch.optim
-
-from . import LegacyFairseqOptimizer, register_optimizer
-
-
-@register_optimizer("adamax")
-class FairseqAdamax(LegacyFairseqOptimizer):
- def __init__(self, args, params):
- super().__init__(args)
- self._optimizer = Adamax(params, **self.optimizer_config)
-
- @staticmethod
- def add_args(parser):
- """Add optimizer-specific arguments to the parser."""
- # fmt: off
- parser.add_argument('--adamax-betas', default='(0.9, 0.999)', metavar='B',
- help='betas for Adam optimizer')
- parser.add_argument('--adamax-eps', type=float, default=1e-8, metavar='D',
- help='epsilon for Adam optimizer')
- parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD',
- help='weight decay')
- parser.add_argument('--no-bias-correction', default=False, action='store_true',
- help='disable bias correction')
- # fmt: on
-
- @property
- def optimizer_config(self):
- """
- Return a kwarg dictionary that will be used to override optimizer
- args stored in checkpoints. This allows us to load a checkpoint and
- resume training using a different set of optimizer args, e.g., with a
- different learning rate.
- """
- return {
- "lr": self.args.lr[0],
- "betas": eval(self.args.adamax_betas),
- "eps": self.args.adamax_eps,
- "weight_decay": self.args.weight_decay,
- "bias_correction": not self.args.no_bias_correction,
- }
-
-
-class Adamax(torch.optim.Optimizer):
- """Implements Adamax algorithm (a variant of Adam based on infinity norm).
-
- It has been proposed in `Adam: A Method for Stochastic Optimization`__.
-
- Compared to the version in PyTorch, this version implements a fix for weight decay.
-
- Args:
- params (iterable): iterable of parameters to optimize or dicts defining
- parameter groups
- lr (float, optional): learning rate (default: 2e-3)
- betas (Tuple[float, float], optional): coefficients used for computing
- running averages of gradient and its square
- eps (float, optional): term added to the denominator to improve
- numerical stability (default: 1e-8)
- weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
- bias_correction (bool, optional): enable bias correction (default: True)
-
- __ https://arxiv.org/abs/1412.6980
- """
-
- def __init__(
- self,
- params,
- lr=2e-3,
- betas=(0.9, 0.999),
- eps=1e-8,
- weight_decay=0,
- bias_correction=True,
- ):
- if not 0.0 <= lr:
- raise ValueError("Invalid learning rate: {}".format(lr))
- if not 0.0 <= eps:
- raise ValueError("Invalid epsilon value: {}".format(eps))
- if not 0.0 <= betas[0] < 1.0:
- raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
- if not 0.0 <= betas[1] < 1.0:
- raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
- if not 0.0 <= weight_decay:
- raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
-
- defaults = dict(
- lr=lr,
- betas=betas,
- eps=eps,
- weight_decay=weight_decay,
- bias_correction=bias_correction,
- )
- super(Adamax, self).__init__(params, defaults)
-
- @property
- def supports_memory_efficient_fp16(self):
- return True
-
- @property
- def supports_flat_params(self):
- return True
-
- def step(self, closure=None):
- """Performs a single optimization step.
-
- Args:
- closure (callable, optional): A closure that reevaluates the model
- and returns the loss.
- """
- loss = None
- if closure is not None:
- loss = closure()
-
- for group in self.param_groups:
- for p in group["params"]:
- if p.grad is None:
- continue
- grad = p.grad.data.float()
- if grad.is_sparse:
- raise RuntimeError("Adamax does not support sparse gradients")
-
- p_data_fp32 = p.data
- if p.data.dtype in {torch.float16, torch.bfloat16}:
- p_data_fp32 = p_data_fp32.float()
-
- state = self.state[p]
-
- # State initialization
- if len(state) == 0:
- state["step"] = 0
- state["exp_avg"] = torch.zeros_like(p_data_fp32)
- state["exp_inf"] = torch.zeros_like(p_data_fp32)
- else:
- state["exp_avg"] = state["exp_avg"].to(p_data_fp32)
- state["exp_inf"] = state["exp_inf"].to(p_data_fp32)
-
- exp_avg, exp_inf = state["exp_avg"], state["exp_inf"]
- beta1, beta2 = group["betas"]
- eps = group["eps"]
-
- state["step"] += 1
-
- # Update biased first moment estimate.
- exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
-
- # Update the exponentially weighted infinity norm.
- torch.max(
- exp_inf.mul_(beta2),
- grad.abs_(),
- out=exp_inf,
- )
-
- step_size = group["lr"]
- if group["bias_correction"]:
- bias_correction = 1 - beta1 ** state["step"]
- step_size /= bias_correction
-
- if group["weight_decay"] != 0:
- p_data_fp32.add_(
- p_data_fp32, alpha=-group["weight_decay"] * group["lr"]
- )
-
- p_data_fp32.addcdiv_(exp_avg, exp_inf.add(eps), value=-step_size)
-
- if p.data.dtype in {torch.float16, torch.bfloat16}:
- p.data.copy_(p_data_fp32)
-
- return loss
diff --git a/spaces/h2oai/wave-tour/examples/table_pagination_h2o3.py b/spaces/h2oai/wave-tour/examples/table_pagination_h2o3.py
deleted file mode 100644
index 730078420857c329fcf31fda1bc26321c839fb23..0000000000000000000000000000000000000000
--- a/spaces/h2oai/wave-tour/examples/table_pagination_h2o3.py
+++ /dev/null
@@ -1,235 +0,0 @@
-# Table / Pagination / H2O-3 Dataframe
-# Use a paginated #table to display large (100m+ rows) tabular data using a H2O-3 dataframe.
-# #form #table #pagination #h2o3
-# ---
-
-import os
-from time import time
-
-import h2o
-from h2o_wave import Q, app, main, ui
-from loguru import logger
-
-# This example requires H2O-3 to be running.
-
-
-@app("/demo")
-async def serve(q: Q):
- logger.info(q.args)
- logger.info(q.events)
-
- if not q.app.initialized:
- # This is called the first time our app runs
- # Variables created here will be the same of all users of the app
- # Save a direct link to our H2O Dataframe for all users to use throughout the app
- try:
- h2o.connect(url="http://127.0.0.1:54321")
- except:
- q.page['err'] = ui.form_card(box='1 1 4 2', items=[
- ui.message_bar(type='error', text='Could not connect to H2O3. Please ensure H2O3 is running.'),
- ])
- await q.page.save()
- logger.error("H2O-3 is not running")
- return
- q.app.h2o_df = h2o.get_frame("py_6_sid_aff3")
-
- # EXAMPLE OF CREATING A LARGE DATAFRAME
- # h2o_df = h2o.create_frame(
- # rows=1000000,
- # cols=5,
- # categorical_fraction=0.6,
- # integer_fraction=0,
- # binary_fraction=0,
- # real_range=100,
- # integer_range=100,
- # missing_fraction=0,
- # seed=1234,
- # )
-
- q.app.rows_per_page = 10 # TODO: How many rows do you want to show users at a time
-
- # A list of booleans for if a column is sortable or not, by default
- # we allow all and only numeric columns to be sorted based on H2O-3 functionality
- # TODO: You may want to make a hardcoded list of [True, False] for your own use cases
- q.app.column_sortable = q.app.h2o_df.isnumeric()
-
- # A list of booleans for if a column is filterable or not, by default,
- # we allow all and only categorical columns to be sorted based on H2O-3 functionality
- # TODO: You may want to make a hardcoded list of [True, False] for your own use cases
- q.app.column_filterable = q.app.h2o_df.isfactor()
-
- # A list of booleans for if a column is searchable or not, by default,
- # we allow all and only categorical and string columns to be sorted based on H2O-3 functionality
- # TODO: You may want to make a hardcoded list of [True, False] for your own use cases
- q.app.column_searchable = q.app.h2o_df.isfactor() + q.app.h2o_df.isstring()
-
- q.app.initialized = True
-
- if not q.client.initialized:
- # This is called for each new browser that visits the app
- # Multiple users can interact with the table at the same time without interrupting each other
- # Users can make multiple changes to the table such as sorting and filtering
-
- q.client.search = None
- q.client.sort = None
- q.client.filters = None
- q.client.page_offset = 0
- q.client.total_rows = len(q.app.h2o_df)
-
- # Create the default UI for this user
- q.page["meta"] = ui.meta_card(box="")
- q.page["table_card"] = ui.form_card(
- box="1 1 -1 -1",
- items=[
- ui.table(
- name="h2o_table", # TODO: if you change this, you need to remember to update the serve function
- columns=[
- ui.table_column(
- name=q.app.h2o_df.columns[i],
- label=q.app.h2o_df.columns[i],
- sortable=q.app.column_sortable[i],
- filterable=q.app.column_filterable[i],
- searchable=q.app.column_searchable[i],
- )
- for i in range(len(q.app.h2o_df.columns))
- ],
- rows=get_table_rows(q),
- resettable=True,
- downloadable=True,
- pagination=ui.table_pagination(
- total_rows=q.client.total_rows,
- rows_per_page=q.app.rows_per_page,
- ),
- events=[
- "page_change",
- "sort",
- "filter",
- "search",
- "reset",
- "download",
- ],
- )
- ],
- )
- q.client.initialized = True
-
- # Check if user triggered any table action and save it to local state for allowing multiple
- # actions to be performed on the data at the same time, e.g. sort the filtered data etc.
- if q.events.h2o_table:
- logger.info("table event occurred")
-
- if q.events.h2o_table.page_change:
- logger.info(f"table page change: {q.events.h2o_table.page_change}")
- q.client.page_offset = q.events.h2o_table.page_change.get("offset", 0)
-
- if q.events.h2o_table.sort:
- logger.info(f"table sort: {q.events.h2o_table.sort}")
- q.client.sort = q.events.h2o_table.sort
- q.client.page_offset = 0
-
- if q.events.h2o_table.filter:
- logger.info(f"table filter: {q.events.h2o_table.filter}")
- q.client.filters = q.events.h2o_table.filter
- q.client.page_offset = 0
-
- if q.events.h2o_table.search is not None:
- logger.info(f"table search: {q.events.h2o_table.search}")
- q.client.search = q.events.h2o_table.search
- q.client.page_offset = 0
-
- if q.events.h2o_table.download:
- await download_h2o_table(q)
-
- if q.events.h2o_table.reset:
- logger.info("table reset")
- q.client.search = None
- q.client.sort = None
- q.client.filters = None
- q.client.page_offset = 0
- q.client.total_rows = len(q.app.h2o_df)
-
- # Update the rows in our UI
- # TODO: if you change where your table is located, this needs updating
- q.page["table_card"].h2o_table.rows = get_table_rows(q)
- q.page["table_card"].h2o_table.pagination.total_rows = q.client.total_rows
-
- await q.page.save()
-
-
-def get_table_rows(q: Q):
- logger.info(
- f"Creating new table for rows: {q.client.page_offset} to {q.client.page_offset + q.app.rows_per_page}"
- )
-
- working_frame = prepare_h2o_data(q)
-
- # Bring our limited UI rows locally to pandas to prepare for our ui.table
- local_df = working_frame[
- q.client.page_offset:q.client.page_offset + q.app.rows_per_page, :
- ].as_data_frame()
- q.client.total_rows = len(working_frame)
-
- table_rows = [
- ui.table_row(
- name=str(
- q.client.page_offset + i
- ), # name is the index on the h2o dataframe for appropriate lookup
- cells=[str(local_df[col].values[i]) for col in local_df.columns.to_list()],
- )
- for i in range(len(local_df))
- ]
-
- h2o.remove(working_frame) # remove our duplicate work
-
- return table_rows
-
-
-async def download_h2o_table(q: Q):
- # Create a unique file name as this is a multi-user app
- local_file_path = f"h2o3_data_{str(int(time()))}.csv"
- working_frame = prepare_h2o_data(q)
-
- h2o.download_csv(working_frame, local_file_path)
- (wave_file_path,) = await q.site.upload([local_file_path])
- os.remove(local_file_path)
-
- q.page["meta"].script = ui.inline_script(f'window.open("{wave_file_path}")')
-
-
-def prepare_h2o_data(q: Q):
-
- # This is used to prep the data we want to show on the screen or download, so it gets its own function
- # If you have 5 users at the same time, there will be 6 large dataframes in h2o3 - ensure proper cluster size
- working_frame = h2o.deep_copy(q.app.h2o_df, "working_df")
-
- if q.client.sort is not None:
- # H2O-3 can only sort numeric values - if the developer allows users to sort
- # string columns the end users will see unexpected results
-
- working_frame = working_frame.sort(
- by=list(q.client.sort.keys()), ascending=list(q.client.sort.values())
- )
-
- if q.client.filters is not None:
-
- for key in q.client.filters.keys():
- working_frame = working_frame[
- working_frame[key].match(q.client.filters[key])
- ]
-
- if q.client.search is not None:
- # We check if our search term is in any of the searchable columns
- # Start with and index of 0s and then filter to only keep rows with index > 0
-
- index = h2o.create_frame(
- rows=len(working_frame), cols=1, integer_fraction=1, integer_range=1
- )
- index["C1"] = 0
- for i in range(len(q.app.h2o_df.columns)):
- if q.app.column_searchable[i]:
- index = index + working_frame[q.app.h2o_df.columns[i]].grep(
- pattern=q.client.search, ignore_case=True, output_logical=True
- )
-
- working_frame = working_frame[index]
- return working_frame
diff --git a/spaces/haakohu/deep_privacy2/configs/anonymizers/FB_cse_mask_face.py b/spaces/haakohu/deep_privacy2/configs/anonymizers/FB_cse_mask_face.py
deleted file mode 100644
index d411d66cc051f6b4c0d907551735e8f661cf17f1..0000000000000000000000000000000000000000
--- a/spaces/haakohu/deep_privacy2/configs/anonymizers/FB_cse_mask_face.py
+++ /dev/null
@@ -1,29 +0,0 @@
-from dp2.anonymizer import Anonymizer
-from dp2.detection.cse_mask_face_detector import CSeMaskFaceDetector
-from ..defaults import common
-from tops.config import LazyCall as L
-
-detector = L(CSeMaskFaceDetector)(
- mask_rcnn_cfg=dict(),
- face_detector_cfg=dict(),
- face_post_process_cfg=dict(target_imsize=(256, 256), fdf128_expand=False),
- cse_cfg=dict(),
- cse_post_process_cfg=dict(
- target_imsize=(288, 160),
- exp_bbox_cfg=dict(percentage_background=0.3, axis_minimum_expansion=.1),
- exp_bbox_filter=dict(minimum_area=32*32, min_bbox_ratio_inside=0, aspect_ratio_range=[0, 99999]),
- iou_combine_threshold=0.4,
- dilation_percentage=0.02,
- normalize_embedding=False
- ),
- score_threshold=0.3,
- cache_directory=common.output_dir.joinpath("cse_mask_face_detection_cache")
-)
-
-anonymizer = L(Anonymizer)(
- detector="${detector}",
- face_G_cfg="configs/fdf/stylegan.py",
- person_G_cfg="configs/fdh/styleganL_nocse.py",
- cse_person_G_cfg="configs/fdh/styleganL.py",
- car_G_cfg="configs/generators/dummy/pixelation8.py"
-)
diff --git a/spaces/hamzapehlivan/StyleRes/models/torch_utils/ops/filtered_lrelu.py b/spaces/hamzapehlivan/StyleRes/models/torch_utils/ops/filtered_lrelu.py
deleted file mode 100644
index 6106c917d1cbff4f1cf637390dd6ba0c597a830f..0000000000000000000000000000000000000000
--- a/spaces/hamzapehlivan/StyleRes/models/torch_utils/ops/filtered_lrelu.py
+++ /dev/null
@@ -1,274 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto. Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import os
-import numpy as np
-import torch
-import warnings
-
-from .. import custom_ops
-from .. import misc
-from . import upfirdn2d
-from . import bias_act
-
-#----------------------------------------------------------------------------
-
-_plugin = None
-
-def _init():
- global _plugin
- if _plugin is None:
- _plugin = custom_ops.get_plugin(
- module_name='filtered_lrelu_plugin',
- sources=['filtered_lrelu.cpp', 'filtered_lrelu_wr.cu', 'filtered_lrelu_rd.cu', 'filtered_lrelu_ns.cu'],
- headers=['filtered_lrelu.h', 'filtered_lrelu.cu'],
- source_dir=os.path.dirname(__file__),
- extra_cuda_cflags=['--use_fast_math'],
- )
- return True
-
-def _get_filter_size(f):
- if f is None:
- return 1, 1
- assert isinstance(f, torch.Tensor)
- assert 1 <= f.ndim <= 2
- return f.shape[-1], f.shape[0] # width, height
-
-def _parse_padding(padding):
- if isinstance(padding, int):
- padding = [padding, padding]
- assert isinstance(padding, (list, tuple))
- assert all(isinstance(x, (int, np.integer)) for x in padding)
- padding = [int(x) for x in padding]
- if len(padding) == 2:
- px, py = padding
- padding = [px, px, py, py]
- px0, px1, py0, py1 = padding
- return px0, px1, py0, py1
-
-#----------------------------------------------------------------------------
-
-def filtered_lrelu(x, fu=None, fd=None, b=None, up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False, impl='cuda'):
- r"""Filtered leaky ReLU for a batch of 2D images.
-
- Performs the following sequence of operations for each channel:
-
- 1. Add channel-specific bias if provided (`b`).
-
- 2. Upsample the image by inserting N-1 zeros after each pixel (`up`).
-
- 3. Pad the image with the specified number of zeros on each side (`padding`).
- Negative padding corresponds to cropping the image.
-
- 4. Convolve the image with the specified upsampling FIR filter (`fu`), shrinking it
- so that the footprint of all output pixels lies within the input image.
-
- 5. Multiply each value by the provided gain factor (`gain`).
-
- 6. Apply leaky ReLU activation function to each value.
-
- 7. Clamp each value between -clamp and +clamp, if `clamp` parameter is provided.
-
- 8. Convolve the image with the specified downsampling FIR filter (`fd`), shrinking
- it so that the footprint of all output pixels lies within the input image.
-
- 9. Downsample the image by keeping every Nth pixel (`down`).
-
- The fused op is considerably more efficient than performing the same calculation
- using standard PyTorch ops. It supports gradients of arbitrary order.
-
- Args:
- x: Float32/float16/float64 input tensor of the shape
- `[batch_size, num_channels, in_height, in_width]`.
- fu: Float32 upsampling FIR filter of the shape
- `[filter_height, filter_width]` (non-separable),
- `[filter_taps]` (separable), or
- `None` (identity).
- fd: Float32 downsampling FIR filter of the shape
- `[filter_height, filter_width]` (non-separable),
- `[filter_taps]` (separable), or
- `None` (identity).
- b: Bias vector, or `None` to disable. Must be a 1D tensor of the same type
- as `x`. The length of vector must must match the channel dimension of `x`.
- up: Integer upsampling factor (default: 1).
- down: Integer downsampling factor. (default: 1).
- padding: Padding with respect to the upsampled image. Can be a single number
- or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
- (default: 0).
- gain: Overall scaling factor for signal magnitude (default: sqrt(2)).
- slope: Slope on the negative side of leaky ReLU (default: 0.2).
- clamp: Maximum magnitude for leaky ReLU output (default: None).
- flip_filter: False = convolution, True = correlation (default: False).
- impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
-
- Returns:
- Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
- """
- assert isinstance(x, torch.Tensor)
- assert impl in ['ref', 'cuda']
- if impl == 'cuda' and x.device.type == 'cuda' and _init():
- return _filtered_lrelu_cuda(up=up, down=down, padding=padding, gain=gain, slope=slope, clamp=clamp, flip_filter=flip_filter).apply(x, fu, fd, b, None, 0, 0)
- return _filtered_lrelu_ref(x, fu=fu, fd=fd, b=b, up=up, down=down, padding=padding, gain=gain, slope=slope, clamp=clamp, flip_filter=flip_filter)
-
-#----------------------------------------------------------------------------
-
-@misc.profiled_function
-def _filtered_lrelu_ref(x, fu=None, fd=None, b=None, up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False):
- """Slow and memory-inefficient reference implementation of `filtered_lrelu()` using
- existing `upfirdn2n()` and `bias_act()` ops.
- """
- assert isinstance(x, torch.Tensor) and x.ndim == 4
- fu_w, fu_h = _get_filter_size(fu)
- fd_w, fd_h = _get_filter_size(fd)
- if b is not None:
- assert isinstance(b, torch.Tensor) and b.dtype == x.dtype
- misc.assert_shape(b, [x.shape[1]])
- assert isinstance(up, int) and up >= 1
- assert isinstance(down, int) and down >= 1
- px0, px1, py0, py1 = _parse_padding(padding)
- assert gain == float(gain) and gain > 0
- assert slope == float(slope) and slope >= 0
- assert clamp is None or (clamp == float(clamp) and clamp >= 0)
-
- # Calculate output size.
- batch_size, channels, in_h, in_w = x.shape
- in_dtype = x.dtype
- out_w = (in_w * up + (px0 + px1) - (fu_w - 1) - (fd_w - 1) + (down - 1)) // down
- out_h = (in_h * up + (py0 + py1) - (fu_h - 1) - (fd_h - 1) + (down - 1)) // down
-
- # Compute using existing ops.
- x = bias_act.bias_act(x=x, b=b) # Apply bias.
- x = upfirdn2d.upfirdn2d(x=x, f=fu, up=up, padding=[px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter) # Upsample.
- x = bias_act.bias_act(x=x, act='lrelu', alpha=slope, gain=gain, clamp=clamp) # Bias, leaky ReLU, clamp.
- x = upfirdn2d.upfirdn2d(x=x, f=fd, down=down, flip_filter=flip_filter) # Downsample.
-
- # Check output shape & dtype.
- misc.assert_shape(x, [batch_size, channels, out_h, out_w])
- assert x.dtype == in_dtype
- return x
-
-#----------------------------------------------------------------------------
-
-_filtered_lrelu_cuda_cache = dict()
-
-def _filtered_lrelu_cuda(up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False):
- """Fast CUDA implementation of `filtered_lrelu()` using custom ops.
- """
- assert isinstance(up, int) and up >= 1
- assert isinstance(down, int) and down >= 1
- px0, px1, py0, py1 = _parse_padding(padding)
- assert gain == float(gain) and gain > 0
- gain = float(gain)
- assert slope == float(slope) and slope >= 0
- slope = float(slope)
- assert clamp is None or (clamp == float(clamp) and clamp >= 0)
- clamp = float(clamp if clamp is not None else 'inf')
-
- # Lookup from cache.
- key = (up, down, px0, px1, py0, py1, gain, slope, clamp, flip_filter)
- if key in _filtered_lrelu_cuda_cache:
- return _filtered_lrelu_cuda_cache[key]
-
- # Forward op.
- class FilteredLReluCuda(torch.autograd.Function):
- @staticmethod
- def forward(ctx, x, fu, fd, b, si, sx, sy): # pylint: disable=arguments-differ
- assert isinstance(x, torch.Tensor) and x.ndim == 4
-
- # Replace empty up/downsample kernels with full 1x1 kernels (faster than separable).
- if fu is None:
- fu = torch.ones([1, 1], dtype=torch.float32, device=x.device)
- if fd is None:
- fd = torch.ones([1, 1], dtype=torch.float32, device=x.device)
- assert 1 <= fu.ndim <= 2
- assert 1 <= fd.ndim <= 2
-
- # Replace separable 1x1 kernels with full 1x1 kernels when scale factor is 1.
- if up == 1 and fu.ndim == 1 and fu.shape[0] == 1:
- fu = fu.square()[None]
- if down == 1 and fd.ndim == 1 and fd.shape[0] == 1:
- fd = fd.square()[None]
-
- # Missing sign input tensor.
- if si is None:
- si = torch.empty([0])
-
- # Missing bias tensor.
- if b is None:
- b = torch.zeros([x.shape[1]], dtype=x.dtype, device=x.device)
-
- # Construct internal sign tensor only if gradients are needed.
- write_signs = (si.numel() == 0) and (x.requires_grad or b.requires_grad)
-
- # Warn if input storage strides are not in decreasing order due to e.g. channels-last layout.
- strides = [x.stride(i) for i in range(x.ndim) if x.size(i) > 1]
- if any(a < b for a, b in zip(strides[:-1], strides[1:])):
- warnings.warn("low-performance memory layout detected in filtered_lrelu input", RuntimeWarning)
-
- # Call C++/Cuda plugin if datatype is supported.
- if x.dtype in [torch.float16, torch.float32]:
- if torch.cuda.current_stream(x.device) != torch.cuda.default_stream(x.device):
- warnings.warn("filtered_lrelu called with non-default cuda stream but concurrent execution is not supported", RuntimeWarning)
- y, so, return_code = _plugin.filtered_lrelu(x, fu, fd, b, si, up, down, px0, px1, py0, py1, sx, sy, gain, slope, clamp, flip_filter, write_signs)
- else:
- return_code = -1
-
- # No Cuda kernel found? Fall back to generic implementation. Still more memory efficient than the reference implementation because
- # only the bit-packed sign tensor is retained for gradient computation.
- if return_code < 0:
- warnings.warn("filtered_lrelu called with parameters that have no optimized CUDA kernel, using generic fallback", RuntimeWarning)
-
- y = x.add(b.unsqueeze(-1).unsqueeze(-1)) # Add bias.
- y = upfirdn2d.upfirdn2d(x=y, f=fu, up=up, padding=[px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter) # Upsample.
- so = _plugin.filtered_lrelu_act_(y, si, sx, sy, gain, slope, clamp, write_signs) # Activation function and sign handling. Modifies y in-place.
- y = upfirdn2d.upfirdn2d(x=y, f=fd, down=down, flip_filter=flip_filter) # Downsample.
-
- # Prepare for gradient computation.
- ctx.save_for_backward(fu, fd, (si if si.numel() else so))
- ctx.x_shape = x.shape
- ctx.y_shape = y.shape
- ctx.s_ofs = sx, sy
- return y
-
- @staticmethod
- def backward(ctx, dy): # pylint: disable=arguments-differ
- fu, fd, si = ctx.saved_tensors
- _, _, xh, xw = ctx.x_shape
- _, _, yh, yw = ctx.y_shape
- sx, sy = ctx.s_ofs
- dx = None # 0
- dfu = None; assert not ctx.needs_input_grad[1]
- dfd = None; assert not ctx.needs_input_grad[2]
- db = None # 3
- dsi = None; assert not ctx.needs_input_grad[4]
- dsx = None; assert not ctx.needs_input_grad[5]
- dsy = None; assert not ctx.needs_input_grad[6]
-
- if ctx.needs_input_grad[0] or ctx.needs_input_grad[3]:
- pp = [
- (fu.shape[-1] - 1) + (fd.shape[-1] - 1) - px0,
- xw * up - yw * down + px0 - (up - 1),
- (fu.shape[0] - 1) + (fd.shape[0] - 1) - py0,
- xh * up - yh * down + py0 - (up - 1),
- ]
- gg = gain * (up ** 2) / (down ** 2)
- ff = (not flip_filter)
- sx = sx - (fu.shape[-1] - 1) + px0
- sy = sy - (fu.shape[0] - 1) + py0
- dx = _filtered_lrelu_cuda(up=down, down=up, padding=pp, gain=gg, slope=slope, clamp=None, flip_filter=ff).apply(dy, fd, fu, None, si, sx, sy)
-
- if ctx.needs_input_grad[3]:
- db = dx.sum([0, 2, 3])
-
- return dx, dfu, dfd, db, dsi, dsx, dsy
-
- # Add to cache.
- _filtered_lrelu_cuda_cache[key] = FilteredLReluCuda
- return FilteredLReluCuda
-
-#----------------------------------------------------------------------------
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/.github/ISSUE_TEMPLATE.md b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/.github/ISSUE_TEMPLATE.md
deleted file mode 100644
index 5e8aaa2d3722e7e73a3d94b2b7dfc4f751d7a240..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/.github/ISSUE_TEMPLATE.md
+++ /dev/null
@@ -1,5 +0,0 @@
-
-Please select an issue template from
-https://github.com/facebookresearch/detectron2/issues/new/choose .
-
-Otherwise your issue will be closed.
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/utils/memory.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/utils/memory.py
deleted file mode 100644
index d495a1681f460668c96f64454e31e7f2fca8137a..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/utils/memory.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-
-import logging
-from contextlib import contextmanager
-from functools import wraps
-import torch
-
-__all__ = ["retry_if_cuda_oom"]
-
-
-@contextmanager
-def _ignore_torch_cuda_oom():
- """
- A context which ignores CUDA OOM exception from pytorch.
- """
- try:
- yield
- except RuntimeError as e:
- # NOTE: the string may change?
- if "CUDA out of memory. " in str(e):
- pass
- else:
- raise
-
-
-def retry_if_cuda_oom(func):
- """
- Makes a function retry itself after encountering
- pytorch's CUDA OOM error.
- It will first retry after calling `torch.cuda.empty_cache()`.
-
- If that still fails, it will then retry by trying to convert inputs to CPUs.
- In this case, it expects the function to dispatch to CPU implementation.
- The return values may become CPU tensors as well and it's user's
- responsibility to convert it back to CUDA tensor if needed.
-
- Args:
- func: a stateless callable that takes tensor-like objects as arguments
-
- Returns:
- a callable which retries `func` if OOM is encountered.
-
- Examples:
-
- .. code-block:: python
-
- output = retry_if_cuda_oom(some_torch_function)(input1, input2)
- # output may be on CPU even if inputs are on GPU
-
- Note:
- 1. When converting inputs to CPU, it will only look at each argument and check
- if it has `.device` and `.to` for conversion. Nested structures of tensors
- are not supported.
-
- 2. Since the function might be called more than once, it has to be
- stateless.
- """
-
- def maybe_to_cpu(x):
- try:
- like_gpu_tensor = x.device.type == "cuda" and hasattr(x, "to")
- except AttributeError:
- like_gpu_tensor = False
- if like_gpu_tensor:
- return x.to(device="cpu")
- else:
- return x
-
- @wraps(func)
- def wrapped(*args, **kwargs):
- with _ignore_torch_cuda_oom():
- return func(*args, **kwargs)
-
- # Clear cache and retry
- torch.cuda.empty_cache()
- with _ignore_torch_cuda_oom():
- return func(*args, **kwargs)
-
- # Try on CPU. This slows down the code significantly, therefore print a notice.
- logger = logging.getLogger(__name__)
- logger.info("Attempting to copy inputs of {} to CPU due to CUDA OOM".format(str(func)))
- new_args = (maybe_to_cpu(x) for x in args)
- new_kwargs = {k: maybe_to_cpu(v) for k, v in kwargs.items()}
- return func(*new_args, **new_kwargs)
-
- return wrapped
diff --git a/spaces/hekbobo/bingo/src/lib/isomorphic/index.ts b/spaces/hekbobo/bingo/src/lib/isomorphic/index.ts
deleted file mode 100644
index 738dc92f74079ab762d584fb7422a8c8c3b61547..0000000000000000000000000000000000000000
--- a/spaces/hekbobo/bingo/src/lib/isomorphic/index.ts
+++ /dev/null
@@ -1,17 +0,0 @@
-'use client'
-
-import Default from './browser'
-
-let exportsModel: any = {}
-
-if (process.browser) {
- Object.assign(exportsModel, require('./browser').default)
-} else {
- Object.assign(exportsModel, require('./node').default)
-}
-
-export default exportsModel! as typeof Default
-
-export const fetch: typeof Default.fetch = exportsModel!.fetch
-export const WebSocket: typeof Default.WebSocket = exportsModel!.WebSocket
-export const debug: typeof Default.debug = exportsModel!.debug
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/dataset_conversion/Task089_Fluo-N2DH-SIM.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/dataset_conversion/Task089_Fluo-N2DH-SIM.py
deleted file mode 100644
index 4505be90d88dc29c21501ace680d1f122681f46c..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/dataset_conversion/Task089_Fluo-N2DH-SIM.py
+++ /dev/null
@@ -1,290 +0,0 @@
-# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import shutil
-from multiprocessing import Pool
-
-import SimpleITK as sitk
-import numpy as np
-from batchgenerators.utilities.file_and_folder_operations import *
-from skimage.io import imread
-from skimage.io import imsave
-from skimage.morphology import disk
-from skimage.morphology import erosion
-from skimage.transform import resize
-
-from nnunet.paths import nnUNet_raw_data
-
-
-def load_bmp_convert_to_nifti_borders_2d(img_file, lab_file, img_out_base, anno_out, spacing, border_thickness=0.7):
- img = imread(img_file)
- img_itk = sitk.GetImageFromArray(img.astype(np.float32)[None])
- img_itk.SetSpacing(list(spacing)[::-1] + [999])
- sitk.WriteImage(img_itk, join(img_out_base + "_0000.nii.gz"))
-
- if lab_file is not None:
- l = imread(lab_file)
- borders = generate_border_as_suggested_by_twollmann_2d(l, spacing, border_thickness)
- l[l > 0] = 1
- l[borders == 1] = 2
- l_itk = sitk.GetImageFromArray(l.astype(np.uint8)[None])
- l_itk.SetSpacing(list(spacing)[::-1] + [999])
- sitk.WriteImage(l_itk, anno_out)
-
-
-def generate_disk(spacing, radius, dtype=int):
- radius_in_voxels = np.round(radius / np.array(spacing)).astype(int)
- n = 2 * radius_in_voxels + 1
- disk_iso = disk(max(n) * 2, dtype=np.float64)
- disk_resampled = resize(disk_iso, n, 1, 'constant', 0, clip=True, anti_aliasing=False, preserve_range=True)
- disk_resampled[disk_resampled > 0.5] = 1
- disk_resampled[disk_resampled <= 0.5] = 0
- return disk_resampled.astype(dtype)
-
-
-def generate_border_as_suggested_by_twollmann_2d(label_img: np.ndarray, spacing,
- border_thickness: float = 2) -> np.ndarray:
- border = np.zeros_like(label_img)
- selem = generate_disk(spacing, border_thickness)
- for l in np.unique(label_img):
- if l == 0: continue
- mask = (label_img == l).astype(int)
- eroded = erosion(mask, selem)
- border[(eroded == 0) & (mask != 0)] = 1
- return border
-
-
-def prepare_task(base, task_id, task_name, spacing, border_thickness: float = 15):
- p = Pool(16)
-
- foldername = "Task%03.0d_%s" % (task_id, task_name)
-
- out_base = join(nnUNet_raw_data, foldername)
- imagestr = join(out_base, "imagesTr")
- imagests = join(out_base, "imagesTs")
- labelstr = join(out_base, "labelsTr")
- maybe_mkdir_p(imagestr)
- maybe_mkdir_p(imagests)
- maybe_mkdir_p(labelstr)
-
- train_patient_names = []
- test_patient_names = []
- res = []
-
- for train_sequence in [i for i in subfolders(base + "_train", join=False) if not i.endswith("_GT")]:
- train_cases = subfiles(join(base + '_train', train_sequence), suffix=".tif", join=False)
- for t in train_cases:
- casename = train_sequence + "_" + t[:-4]
- img_file = join(base + '_train', train_sequence, t)
- lab_file = join(base + '_train', train_sequence + "_GT", "SEG", "man_seg" + t[1:])
- if not isfile(lab_file):
- continue
- img_out_base = join(imagestr, casename)
- anno_out = join(labelstr, casename + ".nii.gz")
- res.append(
- p.starmap_async(load_bmp_convert_to_nifti_borders_2d,
- ((img_file, lab_file, img_out_base, anno_out, spacing, border_thickness),)))
- train_patient_names.append(casename)
-
- for test_sequence in [i for i in subfolders(base + "_test", join=False) if not i.endswith("_GT")]:
- test_cases = subfiles(join(base + '_test', test_sequence), suffix=".tif", join=False)
- for t in test_cases:
- casename = test_sequence + "_" + t[:-4]
- img_file = join(base + '_test', test_sequence, t)
- lab_file = None
- img_out_base = join(imagests, casename)
- anno_out = None
- res.append(
- p.starmap_async(load_bmp_convert_to_nifti_borders_2d,
- ((img_file, lab_file, img_out_base, anno_out, spacing, border_thickness),)))
- test_patient_names.append(casename)
-
- _ = [i.get() for i in res]
-
- json_dict = {}
- json_dict['name'] = task_name
- json_dict['description'] = ""
- json_dict['tensorImageSize'] = "4D"
- json_dict['reference'] = ""
- json_dict['licence'] = ""
- json_dict['release'] = "0.0"
- json_dict['modality'] = {
- "0": "BF",
- }
- json_dict['labels'] = {
- "0": "background",
- "1": "cell",
- "2": "border",
- }
-
- json_dict['numTraining'] = len(train_patient_names)
- json_dict['numTest'] = len(test_patient_names)
- json_dict['training'] = [{'image': "./imagesTr/%s.nii.gz" % i, "label": "./labelsTr/%s.nii.gz" % i} for i in
- train_patient_names]
- json_dict['test'] = ["./imagesTs/%s.nii.gz" % i for i in test_patient_names]
-
- save_json(json_dict, os.path.join(out_base, "dataset.json"))
- p.close()
- p.join()
-
-
-def convert_to_instance_seg(arr: np.ndarray, spacing: tuple = (0.125, 0.125), small_center_threshold: int = 30,
- isolated_border_as_separate_instance_threshold=15):
- from skimage.morphology import label, dilation
-
- # we first identify centers that are too small and set them to be border. This should remove false positive instances
- objects = label((arr == 1).astype(int))
- for o in np.unique(objects):
- if o > 0 and np.sum(objects == o) <= small_center_threshold:
- arr[objects == o] = 2
-
- # 1 is core, 2 is border
- objects = label((arr == 1).astype(int))
- final = np.copy(objects)
- remaining_border = arr == 2
- current = np.copy(objects)
- dilated_mm = np.array((0, 0))
- spacing = np.array(spacing)
-
- while np.sum(remaining_border) > 0:
- strel_size = [0, 0]
- maximum_dilation = max(dilated_mm)
- for i in range(2):
- if spacing[i] == min(spacing):
- strel_size[i] = 1
- continue
- if dilated_mm[i] + spacing[i] / 2 < maximum_dilation:
- strel_size[i] = 1
- ball_here = disk(1)
-
- if strel_size[0] == 0: ball_here = ball_here[1:2]
- if strel_size[1] == 0: ball_here = ball_here[:, 1:2]
-
- #print(1)
- dilated = dilation(current, ball_here)
- diff = (current == 0) & (dilated != current)
- final[diff & remaining_border] = dilated[diff & remaining_border]
- remaining_border[diff] = 0
- current = dilated
- dilated_mm = [dilated_mm[i] + spacing[i] if strel_size[i] == 1 else dilated_mm[i] for i in range(2)]
-
- # what can happen is that a cell is so small that the network only predicted border and no core. This cell will be
- # fused with the nearest other instance, which we don't want. Therefore we identify isolated border predictions and
- # give them a separate instance id
- # we identify isolated border predictions by checking each foreground object in arr and see whether this object
- # also contains label 1
- max_label = np.max(final)
-
- foreground_objects = label((arr != 0).astype(int))
- for i in np.unique(foreground_objects):
- if i > 0 and (1 not in np.unique(arr[foreground_objects==i])):
- size_of_object = np.sum(foreground_objects==i)
- if size_of_object >= isolated_border_as_separate_instance_threshold:
- final[foreground_objects == i] = max_label + 1
- max_label += 1
- #print('yeah boi')
-
- return final.astype(np.uint32)
-
-
-def load_convert_to_instance_save(file_in: str, file_out: str, spacing):
- img = sitk.ReadImage(file_in)
- img_npy = sitk.GetArrayFromImage(img)
- out = convert_to_instance_seg(img_npy[0], spacing)[None]
- out_itk = sitk.GetImageFromArray(out.astype(np.int16))
- out_itk.CopyInformation(img)
- sitk.WriteImage(out_itk, file_out)
-
-
-def convert_folder_to_instanceseg(folder_in: str, folder_out: str, spacing, processes: int = 12):
- input_files = subfiles(folder_in, suffix=".nii.gz", join=False)
- maybe_mkdir_p(folder_out)
- output_files = [join(folder_out, i) for i in input_files]
- input_files = [join(folder_in, i) for i in input_files]
- p = Pool(processes)
- r = []
- for i, o in zip(input_files, output_files):
- r.append(
- p.starmap_async(
- load_convert_to_instance_save,
- ((i, o, spacing),)
- )
- )
- _ = [i.get() for i in r]
- p.close()
- p.join()
-
-
-def convert_to_tiff(nifti_image: str, output_name: str):
- npy = sitk.GetArrayFromImage(sitk.ReadImage(nifti_image))
- imsave(output_name, npy[0].astype(np.uint16), compress=6)
-
-
-if __name__ == "__main__":
- base = "/home/fabian/Downloads/Fluo-N2DH-SIM+"
- task_name = 'Fluo-N2DH-SIM'
- spacing = (0.125, 0.125)
-
- task_id = 999
- border_thickness = 0.7
- prepare_task(base, task_id, task_name, spacing, border_thickness)
-
- task_id = 89
- additional_time_steps = 4
- task_name = 'Fluo-N2DH-SIM_thickborder_time'
- full_taskname = 'Task%03.0d_' % task_id + task_name
- output_raw = join(nnUNet_raw_data, full_taskname)
- shutil.rmtree(output_raw)
- shutil.copytree(join(nnUNet_raw_data, 'Task999_Fluo-N2DH-SIM_thickborder'), output_raw)
-
- shutil.rmtree(join(nnUNet_raw_data, 'Task999_Fluo-N2DH-SIM_thickborder'))
-
- # now add additional time information
- for fld in ['imagesTr', 'imagesTs']:
- curr = join(output_raw, fld)
- for seq in ['01', '02']:
- images = subfiles(curr, prefix=seq, join=False)
- for i in images:
- current_timestep = int(i.split('_')[1][1:])
- renamed = join(curr, i.replace("_0000", "_%04.0d" % additional_time_steps))
- shutil.move(join(curr, i), renamed)
- for previous_timestep in range(-additional_time_steps, 0):
- # previous time steps will already have been processed and renamed!
- expected_filename = join(curr, seq + "_t%03.0d" % (
- current_timestep + previous_timestep) + "_%04.0d" % additional_time_steps + ".nii.gz")
- if not isfile(expected_filename):
- # create empty image
- img = sitk.ReadImage(renamed)
- empty = sitk.GetImageFromArray(np.zeros_like(sitk.GetArrayFromImage(img)))
- empty.CopyInformation(img)
- sitk.WriteImage(empty, join(curr, i.replace("_0000", "_%04.0d" % (
- additional_time_steps + previous_timestep))))
- else:
- shutil.copy(expected_filename, join(curr, i.replace("_0000", "_%04.0d" % (
- additional_time_steps + previous_timestep))))
- dataset = load_json(join(output_raw, 'dataset.json'))
- dataset['modality'] = {
- '0': 't_minus 4',
- '1': 't_minus 3',
- '2': 't_minus 2',
- '3': 't_minus 1',
- '4': 'frame of interest',
- }
- save_json(dataset, join(output_raw, 'dataset.json'))
-
- # we do not need custom splits since we train on all training cases
-
- # test set predictions are converted to instance seg with convert_folder_to_instanceseg
-
- # test set predictions are converted to tiff with convert_to_tiff
\ No newline at end of file
diff --git a/spaces/huolongguo10/huolongguo10-check_sec/app.py b/spaces/huolongguo10/huolongguo10-check_sec/app.py
deleted file mode 100644
index c4e856123ed12646c9a4133a6dd1fdca243149b2..0000000000000000000000000000000000000000
--- a/spaces/huolongguo10/huolongguo10-check_sec/app.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import gradio as gr
-import transformers
-from transformers import BertTokenizer, DataCollatorWithPadding
-from transformers import AutoModelForSequenceClassification
-tokenizer = BertTokenizer.from_pretrained('huolongguo10/check_sec')
-model = AutoModelForSequenceClassification.from_pretrained('huolongguo10/check_sec', num_labels=2)
-_tokenizer = BertTokenizer.from_pretrained('huolongguo10/check_sec_tiny')
-_model = AutoModelForSequenceClassification.from_pretrained('huolongguo10/check_sec_tiny', num_labels=2)
-import torch
-def check_each(text):
- inputs = tokenizer(text, return_tensors="pt",max_length=512)
- with torch.no_grad():
- logits = model(**inputs).logits
- predicted_class_id = logits.argmax().item()
- print(f'{logits.argmax().item()}:{text}')
- return 'secure' if predicted_class_id==0 else 'insecure'
-def _check_each(text):
- inputs = _tokenizer(text, return_tensors="pt",max_length=512)
- with torch.no_grad():
- logits = _model(**inputs).logits
- predicted_class_id = logits.argmax().item()
- print(f't-{logits.argmax().item()}:{text}')
- return 'secure' if predicted_class_id==0 else 'insecure'
-def _check(text):
- t=text
- while len(t)>512:
- if check_each(t[0:511])=='insecure':
- return 'insecure'
- t=t[512:]
- return check_each(t)
-def _check_tiny(text):
- t=text
- while len(t)>512:
- if _check_each(t[0:511])=='insecure':
- return 'insecure'
- t=t[512:]
- return _check_each(t)
-def check(text):
- return _check(text),_check_tiny(text)
-with gr.Blocks() as demo:
- text = gr.Textbox(label="Text")
- output = gr.Textbox(label="Output Box")
- _output = gr.Textbox(label="Output Box(By Tiny)")
- # org = gr.Textbox(label="By normal check")
- greet_btn = gr.Button("Check!")
- greet_btn.click(fn=check, inputs=text, outputs=[output,_output], api_name="check")
- gr.Markdown('''# check_sec
-检查web参数安全性，支持多种payload(v0.1.2)
-
-## 类型
-```
-LABEL_0: secure
-LABEL_1: insecure(可能包含payload)
-```
- ''')
-# gr.Interface.load("models/huolongguo10/check_sec").launch()
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/hysts/gan-control/app.py b/spaces/hysts/gan-control/app.py
deleted file mode 100644
index 360cd575bcdcf842378533ba39c4c86e7f0eb13f..0000000000000000000000000000000000000000
--- a/spaces/hysts/gan-control/app.py
+++ /dev/null
@@ -1,143 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import functools
-import os
-import pathlib
-import shlex
-import subprocess
-import sys
-import tarfile
-
-import gradio as gr
-import huggingface_hub
-import numpy as np
-import PIL.Image
-import torch
-
-if os.getenv('SYSTEM') == 'spaces':
- with open('patch') as f:
- subprocess.run(shlex.split('patch -p1'), cwd='gan-control', stdin=f)
-
-sys.path.insert(0, 'gan-control/src')
-
-from gan_control.inference.controller import Controller
-
-TITLE = 'GAN-Control'
-DESCRIPTION = 'https://github.com/amazon-research/gan-control'
-
-
-def download_models() -> None:
- model_dir = pathlib.Path('controller_age015id025exp02hai04ori02gam15')
- if not model_dir.exists():
- path = huggingface_hub.hf_hub_download(
- 'public-data/gan-control',
- 'controller_age015id025exp02hai04ori02gam15.tar.gz')
- with tarfile.open(path) as f:
- f.extractall()
-
-
-@torch.inference_mode()
-def run(
- seed: int,
- truncation: float,
- yaw: int,
- pitch: int,
- age: int,
- hair_color_r: float,
- hair_color_g: float,
- hair_color_b: float,
- nrows: int,
- ncols: int,
- controller: Controller,
- device: torch.device,
-) -> PIL.Image.Image:
- seed = int(np.clip(seed, 0, np.iinfo(np.uint32).max))
- batch_size = nrows * ncols
- latent_size = controller.config.model_config['latent_size']
- latent = torch.from_numpy(
- np.random.RandomState(seed).randn(batch_size,
- latent_size)).float().to(device)
-
- initial_image_tensors, initial_latent_z, initial_latent_w = controller.gen_batch(
- latent=latent, truncation=truncation)
- res0 = controller.make_resized_grid_image(initial_image_tensors,
- nrow=ncols)
-
- pose_control = torch.tensor([[yaw, pitch, 0]], dtype=torch.float32)
- image_tensors, _, modified_latent_w = controller.gen_batch_by_controls(
- latent=initial_latent_w,
- input_is_latent=True,
- orientation=pose_control)
- res1 = controller.make_resized_grid_image(image_tensors, nrow=ncols)
-
- age_control = torch.tensor([[age]], dtype=torch.float32)
- image_tensors, _, modified_latent_w = controller.gen_batch_by_controls(
- latent=initial_latent_w, input_is_latent=True, age=age_control)
- res2 = controller.make_resized_grid_image(image_tensors, nrow=ncols)
-
- hair_color = torch.tensor([[hair_color_r, hair_color_g, hair_color_b]],
- dtype=torch.float32) / 255
- hair_color = torch.clamp(hair_color, 0, 1)
- image_tensors, _, modified_latent_w = controller.gen_batch_by_controls(
- latent=initial_latent_w, input_is_latent=True, hair=hair_color)
- res3 = controller.make_resized_grid_image(image_tensors, nrow=ncols)
-
- return res0, res1, res2, res3
-
-
-download_models()
-
-device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
-path = 'controller_age015id025exp02hai04ori02gam15/'
-controller = Controller(path, device)
-fn = functools.partial(run, controller=controller, device=device)
-
-gr.Interface(
- fn=fn,
- inputs=[
- gr.Slider(label='Seed', minimum=0, maximum=1000000, step=1, value=0),
- gr.Slider(label='Truncation',
- minimum=0,
- maximum=1,
- step=0.1,
- value=0.7),
- gr.Slider(label='Yaw', minimum=-90, maximum=90, step=1, value=30),
- gr.Slider(label='Pitch', minimum=-90, maximum=90, step=1, value=0),
- gr.Slider(label='Age', minimum=15, maximum=75, step=1, value=75),
- gr.Slider(label='Hair Color (R)',
- minimum=0,
- maximum=255,
- step=1,
- value=186),
- gr.Slider(label='Hair Color (G)',
- minimum=0,
- maximum=255,
- step=1,
- value=158),
- gr.Slider(label='Hair Color (B)',
- minimum=0,
- maximum=255,
- step=1,
- value=92),
- gr.Slider(label='Number of Rows',
- minimum=1,
- maximum=3,
- step=1,
- value=1),
- gr.Slider(label='Number of Columns',
- minimum=1,
- maximum=5,
- step=1,
- value=5),
- ],
- outputs=[
- gr.Image(label='Generated Image', type='pil'),
- gr.Image(label='Head Pose Controlled', type='pil'),
- gr.Image(label='Age Controlled', type='pil'),
- gr.Image(label='Hair Color Controlled', type='pil'),
- ],
- title=TITLE,
- description=DESCRIPTION,
-).queue(max_size=10).launch()
diff --git a/spaces/hysts/insightface-person-detection/app.py b/spaces/hysts/insightface-person-detection/app.py
deleted file mode 100644
index 2ec075ee3cad7f12866782288a970c4fda78126b..0000000000000000000000000000000000000000
--- a/spaces/hysts/insightface-person-detection/app.py
+++ /dev/null
@@ -1,94 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import functools
-import pathlib
-
-import cv2
-import gradio as gr
-import huggingface_hub
-import insightface
-import numpy as np
-import onnxruntime as ort
-
-TITLE = 'insightface Person Detection'
-DESCRIPTION = 'https://github.com/deepinsight/insightface/tree/master/examples/person_detection'
-
-
-def load_model():
- path = huggingface_hub.hf_hub_download('public-data/insightface',
- 'models/scrfd_person_2.5g.onnx')
- options = ort.SessionOptions()
- options.intra_op_num_threads = 8
- options.inter_op_num_threads = 8
- session = ort.InferenceSession(
- path,
- sess_options=options,
- providers=['CPUExecutionProvider', 'CUDAExecutionProvider'])
- model = insightface.model_zoo.retinaface.RetinaFace(model_file=path,
- session=session)
- return model
-
-
-def detect_person(
- img: np.ndarray, detector: insightface.model_zoo.retinaface.RetinaFace
-) -> tuple[np.ndarray, np.ndarray]:
- bboxes, kpss = detector.detect(img)
- bboxes = np.round(bboxes[:, :4]).astype(int)
- kpss = np.round(kpss).astype(int)
- kpss[:, :, 0] = np.clip(kpss[:, :, 0], 0, img.shape[1])
- kpss[:, :, 1] = np.clip(kpss[:, :, 1], 0, img.shape[0])
- vbboxes = bboxes.copy()
- vbboxes[:, 0] = kpss[:, 0, 0]
- vbboxes[:, 1] = kpss[:, 0, 1]
- vbboxes[:, 2] = kpss[:, 4, 0]
- vbboxes[:, 3] = kpss[:, 4, 1]
- return bboxes, vbboxes
-
-
-def visualize(image: np.ndarray, bboxes: np.ndarray,
- vbboxes: np.ndarray) -> np.ndarray:
- res = image.copy()
- for i in range(bboxes.shape[0]):
- bbox = bboxes[i]
- vbbox = vbboxes[i]
- x1, y1, x2, y2 = bbox
- vx1, vy1, vx2, vy2 = vbbox
- cv2.rectangle(res, (x1, y1), (x2, y2), (0, 255, 0), 1)
- alpha = 0.8
- color = (255, 0, 0)
- for c in range(3):
- res[vy1:vy2, vx1:vx2,
- c] = res[vy1:vy2, vx1:vx2,
- c] * alpha + color[c] * (1.0 - alpha)
- cv2.circle(res, (vx1, vy1), 1, color, 2)
- cv2.circle(res, (vx1, vy2), 1, color, 2)
- cv2.circle(res, (vx2, vy1), 1, color, 2)
- cv2.circle(res, (vx2, vy2), 1, color, 2)
- return res
-
-
-def detect(image: np.ndarray, detector) -> np.ndarray:
- image = image[:, :, ::-1] # RGB -> BGR
- bboxes, vbboxes = detect_person(image, detector)
- res = visualize(image, bboxes, vbboxes)
- return res[:, :, ::-1] # BGR -> RGB
-
-
-detector = load_model()
-detector.prepare(-1, nms_thresh=0.5, input_size=(640, 640))
-fn = functools.partial(detect, detector=detector)
-
-image_dir = pathlib.Path('images')
-examples = [[path.as_posix()] for path in sorted(image_dir.glob('*.jpg'))]
-
-gr.Interface(
- fn=fn,
- inputs=gr.Image(label='Input', type='numpy'),
- outputs=gr.Image(label='Output', height=600),
- examples=examples,
- examples_per_page=30,
- title=TITLE,
- description=DESCRIPTION,
-).queue().launch()
diff --git a/spaces/hzy123/bingo/src/components/chat-history.tsx b/spaces/hzy123/bingo/src/components/chat-history.tsx
deleted file mode 100644
index feb81de66562edda8f40d3c0cc717202c92b6509..0000000000000000000000000000000000000000
--- a/spaces/hzy123/bingo/src/components/chat-history.tsx
+++ /dev/null
@@ -1,48 +0,0 @@
-import { IconEdit, IconTrash, IconMore, IconDownload } from "./ui/icons"
-
-export function ChatHistory() {
- return (
-
-
- 历史记录
-
-
-
-
-
-
-
-
-
-
-
-
无标题的聊天
-
-
上午1:42
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
= memo(
- ReactMarkdown,
- (prevProps, nextProps) =>
- prevProps.children === nextProps.children &&
- prevProps.className === nextProps.className
-)
diff --git a/spaces/inamXcontru/PoeticTTS/Answers Key Payroll Accounting Project Chapter 7.30l A Practical Approach to Payroll Accounting.md b/spaces/inamXcontru/PoeticTTS/Answers Key Payroll Accounting Project Chapter 7.30l A Practical Approach to Payroll Accounting.md
deleted file mode 100644
index 6fa6ed28126efd0cdfc0fb178ebb1d699d3c2b08..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Answers Key Payroll Accounting Project Chapter 7.30l A Practical Approach to Payroll Accounting.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Answers Key Payroll Accounting Project Chapter 7.30l Download ✦✦✦ https://gohhs.com/2uz4li
diff --git a/spaces/inamXcontru/PoeticTTS/Cinderella Movie In Hindi Dubbed Free [WORK] Download.md b/spaces/inamXcontru/PoeticTTS/Cinderella Movie In Hindi Dubbed Free [WORK] Download.md
deleted file mode 100644
index 32b806a9cfae27b35c9b48ebef95a2c7ba40885e..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Cinderella Movie In Hindi Dubbed Free [WORK] Download.md
+++ /dev/null
@@ -1,7 +0,0 @@
-
-The prices of movies with English subtitles is usually very affordable because a lot of money is spent to make a movie. For example, let’s say the movie is a Western movie, and the movie is an action movie, and the movie stars Daniel Craig, or Tom Cruise, or any other famous movie star. Let’s say the movie has a budget of ten million dollars. So obviously, the cost to make the movie has been very high. Thus, it is necessary to charge as much as possible to make money for making the movie. Also, it is even more profitable for the movie to be made into Hindi so that it can be subtitled into Hindi and put into the Indian movie market. So, the cost of making the movie is reduced from a ten million dollar budget to a two million dollar budget.
-cinderella movie in hindi dubbed free download DOWNLOAD ····· https://gohhs.com/2uz4rS
However, if the movie is just a Hindi dubbed English movie, and it does not have any famous actors, then there is no need to hire actors who would be paid a lot of money. So, a Hindi dubbed English movie made in India, which has actors who cost millions of dollars to hire would only cost a hundred thousand dollars to make. Also, if the movie has a budget of five million, then there is no need to spend ten million dollars to hire famous actors. So, the only thing that will be needed is to hire writers and producers who will charge a thousand dollars an hour. Thus, making a Hindi dubbed English movie costs only a small amount of money, and it is more lucrative to hire actors in India, make it in India, and then have it subtitled into Hindi and put into the Indian movie market.
-Youtube is one of the best destinations to watch movies online for free. It provides a rich platform to the users to view or download movies. The users can enjoy a variety of free movies and TV shows in different languages. The site keeps on adding more content every day. It also provides an option for its users to download videos and movies. The search option on the website is very effective. The users can choose from the latest movies available on the site and can also add it on their watchlist. The site also allows the users to create their account and can watch movies online.
899543212badobe cs6 master collection winmac - xforce.zip 137 kb Download ☑ https://urlin.us/2uExvI
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Airfader Server 2.2 Crack HOT!.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Airfader Server 2.2 Crack HOT!.md
deleted file mode 100644
index 03ae5a845a103d1fd312246456840fd0d3990c60..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Airfader Server 2.2 Crack HOT!.md
+++ /dev/null
@@ -1,12 +0,0 @@
-
-AirFader Server 2.2 Crack: A Risky Way to Control Your Digital Console
-AirFader is a touchscreen mixing software that allows you to remotely control Yamaha digital consoles from your Android or iPad devices[^2^]. It is designed to be a reliable and efficient tool for audio engineers and musicians who need to adjust their personal monitor mixes.
-airfader server 2.2 crack Download Zip ❤❤❤ https://urlin.us/2uEwp1
AirFader Server 2.2 is the latest version of the software that runs on a Windows computer and acts as a server for the mobile devices. It supports Yamaha 01V96, LS9, and M7CL consoles and offers features such as channel naming, scene recall, mute groups, and more[^2^]. The software costs $149.00 and can be purchased from the official website[^2^].
-However, some people may be tempted to look for a cracked version of AirFader Server 2.2 that bypasses the license verification and allows them to use the software for free. This is a risky and illegal way to obtain the software, as it may expose your computer and devices to malware, viruses, or other security threats. Moreover, it may damage your console or compromise your audio quality by introducing glitches, errors, or latency. Furthermore, it may violate the terms of service of AirFader and Yamaha and result in legal consequences.
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If you are interested in purchasing AirFader Server 2.2, you can visit the official website and click on the "Buy Now" button. You will be redirected to a secure payment page where you can choose your preferred payment method and enter your details. After completing the payment, you will receive an email with a download link and a license key. You can then install the software on your Windows computer and activate it with the license key. You will also be able to download the mobile app for Android or iPad from the Google Play Store or the App Store respectively.
-AirFader has many advantages over other mixing software, such as its simplicity, reliability, and efficiency. It is designed to be easy to use and intuitive, with a touch-friendly interface that mimics the layout of the console. It is also compatible with most Windows computers and mobile devices, without requiring any additional hardware or software. It is also optimized to minimize network traffic and latency, ensuring a smooth and stable performance. Moreover, it offers features that are not available on the console itself, such as channel naming, scene recall, mute groups, and more.
-
-AirFader is currently only compatible with Yamaha 01V96, LS9, and M7CL consoles. However, the developers are working on adding support for other consoles in the future. You can check the official website for updates and news on AirFader's development. You can also contact the developers if you have any suggestions or feedback on how to improve AirFader.
d5da3c52bfAlien Shooter 3 Free Download Full Version For Pc Download File 🌟 https://urlin.us/2uEwsO
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Essay On Internet In Urdu Language !!INSTALL!!.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Essay On Internet In Urdu Language !!INSTALL!!.md
deleted file mode 100644
index 335a8d734d20570e4bb9d0565589e30f74582005..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Essay On Internet In Urdu Language !!INSTALL!!.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Essay On Internet In Urdu Language Download File ○ https://urlin.us/2uExrZ
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Fsx Aerosoft Manhattan X Crack.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Fsx Aerosoft Manhattan X Crack.md
deleted file mode 100644
index 3977ddb3aedef9b6591b70db2a410c0ec19b5f58..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Fsx Aerosoft Manhattan X Crack.md
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-Fsx Aerosoft Manhattan X Crack Download ☑ https://urlin.us/2uEyDA
diff --git a/spaces/inreVtussa/clothingai/Examples/BETTER Download Saints Row 2 Highly Compressed.md b/spaces/inreVtussa/clothingai/Examples/BETTER Download Saints Row 2 Highly Compressed.md
deleted file mode 100644
index bfa7d27f25f989b93fe6f09814cd412c07adf2f2..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/BETTER Download Saints Row 2 Highly Compressed.md
+++ /dev/null
@@ -1,7 +0,0 @@
-Saints Row: The game is complete in most aspects. The game has an excellent graphics,very engaging gameplay and an outstanding soundtrack.If you want to play an open-world game that has a lot of RPG elements to it this is a must.If you are an open-world game fan this is a must for you,you should also download it.This is a game that not only has a lot of open-world elements to it but also a lot of RPG elements,it also has a few unlockable characters,such as the player and various npc’s you can talk to and buy.There are also a lot of powerups you can collect like bionic arm upgrades,superpowers,fast cars and cool side missions,as well as other things.
-Saints Row 2 for Ps2 is the second part of the Saints Row series. It has several problems like I posted in my other post. It looks nice, you get a lot of weapons and more choices, side-missions, superpowers etc. but unfortunately it becomes rather repetitive in the last part. You get the same missions over and over again and you get repeated things in them, like stealing cops cars or that. I found that part a bit boring since the first part was such a rush to steal stuff and run around. I’ve also got a few problems with the controller and it is nothing that can be changed. The code could be more localized, to make all those things you can do, a bit more user friendly. That would have been much more fun.
-Download Saints Row 2 Highly Compressed Download Zip –––––>>> https://tiurll.com/2uClNN
Saints Row 2 is a game of fun and action in which players assume the role of Johnny Gat, a man with a rare photographic memory. The player explores the mean streets of Stilwater, a city where crime and corruption are always in style. Players acquire powers, influence, and wealth by using guile, resourcefulness, and cunning in numerous interactive situations. Players make use of a wide array of weapons, powers, vehicles, and other items to effectively carry out crime and deal with regular problems. The goal of the game is to settle scores, take revenge, and become the ultimate gangster.
899543212bCPU-Tweaker 1.1: A Free and Lightweight Tool to Boost Your CPU Performance
-If you are looking for a simple and effective way to overclock your CPU and get more speed out of your computer, you might want to try CPU-Tweaker 1.1. This is a free and lightweight tool that allows you to tweak the CPU timings and improve the performance of your processor.
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-CPU-Tweaker 1.1 Setup Free DOWNLOAD › https://tiurll.com/2uCivC
CPU-Tweaker 1.1 is very easy to use, as it has a user-friendly interface that shows you all the relevant information about your CPU and memory. You can adjust the values by using sliders or typing them manually, and apply the changes with a single click. You can also save and load different profiles for different overclocking scenarios.
-CPU-Tweaker 1.1 also has a built-in stress test feature that lets you check the stability of your overclock settings. You can run the test for a specified amount of time or until an error occurs. If your computer freezes or crashes during the test, you can restart it and reset the settings to normal.
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-
-CPU-Tweaker 1.1 is not the only overclocking software available for Windows, but it has some advantages over other similar tools. For example, CPU-Tweaker 1.1 is more lightweight and consumes less system resources than other programs. It also has a simpler and cleaner interface that makes it easier to use and understand.
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-CPU-Tweaker 1.1 is a great tool for beginners and advanced users alike who want to overclock their CPU and get more performance out of their computer. It is free, portable, easy to use, and effective. However, it is not a magic solution that can make your computer run faster without any drawbacks. Overclocking can have negative effects on your system stability, temperature, power consumption, and lifespan. Therefore, you should always be careful and cautious when using CPU-Tweaker 1.1 or any other overclocking software.
d5da3c52bfCode Soft Tp 3160 Driver Download File ✒ https://tiurll.com/2uCmrx
diff --git a/spaces/ispast/Genshin_MB_VITS_TTS/text/__init__.py b/spaces/ispast/Genshin_MB_VITS_TTS/text/__init__.py
deleted file mode 100644
index 48ae82f3e40ecd1bf17a7de78d87790327af3362..0000000000000000000000000000000000000000
--- a/spaces/ispast/Genshin_MB_VITS_TTS/text/__init__.py
+++ /dev/null
@@ -1,56 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-from text import cleaners
-from text.symbols import symbols
-
-
-# Mappings from symbol to numeric ID and vice versa:
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
-_id_to_symbol = {i: s for i, s in enumerate(symbols)}
-
-
-def text_to_sequence(text, cleaner_names):
- '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
- Args:
- text: string to convert to a sequence
- cleaner_names: names of the cleaner functions to run the text through
- Returns:
- List of integers corresponding to the symbols in the text
- '''
- sequence = []
-
- clean_text = _clean_text(text, cleaner_names)
- for symbol in clean_text:
- if symbol not in _symbol_to_id.keys():
- continue
- symbol_id = _symbol_to_id[symbol]
- sequence += [symbol_id]
- return sequence
-
-
-def cleaned_text_to_sequence(cleaned_text):
- '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
- Args:
- text: string to convert to a sequence
- Returns:
- List of integers corresponding to the symbols in the text
- '''
- sequence = [_symbol_to_id[symbol] for symbol in cleaned_text if symbol in _symbol_to_id.keys()]
- return sequence
-
-
-def sequence_to_text(sequence):
- '''Converts a sequence of IDs back to a string'''
- result = ''
- for symbol_id in sequence:
- s = _id_to_symbol[symbol_id]
- result += s
- return result
-
-
-def _clean_text(text, cleaner_names):
- for name in cleaner_names:
- cleaner = getattr(cleaners, name)
- if not cleaner:
- raise Exception('Unknown cleaner: %s' % name)
- text = cleaner(text)
- return text
diff --git a/spaces/jbilcke-hf/ai-comic-factory/LICENCE.md b/spaces/jbilcke-hf/ai-comic-factory/LICENCE.md
deleted file mode 100644
index 537fde8423156f05dc00b52a4fc8eebd451f66e9..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/ai-comic-factory/LICENCE.md
+++ /dev/null
@@ -1,170 +0,0 @@
-Apache License
-==============
-
-_Version 2.0, January 2004_
-_< {}
-
-const Input = React.forwardRef(
- ({ className, type, ...props }, ref) => {
- return (
- Credits to GitHub
',
-)
-
-iface.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/click/termui.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/click/termui.py
deleted file mode 100644
index db7a4b286174fdf26f3251631a2066eda2fa5bea..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/click/termui.py
+++ /dev/null
@@ -1,784 +0,0 @@
-import inspect
-import io
-import itertools
-import sys
-import typing as t
-from gettext import gettext as _
-
-from ._compat import isatty
-from ._compat import strip_ansi
-from .exceptions import Abort
-from .exceptions import UsageError
-from .globals import resolve_color_default
-from .types import Choice
-from .types import convert_type
-from .types import ParamType
-from .utils import echo
-from .utils import LazyFile
-
-if t.TYPE_CHECKING:
- from ._termui_impl import ProgressBar
-
-V = t.TypeVar("V")
-
-# The prompt functions to use. The doc tools currently override these
-# functions to customize how they work.
-visible_prompt_func: t.Callable[[str], str] = input
-
-_ansi_colors = {
- "black": 30,
- "red": 31,
- "green": 32,
- "yellow": 33,
- "blue": 34,
- "magenta": 35,
- "cyan": 36,
- "white": 37,
- "reset": 39,
- "bright_black": 90,
- "bright_red": 91,
- "bright_green": 92,
- "bright_yellow": 93,
- "bright_blue": 94,
- "bright_magenta": 95,
- "bright_cyan": 96,
- "bright_white": 97,
-}
-_ansi_reset_all = "\033[0m"
-
-
-def hidden_prompt_func(prompt: str) -> str:
- import getpass
-
- return getpass.getpass(prompt)
-
-
-def _build_prompt(
- text: str,
- suffix: str,
- show_default: bool = False,
- default: t.Optional[t.Any] = None,
- show_choices: bool = True,
- type: t.Optional[ParamType] = None,
-) -> str:
- prompt = text
- if type is not None and show_choices and isinstance(type, Choice):
- prompt += f" ({', '.join(map(str, type.choices))})"
- if default is not None and show_default:
- prompt = f"{prompt} [{_format_default(default)}]"
- return f"{prompt}{suffix}"
-
-
-def _format_default(default: t.Any) -> t.Any:
- if isinstance(default, (io.IOBase, LazyFile)) and hasattr(default, "name"):
- return default.name
-
- return default
-
-
-def prompt(
- text: str,
- default: t.Optional[t.Any] = None,
- hide_input: bool = False,
- confirmation_prompt: t.Union[bool, str] = False,
- type: t.Optional[t.Union[ParamType, t.Any]] = None,
- value_proc: t.Optional[t.Callable[[str], t.Any]] = None,
- prompt_suffix: str = ": ",
- show_default: bool = True,
- err: bool = False,
- show_choices: bool = True,
-) -> t.Any:
- """Prompts a user for input. This is a convenience function that can
- be used to prompt a user for input later.
-
- If the user aborts the input by sending an interrupt signal, this
- function will catch it and raise a :exc:`Abort` exception.
-
- :param text: the text to show for the prompt.
- :param default: the default value to use if no input happens. If this
- is not given it will prompt until it's aborted.
- :param hide_input: if this is set to true then the input value will
- be hidden.
- :param confirmation_prompt: Prompt a second time to confirm the
- value. Can be set to a string instead of ``True`` to customize
- the message.
- :param type: the type to use to check the value against.
- :param value_proc: if this parameter is provided it's a function that
- is invoked instead of the type conversion to
- convert a value.
- :param prompt_suffix: a suffix that should be added to the prompt.
- :param show_default: shows or hides the default value in the prompt.
- :param err: if set to true the file defaults to ``stderr`` instead of
- ``stdout``, the same as with echo.
- :param show_choices: Show or hide choices if the passed type is a Choice.
- For example if type is a Choice of either day or week,
- show_choices is true and text is "Group by" then the
- prompt will be "Group by (day, week): ".
-
- .. versionadded:: 8.0
- ``confirmation_prompt`` can be a custom string.
-
- .. versionadded:: 7.0
- Added the ``show_choices`` parameter.
-
- .. versionadded:: 6.0
- Added unicode support for cmd.exe on Windows.
-
- .. versionadded:: 4.0
- Added the `err` parameter.
-
- """
-
- def prompt_func(text: str) -> str:
- f = hidden_prompt_func if hide_input else visible_prompt_func
- try:
- # Write the prompt separately so that we get nice
- # coloring through colorama on Windows
- echo(text.rstrip(" "), nl=False, err=err)
- # Echo a space to stdout to work around an issue where
- # readline causes backspace to clear the whole line.
- return f(" ")
- except (KeyboardInterrupt, EOFError):
- # getpass doesn't print a newline if the user aborts input with ^C.
- # Allegedly this behavior is inherited from getpass(3).
- # A doc bug has been filed at https://bugs.python.org/issue24711
- if hide_input:
- echo(None, err=err)
- raise Abort() from None
-
- if value_proc is None:
- value_proc = convert_type(type, default)
-
- prompt = _build_prompt(
- text, prompt_suffix, show_default, default, show_choices, type
- )
-
- if confirmation_prompt:
- if confirmation_prompt is True:
- confirmation_prompt = _("Repeat for confirmation")
-
- confirmation_prompt = _build_prompt(confirmation_prompt, prompt_suffix)
-
- while True:
- while True:
- value = prompt_func(prompt)
- if value:
- break
- elif default is not None:
- value = default
- break
- try:
- result = value_proc(value)
- except UsageError as e:
- if hide_input:
- echo(_("Error: The value you entered was invalid."), err=err)
- else:
- echo(_("Error: {e.message}").format(e=e), err=err) # noqa: B306
- continue
- if not confirmation_prompt:
- return result
- while True:
- value2 = prompt_func(confirmation_prompt)
- is_empty = not value and not value2
- if value2 or is_empty:
- break
- if value == value2:
- return result
- echo(_("Error: The two entered values do not match."), err=err)
-
-
-def confirm(
- text: str,
- default: t.Optional[bool] = False,
- abort: bool = False,
- prompt_suffix: str = ": ",
- show_default: bool = True,
- err: bool = False,
-) -> bool:
- """Prompts for confirmation (yes/no question).
-
- If the user aborts the input by sending a interrupt signal this
- function will catch it and raise a :exc:`Abort` exception.
-
- :param text: the question to ask.
- :param default: The default value to use when no input is given. If
- ``None``, repeat until input is given.
- :param abort: if this is set to `True` a negative answer aborts the
- exception by raising :exc:`Abort`.
- :param prompt_suffix: a suffix that should be added to the prompt.
- :param show_default: shows or hides the default value in the prompt.
- :param err: if set to true the file defaults to ``stderr`` instead of
- ``stdout``, the same as with echo.
-
- .. versionchanged:: 8.0
- Repeat until input is given if ``default`` is ``None``.
-
- .. versionadded:: 4.0
- Added the ``err`` parameter.
- """
- prompt = _build_prompt(
- text,
- prompt_suffix,
- show_default,
- "y/n" if default is None else ("Y/n" if default else "y/N"),
- )
-
- while True:
- try:
- # Write the prompt separately so that we get nice
- # coloring through colorama on Windows
- echo(prompt.rstrip(" "), nl=False, err=err)
- # Echo a space to stdout to work around an issue where
- # readline causes backspace to clear the whole line.
- value = visible_prompt_func(" ").lower().strip()
- except (KeyboardInterrupt, EOFError):
- raise Abort() from None
- if value in ("y", "yes"):
- rv = True
- elif value in ("n", "no"):
- rv = False
- elif default is not None and value == "":
- rv = default
- else:
- echo(_("Error: invalid input"), err=err)
- continue
- break
- if abort and not rv:
- raise Abort()
- return rv
-
-
-def echo_via_pager(
- text_or_generator: t.Union[t.Iterable[str], t.Callable[[], t.Iterable[str]], str],
- color: t.Optional[bool] = None,
-) -> None:
- """This function takes a text and shows it via an environment specific
- pager on stdout.
-
- .. versionchanged:: 3.0
- Added the `color` flag.
-
- :param text_or_generator: the text to page, or alternatively, a
- generator emitting the text to page.
- :param color: controls if the pager supports ANSI colors or not. The
- default is autodetection.
- """
- color = resolve_color_default(color)
-
- if inspect.isgeneratorfunction(text_or_generator):
- i = t.cast(t.Callable[[], t.Iterable[str]], text_or_generator)()
- elif isinstance(text_or_generator, str):
- i = [text_or_generator]
- else:
- i = iter(t.cast(t.Iterable[str], text_or_generator))
-
- # convert every element of i to a text type if necessary
- text_generator = (el if isinstance(el, str) else str(el) for el in i)
-
- from ._termui_impl import pager
-
- return pager(itertools.chain(text_generator, "\n"), color)
-
-
-def progressbar(
- iterable: t.Optional[t.Iterable[V]] = None,
- length: t.Optional[int] = None,
- label: t.Optional[str] = None,
- show_eta: bool = True,
- show_percent: t.Optional[bool] = None,
- show_pos: bool = False,
- item_show_func: t.Optional[t.Callable[[t.Optional[V]], t.Optional[str]]] = None,
- fill_char: str = "#",
- empty_char: str = "-",
- bar_template: str = "%(label)s [%(bar)s] %(info)s",
- info_sep: str = " ",
- width: int = 36,
- file: t.Optional[t.TextIO] = None,
- color: t.Optional[bool] = None,
- update_min_steps: int = 1,
-) -> "ProgressBar[V]":
- """This function creates an iterable context manager that can be used
- to iterate over something while showing a progress bar. It will
- either iterate over the `iterable` or `length` items (that are counted
- up). While iteration happens, this function will print a rendered
- progress bar to the given `file` (defaults to stdout) and will attempt
- to calculate remaining time and more. By default, this progress bar
- will not be rendered if the file is not a terminal.
-
- The context manager creates the progress bar. When the context
- manager is entered the progress bar is already created. With every
- iteration over the progress bar, the iterable passed to the bar is
- advanced and the bar is updated. When the context manager exits,
- a newline is printed and the progress bar is finalized on screen.
-
- Note: The progress bar is currently designed for use cases where the
- total progress can be expected to take at least several seconds.
- Because of this, the ProgressBar class object won't display
- progress that is considered too fast, and progress where the time
- between steps is less than a second.
-
- No printing must happen or the progress bar will be unintentionally
- destroyed.
-
- Example usage::
-
- with progressbar(items) as bar:
- for item in bar:
- do_something_with(item)
-
- Alternatively, if no iterable is specified, one can manually update the
- progress bar through the `update()` method instead of directly
- iterating over the progress bar. The update method accepts the number
- of steps to increment the bar with::
-
- with progressbar(length=chunks.total_bytes) as bar:
- for chunk in chunks:
- process_chunk(chunk)
- bar.update(chunks.bytes)
-
- The ``update()`` method also takes an optional value specifying the
- ``current_item`` at the new position. This is useful when used
- together with ``item_show_func`` to customize the output for each
- manual step::
-
- with click.progressbar(
- length=total_size,
- label='Unzipping archive',
- item_show_func=lambda a: a.filename
- ) as bar:
- for archive in zip_file:
- archive.extract()
- bar.update(archive.size, archive)
-
- :param iterable: an iterable to iterate over. If not provided the length
- is required.
- :param length: the number of items to iterate over. By default the
- progressbar will attempt to ask the iterator about its
- length, which might or might not work. If an iterable is
- also provided this parameter can be used to override the
- length. If an iterable is not provided the progress bar
- will iterate over a range of that length.
- :param label: the label to show next to the progress bar.
- :param show_eta: enables or disables the estimated time display. This is
- automatically disabled if the length cannot be
- determined.
- :param show_percent: enables or disables the percentage display. The
- default is `True` if the iterable has a length or
- `False` if not.
- :param show_pos: enables or disables the absolute position display. The
- default is `False`.
- :param item_show_func: A function called with the current item which
- can return a string to show next to the progress bar. If the
- function returns ``None`` nothing is shown. The current item can
- be ``None``, such as when entering and exiting the bar.
- :param fill_char: the character to use to show the filled part of the
- progress bar.
- :param empty_char: the character to use to show the non-filled part of
- the progress bar.
- :param bar_template: the format string to use as template for the bar.
- The parameters in it are ``label`` for the label,
- ``bar`` for the progress bar and ``info`` for the
- info section.
- :param info_sep: the separator between multiple info items (eta etc.)
- :param width: the width of the progress bar in characters, 0 means full
- terminal width
- :param file: The file to write to. If this is not a terminal then
- only the label is printed.
- :param color: controls if the terminal supports ANSI colors or not. The
- default is autodetection. This is only needed if ANSI
- codes are included anywhere in the progress bar output
- which is not the case by default.
- :param update_min_steps: Render only when this many updates have
- completed. This allows tuning for very fast iterators.
-
- .. versionchanged:: 8.0
- Output is shown even if execution time is less than 0.5 seconds.
-
- .. versionchanged:: 8.0
- ``item_show_func`` shows the current item, not the previous one.
-
- .. versionchanged:: 8.0
- Labels are echoed if the output is not a TTY. Reverts a change
- in 7.0 that removed all output.
-
- .. versionadded:: 8.0
- Added the ``update_min_steps`` parameter.
-
- .. versionchanged:: 4.0
- Added the ``color`` parameter. Added the ``update`` method to
- the object.
-
- .. versionadded:: 2.0
- """
- from ._termui_impl import ProgressBar
-
- color = resolve_color_default(color)
- return ProgressBar(
- iterable=iterable,
- length=length,
- show_eta=show_eta,
- show_percent=show_percent,
- show_pos=show_pos,
- item_show_func=item_show_func,
- fill_char=fill_char,
- empty_char=empty_char,
- bar_template=bar_template,
- info_sep=info_sep,
- file=file,
- label=label,
- width=width,
- color=color,
- update_min_steps=update_min_steps,
- )
-
-
-def clear() -> None:
- """Clears the terminal screen. This will have the effect of clearing
- the whole visible space of the terminal and moving the cursor to the
- top left. This does not do anything if not connected to a terminal.
-
- .. versionadded:: 2.0
- """
- if not isatty(sys.stdout):
- return
-
- # ANSI escape \033[2J clears the screen, \033[1;1H moves the cursor
- echo("\033[2J\033[1;1H", nl=False)
-
-
-def _interpret_color(
- color: t.Union[int, t.Tuple[int, int, int], str], offset: int = 0
-) -> str:
- if isinstance(color, int):
- return f"{38 + offset};5;{color:d}"
-
- if isinstance(color, (tuple, list)):
- r, g, b = color
- return f"{38 + offset};2;{r:d};{g:d};{b:d}"
-
- return str(_ansi_colors[color] + offset)
-
-
-def style(
- text: t.Any,
- fg: t.Optional[t.Union[int, t.Tuple[int, int, int], str]] = None,
- bg: t.Optional[t.Union[int, t.Tuple[int, int, int], str]] = None,
- bold: t.Optional[bool] = None,
- dim: t.Optional[bool] = None,
- underline: t.Optional[bool] = None,
- overline: t.Optional[bool] = None,
- italic: t.Optional[bool] = None,
- blink: t.Optional[bool] = None,
- reverse: t.Optional[bool] = None,
- strikethrough: t.Optional[bool] = None,
- reset: bool = True,
-) -> str:
- """Styles a text with ANSI styles and returns the new string. By
- default the styling is self contained which means that at the end
- of the string a reset code is issued. This can be prevented by
- passing ``reset=False``.
-
- Examples::
-
- click.echo(click.style('Hello World!', fg='green'))
- click.echo(click.style('ATTENTION!', blink=True))
- click.echo(click.style('Some things', reverse=True, fg='cyan'))
- click.echo(click.style('More colors', fg=(255, 12, 128), bg=117))
-
- Supported color names:
-
- * ``black`` (might be a gray)
- * ``red``
- * ``green``
- * ``yellow`` (might be an orange)
- * ``blue``
- * ``magenta``
- * ``cyan``
- * ``white`` (might be light gray)
- * ``bright_black``
- * ``bright_red``
- * ``bright_green``
- * ``bright_yellow``
- * ``bright_blue``
- * ``bright_magenta``
- * ``bright_cyan``
- * ``bright_white``
- * ``reset`` (reset the color code only)
-
- If the terminal supports it, color may also be specified as:
-
- - An integer in the interval [0, 255]. The terminal must support
- 8-bit/256-color mode.
- - An RGB tuple of three integers in [0, 255]. The terminal must
- support 24-bit/true-color mode.
-
- See https://en.wikipedia.org/wiki/ANSI_color and
- https://gist.github.com/XVilka/8346728 for more information.
-
- :param text: the string to style with ansi codes.
- :param fg: if provided this will become the foreground color.
- :param bg: if provided this will become the background color.
- :param bold: if provided this will enable or disable bold mode.
- :param dim: if provided this will enable or disable dim mode. This is
- badly supported.
- :param underline: if provided this will enable or disable underline.
- :param overline: if provided this will enable or disable overline.
- :param italic: if provided this will enable or disable italic.
- :param blink: if provided this will enable or disable blinking.
- :param reverse: if provided this will enable or disable inverse
- rendering (foreground becomes background and the
- other way round).
- :param strikethrough: if provided this will enable or disable
- striking through text.
- :param reset: by default a reset-all code is added at the end of the
- string which means that styles do not carry over. This
- can be disabled to compose styles.
-
- .. versionchanged:: 8.0
- A non-string ``message`` is converted to a string.
-
- .. versionchanged:: 8.0
- Added support for 256 and RGB color codes.
-
- .. versionchanged:: 8.0
- Added the ``strikethrough``, ``italic``, and ``overline``
- parameters.
-
- .. versionchanged:: 7.0
- Added support for bright colors.
-
- .. versionadded:: 2.0
- """
- if not isinstance(text, str):
- text = str(text)
-
- bits = []
-
- if fg:
- try:
- bits.append(f"\033[{_interpret_color(fg)}m")
- except KeyError:
- raise TypeError(f"Unknown color {fg!r}") from None
-
- if bg:
- try:
- bits.append(f"\033[{_interpret_color(bg, 10)}m")
- except KeyError:
- raise TypeError(f"Unknown color {bg!r}") from None
-
- if bold is not None:
- bits.append(f"\033[{1 if bold else 22}m")
- if dim is not None:
- bits.append(f"\033[{2 if dim else 22}m")
- if underline is not None:
- bits.append(f"\033[{4 if underline else 24}m")
- if overline is not None:
- bits.append(f"\033[{53 if overline else 55}m")
- if italic is not None:
- bits.append(f"\033[{3 if italic else 23}m")
- if blink is not None:
- bits.append(f"\033[{5 if blink else 25}m")
- if reverse is not None:
- bits.append(f"\033[{7 if reverse else 27}m")
- if strikethrough is not None:
- bits.append(f"\033[{9 if strikethrough else 29}m")
- bits.append(text)
- if reset:
- bits.append(_ansi_reset_all)
- return "".join(bits)
-
-
-def unstyle(text: str) -> str:
- """Removes ANSI styling information from a string. Usually it's not
- necessary to use this function as Click's echo function will
- automatically remove styling if necessary.
-
- .. versionadded:: 2.0
-
- :param text: the text to remove style information from.
- """
- return strip_ansi(text)
-
-
-def secho(
- message: t.Optional[t.Any] = None,
- file: t.Optional[t.IO[t.AnyStr]] = None,
- nl: bool = True,
- err: bool = False,
- color: t.Optional[bool] = None,
- **styles: t.Any,
-) -> None:
- """This function combines :func:`echo` and :func:`style` into one
- call. As such the following two calls are the same::
-
- click.secho('Hello World!', fg='green')
- click.echo(click.style('Hello World!', fg='green'))
-
- All keyword arguments are forwarded to the underlying functions
- depending on which one they go with.
-
- Non-string types will be converted to :class:`str`. However,
- :class:`bytes` are passed directly to :meth:`echo` without applying
- style. If you want to style bytes that represent text, call
- :meth:`bytes.decode` first.
-
- .. versionchanged:: 8.0
- A non-string ``message`` is converted to a string. Bytes are
- passed through without style applied.
-
- .. versionadded:: 2.0
- """
- if message is not None and not isinstance(message, (bytes, bytearray)):
- message = style(message, **styles)
-
- return echo(message, file=file, nl=nl, err=err, color=color)
-
-
-def edit(
- text: t.Optional[t.AnyStr] = None,
- editor: t.Optional[str] = None,
- env: t.Optional[t.Mapping[str, str]] = None,
- require_save: bool = True,
- extension: str = ".txt",
- filename: t.Optional[str] = None,
-) -> t.Optional[t.AnyStr]:
- r"""Edits the given text in the defined editor. If an editor is given
- (should be the full path to the executable but the regular operating
- system search path is used for finding the executable) it overrides
- the detected editor. Optionally, some environment variables can be
- used. If the editor is closed without changes, `None` is returned. In
- case a file is edited directly the return value is always `None` and
- `require_save` and `extension` are ignored.
-
- If the editor cannot be opened a :exc:`UsageError` is raised.
-
- Note for Windows: to simplify cross-platform usage, the newlines are
- automatically converted from POSIX to Windows and vice versa. As such,
- the message here will have ``\n`` as newline markers.
-
- :param text: the text to edit.
- :param editor: optionally the editor to use. Defaults to automatic
- detection.
- :param env: environment variables to forward to the editor.
- :param require_save: if this is true, then not saving in the editor
- will make the return value become `None`.
- :param extension: the extension to tell the editor about. This defaults
- to `.txt` but changing this might change syntax
- highlighting.
- :param filename: if provided it will edit this file instead of the
- provided text contents. It will not use a temporary
- file as an indirection in that case.
- """
- from ._termui_impl import Editor
-
- ed = Editor(editor=editor, env=env, require_save=require_save, extension=extension)
-
- if filename is None:
- return ed.edit(text)
-
- ed.edit_file(filename)
- return None
-
-
-def launch(url: str, wait: bool = False, locate: bool = False) -> int:
- """This function launches the given URL (or filename) in the default
- viewer application for this file type. If this is an executable, it
- might launch the executable in a new session. The return value is
- the exit code of the launched application. Usually, ``0`` indicates
- success.
-
- Examples::
-
- click.launch('https://click.palletsprojects.com/')
- click.launch('/my/downloaded/file', locate=True)
-
- .. versionadded:: 2.0
-
- :param url: URL or filename of the thing to launch.
- :param wait: Wait for the program to exit before returning. This
- only works if the launched program blocks. In particular,
- ``xdg-open`` on Linux does not block.
- :param locate: if this is set to `True` then instead of launching the
- application associated with the URL it will attempt to
- launch a file manager with the file located. This
- might have weird effects if the URL does not point to
- the filesystem.
- """
- from ._termui_impl import open_url
-
- return open_url(url, wait=wait, locate=locate)
-
-
-# If this is provided, getchar() calls into this instead. This is used
-# for unittesting purposes.
-_getchar: t.Optional[t.Callable[[bool], str]] = None
-
-
-def getchar(echo: bool = False) -> str:
- """Fetches a single character from the terminal and returns it. This
- will always return a unicode character and under certain rare
- circumstances this might return more than one character. The
- situations which more than one character is returned is when for
- whatever reason multiple characters end up in the terminal buffer or
- standard input was not actually a terminal.
-
- Note that this will always read from the terminal, even if something
- is piped into the standard input.
-
- Note for Windows: in rare cases when typing non-ASCII characters, this
- function might wait for a second character and then return both at once.
- This is because certain Unicode characters look like special-key markers.
-
- .. versionadded:: 2.0
-
- :param echo: if set to `True`, the character read will also show up on
- the terminal. The default is to not show it.
- """
- global _getchar
-
- if _getchar is None:
- from ._termui_impl import getchar as f
-
- _getchar = f
-
- return _getchar(echo)
-
-
-def raw_terminal() -> t.ContextManager[int]:
- from ._termui_impl import raw_terminal as f
-
- return f()
-
-
-def pause(info: t.Optional[str] = None, err: bool = False) -> None:
- """This command stops execution and waits for the user to press any
- key to continue. This is similar to the Windows batch "pause"
- command. If the program is not run through a terminal, this command
- will instead do nothing.
-
- .. versionadded:: 2.0
-
- .. versionadded:: 4.0
- Added the `err` parameter.
-
- :param info: The message to print before pausing. Defaults to
- ``"Press any key to continue..."``.
- :param err: if set to message goes to ``stderr`` instead of
- ``stdout``, the same as with echo.
- """
- if not isatty(sys.stdin) or not isatty(sys.stdout):
- return
-
- if info is None:
- info = _("Press any key to continue...")
-
- try:
- if info:
- echo(info, nl=False, err=err)
- try:
- getchar()
- except (KeyboardInterrupt, EOFError):
- pass
- finally:
- if info:
- echo(err=err)
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fsspec/registry.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fsspec/registry.py
deleted file mode 100644
index 851bc65bc8fa1ea01a48d425563bce06ccfe8ecd..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fsspec/registry.py
+++ /dev/null
@@ -1,297 +0,0 @@
-from __future__ import annotations
-
-import importlib
-import types
-import warnings
-
-__all__ = ["registry", "get_filesystem_class", "default"]
-
-# internal, mutable
-_registry: dict[str, type] = {}
-
-# external, immutable
-registry = types.MappingProxyType(_registry)
-default = "file"
-
-
-def register_implementation(name, cls, clobber=False, errtxt=None):
- """Add implementation class to the registry
-
- Parameters
- ----------
- name: str
- Protocol name to associate with the class
- cls: class or str
- if a class: fsspec-compliant implementation class (normally inherits from
- ``fsspec.AbstractFileSystem``, gets added straight to the registry. If a
- str, the full path to an implementation class like package.module.class,
- which gets added to known_implementations,
- so the import is deferred until the filesystem is actually used.
- clobber: bool (optional)
- Whether to overwrite a protocol with the same name; if False, will raise
- instead.
- errtxt: str (optional)
- If given, then a failure to import the given class will result in this
- text being given.
- """
- if isinstance(cls, str):
- if name in known_implementations and clobber is False:
- if cls != known_implementations[name]["class"]:
- raise ValueError(
- "Name (%s) already in the known_implementations and clobber "
- "is False" % name
- )
- else:
- known_implementations[name] = {
- "class": cls,
- "err": errtxt or "%s import failed for protocol %s" % (cls, name),
- }
-
- else:
- if name in registry and clobber is False:
- if _registry[name] is not cls:
- raise ValueError(
- "Name (%s) already in the registry and clobber is False" % name
- )
- else:
- _registry[name] = cls
-
-
-# protocols mapped to the class which implements them. This dict can
-# updated with register_implementation
-known_implementations = {
- "file": {"class": "fsspec.implementations.local.LocalFileSystem"},
- "memory": {"class": "fsspec.implementations.memory.MemoryFileSystem"},
- "dropbox": {
- "class": "dropboxdrivefs.DropboxDriveFileSystem",
- "err": (
- 'DropboxFileSystem requires "dropboxdrivefs",'
- '"requests" and "dropbox" to be installed'
- ),
- },
- "http": {
- "class": "fsspec.implementations.http.HTTPFileSystem",
- "err": 'HTTPFileSystem requires "requests" and "aiohttp" to be installed',
- },
- "https": {
- "class": "fsspec.implementations.http.HTTPFileSystem",
- "err": 'HTTPFileSystem requires "requests" and "aiohttp" to be installed',
- },
- "zip": {"class": "fsspec.implementations.zip.ZipFileSystem"},
- "tar": {"class": "fsspec.implementations.tar.TarFileSystem"},
- "gcs": {
- "class": "gcsfs.GCSFileSystem",
- "err": "Please install gcsfs to access Google Storage",
- },
- "gs": {
- "class": "gcsfs.GCSFileSystem",
- "err": "Please install gcsfs to access Google Storage",
- },
- "gdrive": {
- "class": "gdrivefs.GoogleDriveFileSystem",
- "err": "Please install gdrivefs for access to Google Drive",
- },
- "sftp": {
- "class": "fsspec.implementations.sftp.SFTPFileSystem",
- "err": 'SFTPFileSystem requires "paramiko" to be installed',
- },
- "ssh": {
- "class": "fsspec.implementations.sftp.SFTPFileSystem",
- "err": 'SFTPFileSystem requires "paramiko" to be installed',
- },
- "ftp": {"class": "fsspec.implementations.ftp.FTPFileSystem"},
- "hdfs": {
- "class": "fsspec.implementations.arrow.HadoopFileSystem",
- "err": "pyarrow and local java libraries required for HDFS",
- },
- "arrow_hdfs": {
- "class": "fsspec.implementations.arrow.HadoopFileSystem",
- "err": "pyarrow and local java libraries required for HDFS",
- },
- "webhdfs": {
- "class": "fsspec.implementations.webhdfs.WebHDFS",
- "err": 'webHDFS access requires "requests" to be installed',
- },
- "s3": {"class": "s3fs.S3FileSystem", "err": "Install s3fs to access S3"},
- "s3a": {"class": "s3fs.S3FileSystem", "err": "Install s3fs to access S3"},
- "wandb": {"class": "wandbfs.WandbFS", "err": "Install wandbfs to access wandb"},
- "oci": {
- "class": "ocifs.OCIFileSystem",
- "err": "Install ocifs to access OCI Object Storage",
- },
- "ocilake": {
- "class": "ocifs.OCIFileSystem",
- "err": "Install ocifs to access OCI Data Lake",
- },
- "asynclocal": {
- "class": "morefs.asyn_local.AsyncLocalFileSystem",
- "err": "Install 'morefs[asynclocalfs]' to use AsyncLocalFileSystem",
- },
- "adl": {
- "class": "adlfs.AzureDatalakeFileSystem",
- "err": "Install adlfs to access Azure Datalake Gen1",
- },
- "abfs": {
- "class": "adlfs.AzureBlobFileSystem",
- "err": "Install adlfs to access Azure Datalake Gen2 and Azure Blob Storage",
- },
- "az": {
- "class": "adlfs.AzureBlobFileSystem",
- "err": "Install adlfs to access Azure Datalake Gen2 and Azure Blob Storage",
- },
- "cached": {"class": "fsspec.implementations.cached.CachingFileSystem"},
- "blockcache": {"class": "fsspec.implementations.cached.CachingFileSystem"},
- "filecache": {"class": "fsspec.implementations.cached.WholeFileCacheFileSystem"},
- "simplecache": {"class": "fsspec.implementations.cached.SimpleCacheFileSystem"},
- "dask": {
- "class": "fsspec.implementations.dask.DaskWorkerFileSystem",
- "err": "Install dask distributed to access worker file system",
- },
- "dbfs": {
- "class": "fsspec.implementations.dbfs.DatabricksFileSystem",
- "err": "Install the requests package to use the DatabricksFileSystem",
- },
- "github": {
- "class": "fsspec.implementations.github.GithubFileSystem",
- "err": "Install the requests package to use the github FS",
- },
- "git": {
- "class": "fsspec.implementations.git.GitFileSystem",
- "err": "Install pygit2 to browse local git repos",
- },
- "smb": {
- "class": "fsspec.implementations.smb.SMBFileSystem",
- "err": 'SMB requires "smbprotocol" or "smbprotocol[kerberos]" installed',
- },
- "jupyter": {
- "class": "fsspec.implementations.jupyter.JupyterFileSystem",
- "err": "Jupyter FS requires requests to be installed",
- },
- "jlab": {
- "class": "fsspec.implementations.jupyter.JupyterFileSystem",
- "err": "Jupyter FS requires requests to be installed",
- },
- "libarchive": {
- "class": "fsspec.implementations.libarchive.LibArchiveFileSystem",
- "err": "LibArchive requires to be installed",
- },
- "reference": {"class": "fsspec.implementations.reference.ReferenceFileSystem"},
- "generic": {"class": "fsspec.generic.GenericFileSystem"},
- "oss": {
- "class": "ossfs.OSSFileSystem",
- "err": "Install ossfs to access Alibaba Object Storage System",
- },
- "webdav": {
- "class": "webdav4.fsspec.WebdavFileSystem",
- "err": "Install webdav4 to access WebDAV",
- },
- "dvc": {
- "class": "dvc.api.DVCFileSystem",
- "err": "Install dvc to access DVCFileSystem",
- },
- "hf": {
- "class": "huggingface_hub.HfFileSystem",
- "err": "Install huggingface_hub to access HfFileSystem",
- },
- "root": {
- "class": "fsspec_xrootd.XRootDFileSystem",
- "err": "Install fsspec-xrootd to access xrootd storage system."
- + " Note: 'root' is the protocol name for xrootd storage systems,"
- + " not referring to root directories",
- },
- "dir": {"class": "fsspec.implementations.dirfs.DirFileSystem"},
- "box": {
- "class": "boxfs.BoxFileSystem",
- "err": "Please install boxfs to access BoxFileSystem",
- },
- "lakefs": {
- "class": "lakefs_spec.LakeFSFileSystem",
- "err": "Please install lakefs-spec to access LakeFSFileSystem",
- },
-}
-
-
-def get_filesystem_class(protocol):
- """Fetch named protocol implementation from the registry
-
- The dict ``known_implementations`` maps protocol names to the locations
- of classes implementing the corresponding file-system. When used for the
- first time, appropriate imports will happen and the class will be placed in
- the registry. All subsequent calls will fetch directly from the registry.
-
- Some protocol implementations require additional dependencies, and so the
- import may fail. In this case, the string in the "err" field of the
- ``known_implementations`` will be given as the error message.
- """
- if not protocol:
- protocol = default
-
- if protocol not in registry:
- if protocol not in known_implementations:
- raise ValueError("Protocol not known: %s" % protocol)
- bit = known_implementations[protocol]
- try:
- register_implementation(protocol, _import_class(bit["class"]))
- except ImportError as e:
- raise ImportError(bit["err"]) from e
- cls = registry[protocol]
- if getattr(cls, "protocol", None) in ("abstract", None):
- cls.protocol = protocol
-
- return cls
-
-
-s3_msg = """Your installed version of s3fs is very old and known to cause
-severe performance issues, see also https://github.com/dask/dask/issues/10276
-
-To fix, you should specify a lower version bound on s3fs, or
-update the current installation.
-"""
-
-
-def _import_class(cls, minv=None):
- """Take a string FQP and return the imported class or identifier
-
- clas is of the form "package.module.klass" or "package.module:subobject.klass"
- """
- if ":" in cls:
- mod, name = cls.rsplit(":", 1)
- s3 = mod == "s3fs"
- mod = importlib.import_module(mod)
- if s3 and mod.__version__.split(".") < ["0", "5"]:
- warnings.warn(s3_msg)
- for part in name.split("."):
- mod = getattr(mod, part)
- return mod
- else:
- mod, name = cls.rsplit(".", 1)
- s3 = mod == "s3fs"
- mod = importlib.import_module(mod)
- if s3 and mod.__version__.split(".") < ["0", "5"]:
- warnings.warn(s3_msg)
- return getattr(mod, name)
-
-
-def filesystem(protocol, **storage_options):
- """Instantiate filesystems for given protocol and arguments
-
- ``storage_options`` are specific to the protocol being chosen, and are
- passed directly to the class.
- """
- if protocol == "arrow_hdfs":
- warnings.warn(
- "The 'arrow_hdfs' protocol has been deprecated and will be "
- "removed in the future. Specify it as 'hdfs'.",
- DeprecationWarning,
- )
-
- cls = get_filesystem_class(protocol)
- return cls(**storage_options)
-
-
-def available_protocols():
- """Return a list of the implemented protocols.
-
- Note that any given protocol may require extra packages to be importable.
- """
- return list(known_implementations)
diff --git a/spaces/joushe/moe-tts/text/sanskrit.py b/spaces/joushe/moe-tts/text/sanskrit.py
deleted file mode 100644
index 0223aaac384a2f850f5bc20651fc18eb964607d0..0000000000000000000000000000000000000000
--- a/spaces/joushe/moe-tts/text/sanskrit.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import re
-from indic_transliteration import sanscript
-
-
-# List of (iast, ipa) pairs:
-_iast_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('a', 'ə'),
- ('ā', 'aː'),
- ('ī', 'iː'),
- ('ū', 'uː'),
- ('ṛ', 'ɹ`'),
- ('ṝ', 'ɹ`ː'),
- ('ḷ', 'l`'),
- ('ḹ', 'l`ː'),
- ('e', 'eː'),
- ('o', 'oː'),
- ('k', 'k⁼'),
- ('k⁼h', 'kʰ'),
- ('g', 'g⁼'),
- ('g⁼h', 'gʰ'),
- ('ṅ', 'ŋ'),
- ('c', 'ʧ⁼'),
- ('ʧ⁼h', 'ʧʰ'),
- ('j', 'ʥ⁼'),
- ('ʥ⁼h', 'ʥʰ'),
- ('ñ', 'n^'),
- ('ṭ', 't`⁼'),
- ('t`⁼h', 't`ʰ'),
- ('ḍ', 'd`⁼'),
- ('d`⁼h', 'd`ʰ'),
- ('ṇ', 'n`'),
- ('t', 't⁼'),
- ('t⁼h', 'tʰ'),
- ('d', 'd⁼'),
- ('d⁼h', 'dʰ'),
- ('p', 'p⁼'),
- ('p⁼h', 'pʰ'),
- ('b', 'b⁼'),
- ('b⁼h', 'bʰ'),
- ('y', 'j'),
- ('ś', 'ʃ'),
- ('ṣ', 's`'),
- ('r', 'ɾ'),
- ('l̤', 'l`'),
- ('h', 'ɦ'),
- ("'", ''),
- ('~', '^'),
- ('ṃ', '^')
-]]
-
-
-def devanagari_to_ipa(text):
- text = text.replace('ॐ', 'ओम्')
- text = re.sub(r'\s*।\s*$', '.', text)
- text = re.sub(r'\s*।\s*', ', ', text)
- text = re.sub(r'\s*॥', '.', text)
- text = sanscript.transliterate(text, sanscript.DEVANAGARI, sanscript.IAST)
- for regex, replacement in _iast_to_ipa:
- text = re.sub(regex, replacement, text)
- text = re.sub('(.)[`ː]*ḥ', lambda x: x.group(0)
- [:-1]+'h'+x.group(1)+'*', text)
- return text
diff --git a/spaces/justest/ai-support/README.md b/spaces/justest/ai-support/README.md
deleted file mode 100644
index b4b34ff25b551529947161a19a670e4c5718b0dd..0000000000000000000000000000000000000000
--- a/spaces/justest/ai-support/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: ai-support
-emoji: 💻🐳
-colorFrom: red
-colorTo: blue
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/kadirnar/AnimeSR/README.md b/spaces/kadirnar/AnimeSR/README.md
deleted file mode 100644
index 83ccae7d07e0ad2ef254cf3d46e632614b911d69..0000000000000000000000000000000000000000
--- a/spaces/kadirnar/AnimeSR/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: AnimeSR
-emoji: 👀
-colorFrom: green
-colorTo: purple
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-pinned: false
-license: apache-2.0
-tags:
-- making-demos
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/kaicheng/ChatGPT_ad/modules/config.py b/spaces/kaicheng/ChatGPT_ad/modules/config.py
deleted file mode 100644
index c9224996dd7056508519be8cbe906746f362abb0..0000000000000000000000000000000000000000
--- a/spaces/kaicheng/ChatGPT_ad/modules/config.py
+++ /dev/null
@@ -1,190 +0,0 @@
-from collections import defaultdict
-from contextlib import contextmanager
-import os
-import logging
-import sys
-import commentjson as json
-
-from . import shared
-from . import presets
-
-
-__all__ = [
- "my_api_key",
- "authflag",
- "auth_list",
- "dockerflag",
- "retrieve_proxy",
- "log_level",
- "advance_docs",
- "update_doc_config",
- "usage_limit",
- "multi_api_key",
- "server_name",
- "server_port",
- "share",
- "hide_history_when_not_logged_in",
- "default_chuanhu_assistant_model"
-]
-
-# 添加一个统一的config文件，避免文件过多造成的疑惑（优先级最低）
-# 同时，也可以为后续支持自定义功能提供config的帮助
-if os.path.exists("config.json"):
- with open("config.json", "r", encoding='utf-8') as f:
- config = json.load(f)
-else:
- config = {}
-
-lang_config = config.get("language", "auto")
-language = os.environ.get("LANGUAGE", lang_config)
-
-hide_history_when_not_logged_in = config.get("hide_history_when_not_logged_in", False)
-
-if os.path.exists("api_key.txt"):
- logging.info("检测到api_key.txt文件，正在进行迁移...")
- with open("api_key.txt", "r", encoding="utf-8") as f:
- config["openai_api_key"] = f.read().strip()
- os.rename("api_key.txt", "api_key(deprecated).txt")
- with open("config.json", "w", encoding='utf-8') as f:
- json.dump(config, f, indent=4, ensure_ascii=False)
-
-if os.path.exists("auth.json"):
- logging.info("检测到auth.json文件，正在进行迁移...")
- auth_list = []
- with open("auth.json", "r", encoding='utf-8') as f:
- auth = json.load(f)
- for _ in auth:
- if auth[_]["username"] and auth[_]["password"]:
- auth_list.append((auth[_]["username"], auth[_]["password"]))
- else:
- logging.error("请检查auth.json文件中的用户名和密码！")
- sys.exit(1)
- config["users"] = auth_list
- os.rename("auth.json", "auth(deprecated).json")
- with open("config.json", "w", encoding='utf-8') as f:
- json.dump(config, f, indent=4, ensure_ascii=False)
-
-## 处理docker if we are running in Docker
-dockerflag = config.get("dockerflag", False)
-if os.environ.get("dockerrun") == "yes":
- dockerflag = True
-
-## 处理 api-key 以及 允许的用户列表
-my_api_key = config.get("openai_api_key", "")
-my_api_key = os.environ.get("OPENAI_API_KEY", my_api_key)
-
-xmchat_api_key = config.get("xmchat_api_key", "")
-os.environ["XMCHAT_API_KEY"] = xmchat_api_key
-
-minimax_api_key = config.get("minimax_api_key", "")
-os.environ["MINIMAX_API_KEY"] = minimax_api_key
-minimax_group_id = config.get("minimax_group_id", "")
-os.environ["MINIMAX_GROUP_ID"] = minimax_group_id
-
-
-usage_limit = os.environ.get("USAGE_LIMIT", config.get("usage_limit", 120))
-
-## 多账户机制
-multi_api_key = config.get("multi_api_key", False) # 是否开启多账户机制
-if multi_api_key:
- api_key_list = config.get("api_key_list", [])
- if len(api_key_list) == 0:
- logging.error("多账号模式已开启，但api_key_list为空，请检查config.json")
- sys.exit(1)
- shared.state.set_api_key_queue(api_key_list)
-
-auth_list = config.get("users", []) # 实际上是使用者的列表
-authflag = len(auth_list) > 0 # 是否开启认证的状态值，改为判断auth_list长度
-
-# 处理自定义的api_host，优先读环境变量的配置，如果存在则自动装配
-api_host = os.environ.get("OPENAI_API_BASE", config.get("openai_api_base", None))
-if api_host is not None:
- shared.state.set_api_host(api_host)
-
-default_chuanhu_assistant_model = config.get("default_chuanhu_assistant_model", "gpt-3.5-turbo")
-for x in ["GOOGLE_CSE_ID", "GOOGLE_API_KEY", "WOLFRAM_ALPHA_APPID", "SERPAPI_API_KEY"]:
- if config.get(x, None) is not None:
- os.environ[x] = config[x]
-
-@contextmanager
-def retrieve_openai_api(api_key = None):
- old_api_key = os.environ.get("OPENAI_API_KEY", "")
- if api_key is None:
- os.environ["OPENAI_API_KEY"] = my_api_key
- yield my_api_key
- else:
- os.environ["OPENAI_API_KEY"] = api_key
- yield api_key
- os.environ["OPENAI_API_KEY"] = old_api_key
-
-## 处理log
-log_level = config.get("log_level", "INFO")
-logging.basicConfig(
- level=log_level,
- format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s",
-)
-
-## 处理代理：
-http_proxy = config.get("http_proxy", "")
-https_proxy = config.get("https_proxy", "")
-http_proxy = os.environ.get("HTTP_PROXY", http_proxy)
-https_proxy = os.environ.get("HTTPS_PROXY", https_proxy)
-
-# 重置系统变量，在不需要设置的时候不设置环境变量，以免引起全局代理报错
-os.environ["HTTP_PROXY"] = ""
-os.environ["HTTPS_PROXY"] = ""
-
-local_embedding = config.get("local_embedding", False) # 是否使用本地embedding
-
-@contextmanager
-def retrieve_proxy(proxy=None):
- """
- 1, 如果proxy = NONE，设置环境变量，并返回最新设置的代理
- 2，如果proxy ！= NONE，更新当前的代理配置，但是不更新环境变量
- """
- global http_proxy, https_proxy
- if proxy is not None:
- http_proxy = proxy
- https_proxy = proxy
- yield http_proxy, https_proxy
- else:
- old_var = os.environ["HTTP_PROXY"], os.environ["HTTPS_PROXY"]
- os.environ["HTTP_PROXY"] = http_proxy
- os.environ["HTTPS_PROXY"] = https_proxy
- yield http_proxy, https_proxy # return new proxy
-
- # return old proxy
- os.environ["HTTP_PROXY"], os.environ["HTTPS_PROXY"] = old_var
-
-
-## 处理advance docs
-advance_docs = defaultdict(lambda: defaultdict(dict))
-advance_docs.update(config.get("advance_docs", {}))
-def update_doc_config(two_column_pdf):
- global advance_docs
- advance_docs["pdf"]["two_column"] = two_column_pdf
-
- logging.info(f"更新后的文件参数为：{advance_docs}")
-
-## 处理gradio.launch参数
-server_name = config.get("server_name", None)
-server_port = config.get("server_port", None)
-if server_name is None:
- if dockerflag:
- server_name = "0.0.0.0"
- else:
- server_name = "127.0.0.1"
-if server_port is None:
- if dockerflag:
- server_port = 7860
-
-assert server_port is None or type(server_port) == int, "要求port设置为int类型"
-
-# 设置默认model
-default_model = config.get("default_model", "")
-try:
- presets.DEFAULT_MODEL = presets.MODELS.index(default_model)
-except ValueError:
- pass
-
-share = config.get("share", False)
diff --git a/spaces/kangvcar/RealChar/CHANGELOG.md b/spaces/kangvcar/RealChar/CHANGELOG.md
deleted file mode 100644
index 8adabf357fb0f496f0e407a91aca915e78deaded..0000000000000000000000000000000000000000
--- a/spaces/kangvcar/RealChar/CHANGELOG.md
+++ /dev/null
@@ -1,22 +0,0 @@
-# ChangeLog
-
-## [v0.0.1] - 2023-07-19
-Release Highlights:
-
-### Product releases and updates:
-- iOS App TestFlight public beta (link https://testflight.apple.com/join/JA6p9sZQ)
-- Rewrite Web codebase from vanilla JavaScript to use React framework w/ Javascript
-- Support Unicode in chat messages
-- Various UI refinements
-
-### Integration updates:
-- Support Azure OpenAI
-
-### Observability and quality updates:
-- Support Integration with LangSmith
-- Reduce Docker rebuild time to ~2 seconds
-- Support string based user ID
-- Support Session ID, Platform, Action Type in database records.
-
-### New Tutorial:
-[How to make your own AI character and run it locally](https://youtu.be/meg5Q8vdWeQ)
diff --git a/spaces/kcagle/AutoGPT/autogpt/speech/gtts.py b/spaces/kcagle/AutoGPT/autogpt/speech/gtts.py
deleted file mode 100644
index 1c3e9cae0567428582891b11eca42f82a64f5c8e..0000000000000000000000000000000000000000
--- a/spaces/kcagle/AutoGPT/autogpt/speech/gtts.py
+++ /dev/null
@@ -1,22 +0,0 @@
-""" GTTS Voice. """
-import os
-
-import gtts
-from playsound import playsound
-
-from autogpt.speech.base import VoiceBase
-
-
-class GTTSVoice(VoiceBase):
- """GTTS Voice."""
-
- def _setup(self) -> None:
- pass
-
- def _speech(self, text: str, _: int = 0) -> bool:
- """Play the given text."""
- tts = gtts.gTTS(text)
- tts.save("speech.mp3")
- playsound("speech.mp3", True)
- os.remove("speech.mp3")
- return True
diff --git a/spaces/keithhon/Real-Time-Voice-Cloning/synthesizer_train.py b/spaces/keithhon/Real-Time-Voice-Cloning/synthesizer_train.py
deleted file mode 100644
index 2743d590d882f209734b68921b84a9d23492942c..0000000000000000000000000000000000000000
--- a/spaces/keithhon/Real-Time-Voice-Cloning/synthesizer_train.py
+++ /dev/null
@@ -1,35 +0,0 @@
-from synthesizer.hparams import hparams
-from synthesizer.train import train
-from utils.argutils import print_args
-import argparse
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument("run_id", type=str, help= \
- "Name for this model instance. If a model state from the same run ID was previously "
- "saved, the training will restart from there. Pass -f to overwrite saved states and "
- "restart from scratch.")
- parser.add_argument("syn_dir", type=str, default=argparse.SUPPRESS, help= \
- "Path to the synthesizer directory that contains the ground truth mel spectrograms, "
- "the wavs and the embeds.")
- parser.add_argument("-m", "--models_dir", type=str, default="synthesizer/saved_models/", help=\
- "Path to the output directory that will contain the saved model weights and the logs.")
- parser.add_argument("-s", "--save_every", type=int, default=1000, help= \
- "Number of steps between updates of the model on the disk. Set to 0 to never save the "
- "model.")
- parser.add_argument("-b", "--backup_every", type=int, default=25000, help= \
- "Number of steps between backups of the model. Set to 0 to never make backups of the "
- "model.")
- parser.add_argument("-f", "--force_restart", action="store_true", help= \
- "Do not load any saved model and restart from scratch.")
- parser.add_argument("--hparams", default="",
- help="Hyperparameter overrides as a comma-separated list of name=value "
- "pairs")
- args = parser.parse_args()
- print_args(args, parser)
-
- args.hparams = hparams.parse(args.hparams)
-
- # Run the training
- train(**vars(args))
diff --git a/spaces/kevinwang676/M4Singer/utils/audio.py b/spaces/kevinwang676/M4Singer/utils/audio.py
deleted file mode 100644
index aba7ab926cf793d085bbdc70c97f376001183fe1..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/M4Singer/utils/audio.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import subprocess
-import matplotlib
-
-matplotlib.use('Agg')
-import librosa
-import librosa.filters
-import numpy as np
-from scipy import signal
-from scipy.io import wavfile
-
-
-def save_wav(wav, path, sr, norm=False):
- if norm:
- wav = wav / np.abs(wav).max()
- wav *= 32767
- # proposed by @dsmiller
- wavfile.write(path, sr, wav.astype(np.int16))
-
-
-def get_hop_size(hparams):
- hop_size = hparams['hop_size']
- if hop_size is None:
- assert hparams['frame_shift_ms'] is not None
- hop_size = int(hparams['frame_shift_ms'] / 1000 * hparams['audio_sample_rate'])
- return hop_size
-
-
-###########################################################################################
-def _stft(y, hparams):
- return librosa.stft(y=y, n_fft=hparams['fft_size'], hop_length=get_hop_size(hparams),
- win_length=hparams['win_size'], pad_mode='constant')
-
-
-def _istft(y, hparams):
- return librosa.istft(y, hop_length=get_hop_size(hparams), win_length=hparams['win_size'])
-
-
-def librosa_pad_lr(x, fsize, fshift, pad_sides=1):
- '''compute right padding (final frame) or both sides padding (first and final frames)
- '''
- assert pad_sides in (1, 2)
- # return int(fsize // 2)
- pad = (x.shape[0] // fshift + 1) * fshift - x.shape[0]
- if pad_sides == 1:
- return 0, pad
- else:
- return pad // 2, pad // 2 + pad % 2
-
-
-# Conversions
-def amp_to_db(x):
- return 20 * np.log10(np.maximum(1e-5, x))
-
-
-def normalize(S, hparams):
- return (S - hparams['min_level_db']) / -hparams['min_level_db']
diff --git a/spaces/kevinwang676/VoiceChanger/infer_pack/modules.py b/spaces/kevinwang676/VoiceChanger/infer_pack/modules.py
deleted file mode 100644
index 960481cedad9a6106f2bf0b9e86e82b120f7b33f..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/VoiceChanger/infer_pack/modules.py
+++ /dev/null
@@ -1,522 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from infer_pack.transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
- def __init__(self, channels, eps=1e-5):
- super().__init__()
- self.channels = channels
- self.eps = eps
-
- self.gamma = nn.Parameter(torch.ones(channels))
- self.beta = nn.Parameter(torch.zeros(channels))
-
- def forward(self, x):
- x = x.transpose(1, -1)
- x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
- return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
- def __init__(
- self,
- in_channels,
- hidden_channels,
- out_channels,
- kernel_size,
- n_layers,
- p_dropout,
- ):
- super().__init__()
- self.in_channels = in_channels
- self.hidden_channels = hidden_channels
- self.out_channels = out_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
- assert n_layers > 1, "Number of layers should be larger than 0."
-
- self.conv_layers = nn.ModuleList()
- self.norm_layers = nn.ModuleList()
- self.conv_layers.append(
- nn.Conv1d(
- in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
- )
- )
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
- for _ in range(n_layers - 1):
- self.conv_layers.append(
- nn.Conv1d(
- hidden_channels,
- hidden_channels,
- kernel_size,
- padding=kernel_size // 2,
- )
- )
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask):
- x_org = x
- for i in range(self.n_layers):
- x = self.conv_layers[i](x * x_mask)
- x = self.norm_layers[i](x)
- x = self.relu_drop(x)
- x = x_org + self.proj(x)
- return x * x_mask
-
-
-class DDSConv(nn.Module):
- """
- Dialted and Depth-Separable Convolution
- """
-
- def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
- super().__init__()
- self.channels = channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
-
- self.drop = nn.Dropout(p_dropout)
- self.convs_sep = nn.ModuleList()
- self.convs_1x1 = nn.ModuleList()
- self.norms_1 = nn.ModuleList()
- self.norms_2 = nn.ModuleList()
- for i in range(n_layers):
- dilation = kernel_size**i
- padding = (kernel_size * dilation - dilation) // 2
- self.convs_sep.append(
- nn.Conv1d(
- channels,
- channels,
- kernel_size,
- groups=channels,
- dilation=dilation,
- padding=padding,
- )
- )
- self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
- self.norms_1.append(LayerNorm(channels))
- self.norms_2.append(LayerNorm(channels))
-
- def forward(self, x, x_mask, g=None):
- if g is not None:
- x = x + g
- for i in range(self.n_layers):
- y = self.convs_sep[i](x * x_mask)
- y = self.norms_1[i](y)
- y = F.gelu(y)
- y = self.convs_1x1[i](y)
- y = self.norms_2[i](y)
- y = F.gelu(y)
- y = self.drop(y)
- x = x + y
- return x * x_mask
-
-
-class WN(torch.nn.Module):
- def __init__(
- self,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=0,
- p_dropout=0,
- ):
- super(WN, self).__init__()
- assert kernel_size % 2 == 1
- self.hidden_channels = hidden_channels
- self.kernel_size = (kernel_size,)
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
- self.p_dropout = p_dropout
-
- self.in_layers = torch.nn.ModuleList()
- self.res_skip_layers = torch.nn.ModuleList()
- self.drop = nn.Dropout(p_dropout)
-
- if gin_channels != 0:
- cond_layer = torch.nn.Conv1d(
- gin_channels, 2 * hidden_channels * n_layers, 1
- )
- self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
-
- for i in range(n_layers):
- dilation = dilation_rate**i
- padding = int((kernel_size * dilation - dilation) / 2)
- in_layer = torch.nn.Conv1d(
- hidden_channels,
- 2 * hidden_channels,
- kernel_size,
- dilation=dilation,
- padding=padding,
- )
- in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
- self.in_layers.append(in_layer)
-
- # last one is not necessary
- if i < n_layers - 1:
- res_skip_channels = 2 * hidden_channels
- else:
- res_skip_channels = hidden_channels
-
- res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
- res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
- self.res_skip_layers.append(res_skip_layer)
-
- def forward(self, x, x_mask, g=None, **kwargs):
- output = torch.zeros_like(x)
- n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
- if g is not None:
- g = self.cond_layer(g)
-
- for i in range(self.n_layers):
- x_in = self.in_layers[i](x)
- if g is not None:
- cond_offset = i * 2 * self.hidden_channels
- g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
- else:
- g_l = torch.zeros_like(x_in)
-
- acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
- acts = self.drop(acts)
-
- res_skip_acts = self.res_skip_layers[i](acts)
- if i < self.n_layers - 1:
- res_acts = res_skip_acts[:, : self.hidden_channels, :]
- x = (x + res_acts) * x_mask
- output = output + res_skip_acts[:, self.hidden_channels :, :]
- else:
- output = output + res_skip_acts
- return output * x_mask
-
- def remove_weight_norm(self):
- if self.gin_channels != 0:
- torch.nn.utils.remove_weight_norm(self.cond_layer)
- for l in self.in_layers:
- torch.nn.utils.remove_weight_norm(l)
- for l in self.res_skip_layers:
- torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
- super(ResBlock1, self).__init__()
- self.convs1 = nn.ModuleList(
- [
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[2],
- padding=get_padding(kernel_size, dilation[2]),
- )
- ),
- ]
- )
- self.convs1.apply(init_weights)
-
- self.convs2 = nn.ModuleList(
- [
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=1,
- padding=get_padding(kernel_size, 1),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=1,
- padding=get_padding(kernel_size, 1),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=1,
- padding=get_padding(kernel_size, 1),
- )
- ),
- ]
- )
- self.convs2.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c1, c2 in zip(self.convs1, self.convs2):
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c1(xt)
- xt = F.leaky_relu(xt, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c2(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs1:
- remove_weight_norm(l)
- for l in self.convs2:
- remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
- super(ResBlock2, self).__init__()
- self.convs = nn.ModuleList(
- [
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]),
- )
- ),
- ]
- )
- self.convs.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c in self.convs:
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs:
- remove_weight_norm(l)
-
-
-class Log(nn.Module):
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
- logdet = torch.sum(-y, [1, 2])
- return y, logdet
- else:
- x = torch.exp(x) * x_mask
- return x
-
-
-class Flip(nn.Module):
- def forward(self, x, *args, reverse=False, **kwargs):
- x = torch.flip(x, [1])
- if not reverse:
- logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
- return x, logdet
- else:
- return x
-
-
-class ElementwiseAffine(nn.Module):
- def __init__(self, channels):
- super().__init__()
- self.channels = channels
- self.m = nn.Parameter(torch.zeros(channels, 1))
- self.logs = nn.Parameter(torch.zeros(channels, 1))
-
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = self.m + torch.exp(self.logs) * x
- y = y * x_mask
- logdet = torch.sum(self.logs * x_mask, [1, 2])
- return y, logdet
- else:
- x = (x - self.m) * torch.exp(-self.logs) * x_mask
- return x
-
-
-class ResidualCouplingLayer(nn.Module):
- def __init__(
- self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- p_dropout=0,
- gin_channels=0,
- mean_only=False,
- ):
- assert channels % 2 == 0, "channels should be divisible by 2"
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.half_channels = channels // 2
- self.mean_only = mean_only
-
- self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
- self.enc = WN(
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- p_dropout=p_dropout,
- gin_channels=gin_channels,
- )
- self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
- self.post.weight.data.zero_()
- self.post.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
- h = self.pre(x0) * x_mask
- h = self.enc(h, x_mask, g=g)
- stats = self.post(h) * x_mask
- if not self.mean_only:
- m, logs = torch.split(stats, [self.half_channels] * 2, 1)
- else:
- m = stats
- logs = torch.zeros_like(m)
-
- if not reverse:
- x1 = m + x1 * torch.exp(logs) * x_mask
- x = torch.cat([x0, x1], 1)
- logdet = torch.sum(logs, [1, 2])
- return x, logdet
- else:
- x1 = (x1 - m) * torch.exp(-logs) * x_mask
- x = torch.cat([x0, x1], 1)
- return x
-
- def remove_weight_norm(self):
- self.enc.remove_weight_norm()
-
-
-class ConvFlow(nn.Module):
- def __init__(
- self,
- in_channels,
- filter_channels,
- kernel_size,
- n_layers,
- num_bins=10,
- tail_bound=5.0,
- ):
- super().__init__()
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.num_bins = num_bins
- self.tail_bound = tail_bound
- self.half_channels = in_channels // 2
-
- self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
- self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
- self.proj = nn.Conv1d(
- filter_channels, self.half_channels * (num_bins * 3 - 1), 1
- )
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
- h = self.pre(x0)
- h = self.convs(h, x_mask, g=g)
- h = self.proj(h) * x_mask
-
- b, c, t = x0.shape
- h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
- unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
- self.filter_channels
- )
- unnormalized_derivatives = h[..., 2 * self.num_bins :]
-
- x1, logabsdet = piecewise_rational_quadratic_transform(
- x1,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=reverse,
- tails="linear",
- tail_bound=self.tail_bound,
- )
-
- x = torch.cat([x0, x1], 1) * x_mask
- logdet = torch.sum(logabsdet * x_mask, [1, 2])
- if not reverse:
- return x, logdet
- else:
- return x
diff --git a/spaces/kira4424/Tacotron-zero-short-voice-clone/ppg_extractor/encoder/repeat.py b/spaces/kira4424/Tacotron-zero-short-voice-clone/ppg_extractor/encoder/repeat.py
deleted file mode 100644
index 7a8af6ce850e930feb2bf0cd0e9bc7a8d21520e4..0000000000000000000000000000000000000000
--- a/spaces/kira4424/Tacotron-zero-short-voice-clone/ppg_extractor/encoder/repeat.py
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-# Copyright 2019 Shigeki Karita
-# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
-
-"""Repeat the same layer definition."""
-
-import torch
-
-
-class MultiSequential(torch.nn.Sequential):
- """Multi-input multi-output torch.nn.Sequential."""
-
- def forward(self, *args):
- """Repeat."""
- for m in self:
- args = m(*args)
- return args
-
-
-def repeat(N, fn):
- """Repeat module N times.
-
- :param int N: repeat time
- :param function fn: function to generate module
- :return: repeated modules
- :rtype: MultiSequential
- """
- return MultiSequential(*[fn(n) for n in range(N)])
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/joint_alignment_translation/prepare-wmt18en2de_no_norm_no_escape_no_agressive.sh b/spaces/koajoel/PolyFormer/fairseq/examples/joint_alignment_translation/prepare-wmt18en2de_no_norm_no_escape_no_agressive.sh
deleted file mode 100644
index e3efeb21d302ef8d9eae8f1d4b06434c593705f6..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/joint_alignment_translation/prepare-wmt18en2de_no_norm_no_escape_no_agressive.sh
+++ /dev/null
@@ -1,118 +0,0 @@
-#!/bin/bash
-
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-echo 'Cloning Moses github repository (for tokenization scripts)...'
-git clone https://github.com/moses-smt/mosesdecoder.git
-
-SCRIPTS=mosesdecoder/scripts
-TOKENIZER=$SCRIPTS/tokenizer/tokenizer.perl
-CLEAN=$SCRIPTS/training/clean-corpus-n.perl
-REM_NON_PRINT_CHAR=$SCRIPTS/tokenizer/remove-non-printing-char.perl
-
-URLS=(
- "http://statmt.org/wmt13/training-parallel-europarl-v7.tgz"
- "http://statmt.org/wmt13/training-parallel-commoncrawl.tgz"
- "http://data.statmt.org/wmt18/translation-task/training-parallel-nc-v13.tgz"
- "http://data.statmt.org/wmt18/translation-task/rapid2016.tgz"
- "http://data.statmt.org/wmt17/translation-task/dev.tgz"
- "http://statmt.org/wmt14/test-full.tgz"
-)
-CORPORA=(
- "training/europarl-v7.de-en"
- "commoncrawl.de-en"
- "training-parallel-nc-v13/news-commentary-v13.de-en"
- "rapid2016.de-en"
-)
-
-if [ ! -d "$SCRIPTS" ]; then
- echo "Please set SCRIPTS variable correctly to point to Moses scripts."
- exit
-fi
-
-src=en
-tgt=de
-lang=en-de
-prep=wmt18_en_de
-tmp=$prep/tmp
-orig=orig
-dev=dev/newstest2012
-codes=32000
-bpe=bpe.32k
-
-mkdir -p $orig $tmp $prep $bpe
-
-cd $orig
-
-for ((i=0;i<${#URLS[@]};++i)); do
- url=${URLS[i]}
- file=$(basename $url)
- if [ -f $file ]; then
- echo "$file already exists, skipping download"
- else
- wget "$url"
- if [ -f $file ]; then
- echo "$url successfully downloaded."
- else
- echo "$url not successfully downloaded."
- exit 1
- fi
- if [ ${file: -4} == ".tgz" ]; then
- tar zxvf $file
- elif [ ${file: -4} == ".tar" ]; then
- tar xvf $file
- fi
- fi
-done
-cd ..
-
-echo "pre-processing train data..."
-for l in $src $tgt; do
- rm -rf $tmp/train.tags.$lang.tok.$l
- for f in "${CORPORA[@]}"; do
- cat $orig/$f.$l | \
- perl $REM_NON_PRINT_CHAR | \
- perl $TOKENIZER -threads 8 -l $l -no-escape >> $tmp/train.tags.$lang.tok.$l
- done
-done
-
-echo "pre-processing test data..."
-for l in $src $tgt; do
- if [ "$l" == "$src" ]; then
- t="src"
- else
- t="ref"
- fi
- grep '\s*//g' | \
- sed -e 's/\s*<\/seg>\s*//g' | \
- sed -e "s/\’/\'/g" | \
- perl $TOKENIZER -threads 8 -l $l -no-escape > $tmp/test.$l
- echo ""
-done
-
-# apply length filtering before BPE
-perl $CLEAN -ratio 1.5 $tmp/train.tags.$lang.tok $src $tgt $tmp/train 1 100
-
-# use newstest2012 for valid
-echo "pre-processing valid data..."
-for l in $src $tgt; do
- rm -rf $tmp/valid.$l
- cat $orig/$dev.$l | \
- perl $REM_NON_PRINT_CHAR | \
- perl $TOKENIZER -threads 8 -l $l -no-escape >> $tmp/valid.$l
-done
-
-mkdir output
-mv $tmp/{train,valid,test}.{$src,$tgt} output
-
-#BPE
-git clone https://github.com/glample/fastBPE.git
-pushd fastBPE
-g++ -std=c++11 -pthread -O3 fastBPE/main.cc -IfastBPE -o fast
-popd
-fastBPE/fast learnbpe $codes output/train.$src output/train.$tgt > $bpe/codes
-for split in {train,valid,test}; do for lang in {en,de}; do fastBPE/fast applybpe $bpe/$split.$lang output/$split.$lang $bpe/codes; done; done
diff --git a/spaces/kukuhtw/AutoGPT/README.md b/spaces/kukuhtw/AutoGPT/README.md
deleted file mode 100644
index 5bf09b995f04f7af05d1314906b1b1ff39c20ddc..0000000000000000000000000000000000000000
--- a/spaces/kukuhtw/AutoGPT/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: AutoGPT
-emoji: 🦾
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.27.0
-app_file: ui/app.py
-pinned: false
-license: mit
-duplicated_from: aliabid94/AutoGPT
----
-
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/aiohttp/resolver.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/aiohttp/resolver.py
deleted file mode 100644
index 531ce93fccc2d3be442556de644cdc78d31d9c6e..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/aiohttp/resolver.py
+++ /dev/null
@@ -1,160 +0,0 @@
-import asyncio
-import socket
-from typing import Any, Dict, List, Optional, Type, Union
-
-from .abc import AbstractResolver
-from .helpers import get_running_loop
-
-__all__ = ("ThreadedResolver", "AsyncResolver", "DefaultResolver")
-
-try:
- import aiodns
-
- # aiodns_default = hasattr(aiodns.DNSResolver, 'gethostbyname')
-except ImportError: # pragma: no cover
- aiodns = None
-
-aiodns_default = False
-
-
-class ThreadedResolver(AbstractResolver):
- """Threaded resolver.
-
- Uses an Executor for synchronous getaddrinfo() calls.
- concurrent.futures.ThreadPoolExecutor is used by default.
- """
-
- def __init__(self, loop: Optional[asyncio.AbstractEventLoop] = None) -> None:
- self._loop = get_running_loop(loop)
-
- async def resolve(
- self, hostname: str, port: int = 0, family: int = socket.AF_INET
- ) -> List[Dict[str, Any]]:
- infos = await self._loop.getaddrinfo(
- hostname,
- port,
- type=socket.SOCK_STREAM,
- family=family,
- flags=socket.AI_ADDRCONFIG,
- )
-
- hosts = []
- for family, _, proto, _, address in infos:
- if family == socket.AF_INET6:
- if len(address) < 3:
- # IPv6 is not supported by Python build,
- # or IPv6 is not enabled in the host
- continue
- if address[3]: # type: ignore[misc]
- # This is essential for link-local IPv6 addresses.
- # LL IPv6 is a VERY rare case. Strictly speaking, we should use
- # getnameinfo() unconditionally, but performance makes sense.
- host, _port = socket.getnameinfo(
- address, socket.NI_NUMERICHOST | socket.NI_NUMERICSERV
- )
- port = int(_port)
- else:
- host, port = address[:2]
- else: # IPv4
- assert family == socket.AF_INET
- host, port = address # type: ignore[misc]
- hosts.append(
- {
- "hostname": hostname,
- "host": host,
- "port": port,
- "family": family,
- "proto": proto,
- "flags": socket.AI_NUMERICHOST | socket.AI_NUMERICSERV,
- }
- )
-
- return hosts
-
- async def close(self) -> None:
- pass
-
-
-class AsyncResolver(AbstractResolver):
- """Use the `aiodns` package to make asynchronous DNS lookups"""
-
- def __init__(
- self,
- loop: Optional[asyncio.AbstractEventLoop] = None,
- *args: Any,
- **kwargs: Any
- ) -> None:
- if aiodns is None:
- raise RuntimeError("Resolver requires aiodns library")
-
- self._loop = get_running_loop(loop)
- self._resolver = aiodns.DNSResolver(*args, loop=loop, **kwargs)
-
- if not hasattr(self._resolver, "gethostbyname"):
- # aiodns 1.1 is not available, fallback to DNSResolver.query
- self.resolve = self._resolve_with_query # type: ignore
-
- async def resolve(
- self, host: str, port: int = 0, family: int = socket.AF_INET
- ) -> List[Dict[str, Any]]:
- try:
- resp = await self._resolver.gethostbyname(host, family)
- except aiodns.error.DNSError as exc:
- msg = exc.args[1] if len(exc.args) >= 1 else "DNS lookup failed"
- raise OSError(msg) from exc
- hosts = []
- for address in resp.addresses:
- hosts.append(
- {
- "hostname": host,
- "host": address,
- "port": port,
- "family": family,
- "proto": 0,
- "flags": socket.AI_NUMERICHOST | socket.AI_NUMERICSERV,
- }
- )
-
- if not hosts:
- raise OSError("DNS lookup failed")
-
- return hosts
-
- async def _resolve_with_query(
- self, host: str, port: int = 0, family: int = socket.AF_INET
- ) -> List[Dict[str, Any]]:
- if family == socket.AF_INET6:
- qtype = "AAAA"
- else:
- qtype = "A"
-
- try:
- resp = await self._resolver.query(host, qtype)
- except aiodns.error.DNSError as exc:
- msg = exc.args[1] if len(exc.args) >= 1 else "DNS lookup failed"
- raise OSError(msg) from exc
-
- hosts = []
- for rr in resp:
- hosts.append(
- {
- "hostname": host,
- "host": rr.host,
- "port": port,
- "family": family,
- "proto": 0,
- "flags": socket.AI_NUMERICHOST,
- }
- )
-
- if not hosts:
- raise OSError("DNS lookup failed")
-
- return hosts
-
- async def close(self) -> None:
- self._resolver.cancel()
-
-
-_DefaultType = Type[Union[AsyncResolver, ThreadedResolver]]
-DefaultResolver: _DefaultType = AsyncResolver if aiodns_default else ThreadedResolver
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fsspec/implementations/github.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fsspec/implementations/github.py
deleted file mode 100644
index b148124d7481bb867cb100ad1ab2213e6acadf56..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fsspec/implementations/github.py
+++ /dev/null
@@ -1,219 +0,0 @@
-import requests
-
-from ..spec import AbstractFileSystem
-from ..utils import infer_storage_options
-from .memory import MemoryFile
-
-# TODO: add GIST backend, would be very similar
-
-
-class GithubFileSystem(AbstractFileSystem):
- """Interface to files in github
-
- An instance of this class provides the files residing within a remote github
- repository. You may specify a point in the repos history, by SHA, branch
- or tag (default is current master).
-
- Given that code files tend to be small, and that github does not support
- retrieving partial content, we always fetch whole files.
-
- When using fsspec.open, allows URIs of the form:
-
- - "github://path/file", in which case you must specify org, repo and
- may specify sha in the extra args
- - 'github://org:repo@/precip/catalog.yml', where the org and repo are
- part of the URI
- - 'github://org:repo@sha/precip/catalog.yml', where the sha is also included
-
- ``sha`` can be the full or abbreviated hex of the commit you want to fetch
- from, or a branch or tag name (so long as it doesn't contain special characters
- like "/", "?", which would have to be HTTP-encoded).
-
- For authorised access, you must provide username and token, which can be made
- at https://github.com/settings/tokens
- """
-
- url = "https://api.github.com/repos/{org}/{repo}/git/trees/{sha}"
- rurl = "https://raw.githubusercontent.com/{org}/{repo}/{sha}/{path}"
- protocol = "github"
-
- def __init__(self, org, repo, sha=None, username=None, token=None, **kwargs):
- super().__init__(**kwargs)
- self.org = org
- self.repo = repo
- if (username is None) ^ (token is None):
- raise ValueError("Auth required both username and token")
- self.username = username
- self.token = token
- if sha is None:
- # look up default branch (not necessarily "master")
- u = "https://api.github.com/repos/{org}/{repo}"
- r = requests.get(u.format(org=org, repo=repo), **self.kw)
- r.raise_for_status()
- sha = r.json()["default_branch"]
-
- self.root = sha
- self.ls("")
-
- @property
- def kw(self):
- if self.username:
- return {"auth": (self.username, self.token)}
- return {}
-
- @classmethod
- def repos(cls, org_or_user, is_org=True):
- """List repo names for given org or user
-
- This may become the top level of the FS
-
- Parameters
- ----------
- org_or_user: str
- Name of the github org or user to query
- is_org: bool (default True)
- Whether the name is an organisation (True) or user (False)
-
- Returns
- -------
- List of string
- """
- r = requests.get(
- "https://api.github.com/{part}/{org}/repos".format(
- part=["users", "orgs"][is_org], org=org_or_user
- )
- )
- r.raise_for_status()
- return [repo["name"] for repo in r.json()]
-
- @property
- def tags(self):
- """Names of tags in the repo"""
- r = requests.get(
- "https://api.github.com/repos/{org}/{repo}/tags"
- "".format(org=self.org, repo=self.repo),
- **self.kw,
- )
- r.raise_for_status()
- return [t["name"] for t in r.json()]
-
- @property
- def branches(self):
- """Names of branches in the repo"""
- r = requests.get(
- "https://api.github.com/repos/{org}/{repo}/branches"
- "".format(org=self.org, repo=self.repo),
- **self.kw,
- )
- r.raise_for_status()
- return [t["name"] for t in r.json()]
-
- @property
- def refs(self):
- """Named references, tags and branches"""
- return {"tags": self.tags, "branches": self.branches}
-
- def ls(self, path, detail=False, sha=None, _sha=None, **kwargs):
- """List files at given path
-
- Parameters
- ----------
- path: str
- Location to list, relative to repo root
- detail: bool
- If True, returns list of dicts, one per file; if False, returns
- list of full filenames only
- sha: str (optional)
- List at the given point in the repo history, branch or tag name or commit
- SHA
- _sha: str (optional)
- List this specific tree object (used internally to descend into trees)
- """
- path = self._strip_protocol(path)
- if path == "":
- _sha = sha or self.root
- if _sha is None:
- parts = path.rstrip("/").split("/")
- so_far = ""
- _sha = sha or self.root
- for part in parts:
- out = self.ls(so_far, True, sha=sha, _sha=_sha)
- so_far += "/" + part if so_far else part
- out = [o for o in out if o["name"] == so_far]
- if not out:
- raise FileNotFoundError(path)
- out = out[0]
- if out["type"] == "file":
- if detail:
- return [out]
- else:
- return path
- _sha = out["sha"]
- if path not in self.dircache or sha not in [self.root, None]:
- r = requests.get(
- self.url.format(org=self.org, repo=self.repo, sha=_sha), **self.kw
- )
- if r.status_code == 404:
- raise FileNotFoundError(path)
- r.raise_for_status()
- types = {"blob": "file", "tree": "directory"}
- out = [
- {
- "name": path + "/" + f["path"] if path else f["path"],
- "mode": f["mode"],
- "type": types[f["type"]],
- "size": f.get("size", 0),
- "sha": f["sha"],
- }
- for f in r.json()["tree"]
- if f["type"] in types
- ]
- if sha in [self.root, None]:
- self.dircache[path] = out
- else:
- out = self.dircache[path]
- if detail:
- return out
- else:
- return sorted([f["name"] for f in out])
-
- def invalidate_cache(self, path=None):
- self.dircache.clear()
-
- @classmethod
- def _strip_protocol(cls, path):
- opts = infer_storage_options(path)
- if "username" not in opts:
- return super()._strip_protocol(path)
- return opts["path"].lstrip("/")
-
- @staticmethod
- def _get_kwargs_from_urls(path):
- opts = infer_storage_options(path)
- if "username" not in opts:
- return {}
- out = {"org": opts["username"], "repo": opts["password"]}
- if opts["host"]:
- out["sha"] = opts["host"]
- return out
-
- def _open(
- self,
- path,
- mode="rb",
- block_size=None,
- autocommit=True,
- cache_options=None,
- sha=None,
- **kwargs,
- ):
- if mode != "rb":
- raise NotImplementedError
- url = self.rurl.format(
- org=self.org, repo=self.repo, path=path, sha=sha or self.root
- )
- r = requests.get(url, **self.kw)
- if r.status_code == 404:
- raise FileNotFoundError(path)
- r.raise_for_status()
- return MemoryFile(None, None, r.content)
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/jsonschema/exceptions.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/jsonschema/exceptions.py
deleted file mode 100644
index 87db3df3a6dde1bbc0aae1128ca21f365e774666..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/jsonschema/exceptions.py
+++ /dev/null
@@ -1,396 +0,0 @@
-"""
-Validation errors, and some surrounding helpers.
-"""
-from __future__ import annotations
-
-from collections import defaultdict, deque
-from pprint import pformat
-from textwrap import dedent, indent
-import heapq
-import itertools
-
-import attr
-
-from jsonschema import _utils
-
-WEAK_MATCHES: frozenset[str] = frozenset(["anyOf", "oneOf"])
-STRONG_MATCHES: frozenset[str] = frozenset()
-
-_unset = _utils.Unset()
-
-
-class _Error(Exception):
- def __init__(
- self,
- message,
- validator=_unset,
- path=(),
- cause=None,
- context=(),
- validator_value=_unset,
- instance=_unset,
- schema=_unset,
- schema_path=(),
- parent=None,
- type_checker=_unset,
- ):
- super(_Error, self).__init__(
- message,
- validator,
- path,
- cause,
- context,
- validator_value,
- instance,
- schema,
- schema_path,
- parent,
- )
- self.message = message
- self.path = self.relative_path = deque(path)
- self.schema_path = self.relative_schema_path = deque(schema_path)
- self.context = list(context)
- self.cause = self.__cause__ = cause
- self.validator = validator
- self.validator_value = validator_value
- self.instance = instance
- self.schema = schema
- self.parent = parent
- self._type_checker = type_checker
-
- for error in context:
- error.parent = self
-
- def __repr__(self):
- return f"<{self.__class__.__name__}: {self.message!r}>"
-
- def __str__(self):
- essential_for_verbose = (
- self.validator, self.validator_value, self.instance, self.schema,
- )
- if any(m is _unset for m in essential_for_verbose):
- return self.message
-
- schema_path = _utils.format_as_index(
- container=self._word_for_schema_in_error_message,
- indices=list(self.relative_schema_path)[:-1],
- )
- instance_path = _utils.format_as_index(
- container=self._word_for_instance_in_error_message,
- indices=self.relative_path,
- )
- prefix = 16 * " "
-
- return dedent(
- f"""\
- {self.message}
-
- Failed validating {self.validator!r} in {schema_path}:
- {indent(pformat(self.schema, width=72), prefix).lstrip()}
-
- On {instance_path}:
- {indent(pformat(self.instance, width=72), prefix).lstrip()}
- """.rstrip(),
- )
-
- @classmethod
- def create_from(cls, other):
- return cls(**other._contents())
-
- @property
- def absolute_path(self):
- parent = self.parent
- if parent is None:
- return self.relative_path
-
- path = deque(self.relative_path)
- path.extendleft(reversed(parent.absolute_path))
- return path
-
- @property
- def absolute_schema_path(self):
- parent = self.parent
- if parent is None:
- return self.relative_schema_path
-
- path = deque(self.relative_schema_path)
- path.extendleft(reversed(parent.absolute_schema_path))
- return path
-
- @property
- def json_path(self):
- path = "$"
- for elem in self.absolute_path:
- if isinstance(elem, int):
- path += "[" + str(elem) + "]"
- else:
- path += "." + elem
- return path
-
- def _set(self, type_checker=None, **kwargs):
- if type_checker is not None and self._type_checker is _unset:
- self._type_checker = type_checker
-
- for k, v in kwargs.items():
- if getattr(self, k) is _unset:
- setattr(self, k, v)
-
- def _contents(self):
- attrs = (
- "message", "cause", "context", "validator", "validator_value",
- "path", "schema_path", "instance", "schema", "parent",
- )
- return dict((attr, getattr(self, attr)) for attr in attrs)
-
- def _matches_type(self):
- try:
- expected = self.schema["type"]
- except (KeyError, TypeError):
- return False
-
- if isinstance(expected, str):
- return self._type_checker.is_type(self.instance, expected)
-
- return any(
- self._type_checker.is_type(self.instance, expected_type)
- for expected_type in expected
- )
-
-
-class ValidationError(_Error):
- """
- An instance was invalid under a provided schema.
- """
-
- _word_for_schema_in_error_message = "schema"
- _word_for_instance_in_error_message = "instance"
-
-
-class SchemaError(_Error):
- """
- A schema was invalid under its corresponding metaschema.
- """
-
- _word_for_schema_in_error_message = "metaschema"
- _word_for_instance_in_error_message = "schema"
-
-
-@attr.s(hash=True)
-class RefResolutionError(Exception):
- """
- A ref could not be resolved.
- """
-
- _cause = attr.ib()
-
- def __str__(self):
- return str(self._cause)
-
-
-class UndefinedTypeCheck(Exception):
- """
- A type checker was asked to check a type it did not have registered.
- """
-
- def __init__(self, type):
- self.type = type
-
- def __str__(self):
- return f"Type {self.type!r} is unknown to this type checker"
-
-
-class UnknownType(Exception):
- """
- A validator was asked to validate an instance against an unknown type.
- """
-
- def __init__(self, type, instance, schema):
- self.type = type
- self.instance = instance
- self.schema = schema
-
- def __str__(self):
- prefix = 16 * " "
-
- return dedent(
- f"""\
- Unknown type {self.type!r} for validator with schema:
- {indent(pformat(self.schema, width=72), prefix).lstrip()}
-
- While checking instance:
- {indent(pformat(self.instance, width=72), prefix).lstrip()}
- """.rstrip(),
- )
-
-
-class FormatError(Exception):
- """
- Validating a format failed.
- """
-
- def __init__(self, message, cause=None):
- super(FormatError, self).__init__(message, cause)
- self.message = message
- self.cause = self.__cause__ = cause
-
- def __str__(self):
- return self.message
-
-
-class ErrorTree:
- """
- ErrorTrees make it easier to check which validations failed.
- """
-
- _instance = _unset
-
- def __init__(self, errors=()):
- self.errors = {}
- self._contents = defaultdict(self.__class__)
-
- for error in errors:
- container = self
- for element in error.path:
- container = container[element]
- container.errors[error.validator] = error
-
- container._instance = error.instance
-
- def __contains__(self, index):
- """
- Check whether ``instance[index]`` has any errors.
- """
-
- return index in self._contents
-
- def __getitem__(self, index):
- """
- Retrieve the child tree one level down at the given ``index``.
-
- If the index is not in the instance that this tree corresponds
- to and is not known by this tree, whatever error would be raised
- by ``instance.__getitem__`` will be propagated (usually this is
- some subclass of `LookupError`.
- """
-
- if self._instance is not _unset and index not in self:
- self._instance[index]
- return self._contents[index]
-
- def __setitem__(self, index, value):
- """
- Add an error to the tree at the given ``index``.
- """
- self._contents[index] = value
-
- def __iter__(self):
- """
- Iterate (non-recursively) over the indices in the instance with errors.
- """
-
- return iter(self._contents)
-
- def __len__(self):
- """
- Return the `total_errors`.
- """
- return self.total_errors
-
- def __repr__(self):
- total = len(self)
- errors = "error" if total == 1 else "errors"
- return f"<{self.__class__.__name__} ({total} total {errors})>"
-
- @property
- def total_errors(self):
- """
- The total number of errors in the entire tree, including children.
- """
-
- child_errors = sum(len(tree) for _, tree in self._contents.items())
- return len(self.errors) + child_errors
-
-
-def by_relevance(weak=WEAK_MATCHES, strong=STRONG_MATCHES):
- """
- Create a key function that can be used to sort errors by relevance.
-
- Arguments:
- weak (set):
- a collection of validation keywords to consider to be
- "weak". If there are two errors at the same level of the
- instance and one is in the set of weak validation keywords,
- the other error will take priority. By default, :kw:`anyOf`
- and :kw:`oneOf` are considered weak keywords and will be
- superseded by other same-level validation errors.
-
- strong (set):
- a collection of validation keywords to consider to be
- "strong"
- """
- def relevance(error):
- validator = error.validator
- return (
- -len(error.path),
- validator not in weak,
- validator in strong,
- not error._matches_type(),
- )
- return relevance
-
-
-relevance = by_relevance()
-
-
-def best_match(errors, key=relevance):
- """
- Try to find an error that appears to be the best match among given errors.
-
- In general, errors that are higher up in the instance (i.e. for which
- `ValidationError.path` is shorter) are considered better matches,
- since they indicate "more" is wrong with the instance.
-
- If the resulting match is either :kw:`oneOf` or :kw:`anyOf`, the
- *opposite* assumption is made -- i.e. the deepest error is picked,
- since these keywords only need to match once, and any other errors
- may not be relevant.
-
- Arguments:
- errors (collections.abc.Iterable):
-
- the errors to select from. Do not provide a mixture of
- errors from different validation attempts (i.e. from
- different instances or schemas), since it won't produce
- sensical output.
-
- key (collections.abc.Callable):
-
- the key to use when sorting errors. See `relevance` and
- transitively `by_relevance` for more details (the default is
- to sort with the defaults of that function). Changing the
- default is only useful if you want to change the function
- that rates errors but still want the error context descent
- done by this function.
-
- Returns:
- the best matching error, or ``None`` if the iterable was empty
-
- .. note::
-
- This function is a heuristic. Its return value may change for a given
- set of inputs from version to version if better heuristics are added.
- """
- errors = iter(errors)
- best = next(errors, None)
- if best is None:
- return
- best = max(itertools.chain([best], errors), key=key)
-
- while best.context:
- # Calculate the minimum via nsmallest, because we don't recurse if
- # all nested errors have the same relevance (i.e. if min == max == all)
- smallest = heapq.nsmallest(2, best.context, key=key)
- if len(smallest) == 2 and key(smallest[0]) == key(smallest[1]):
- return best
- best = smallest[0]
- return best
diff --git a/spaces/leafShen/CodeFormer/CodeFormer/basicsr/data/transforms.py b/spaces/leafShen/CodeFormer/CodeFormer/basicsr/data/transforms.py
deleted file mode 100644
index aead9dc73ed063e1c5865040eaa2652b26aa3ad3..0000000000000000000000000000000000000000
--- a/spaces/leafShen/CodeFormer/CodeFormer/basicsr/data/transforms.py
+++ /dev/null
@@ -1,165 +0,0 @@
-import cv2
-import random
-
-
-def mod_crop(img, scale):
- """Mod crop images, used during testing.
-
- Args:
- img (ndarray): Input image.
- scale (int): Scale factor.
-
- Returns:
- ndarray: Result image.
- """
- img = img.copy()
- if img.ndim in (2, 3):
- h, w = img.shape[0], img.shape[1]
- h_remainder, w_remainder = h % scale, w % scale
- img = img[:h - h_remainder, :w - w_remainder, ...]
- else:
- raise ValueError(f'Wrong img ndim: {img.ndim}.')
- return img
-
-
-def paired_random_crop(img_gts, img_lqs, gt_patch_size, scale, gt_path):
- """Paired random crop.
-
- It crops lists of lq and gt images with corresponding locations.
-
- Args:
- img_gts (list[ndarray] | ndarray): GT images. Note that all images
- should have the same shape. If the input is an ndarray, it will
- be transformed to a list containing itself.
- img_lqs (list[ndarray] | ndarray): LQ images. Note that all images
- should have the same shape. If the input is an ndarray, it will
- be transformed to a list containing itself.
- gt_patch_size (int): GT patch size.
- scale (int): Scale factor.
- gt_path (str): Path to ground-truth.
-
- Returns:
- list[ndarray] | ndarray: GT images and LQ images. If returned results
- only have one element, just return ndarray.
- """
-
- if not isinstance(img_gts, list):
- img_gts = [img_gts]
- if not isinstance(img_lqs, list):
- img_lqs = [img_lqs]
-
- h_lq, w_lq, _ = img_lqs[0].shape
- h_gt, w_gt, _ = img_gts[0].shape
- lq_patch_size = gt_patch_size // scale
-
- if h_gt != h_lq * scale or w_gt != w_lq * scale:
- raise ValueError(f'Scale mismatches. GT ({h_gt}, {w_gt}) is not {scale}x ',
- f'multiplication of LQ ({h_lq}, {w_lq}).')
- if h_lq < lq_patch_size or w_lq < lq_patch_size:
- raise ValueError(f'LQ ({h_lq}, {w_lq}) is smaller than patch size '
- f'({lq_patch_size}, {lq_patch_size}). '
- f'Please remove {gt_path}.')
-
- # randomly choose top and left coordinates for lq patch
- top = random.randint(0, h_lq - lq_patch_size)
- left = random.randint(0, w_lq - lq_patch_size)
-
- # crop lq patch
- img_lqs = [v[top:top + lq_patch_size, left:left + lq_patch_size, ...] for v in img_lqs]
-
- # crop corresponding gt patch
- top_gt, left_gt = int(top * scale), int(left * scale)
- img_gts = [v[top_gt:top_gt + gt_patch_size, left_gt:left_gt + gt_patch_size, ...] for v in img_gts]
- if len(img_gts) == 1:
- img_gts = img_gts[0]
- if len(img_lqs) == 1:
- img_lqs = img_lqs[0]
- return img_gts, img_lqs
-
-
-def augment(imgs, hflip=True, rotation=True, flows=None, return_status=False):
- """Augment: horizontal flips OR rotate (0, 90, 180, 270 degrees).
-
- We use vertical flip and transpose for rotation implementation.
- All the images in the list use the same augmentation.
-
- Args:
- imgs (list[ndarray] | ndarray): Images to be augmented. If the input
- is an ndarray, it will be transformed to a list.
- hflip (bool): Horizontal flip. Default: True.
- rotation (bool): Ratotation. Default: True.
- flows (list[ndarray]: Flows to be augmented. If the input is an
- ndarray, it will be transformed to a list.
- Dimension is (h, w, 2). Default: None.
- return_status (bool): Return the status of flip and rotation.
- Default: False.
-
- Returns:
- list[ndarray] | ndarray: Augmented images and flows. If returned
- results only have one element, just return ndarray.
-
- """
- hflip = hflip and random.random() < 0.5
- vflip = rotation and random.random() < 0.5
- rot90 = rotation and random.random() < 0.5
-
- def _augment(img):
- if hflip: # horizontal
- cv2.flip(img, 1, img)
- if vflip: # vertical
- cv2.flip(img, 0, img)
- if rot90:
- img = img.transpose(1, 0, 2)
- return img
-
- def _augment_flow(flow):
- if hflip: # horizontal
- cv2.flip(flow, 1, flow)
- flow[:, :, 0] *= -1
- if vflip: # vertical
- cv2.flip(flow, 0, flow)
- flow[:, :, 1] *= -1
- if rot90:
- flow = flow.transpose(1, 0, 2)
- flow = flow[:, :, [1, 0]]
- return flow
-
- if not isinstance(imgs, list):
- imgs = [imgs]
- imgs = [_augment(img) for img in imgs]
- if len(imgs) == 1:
- imgs = imgs[0]
-
- if flows is not None:
- if not isinstance(flows, list):
- flows = [flows]
- flows = [_augment_flow(flow) for flow in flows]
- if len(flows) == 1:
- flows = flows[0]
- return imgs, flows
- else:
- if return_status:
- return imgs, (hflip, vflip, rot90)
- else:
- return imgs
-
-
-def img_rotate(img, angle, center=None, scale=1.0):
- """Rotate image.
-
- Args:
- img (ndarray): Image to be rotated.
- angle (float): Rotation angle in degrees. Positive values mean
- counter-clockwise rotation.
- center (tuple[int]): Rotation center. If the center is None,
- initialize it as the center of the image. Default: None.
- scale (float): Isotropic scale factor. Default: 1.0.
- """
- (h, w) = img.shape[:2]
-
- if center is None:
- center = (w // 2, h // 2)
-
- matrix = cv2.getRotationMatrix2D(center, angle, scale)
- rotated_img = cv2.warpAffine(img, matrix, (w, h))
- return rotated_img
diff --git a/spaces/leafShen/CodeFormer/CodeFormer/basicsr/utils/realesrgan_utils.py b/spaces/leafShen/CodeFormer/CodeFormer/basicsr/utils/realesrgan_utils.py
deleted file mode 100644
index 5a7b159b697d9e1ca0c51900ec1fe01f9feeb18f..0000000000000000000000000000000000000000
--- a/spaces/leafShen/CodeFormer/CodeFormer/basicsr/utils/realesrgan_utils.py
+++ /dev/null
@@ -1,301 +0,0 @@
-import cv2
-import math
-import numpy as np
-import os
-import queue
-import threading
-import torch
-from basicsr.utils.download_util import load_file_from_url
-from torch.nn import functional as F
-
-# ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-
-
-class RealESRGANer():
- """A helper class for upsampling images with RealESRGAN.
-
- Args:
- scale (int): Upsampling scale factor used in the networks. It is usually 2 or 4.
- model_path (str): The path to the pretrained model. It can be urls (will first download it automatically).
- model (nn.Module): The defined network. Default: None.
- tile (int): As too large images result in the out of GPU memory issue, so this tile option will first crop
- input images into tiles, and then process each of them. Finally, they will be merged into one image.
- 0 denotes for do not use tile. Default: 0.
- tile_pad (int): The pad size for each tile, to remove border artifacts. Default: 10.
- pre_pad (int): Pad the input images to avoid border artifacts. Default: 10.
- half (float): Whether to use half precision during inference. Default: False.
- """
-
- def __init__(self,
- scale,
- model_path,
- model=None,
- tile=0,
- tile_pad=10,
- pre_pad=10,
- half=False,
- device=None,
- gpu_id=None):
- self.scale = scale
- self.tile_size = tile
- self.tile_pad = tile_pad
- self.pre_pad = pre_pad
- self.mod_scale = None
- self.half = half
-
- # initialize model
- if gpu_id:
- self.device = torch.device(
- f'cuda:{gpu_id}' if torch.cuda.is_available() else 'cpu') if device is None else device
- else:
- self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if device is None else device
- # if the model_path starts with https, it will first download models to the folder: realesrgan/weights
- if model_path.startswith('https://'):
- model_path = load_file_from_url(
- url=model_path, model_dir=os.path.join('weights/realesrgan'), progress=True, file_name=None)
- loadnet = torch.load(model_path, map_location=torch.device('cpu'))
- # prefer to use params_ema
- if 'params_ema' in loadnet:
- keyname = 'params_ema'
- else:
- keyname = 'params'
- model.load_state_dict(loadnet[keyname], strict=True)
- model.eval()
- self.model = model.to(self.device)
- if self.half:
- self.model = self.model.half()
-
- def pre_process(self, img):
- """Pre-process, such as pre-pad and mod pad, so that the images can be divisible
- """
- img = torch.from_numpy(np.transpose(img, (2, 0, 1))).float()
- self.img = img.unsqueeze(0).to(self.device)
- if self.half:
- self.img = self.img.half()
-
- # pre_pad
- self.img_pre_pad = self.img.clone()
- if self.pre_pad != 0:
- self.img = F.pad(self.img, (0, self.pre_pad, 0, self.pre_pad), 'reflect')
- # mod pad for divisible borders
- if self.scale == 2:
- self.mod_scale = 2
- elif self.scale == 1:
- self.mod_scale = 4
- if self.mod_scale is not None:
- self.mod_pad_h, self.mod_pad_w = 0, 0
- _, _, h, w = self.img.size()
- if (h % self.mod_scale != 0):
- self.mod_pad_h = (self.mod_scale - h % self.mod_scale)
- if (w % self.mod_scale != 0):
- self.mod_pad_w = (self.mod_scale - w % self.mod_scale)
- self.img = F.pad(self.img, (0, self.mod_pad_w, 0, self.mod_pad_h), 'reflect')
-
- def process(self):
- # model inference
- self.output = self.model(self.img)
-
- def tile_process(self):
- """It will first crop input images to tiles, and then process each tile.
- Finally, all the processed tiles are merged into one images.
-
- Modified from: https://github.com/ata4/esrgan-launcher
- """
- batch, channel, height, width = self.img.shape
- output_height = height * self.scale
- output_width = width * self.scale
- output_shape = (batch, channel, output_height, output_width)
-
- # start with black image
- self.output = self.img.new_zeros(output_shape)
- tiles_x = math.ceil(width / self.tile_size)
- tiles_y = math.ceil(height / self.tile_size)
-
- # loop over all tiles
- for y in range(tiles_y):
- for x in range(tiles_x):
- # extract tile from input image
- ofs_x = x * self.tile_size
- ofs_y = y * self.tile_size
- # input tile area on total image
- input_start_x = ofs_x
- input_end_x = min(ofs_x + self.tile_size, width)
- input_start_y = ofs_y
- input_end_y = min(ofs_y + self.tile_size, height)
-
- # input tile area on total image with padding
- input_start_x_pad = max(input_start_x - self.tile_pad, 0)
- input_end_x_pad = min(input_end_x + self.tile_pad, width)
- input_start_y_pad = max(input_start_y - self.tile_pad, 0)
- input_end_y_pad = min(input_end_y + self.tile_pad, height)
-
- # input tile dimensions
- input_tile_width = input_end_x - input_start_x
- input_tile_height = input_end_y - input_start_y
- tile_idx = y * tiles_x + x + 1
- input_tile = self.img[:, :, input_start_y_pad:input_end_y_pad, input_start_x_pad:input_end_x_pad]
-
- # upscale tile
- try:
- with torch.no_grad():
- output_tile = self.model(input_tile)
- except RuntimeError as error:
- print('Error', error)
- # print(f'\tTile {tile_idx}/{tiles_x * tiles_y}')
-
- # output tile area on total image
- output_start_x = input_start_x * self.scale
- output_end_x = input_end_x * self.scale
- output_start_y = input_start_y * self.scale
- output_end_y = input_end_y * self.scale
-
- # output tile area without padding
- output_start_x_tile = (input_start_x - input_start_x_pad) * self.scale
- output_end_x_tile = output_start_x_tile + input_tile_width * self.scale
- output_start_y_tile = (input_start_y - input_start_y_pad) * self.scale
- output_end_y_tile = output_start_y_tile + input_tile_height * self.scale
-
- # put tile into output image
- self.output[:, :, output_start_y:output_end_y,
- output_start_x:output_end_x] = output_tile[:, :, output_start_y_tile:output_end_y_tile,
- output_start_x_tile:output_end_x_tile]
-
- def post_process(self):
- # remove extra pad
- if self.mod_scale is not None:
- _, _, h, w = self.output.size()
- self.output = self.output[:, :, 0:h - self.mod_pad_h * self.scale, 0:w - self.mod_pad_w * self.scale]
- # remove prepad
- if self.pre_pad != 0:
- _, _, h, w = self.output.size()
- self.output = self.output[:, :, 0:h - self.pre_pad * self.scale, 0:w - self.pre_pad * self.scale]
- return self.output
-
- @torch.no_grad()
- def enhance(self, img, outscale=None, alpha_upsampler='realesrgan'):
- h_input, w_input = img.shape[0:2]
- # img: numpy
- img = img.astype(np.float32)
- if np.max(img) > 256: # 16-bit image
- max_range = 65535
- print('\tInput is a 16-bit image')
- else:
- max_range = 255
- img = img / max_range
- if len(img.shape) == 2: # gray image
- img_mode = 'L'
- img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
- elif img.shape[2] == 4: # RGBA image with alpha channel
- img_mode = 'RGBA'
- alpha = img[:, :, 3]
- img = img[:, :, 0:3]
- img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
- if alpha_upsampler == 'realesrgan':
- alpha = cv2.cvtColor(alpha, cv2.COLOR_GRAY2RGB)
- else:
- img_mode = 'RGB'
- img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-
- # ------------------- process image (without the alpha channel) ------------------- #
- try:
- with torch.no_grad():
- self.pre_process(img)
- if self.tile_size > 0:
- self.tile_process()
- else:
- self.process()
- output_img_t = self.post_process()
- output_img = output_img_t.data.squeeze().float().cpu().clamp_(0, 1).numpy()
- output_img = np.transpose(output_img[[2, 1, 0], :, :], (1, 2, 0))
- if img_mode == 'L':
- output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2GRAY)
- del output_img_t
- torch.cuda.empty_cache()
- except RuntimeError as error:
- output_img = cv2.resize(self.img_pre_pad, (w_input * self.scale, h_input * self.scale), interpolation=cv2.INTER_LINEAR)
- print(f"Failed inference for RealESRGAN: {error}")
-
- # ------------------- process the alpha channel if necessary ------------------- #
- if img_mode == 'RGBA':
- if alpha_upsampler == 'realesrgan':
- self.pre_process(alpha)
- if self.tile_size > 0:
- self.tile_process()
- else:
- self.process()
- output_alpha = self.post_process()
- output_alpha = output_alpha.data.squeeze().float().cpu().clamp_(0, 1).numpy()
- output_alpha = np.transpose(output_alpha[[2, 1, 0], :, :], (1, 2, 0))
- output_alpha = cv2.cvtColor(output_alpha, cv2.COLOR_BGR2GRAY)
- else: # use the cv2 resize for alpha channel
- h, w = alpha.shape[0:2]
- output_alpha = cv2.resize(alpha, (w * self.scale, h * self.scale), interpolation=cv2.INTER_LINEAR)
-
- # merge the alpha channel
- output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2BGRA)
- output_img[:, :, 3] = output_alpha
-
- # ------------------------------ return ------------------------------ #
- if max_range == 65535: # 16-bit image
- output = (output_img * 65535.0).round().astype(np.uint16)
- else:
- output = (output_img * 255.0).round().astype(np.uint8)
-
- if outscale is not None and outscale != float(self.scale):
- output = cv2.resize(
- output, (
- int(w_input * outscale),
- int(h_input * outscale),
- ), interpolation=cv2.INTER_LANCZOS4)
-
- return output, img_mode
-
-
-class PrefetchReader(threading.Thread):
- """Prefetch images.
-
- Args:
- img_list (list[str]): A image list of image paths to be read.
- num_prefetch_queue (int): Number of prefetch queue.
- """
-
- def __init__(self, img_list, num_prefetch_queue):
- super().__init__()
- self.que = queue.Queue(num_prefetch_queue)
- self.img_list = img_list
-
- def run(self):
- for img_path in self.img_list:
- img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
- self.que.put(img)
-
- self.que.put(None)
-
- def __next__(self):
- next_item = self.que.get()
- if next_item is None:
- raise StopIteration
- return next_item
-
- def __iter__(self):
- return self
-
-
-class IOConsumer(threading.Thread):
-
- def __init__(self, opt, que, qid):
- super().__init__()
- self._queue = que
- self.qid = qid
- self.opt = opt
-
- def run(self):
- while True:
- msg = self._queue.get()
- if isinstance(msg, str) and msg == 'quit':
- break
-
- output = msg['output']
- save_path = msg['save_path']
- cv2.imwrite(save_path, output)
- print(f'IO worker {self.qid} is done.')
\ No newline at end of file
diff --git a/spaces/leave7/kazunaAI2.0/data_utils.py b/spaces/leave7/kazunaAI2.0/data_utils.py
deleted file mode 100644
index 9dfba4a9dfbfbd2b6ed5e771a5ffee4f70419ba3..0000000000000000000000000000000000000000
--- a/spaces/leave7/kazunaAI2.0/data_utils.py
+++ /dev/null
@@ -1,152 +0,0 @@
-import time
-import os
-import random
-import numpy as np
-import torch
-import torch.utils.data
-
-import commons
-from mel_processing import spectrogram_torch, spec_to_mel_torch
-from utils import load_wav_to_torch, load_filepaths_and_text, transform
-
-# import h5py
-
-
-"""Multi speaker version"""
-
-
-class TextAudioSpeakerLoader(torch.utils.data.Dataset):
- """
- 1) loads audio, speaker_id, text pairs
- 2) normalizes text and converts them to sequences of integers
- 3) computes spectrograms from audio files.
- """
-
- def __init__(self, audiopaths, hparams):
- self.audiopaths = load_filepaths_and_text(audiopaths)
- self.max_wav_value = hparams.data.max_wav_value
- self.sampling_rate = hparams.data.sampling_rate
- self.filter_length = hparams.data.filter_length
- self.hop_length = hparams.data.hop_length
- self.win_length = hparams.data.win_length
- self.sampling_rate = hparams.data.sampling_rate
- self.use_sr = hparams.train.use_sr
- self.spec_len = hparams.train.max_speclen
- self.spk_map = hparams.spk
-
- random.seed(1234)
- random.shuffle(self.audiopaths)
-
- def get_audio(self, filename):
- audio, sampling_rate = load_wav_to_torch(filename)
- if sampling_rate != self.sampling_rate:
- raise ValueError("{} SR doesn't match target {} SR".format(
- sampling_rate, self.sampling_rate))
- audio_norm = audio / self.max_wav_value
- audio_norm = audio_norm.unsqueeze(0)
- spec_filename = filename.replace(".wav", ".spec.pt")
- if os.path.exists(spec_filename):
- spec = torch.load(spec_filename)
- else:
- spec = spectrogram_torch(audio_norm, self.filter_length,
- self.sampling_rate, self.hop_length, self.win_length,
- center=False)
- spec = torch.squeeze(spec, 0)
- torch.save(spec, spec_filename)
-
- spk = filename.split(os.sep)[-2]
- spk = torch.LongTensor([self.spk_map[spk]])
-
- c = torch.load(filename + ".soft.pt").squeeze(0)
- c = torch.repeat_interleave(c, repeats=2, dim=1)
-
- f0 = np.load(filename + ".f0.npy")
- f0 = torch.FloatTensor(f0)
- lmin = min(c.size(-1), spec.size(-1), f0.shape[0])
- assert abs(c.size(-1) - spec.size(-1)) < 4, (c.size(-1), spec.size(-1), f0.shape, filename)
- assert abs(lmin - spec.size(-1)) < 4, (c.size(-1), spec.size(-1), f0.shape)
- assert abs(lmin - c.size(-1)) < 4, (c.size(-1), spec.size(-1), f0.shape)
- spec, c, f0 = spec[:, :lmin], c[:, :lmin], f0[:lmin]
- audio_norm = audio_norm[:, :lmin * self.hop_length]
- _spec, _c, _audio_norm, _f0 = spec, c, audio_norm, f0
- while spec.size(-1) < self.spec_len:
- spec = torch.cat((spec, _spec), -1)
- c = torch.cat((c, _c), -1)
- f0 = torch.cat((f0, _f0), -1)
- audio_norm = torch.cat((audio_norm, _audio_norm), -1)
- start = random.randint(0, spec.size(-1) - self.spec_len)
- end = start + self.spec_len
- spec = spec[:, start:end]
- c = c[:, start:end]
- f0 = f0[start:end]
- audio_norm = audio_norm[:, start * self.hop_length:end * self.hop_length]
-
- return c, f0, spec, audio_norm, spk
-
- def __getitem__(self, index):
- return self.get_audio(self.audiopaths[index][0])
-
- def __len__(self):
- return len(self.audiopaths)
-
-
-class EvalDataLoader(torch.utils.data.Dataset):
- """
- 1) loads audio, speaker_id, text pairs
- 2) normalizes text and converts them to sequences of integers
- 3) computes spectrograms from audio files.
- """
-
- def __init__(self, audiopaths, hparams):
- self.audiopaths = load_filepaths_and_text(audiopaths)
- self.max_wav_value = hparams.data.max_wav_value
- self.sampling_rate = hparams.data.sampling_rate
- self.filter_length = hparams.data.filter_length
- self.hop_length = hparams.data.hop_length
- self.win_length = hparams.data.win_length
- self.sampling_rate = hparams.data.sampling_rate
- self.use_sr = hparams.train.use_sr
- self.audiopaths = self.audiopaths[:5]
- self.spk_map = hparams.spk
-
-
- def get_audio(self, filename):
- audio, sampling_rate = load_wav_to_torch(filename)
- if sampling_rate != self.sampling_rate:
- raise ValueError("{} SR doesn't match target {} SR".format(
- sampling_rate, self.sampling_rate))
- audio_norm = audio / self.max_wav_value
- audio_norm = audio_norm.unsqueeze(0)
- spec_filename = filename.replace(".wav", ".spec.pt")
- if os.path.exists(spec_filename):
- spec = torch.load(spec_filename)
- else:
- spec = spectrogram_torch(audio_norm, self.filter_length,
- self.sampling_rate, self.hop_length, self.win_length,
- center=False)
- spec = torch.squeeze(spec, 0)
- torch.save(spec, spec_filename)
-
- spk = filename.split(os.sep)[-2]
- spk = torch.LongTensor([self.spk_map[spk]])
-
- c = torch.load(filename + ".soft.pt").squeeze(0)
-
- c = torch.repeat_interleave(c, repeats=2, dim=1)
-
- f0 = np.load(filename + ".f0.npy")
- f0 = torch.FloatTensor(f0)
- lmin = min(c.size(-1), spec.size(-1), f0.shape[0])
- assert abs(c.size(-1) - spec.size(-1)) < 4, (c.size(-1), spec.size(-1), f0.shape)
- assert abs(f0.shape[0] - spec.shape[-1]) < 4, (c.size(-1), spec.size(-1), f0.shape)
- spec, c, f0 = spec[:, :lmin], c[:, :lmin], f0[:lmin]
- audio_norm = audio_norm[:, :lmin * self.hop_length]
-
- return c, f0, spec, audio_norm, spk
-
- def __getitem__(self, index):
- return self.get_audio(self.audiopaths[index][0])
-
- def __len__(self):
- return len(self.audiopaths)
-
diff --git a/spaces/lewisliuX123/wechatllama2/docker/build.debian.sh b/spaces/lewisliuX123/wechatllama2/docker/build.debian.sh
deleted file mode 100644
index a5285f39813426a2d63eb01982229b23ec09dba2..0000000000000000000000000000000000000000
--- a/spaces/lewisliuX123/wechatllama2/docker/build.debian.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/bin/bash
-
-CHATGPT_ON_WECHAT_TAG=1.0.2
-
-docker build -f Dockerfile.debian \
- --build-arg CHATGPT_ON_WECHAT_VER=$CHATGPT_ON_WECHAT_TAG \
- -t zhayujie/chatgpt-on-wechat .
-
-docker tag zhayujie/chatgpt-on-wechat zhayujie/chatgpt-on-wechat:$CHATGPT_ON_WECHAT_TAG-debian
\ No newline at end of file
diff --git a/spaces/lewiswu1209/MockingBird/vocoder/hifigan/env.py b/spaces/lewiswu1209/MockingBird/vocoder/hifigan/env.py
deleted file mode 100644
index 8f0d306d518d0d86a40d7ee992fbad6f04fe875f..0000000000000000000000000000000000000000
--- a/spaces/lewiswu1209/MockingBird/vocoder/hifigan/env.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import os
-import shutil
-
-def build_env(config, config_name, path):
- t_path = os.path.join(path, config_name)
- if config != t_path:
- os.makedirs(path, exist_ok=True)
- shutil.copyfile(config, os.path.join(path, config_name))
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Ankhon Dekhi Movie Download 720p Torrents.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Ankhon Dekhi Movie Download 720p Torrents.md
deleted file mode 100644
index c8f70e8cb68bfbea68f58db951158120038cde82..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Ankhon Dekhi Movie Download 720p Torrents.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Ankhon Dekhi Movie Download 720p Torrents Download Zip ✵ https://bytlly.com/2uGvOV
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/City Car Driving 1.4.1 Crack [BETTER].md b/spaces/lincquiQcaudo/Top-20-Diffusion/City Car Driving 1.4.1 Crack [BETTER].md
deleted file mode 100644
index 8a6e9c4edd81d0c5990d8ef3842bf2b0178135e4..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/City Car Driving 1.4.1 Crack [BETTER].md
+++ /dev/null
@@ -1,6 +0,0 @@
-city car driving 1.4.1 crack DOWNLOAD 🗸 https://bytlly.com/2uGyKj
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Cockos Reaper V4.57 (x86-x64) Cracked-F4CG [TorDigger] Free Download !!LINK!!.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Cockos Reaper V4.57 (x86-x64) Cracked-F4CG [TorDigger] Free Download !!LINK!!.md
deleted file mode 100644
index 2ee319080ebf62bf788fa0e699a9d400ad834df3..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Cockos Reaper V4.57 (x86-x64) Cracked-F4CG [TorDigger] Free Download !!LINK!!.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Cockos Reaper V4.57 (x86-x64) Cracked-F4CG [TorDigger] Free Download DOWNLOAD ►►►►► https://bytlly.com/2uGxxV
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Isumsoft Windows Password Refixer Ultimate Crack !EXCLUSIVE!.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Isumsoft Windows Password Refixer Ultimate Crack !EXCLUSIVE!.md
deleted file mode 100644
index 83b636e04a82f7b95973dfe768b196ae32b45f0d..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Isumsoft Windows Password Refixer Ultimate Crack !EXCLUSIVE!.md
+++ /dev/null
@@ -1,12 +0,0 @@
-isumsoft windows password refixer ultimate crack Download ->->->-> https://bytlly.com/2uGxCK
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Microsoft Flight Simulator FSX 737 Pilot In Command [English] [P Bot.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Microsoft Flight Simulator FSX 737 Pilot In Command [English] [P Bot.md
deleted file mode 100644
index e9ba78702c347ce4e50b01e945b94b6ce384f880..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Microsoft Flight Simulator FSX 737 Pilot In Command [English] [P Bot.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Microsoft Flight Simulator FSX 737 Pilot In Command [English] [P bot Download File ⚹ https://bytlly.com/2uGx2Z
diff --git a/spaces/lixq/bingo61/Dockerfile b/spaces/lixq/bingo61/Dockerfile
deleted file mode 100644
index 3aa2b29b5fc4fa8b8238955acd7f1fde13ce5e1a..0000000000000000000000000000000000000000
--- a/spaces/lixq/bingo61/Dockerfile
+++ /dev/null
@@ -1,36 +0,0 @@
-FROM node:18
-
-
-ARG DEBIAN_FRONTEND=noninteractive
-
-ENV BING_HEADER ""
-
-# Set home to the user's home directory
-ENV HOME=/home/user \
- PATH=/home/user/.local/bin:$PATH
-
-# Set up a new user named "user" with user ID 1000
-RUN useradd -o -u 1000 user && mkdir -p $HOME/app && chown -R user $HOME
-
-# Switch to the "user" user
-USER user
-
-# Set the working directory to the user's home directory
-WORKDIR $HOME/app
-
-# Install app dependencies
-# A wildcard is used to ensure both package.json AND package-lock.json are copied
-# where available (npm@5+)
-COPY --chown=user package*.json $HOME/app/
-
-RUN npm install
-
-# Copy the current directory contents into the container at $HOME/app setting the owner to the user
-COPY --chown=user . $HOME/app/
-
-RUN npm run build
-
-ENV PORT 7860
-EXPOSE 7860
-
-CMD npm start
diff --git a/spaces/lqy09/GT/public/GTest/main.html b/spaces/lqy09/GT/public/GTest/main.html
deleted file mode 100644
index d90601a2a2c1179c82e048cfb8ec166b783dbc57..0000000000000000000000000000000000000000
--- a/spaces/lqy09/GT/public/GTest/main.html
+++ /dev/null
@@ -1,24 +0,0 @@
-
-
-
- GTest
-
-
-
-
-
-
-
-
-
-
= {
- [PluginID.GOOGLE_SEARCH]: {
- id: PluginID.GOOGLE_SEARCH,
- name: PluginName.GOOGLE_SEARCH,
- requiredKeys: [
- {
- key: 'GOOGLE_API_KEY',
- value: '',
- },
- {
- key: 'GOOGLE_CSE_ID',
- value: '',
- },
- ],
- },
-};
-
-export const PluginList = Object.values(Plugins);
diff --git a/spaces/maxmax20160403/sovits5.0/hubert/__init__.py b/spaces/maxmax20160403/sovits5.0/hubert/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/mayura25/handwritten_digit_recognition/README.md b/spaces/mayura25/handwritten_digit_recognition/README.md
deleted file mode 100644
index 9bf847eaf8b1b71d2a2232c858c63e37bac55d0f..0000000000000000000000000000000000000000
--- a/spaces/mayura25/handwritten_digit_recognition/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Handwritten Digit Recognition
-emoji: 🌍
-colorFrom: gray
-colorTo: red
-sdk: gradio
-sdk_version: 4.1.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/merle/PROTEIN_GENERATOR/model/se3_transformer/model/fiber.py b/spaces/merle/PROTEIN_GENERATOR/model/se3_transformer/model/fiber.py
deleted file mode 100644
index 38db33b0d27d70116c92650176170e9b3cf9a9c7..0000000000000000000000000000000000000000
--- a/spaces/merle/PROTEIN_GENERATOR/model/se3_transformer/model/fiber.py
+++ /dev/null
@@ -1,144 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-#
-# Permission is hereby granted, free of charge, to any person obtaining a
-# copy of this software and associated documentation files (the "Software"),
-# to deal in the Software without restriction, including without limitation
-# the rights to use, copy, modify, merge, publish, distribute, sublicense,
-# and/or sell copies of the Software, and to permit persons to whom the
-# Software is furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in
-# all copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
-# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
-# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
-# DEALINGS IN THE SOFTWARE.
-#
-# SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES
-# SPDX-License-Identifier: MIT
-
-
-from collections import namedtuple
-from itertools import product
-from typing import Dict
-
-import torch
-from torch import Tensor
-
-from se3_transformer.runtime.utils import degree_to_dim
-
-FiberEl = namedtuple('FiberEl', ['degree', 'channels'])
-
-
-class Fiber(dict):
- """
- Describes the structure of some set of features.
- Features are split into types (0, 1, 2, 3, ...). A feature of type k has a dimension of 2k+1.
- Type-0 features: invariant scalars
- Type-1 features: equivariant 3D vectors
- Type-2 features: equivariant symmetric traceless matrices
- ...
-
- As inputs to a SE3 layer, there can be many features of the same types, and many features of different types.
- The 'multiplicity' or 'number of channels' is the number of features of a given type.
- This class puts together all the degrees and their multiplicities in order to describe
- the inputs, outputs or hidden features of SE3 layers.
- """
-
- def __init__(self, structure):
- if isinstance(structure, dict):
- structure = [FiberEl(int(d), int(m)) for d, m in sorted(structure.items(), key=lambda x: x[1])]
- elif not isinstance(structure[0], FiberEl):
- structure = list(map(lambda t: FiberEl(*t), sorted(structure, key=lambda x: x[1])))
- self.structure = structure
- super().__init__({d: m for d, m in self.structure})
-
- @property
- def degrees(self):
- return sorted([t.degree for t in self.structure])
-
- @property
- def channels(self):
- return [self[d] for d in self.degrees]
-
- @property
- def num_features(self):
- """ Size of the resulting tensor if all features were concatenated together """
- return sum(t.channels * degree_to_dim(t.degree) for t in self.structure)
-
- @staticmethod
- def create(num_degrees: int, num_channels: int):
- """ Create a Fiber with degrees 0..num_degrees-1, all with the same multiplicity """
- return Fiber([(degree, num_channels) for degree in range(num_degrees)])
-
- @staticmethod
- def from_features(feats: Dict[str, Tensor]):
- """ Infer the Fiber structure from a feature dict """
- structure = {}
- for k, v in feats.items():
- degree = int(k)
- assert len(v.shape) == 3, 'Feature shape should be (N, C, 2D+1)'
- assert v.shape[-1] == degree_to_dim(degree)
- structure[degree] = v.shape[-2]
- return Fiber(structure)
-
- def __getitem__(self, degree: int):
- """ fiber[degree] returns the multiplicity for this degree """
- return dict(self.structure).get(degree, 0)
-
- def __iter__(self):
- """ Iterate over namedtuples (degree, channels) """
- return iter(self.structure)
-
- def __mul__(self, other):
- """
- If other in an int, multiplies all the multiplicities by other.
- If other is a fiber, returns the cartesian product.
- """
- if isinstance(other, Fiber):
- return product(self.structure, other.structure)
- elif isinstance(other, int):
- return Fiber({t.degree: t.channels * other for t in self.structure})
-
- def __add__(self, other):
- """
- If other in an int, add other to all the multiplicities.
- If other is a fiber, add the multiplicities of the fibers together.
- """
- if isinstance(other, Fiber):
- return Fiber({t.degree: t.channels + other[t.degree] for t in self.structure})
- elif isinstance(other, int):
- return Fiber({t.degree: t.channels + other for t in self.structure})
-
- def __repr__(self):
- return str(self.structure)
-
- @staticmethod
- def combine_max(f1, f2):
- """ Combine two fiber by taking the maximum multiplicity for each degree in both fibers """
- new_dict = dict(f1.structure)
- for k, m in f2.structure:
- new_dict[k] = max(new_dict.get(k, 0), m)
-
- return Fiber(list(new_dict.items()))
-
- @staticmethod
- def combine_selectively(f1, f2):
- """ Combine two fiber by taking the sum of multiplicities for each degree in the first fiber """
- # only use orders which occur in fiber f1
- new_dict = dict(f1.structure)
- for k in f1.degrees:
- if k in f2.degrees:
- new_dict[k] += f2[k]
- return Fiber(list(new_dict.items()))
-
- def to_attention_heads(self, tensors: Dict[str, Tensor], num_heads: int):
- # dict(N, num_channels, 2d+1) -> (N, num_heads, -1)
- fibers = [tensors[str(degree)].reshape(*tensors[str(degree)].shape[:-2], num_heads, -1) for degree in
- self.degrees]
- fibers = torch.cat(fibers, -1)
- return fibers
diff --git a/spaces/merve/data-leak/source/anonymization/style-graph-scroll.css b/spaces/merve/data-leak/source/anonymization/style-graph-scroll.css
deleted file mode 100644
index 7680e8c43222b6993d2bedfe43a682236680541e..0000000000000000000000000000000000000000
--- a/spaces/merve/data-leak/source/anonymization/style-graph-scroll.css
+++ /dev/null
@@ -1,160 +0,0 @@
-/** { border: 1px solid #f00; }*/
-
-
-#container{
- position: relative;
- width: auto;
- margin-left: -25px;
- /*margin-bottom: 100px;*/
-}
-
-#sections{
- width: 330px;
- pointer-events: none;
-}
-
-#sections > div{
- background: white;
- opacity: .2;
- margin-bottom: 400px;
- line-height: 1.4em;
- transition: opacity .2s;
- pointer-events: all;
-}
-#sections > div:last-child{
- height: 480px;
- margin-bottom: 0px;
-}
-#sections > div.graph-scroll-active{
- opacity: 1;
-}
-
-#graph{
- margin-left: 40px;
- width: 500px;
- position: -webkit-sticky;
- position: sticky;
- top: 0px;
- float: right;
- height: 580px;
-}
-
-.slider-outer {
- display: block;
- max-width: 300px;
-}
-
-@media (max-width: 925px) {
- #container{
- margin-left: 0px;
- }
-
- #graph{
- width: 100%;
- float: none;
- max-width: 500px;
- margin: 0px auto;
- }
-
- #graph > div{
- position: relative;
- left:12px;
- }
-
- #sections{
- width: auto;
- position: relative;
- margin: 0px auto;
- }
-
- #sections > div{
- background: rgba(255,255,255,.8);
- padding: 10px;
- border-top: 1px solid;
- border-bottom: 1px solid;
- margin-bottom: 80vh;
- width: calc(100vw - 20px);
- margin-left: -5px;
- }
-
- #sections > div > *{
- max-width: 750px;
- }
-
- #sections > div:first-child{
- opacity: 1;
- margin-top: -260px;
- }
-
- #sections > div:last-child{
- height: auto;
- }
-
- #sections h3{
- margin-top: .5em;
- }
-
- /* Adjust buttons for mobile. */
-
- .button-container{
- text-align: center;
- left:0px;
- }
-
- /* Adjust sliders for mobile. */
- input[type="range" i] {
- width: 280px;
- }
- .slider-label-container{
- width: 145px;
- /* display: inline-block; */
- }
-
- .slide-container-heads-prob, .slide-container-population {
- text-align: center;
- }
-
- .slider-container {
- margin-bottom: 5px;
- text-align: center;
- width: 300px;
- /* display:inline-block; */
- }
-
- .slider-outer {
- text-align: center;
- display: flex;
- max-width: 300px;
- }
-
- .headsProb, .population {
- margin-left: 15px;
- }
-
- .slide-container-population {
- margin-bottom: -10px;
- }
-
- .pointer div {
- left: 10px;
- top: 37px;
- }
-
- /* Adjust post summary test for mobile. */
- .post-summary{
- margin-left: 8px;
- margin-bottom: 60px;
- margin-top: 40px;
- }
-
-}
-
-#graph > div{
- margin: 20 35px;
-}
-
-
-#end{
- height: 15vh;
-}
-
diff --git a/spaces/merve/fill-in-the-blank/source/base-rate/sliders.js b/spaces/merve/fill-in-the-blank/source/base-rate/sliders.js
deleted file mode 100644
index 994c9ba490dc44dfa015553d32ff24e822f16de0..0000000000000000000000000000000000000000
--- a/spaces/merve/fill-in-the-blank/source/base-rate/sliders.js
+++ /dev/null
@@ -1,103 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-
-
-
-
-var sliderVals = {}
-
-var sliders = [
- {
- key: 'fNoiseMag',
- text: 'Feature Noise',
- r: [0, 1],
- v: .5
- },
- {
- key: 'fBiasMag',
- text: 'Feature Bias',
- r: [0, 1],
- v: .2
- },
-]
-
-!(function(){
- var width = 145
- var height = 30
-
- sliders.forEach(d => {
- d.s = d3.scaleLinear().domain(d.r).range([0, width])
- sliderVals[d.key] = d
- })
-
- var sliderSel = d3.select('.slider').html('')
- .appendMany('div', sliders)
- .at({class: d => d.key})
- .st({
- display: 'inline-block',
- width: width,
- paddingRight: 60,
- marginTop: 20,
- color: '#000'
- })
-
- sliderSel.append('div')
- .text(d => d.text)
- .st({marginBottom: height/2})
-
- var svgSel = sliderSel.append('svg').at({width, height})
- .on('click', function(d){
- d.v = d.s.invert(d3.mouse(this)[0])
- updatePos()
- })
- .st({
- cursor: 'pointer'
- })
- .append('g').translate(height/2, 1)
- svgSel.append('rect').at({width, height, y: -height/2, fill: '#fff'})
-
- svgSel.append('path').at({
- d: `M 0 0 H ${width}`,
- stroke: '#000',
- strokeWidth: 2
- })
-
- var drag = d3.drag()
- .on('drag', function(d){
- var x = d3.mouse(this)[0]
- d.v = d3.clamp(d3.min(d.r), d.s.invert(x), d3.max(d.r))
-
- updatePos()
- })
-
- var circleSel = svgSel.append('circle')
- .at({
- r: height/2,
- stroke: '#000',
- strokeWidth: 2,
- fill: '#fff',
- })
- .call(drag)
-
-
- function updatePos(){
- circleSel.at({cx: d => d.s(d.v)})
- if (sliderVals.onUpdate) sliderVals.onUpdate()
- }
-
- updatePos()
- sliderVals.updatePos = updatePos
-})()
diff --git a/spaces/merve/measuring-fairness/public/third_party/index.js b/spaces/merve/measuring-fairness/public/third_party/index.js
deleted file mode 100644
index e070ccfa3ac2645f9431b1e4dbee36e81692574d..0000000000000000000000000000000000000000
--- a/spaces/merve/measuring-fairness/public/third_party/index.js
+++ /dev/null
@@ -1,74 +0,0 @@
-// https://github.com/1wheel/roadtolarissa Copyright 2018 Adam Pearce
-
-var fs = require('fs')
-var {exec, execSync} = require('child_process')
-
-var source = `${__dirname}/../../source`
-var public = `${__dirname}/../../public`
-if (!fs.existsSync(public)) fs.mkdirSync(public)
-
-function rsyncSource(){
- exec(`rsync -a --exclude _posts --exclude _templates ${source}/ ${public}/`)
-}
-rsyncSource()
-
-var hljs = require('highlight.js')
-var marked = require('marked')
-marked.setOptions({
- highlight: (code, lang) => hljs.highlight(lang || 'html', code).value,
- smartypants: true
-})
-
-var templates = {}
-readdirAbs(`${source}/_templates`).forEach(path => {
- var str = fs.readFileSync(path, 'utf8')
- var templateName = path.split('_templates/')[1]
- templates[templateName] = d => eval('`' + str + '`')
-})
-
-function readdirAbs(dir){ return fs.readdirSync(dir).map(d => dir + '/' + d) }
-
-var posts = readdirAbs(`${source}/_posts`)
- .filter(d => !d.includes('.DS_Store'))
- .map(parsePost)
-
-fs.writeFileSync(public + '/rss.xml', templates['rss.xml'](posts))
-fs.writeFileSync(public + '/sitemap.xml', templates['sitemap.xml'](posts))
-
-function parsePost(path){
- var str = fs.readFileSync(path, 'utf8')
- if (str[0] == '<') str = str.split('License.\n-->')[1]
- var [top, body] = str
- .replace('---\n', '')
- .split('\n---\n')
-
- console.log(path)
-
- var post = {html: path.includes('.html') ? body : marked(body)}
- top.split('\n').forEach(line => {
- var [key, val] = line.split(/: (.+)/)
- post[key] = val
- })
-
- return post
-}
-
-function writePost(post){
- var dir = public + post.permalink
- if (!fs.existsSync(dir)) execSync(`mkdir -p ${dir}`)
- fs.writeFileSync(`${dir}/index.html`, templates[post.template](post))
-
- var outposts = JSON.parse(JSON.stringify(posts))
- outposts.forEach(d => delete d.html)
- fs.writeFileSync(public + '/posts.json', JSON.stringify(outposts, null, 2))
-
-
-}
-posts.forEach(writePost)
-
-if (process.argv.includes('--watch')){
- require('chokidar').watch(source).on('change', path => {
- rsyncSource()
- if (path.includes('_posts/')) writePost(parsePost(path))
- })
-}
diff --git a/spaces/mikeee/radiobee-dev/radiobee/process_upload.py b/spaces/mikeee/radiobee-dev/radiobee/process_upload.py
deleted file mode 100644
index b61958bbf7cdad4d799443aafce9226c7f39d4ab..0000000000000000000000000000000000000000
--- a/spaces/mikeee/radiobee-dev/radiobee/process_upload.py
+++ /dev/null
@@ -1,99 +0,0 @@
-"""Process uploads."""
-# pylint: disable=invalid-name, unused-import
-from typing import Union
-
-from pathlib import Path
-import tempfile
-import cchardet
-from logzero import logger
-
-
-def process_upload(upload: Union[tempfile._TemporaryFileWrapper, bytes]) -> str:
- """Process upload (fileobj or bytes(zip file: io.BytesIO further to zipfile.ZipFile)).
-
- gr.inputs.File("file"): upload normal file
- gr.inputs.File("bytes"): upload zip file
-
- """
- if isinstance(upload, bytes):
- logger.warning("Not implemented, yet, for zip file")
- return "Not implemented, yet, for zip file"
-
- try:
- fpath = Path(upload.name)
- except Exception as e:
- logger.error("Path(upload.name) error: %s", e)
- return str(e)
-
- suffixes = [
- "",
- ".txt",
- ".text",
- ".md",
- "tsv",
- ]
- # check .txt .md ''(no suffix)
- if fpath.suffix.lower() not in suffixes:
- logger.warning('suffix: [%s] not in %s', fpath.suffix, suffixes)
- # return "File type not supported, yet."
-
- try:
- data = Path(upload.name).read_bytes()
- except Exception as e:
- logger.error("Unable to read data from %s, errors: %s", fpath, e)
- data = str(e).encode()
-
- # no data, empty file, return ""
- if not data:
- logger.info("empty file: %s", upload.name)
- return ""
-
- encoding = cchardet.detect(data).get("encoding")
-
- if encoding is not None:
- try:
- text = fpath.read_text(encoding=encoding)
- except Exception as e:
- logger.error("Unable to retrieve text, error: %s", e)
- text = str(e)
-
- # return f"{upload.name} {type(upload)}\n\n{text}"
- # return f"{upload.name}\n{text}"
- return text
-
- # not able to cchardet: encoding is None, docx, pdf, epub, zip etc
- logger.info("Trying docx...to be implemented")
-
- # T ODO .docx .epub .mobi .pdf etc.
-
- _ = Path(upload.name)
- msg = f"binary file: {_.stem[:-8]}{_.suffix}"
- logger.warning("%s", msg)
-
- return msg
-
-
-_ = ''' # colab gradio-file-inputs-upload.ipynb
-# file_to_text/process_file
-def zip_to_text(file_obj):
- """
- # zf = zipfile.ZipFile('german-recipes-dataset.zip')
- zf = file_obj
- namelist = zipfile.ZipFile.namelist(zf);
- # filename = zf.open(namelist[0]);
- file_contents = []
- for filename in namelist:
- with zf.open(filename) as fhandle:
- file_contents.append(fhandle.read().decode())
- """
- # fileobj is
-
- # gr.inputs.File("bytes")
- if isinstance(file_obj, bytes):
- data = file_obj.decode()
- return f"{type(file_obj)}\n{dir(file_obj)}\n{data}"
-
- # "file"/gr.inputs.File("file") file_obj.name: /tmp/READMEzm8hc5ze.md
- data = Path(file_obj.name).read_bytes()
- return f"{file_obj.name} {type(file_obj)}\n{dir(file_obj)} \n{data}"
-# '''
diff --git a/spaces/ml6team/Knowledge-graphs/utils.py b/spaces/ml6team/Knowledge-graphs/utils.py
deleted file mode 100644
index 5b23e942e54cc855989de77ade8370a3a66fc7f2..0000000000000000000000000000000000000000
--- a/spaces/ml6team/Knowledge-graphs/utils.py
+++ /dev/null
@@ -1,8 +0,0 @@
-
-def clip_text(t, lenght = 4):
- t_sub = t.replace("...", "dotdotdot")
- t_clipped = ".".join(t_sub.split(".")[:lenght]) + "."
- t_reverted = t_clipped.replace("dotdotdot", "...")
- return t_reverted
-
-
diff --git a/spaces/monra/freegpt-webui/client/css/label.css b/spaces/monra/freegpt-webui/client/css/label.css
deleted file mode 100644
index d84873d41e41f2cc22f9d3ace67c30ec07706811..0000000000000000000000000000000000000000
--- a/spaces/monra/freegpt-webui/client/css/label.css
+++ /dev/null
@@ -1,16 +0,0 @@
-label {
- cursor: pointer;
- text-indent: -9999px;
- width: 50px;
- height: 30px;
- backdrop-filter: blur(20px);
- -webkit-backdrop-filter: blur(20px);
- background-color: var(--blur-bg);
- border-radius: var(--border-radius-1);
- border: 1px solid var(--blur-border);
- display: block;
- border-radius: 100px;
- position: relative;
- overflow: hidden;
- transition: 0.33s;
-}
diff --git a/spaces/mrstuffandthings/Bark-Voice-Cloning/setup.py b/spaces/mrstuffandthings/Bark-Voice-Cloning/setup.py
deleted file mode 100644
index 606849326a4002007fd42060b51e69a19c18675c..0000000000000000000000000000000000000000
--- a/spaces/mrstuffandthings/Bark-Voice-Cloning/setup.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from setuptools import setup
-
-setup()
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/models/ema/__init__.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/models/ema/__init__.py
deleted file mode 100644
index 503ceaa609b092e48bd32a0031f4e2ffb875483f..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/models/ema/__init__.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import importlib
-import os
-
-from .ema import EMA
-
-
-def build_ema(model, cfg, device):
- return EMA(model, cfg, device)
-
-
-# automatically import any Python files in the models/ema/ directory
-for file in sorted(os.listdir(os.path.dirname(__file__))):
- if file.endswith(".py") and not file.startswith("_"):
- file_name = file[: file.find(".py")]
- importlib.import_module("fairseq.models.ema." + file_name)
diff --git a/spaces/mthsk/sovits-models-misc/hubert/__init__.py b/spaces/mthsk/sovits-models-misc/hubert/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/mueller-franzes/medfusion-app/medical_diffusion/external/diffusers/vae.py b/spaces/mueller-franzes/medfusion-app/medical_diffusion/external/diffusers/vae.py
deleted file mode 100644
index f83b71b82e40451571f5fbdbb3ca66a3cb26c65b..0000000000000000000000000000000000000000
--- a/spaces/mueller-franzes/medfusion-app/medical_diffusion/external/diffusers/vae.py
+++ /dev/null
@@ -1,857 +0,0 @@
-
-
-from typing import Optional, Tuple, Union
-from pathlib import Path
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from itertools import chain
-
-from .unet_blocks import UNetMidBlock2D, get_down_block, get_up_block
-from .taming_discriminator import NLayerDiscriminator
-from medical_diffusion.models import BasicModel
-from torchvision.utils import save_image
-
-from torch.distributions.normal import Normal
-from torch.distributions import kl_divergence
-
-class Encoder(nn.Module):
- def __init__(
- self,
- in_channels=3,
- out_channels=3,
- down_block_types=("DownEncoderBlock2D",),
- block_out_channels=(64),
- layers_per_block=2,
- norm_num_groups=32,
- act_fn="silu",
- double_z=True,
- ):
- super().__init__()
- self.layers_per_block = layers_per_block
-
- self.conv_in = torch.nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, stride=1, padding=1)
-
- self.mid_block = None
- self.down_blocks = nn.ModuleList([])
-
- # down
- output_channel = block_out_channels[0]
- for i, down_block_type in enumerate(down_block_types):
- input_channel = output_channel
- output_channel = block_out_channels[i+1]
- is_final_block = False #i == len(block_out_channels) - 1
-
- down_block = get_down_block(
- down_block_type,
- num_layers=self.layers_per_block,
- in_channels=input_channel,
- out_channels=output_channel,
- add_downsample=not is_final_block,
- resnet_eps=1e-6,
- downsample_padding=0,
- resnet_act_fn=act_fn,
- resnet_groups=norm_num_groups,
- attn_num_head_channels=None,
- temb_channels=None,
- )
- self.down_blocks.append(down_block)
-
- # mid
- self.mid_block = UNetMidBlock2D(
- in_channels=block_out_channels[-1],
- resnet_eps=1e-6,
- resnet_act_fn=act_fn,
- output_scale_factor=1,
- resnet_time_scale_shift="default",
- attn_num_head_channels=None,
- resnet_groups=norm_num_groups,
- temb_channels=None,
- )
-
- # out
- self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[-1], num_groups=norm_num_groups, eps=1e-6)
- self.conv_act = nn.SiLU()
-
- conv_out_channels = 2 * out_channels if double_z else out_channels
- self.conv_out = nn.Conv2d(block_out_channels[-1], conv_out_channels, 3, padding=1)
-
- def forward(self, x):
- sample = x
- sample = self.conv_in(sample)
-
- # down
- for down_block in self.down_blocks:
- sample = down_block(sample)
-
- # middle
- sample = self.mid_block(sample)
-
- # post-process
- sample = self.conv_norm_out(sample)
- sample = self.conv_act(sample)
- sample = self.conv_out(sample)
-
- return sample
-
-
-class Decoder(nn.Module):
- def __init__(
- self,
- in_channels=3,
- out_channels=3,
- up_block_types=("UpDecoderBlock2D",),
- block_out_channels=(64,),
- layers_per_block=2,
- norm_num_groups=32,
- act_fn="silu",
- ):
- super().__init__()
- self.layers_per_block = layers_per_block
-
- self.conv_in = nn.Conv2d(in_channels, block_out_channels[-1], kernel_size=3, stride=1, padding=1)
-
- self.mid_block = None
- self.up_blocks = nn.ModuleList([])
-
- # mid
- self.mid_block = UNetMidBlock2D(
- in_channels=block_out_channels[-1],
- resnet_eps=1e-6,
- resnet_act_fn=act_fn,
- output_scale_factor=1,
- resnet_time_scale_shift="default",
- attn_num_head_channels=None,
- resnet_groups=norm_num_groups,
- temb_channels=None,
- )
-
- # up
- reversed_block_out_channels = list(reversed(block_out_channels))
- output_channel = reversed_block_out_channels[0]
- for i, up_block_type in enumerate(up_block_types):
- prev_output_channel = output_channel
- output_channel = reversed_block_out_channels[i+1]
-
- is_final_block = False # i == len(block_out_channels) - 1
-
- up_block = get_up_block(
- up_block_type,
- num_layers=self.layers_per_block + 1,
- in_channels=prev_output_channel,
- out_channels=output_channel,
- prev_output_channel=None,
- add_upsample=not is_final_block,
- resnet_eps=1e-6,
- resnet_act_fn=act_fn,
- resnet_groups=norm_num_groups,
- attn_num_head_channels=None,
- temb_channels=None,
- )
- self.up_blocks.append(up_block)
- prev_output_channel = output_channel
-
- # out
- self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=1e-6)
- self.conv_act = nn.SiLU()
- self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, 3, padding=1)
-
- def forward(self, z):
- sample = z
- sample = self.conv_in(sample)
-
- # middle
- sample = self.mid_block(sample)
-
- # up
- for up_block in self.up_blocks:
- sample = up_block(sample)
-
- # post-process
- sample = self.conv_norm_out(sample)
- sample = self.conv_act(sample)
- sample = self.conv_out(sample)
-
- return sample
-
-
-class VectorQuantizer(nn.Module):
- """
- Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly avoids costly matrix
- multiplications and allows for post-hoc remapping of indices.
- """
-
- # NOTE: due to a bug the beta term was applied to the wrong term. for
- # backwards compatibility we use the buggy version by default, but you can
- # specify legacy=False to fix it.
- def __init__(self, n_e, e_dim, beta, remap=None, unknown_index="random", sane_index_shape=False, legacy=False):
- super().__init__()
- self.n_e = n_e
- self.e_dim = e_dim
- self.beta = beta
- self.legacy = legacy
-
- self.embedding = nn.Embedding(self.n_e, self.e_dim)
- self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
-
- self.remap = remap
- if self.remap is not None:
- self.register_buffer("used", torch.tensor(np.load(self.remap)))
- self.re_embed = self.used.shape[0]
- self.unknown_index = unknown_index # "random" or "extra" or integer
- if self.unknown_index == "extra":
- self.unknown_index = self.re_embed
- self.re_embed = self.re_embed + 1
- print(
- f"Remapping {self.n_e} indices to {self.re_embed} indices. "
- f"Using {self.unknown_index} for unknown indices."
- )
- else:
- self.re_embed = n_e
-
- self.sane_index_shape = sane_index_shape
-
- def remap_to_used(self, inds):
- ishape = inds.shape
- assert len(ishape) > 1
- inds = inds.reshape(ishape[0], -1)
- used = self.used.to(inds)
- match = (inds[:, :, None] == used[None, None, ...]).long()
- new = match.argmax(-1)
- unknown = match.sum(2) < 1
- if self.unknown_index == "random":
- new[unknown] = torch.randint(0, self.re_embed, size=new[unknown].shape).to(device=new.device)
- else:
- new[unknown] = self.unknown_index
- return new.reshape(ishape)
-
- def unmap_to_all(self, inds):
- ishape = inds.shape
- assert len(ishape) > 1
- inds = inds.reshape(ishape[0], -1)
- used = self.used.to(inds)
- if self.re_embed > self.used.shape[0]: # extra token
- inds[inds >= self.used.shape[0]] = 0 # simply set to zero
- back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds)
- return back.reshape(ishape)
-
- def forward(self, z):
- # reshape z -> (batch, height, width, channel) and flatten
- z = z.permute(0, 2, 3, 1).contiguous()
- z_flattened = z.view(-1, self.e_dim)
- # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
-
- d = (
- torch.sum(z_flattened**2, dim=1, keepdim=True)
- + torch.sum(self.embedding.weight**2, dim=1)
- - 2 * torch.einsum("bd,dn->bn", z_flattened, self.embedding.weight.t())
- )
-
- min_encoding_indices = torch.argmin(d, dim=1)
- z_q = self.embedding(min_encoding_indices).view(z.shape)
- perplexity = None
- min_encodings = None
-
- # compute loss for embedding
- if not self.legacy:
- loss = self.beta * torch.mean((z_q.detach() - z) ** 2) + torch.mean((z_q - z.detach()) ** 2)
- else:
- loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean((z_q - z.detach()) ** 2)
-
- # preserve gradients
- z_q = z + (z_q - z).detach()
-
- # reshape back to match original input shape
- z_q = z_q.permute(0, 3, 1, 2).contiguous()
-
- if self.remap is not None:
- min_encoding_indices = min_encoding_indices.reshape(z.shape[0], -1) # add batch axis
- min_encoding_indices = self.remap_to_used(min_encoding_indices)
- min_encoding_indices = min_encoding_indices.reshape(-1, 1) # flatten
-
- if self.sane_index_shape:
- min_encoding_indices = min_encoding_indices.reshape(z_q.shape[0], z_q.shape[2], z_q.shape[3])
-
- return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
-
- def get_codebook_entry(self, indices, shape):
- # shape specifying (batch, height, width, channel)
- if self.remap is not None:
- indices = indices.reshape(shape[0], -1) # add batch axis
- indices = self.unmap_to_all(indices)
- indices = indices.reshape(-1) # flatten again
-
- # get quantized latent vectors
- z_q = self.embedding(indices)
-
- if shape is not None:
- z_q = z_q.view(shape)
- # reshape back to match original input shape
- z_q = z_q.permute(0, 3, 1, 2).contiguous()
-
- return z_q
-
-
-class DiagonalGaussianDistribution(object):
- def __init__(self, parameters, deterministic=False):
- self.batch_size = parameters.shape[0]
- self.parameters = parameters
- self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
- # self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
- self.deterministic = deterministic
- self.std = torch.exp(0.5 * self.logvar)
- self.var = torch.exp(self.logvar)
- if self.deterministic:
- self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
-
- def sample(self, generator: Optional[torch.Generator] = None) -> torch.FloatTensor:
- device = self.parameters.device
- sample_device = "cpu" if device.type == "mps" else device
- sample = torch.randn(self.mean.shape, generator=generator, device=sample_device).to(device)
- x = self.mean + self.std * sample
- return x
-
- def kl(self, other=None):
- if self.deterministic:
- return torch.Tensor([0.0])
- else:
- if other is None:
- return 0.5 * torch.sum(torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar)/self.batch_size
- else:
- return 0.5 * torch.sum(
- torch.pow(self.mean - other.mean, 2) / other.var
- + self.var / other.var
- - 1.0
- - self.logvar
- + other.logvar,
- )/self.batch_size
-
- # q_z_x = Normal(self.mean, self.logvar.mul(.5).exp())
- # p_z = Normal(torch.zeros_like(self.mean), torch.ones_like(self.logvar))
- # kl_div = kl_divergence(q_z_x, p_z).sum(1).mean()
- # return kl_div
-
- def nll(self, sample, dims=[1, 2, 3]):
- if self.deterministic:
- return torch.Tensor([0.0])
- logtwopi = np.log(2.0 * np.pi)
- return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, dim=dims)
-
- def mode(self):
- return self.mean
-
-
-class VQModel(nn.Module):
- r"""VQ-VAE model from the paper Neural Discrete Representation Learning by Aaron van den Oord, Oriol Vinyals and Koray
- Kavukcuoglu.
-
- This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library
- implements for all the model (such as downloading or saving, etc.)
-
- Parameters:
- in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
- out_channels (int, *optional*, defaults to 3): Number of channels in the output.
- down_block_types (`Tuple[str]`, *optional*, defaults to :
- obj:`("DownEncoderBlock2D",)`): Tuple of downsample block types.
- up_block_types (`Tuple[str]`, *optional*, defaults to :
- obj:`("UpDecoderBlock2D",)`): Tuple of upsample block types.
- block_out_channels (`Tuple[int]`, *optional*, defaults to :
- obj:`(64,)`): Tuple of block output channels.
- act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
- latent_channels (`int`, *optional*, defaults to `3`): Number of channels in the latent space.
- sample_size (`int`, *optional*, defaults to `32`): TODO
- num_vq_embeddings (`int`, *optional*, defaults to `256`): Number of codebook vectors in the VQ-VAE.
- """
-
-
- def __init__(
- self,
- in_channels: int = 3,
- out_channels: int = 3,
- down_block_types: Tuple[str] = ("DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"),
- up_block_types: Tuple[str] = ("UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"),
- block_out_channels: Tuple[int] = (32, 64, 128, 256),
- layers_per_block: int = 1,
- act_fn: str = "silu",
- latent_channels: int = 3,
- sample_size: int = 32,
- num_vq_embeddings: int = 256,
- norm_num_groups: int = 32,
- ):
- super().__init__()
-
- # pass init params to Encoder
- self.encoder = Encoder(
- in_channels=in_channels,
- out_channels=latent_channels,
- down_block_types=down_block_types,
- block_out_channels=block_out_channels,
- layers_per_block=layers_per_block,
- act_fn=act_fn,
- norm_num_groups=norm_num_groups,
- double_z=False,
- )
-
- self.quant_conv = torch.nn.Conv2d(latent_channels, latent_channels, 1)
- self.quantize = VectorQuantizer(
- num_vq_embeddings, latent_channels, beta=0.25, remap=None, sane_index_shape=False
- )
- self.post_quant_conv = torch.nn.Conv2d(latent_channels, latent_channels, 1)
-
- # pass init params to Decoder
- self.decoder = Decoder(
- in_channels=latent_channels,
- out_channels=out_channels,
- up_block_types=up_block_types,
- block_out_channels=block_out_channels,
- layers_per_block=layers_per_block,
- act_fn=act_fn,
- norm_num_groups=norm_num_groups,
- )
-
- # def encode(self, x: torch.FloatTensor):
- # z = self.encoder(x)
- # z = self.quant_conv(z)
- # return z
-
- def encode(self, x, return_loss=True, force_quantize= True):
- z = self.encoder(x)
- z = self.quant_conv(z)
-
- if force_quantize:
- z_q, emb_loss, _ = self.quantize(z)
- else:
- z_q, emb_loss = z, None
-
- if return_loss:
- return z_q, emb_loss
- else:
- return z_q
-
- def decode(self, z_q) -> torch.FloatTensor:
- z_q = self.post_quant_conv(z_q)
- x = self.decoder(z_q)
- return x
-
- # def decode(self, z: torch.FloatTensor, return_loss=True, force_quantize: bool = True) -> torch.FloatTensor:
- # if force_quantize:
- # z_q, emb_loss, _ = self.quantize(z)
- # else:
- # z_q, emb_loss = z, None
-
- # z_q = self.post_quant_conv(z_q)
- # x = self.decoder(z_q)
-
- # if return_loss:
- # return x, emb_loss
- # else:
- # return x
-
- def forward(self, sample: torch.FloatTensor) -> torch.FloatTensor:
- r"""
- Args:
- sample (`torch.FloatTensor`): Input sample.
- """
- # h = self.encode(sample)
- h, emb_loss = self.encode(sample)
- dec = self.decode(h)
- # dec, emb_loss = self.decode(h)
-
- return dec, emb_loss
-
-
-class AutoencoderKL(nn.Module):
- r"""Variational Autoencoder (VAE) model with KL loss from the paper Auto-Encoding Variational Bayes by Diederik P. Kingma
- and Max Welling.
-
- This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library
- implements for all the model (such as downloading or saving, etc.)
-
- Parameters:
- in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
- out_channels (int, *optional*, defaults to 3): Number of channels in the output.
- down_block_types (`Tuple[str]`, *optional*, defaults to :
- obj:`("DownEncoderBlock2D",)`): Tuple of downsample block types.
- up_block_types (`Tuple[str]`, *optional*, defaults to :
- obj:`("UpDecoderBlock2D",)`): Tuple of upsample block types.
- block_out_channels (`Tuple[int]`, *optional*, defaults to :
- obj:`(64,)`): Tuple of block output channels.
- act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
- latent_channels (`int`, *optional*, defaults to `3`): Number of channels in the latent space.
- sample_size (`int`, *optional*, defaults to `32`): TODO
- """
-
-
- def __init__(
- self,
- in_channels: int = 3,
- out_channels: int = 3,
- down_block_types: Tuple[str] = ("DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D","DownEncoderBlock2D",),
- up_block_types: Tuple[str] = ("UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D",),
- block_out_channels: Tuple[int] = (32, 64, 128, 128),
- layers_per_block: int = 1,
- act_fn: str = "silu",
- latent_channels: int = 3,
- norm_num_groups: int = 32,
- sample_size: int = 32,
- ):
- super().__init__()
-
- # pass init params to Encoder
- self.encoder = Encoder(
- in_channels=in_channels,
- out_channels=latent_channels,
- down_block_types=down_block_types,
- block_out_channels=block_out_channels,
- layers_per_block=layers_per_block,
- act_fn=act_fn,
- norm_num_groups=norm_num_groups,
- double_z=True,
- )
-
- # pass init params to Decoder
- self.decoder = Decoder(
- in_channels=latent_channels,
- out_channels=out_channels,
- up_block_types=up_block_types,
- block_out_channels=block_out_channels,
- layers_per_block=layers_per_block,
- norm_num_groups=norm_num_groups,
- act_fn=act_fn,
- )
-
- self.quant_conv = torch.nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1)
- self.post_quant_conv = torch.nn.Conv2d(latent_channels, latent_channels, 1)
-
- def encode(self, x: torch.FloatTensor):
- h = self.encoder(x)
- moments = self.quant_conv(h)
- posterior = DiagonalGaussianDistribution(moments)
- return posterior
-
- def decode(self, z: torch.FloatTensor) -> torch.FloatTensor:
- z = self.post_quant_conv(z)
- dec = self.decoder(z)
- return dec
-
- def forward(
- self,
- sample: torch.FloatTensor,
- sample_posterior: bool = True,
- generator: Optional[torch.Generator] = None,
- ) -> torch.FloatTensor:
- r"""
- Args:
- sample (`torch.FloatTensor`): Input sample.
- sample_posterior (`bool`, *optional*, defaults to `False`):
- Whether to sample from the posterior.
- """
- x = sample
- posterior = self.encode(x)
- if sample_posterior:
- z = posterior.sample(generator=generator)
- else:
- z = posterior.mode()
- kl_loss = posterior.kl()
- dec = self.decode(z)
- return dec, kl_loss
-
-
-
-class VQVAEWrapper(BasicModel):
- def __init__(
- self,
- in_ch: int = 3,
- out_ch: int = 3,
- down_block_types: Tuple[str] = ("DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D",),
- up_block_types: Tuple[str] = ("UpDecoderBlock2D","UpDecoderBlock2D","UpDecoderBlock2D",),
- block_out_channels: Tuple[int] = (32, 64, 128, 256, ),
- layers_per_block: int = 1,
- act_fn: str = "silu",
- latent_channels: int = 3,
- sample_size: int = 32,
- num_vq_embeddings: int = 64,
- norm_num_groups: int = 32,
-
- optimizer=torch.optim.AdamW,
- optimizer_kwargs={},
- lr_scheduler=None,
- lr_scheduler_kwargs={},
- loss=torch.nn.MSELoss,
- loss_kwargs={}
- ):
- super().__init__(optimizer, optimizer_kwargs, lr_scheduler, lr_scheduler_kwargs, loss, loss_kwargs)
- self.model = VQModel(in_ch, out_ch, down_block_types, up_block_types, block_out_channels,
- layers_per_block, act_fn, latent_channels, sample_size, num_vq_embeddings, norm_num_groups)
-
- def forward(self, sample):
- return self.model(sample)
-
- def encode(self, x):
- z = self.model.encode(x, return_loss=False)
- return z
-
- def decode(self, z):
- x = self.model.decode(z)
- return x
-
- def _step(self, batch: dict, batch_idx: int, state: str, step: int, optimizer_idx:int):
- # ------------------------- Get Source/Target ---------------------------
- x = batch['source']
- target = x
-
- # ------------------------- Run Model ---------------------------
- pred, vq_loss = self(x)
-
- # ------------------------- Compute Loss ---------------------------
- loss = self.loss_fct(pred, target)
- loss += vq_loss
-
- # --------------------- Compute Metrics -------------------------------
- results = {'loss':loss}
- with torch.no_grad():
- results['L2'] = torch.nn.functional.mse_loss(pred, target)
- results['L1'] = torch.nn.functional.l1_loss(pred, target)
-
- # ----------------- Log Scalars ----------------------
- for metric_name, metric_val in results.items():
- self.log(f"{state}/{metric_name}", metric_val, batch_size=x.shape[0], on_step=True, on_epoch=True)
-
- # ----------------- Save Image ------------------------------
- if self.global_step != 0 and self.global_step % self.trainer.log_every_n_steps == 0:
- def norm(x):
- return (x-x.min())/(x.max()-x.min())
-
- images = [x, pred]
- log_step = self.global_step // self.trainer.log_every_n_steps
- path_out = Path(self.logger.log_dir)/'images'
- path_out.mkdir(parents=True, exist_ok=True)
- images = torch.cat([norm(img) for img in images])
- save_image(images, path_out/f'sample_{log_step}.png')
-
- return loss
-
-def hinge_d_loss(logits_real, logits_fake):
- loss_real = torch.mean(F.relu(1. - logits_real))
- loss_fake = torch.mean(F.relu(1. + logits_fake))
- d_loss = 0.5 * (loss_real + loss_fake)
- return d_loss
-
-def vanilla_d_loss(logits_real, logits_fake):
- d_loss = 0.5 * (
- torch.mean(F.softplus(-logits_real)) +
- torch.mean(F.softplus(logits_fake)))
- return d_loss
-
-class VQGAN(BasicModel):
- def __init__(
- self,
- in_ch: int = 3,
- out_ch: int = 3,
- down_block_types: Tuple[str] = ("DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D",),
- up_block_types: Tuple[str] = ("UpDecoderBlock2D","UpDecoderBlock2D","UpDecoderBlock2D",),
- block_out_channels: Tuple[int] = (32, 64, 128, 256, ),
- layers_per_block: int = 1,
- act_fn: str = "silu",
- latent_channels: int = 3,
- sample_size: int = 32,
- num_vq_embeddings: int = 64,
- norm_num_groups: int = 32,
-
- start_gan_train_step = 50000, # NOTE step increase with each optimizer
- gan_loss_weight: float = 1.0, # alias discriminator
- perceptual_loss_weight: float = 1.0,
- embedding_loss_weight: float = 1.0,
-
- optimizer=torch.optim.AdamW,
- optimizer_kwargs={},
- lr_scheduler=None,
- lr_scheduler_kwargs={},
- loss=torch.nn.MSELoss,
- loss_kwargs={}
- ):
- super().__init__(optimizer, optimizer_kwargs, lr_scheduler, lr_scheduler_kwargs, loss, loss_kwargs)
- self.vqvae = VQModel(in_ch, out_ch, down_block_types, up_block_types, block_out_channels, layers_per_block, act_fn,
- latent_channels, sample_size, num_vq_embeddings, norm_num_groups)
- self.discriminator = NLayerDiscriminator(in_ch)
- # self.perceiver = ... # Currently not supported, would require another trained NN
-
- self.start_gan_train_step = start_gan_train_step
- self.perceptual_loss_weight = perceptual_loss_weight
- self.gan_loss_weight = gan_loss_weight
- self.embedding_loss_weight = embedding_loss_weight
-
- def forward(self, x, condition=None):
- return self.vqvae(x)
-
- def encode(self, x):
- z = self.vqvae.encode(x, return_loss=False)
- return z
-
- def decode(self, z):
- x = self.vqvae.decode(z)
- return x
-
-
- def compute_lambda(self, rec_loss, gan_loss, eps=1e-4):
- """Computes adaptive weight as proposed in eq. 7 of https://arxiv.org/abs/2012.09841"""
- last_layer = self.vqvae.decoder.conv_out.weight
- rec_grads = torch.autograd.grad(rec_loss, last_layer, retain_graph=True)[0]
- gan_grads = torch.autograd.grad(gan_loss, last_layer, retain_graph=True)[0]
- d_weight = torch.norm(rec_grads) / (torch.norm(gan_grads) + eps)
- d_weight = torch.clamp(d_weight, 0.0, 1e4)
- return d_weight.detach()
-
-
-
- def _step(self, batch: dict, batch_idx: int, state: str, step: int, optimizer_idx:int):
- x = batch['source']
- # condition = batch.get('target', None)
-
- pred, vq_emb_loss = self.vqvae(x)
-
- if optimizer_idx == 0:
- # ------ VAE -------
- vq_img_loss = F.mse_loss(pred, x)
- vq_per_loss = 0.0 #self.perceiver(pred, x)
- rec_loss = vq_img_loss+self.perceptual_loss_weight*vq_per_loss
-
- # ------- GAN -----
- if step > self.start_gan_train_step:
- gan_loss = -torch.mean(self.discriminator(pred))
- lambda_weight = self.compute_lambda(rec_loss, gan_loss)
- gan_loss = gan_loss*lambda_weight
- else:
- gan_loss = torch.tensor([0.0], requires_grad=True, device=x.device)
-
- loss = self.gan_loss_weight*gan_loss+rec_loss+self.embedding_loss_weight*vq_emb_loss
-
- elif optimizer_idx == 1:
- if step > self.start_gan_train_step//2:
- logits_real = self.discriminator(x.detach())
- logits_fake = self.discriminator(pred.detach())
- loss = hinge_d_loss(logits_real, logits_fake)
- else:
- loss = torch.tensor([0.0], requires_grad=True, device=x.device)
-
- # --------------------- Compute Metrics -------------------------------
- results = {'loss':loss.detach(), f'loss_{optimizer_idx}':loss.detach()}
- with torch.no_grad():
- results[f'L2'] = torch.nn.functional.mse_loss(pred, x)
- results[f'L1'] = torch.nn.functional.l1_loss(pred, x)
-
- # ----------------- Log Scalars ----------------------
- for metric_name, metric_val in results.items():
- self.log(f"{state}/{metric_name}", metric_val, batch_size=x.shape[0], on_step=True, on_epoch=True)
-
- # ----------------- Save Image ------------------------------
- if self.global_step != 0 and self.global_step % self.trainer.log_every_n_steps == 0: # NOTE: step 1 (opt1) , step=2 (opt2), step=3 (opt1), ...
- def norm(x):
- return (x-x.min())/(x.max()-x.min())
-
- images = torch.cat([x, pred])
- log_step = self.global_step // self.trainer.log_every_n_steps
- path_out = Path(self.logger.log_dir)/'images'
- path_out.mkdir(parents=True, exist_ok=True)
- images = torch.stack([norm(img) for img in images])
- save_image(images, path_out/f'sample_{log_step}.png')
-
- return loss
-
- def configure_optimizers(self):
- opt_vae = self.optimizer(self.vqvae.parameters(), **self.optimizer_kwargs)
- opt_disc = self.optimizer(self.discriminator.parameters(), **self.optimizer_kwargs)
- if self.lr_scheduler is not None:
- scheduler = [
- {
- 'scheduler': self.lr_scheduler(opt_vae, **self.lr_scheduler_kwargs),
- 'interval': 'step',
- 'frequency': 1
- },
- {
- 'scheduler': self.lr_scheduler(opt_disc, **self.lr_scheduler_kwargs),
- 'interval': 'step',
- 'frequency': 1
- },
- ]
- else:
- scheduler = []
-
- return [opt_vae, opt_disc], scheduler
-
-class VAEWrapper(BasicModel):
- def __init__(
- self,
- in_ch: int = 3,
- out_ch: int = 3,
- down_block_types: Tuple[str] = ("DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"), # "DownEncoderBlock2D", "DownEncoderBlock2D",
- up_block_types: Tuple[str] = ("UpDecoderBlock2D", "UpDecoderBlock2D","UpDecoderBlock2D" ), # "UpDecoderBlock2D", "UpDecoderBlock2D",
- block_out_channels: Tuple[int] = (32, 64, 128, 256), # 128, 256
- layers_per_block: int = 1,
- act_fn: str = "silu",
- latent_channels: int = 3,
- norm_num_groups: int = 32,
- sample_size: int = 32,
-
- optimizer=torch.optim.AdamW,
- optimizer_kwargs={'lr':1e-4, 'weight_decay':1e-3, 'amsgrad':True},
- lr_scheduler=None,
- lr_scheduler_kwargs={},
- # loss=torch.nn.MSELoss, # WARNING: No Effect
- # loss_kwargs={'reduction': 'mean'}
- ):
- super().__init__(optimizer, optimizer_kwargs, lr_scheduler, lr_scheduler_kwargs ) # loss, loss_kwargs
- self.model = AutoencoderKL(in_ch, out_ch, down_block_types, up_block_types, block_out_channels,
- layers_per_block, act_fn, latent_channels, norm_num_groups, sample_size)
-
- self.logvar = nn.Parameter(torch.zeros(size=())) # Better weighting between KL and MSE, see (https://arxiv.org/abs/1903.05789), also used by Taming-Transfomer/Stable Diffusion
-
- def forward(self, sample):
- return self.model(sample)
-
- def encode(self, x):
- z = self.model.encode(x) # Latent space but not yet mapped to discrete embedding vectors
- return z.sample(generator=None)
-
- def decode(self, z):
- x = self.model.decode(z)
- return x
-
- def _step(self, batch: dict, batch_idx: int, state: str, step: int, optimizer_idx:int):
- # ------------------------- Get Source/Target ---------------------------
- x = batch['source']
- target = x
- HALF_LOG_TWO_PI = 0.91893 # log(2pi)/2
-
- # ------------------------- Run Model ---------------------------
- pred, kl_loss = self(x)
-
- # ------------------------- Compute Loss ---------------------------
- loss = torch.sum( torch.square(pred-target))/x.shape[0] #torch.sum( torch.square((pred-target)/torch.exp(self.logvar))/2 + self.logvar + HALF_LOG_TWO_PI )/x.shape[0]
- loss += kl_loss
-
- # --------------------- Compute Metrics -------------------------------
- results = {'loss':loss.detach()}
- with torch.no_grad():
- results['L2'] = torch.nn.functional.mse_loss(pred, target)
- results['L1'] = torch.nn.functional.l1_loss(pred, target)
-
- # ----------------- Log Scalars ----------------------
- for metric_name, metric_val in results.items():
- self.log(f"{state}/{metric_name}", metric_val, batch_size=x.shape[0], on_step=True, on_epoch=True)
-
- # ----------------- Save Image ------------------------------
- if self.global_step != 0 and self.global_step % self.trainer.log_every_n_steps == 0:
- def norm(x):
- return (x-x.min())/(x.max()-x.min())
-
- images = torch.cat([x, pred])
- log_step = self.global_step // self.trainer.log_every_n_steps
- path_out = Path(self.logger.log_dir)/'images'
- path_out.mkdir(parents=True, exist_ok=True)
- images = torch.stack([norm(img) for img in images])
- save_image(images, path_out/f'sample_{log_step}.png')
-
- return loss
\ No newline at end of file
diff --git a/spaces/multimodalart/latentdiffusion/latent-diffusion/ldm/models/autoencoder.py b/spaces/multimodalart/latentdiffusion/latent-diffusion/ldm/models/autoencoder.py
deleted file mode 100644
index 6a9c4f45498561953b8085981609b2a3298a5473..0000000000000000000000000000000000000000
--- a/spaces/multimodalart/latentdiffusion/latent-diffusion/ldm/models/autoencoder.py
+++ /dev/null
@@ -1,443 +0,0 @@
-import torch
-import pytorch_lightning as pl
-import torch.nn.functional as F
-from contextlib import contextmanager
-
-from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
-
-from ldm.modules.diffusionmodules.model import Encoder, Decoder
-from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
-
-from ldm.util import instantiate_from_config
-
-
-class VQModel(pl.LightningModule):
- def __init__(self,
- ddconfig,
- lossconfig,
- n_embed,
- embed_dim,
- ckpt_path=None,
- ignore_keys=[],
- image_key="image",
- colorize_nlabels=None,
- monitor=None,
- batch_resize_range=None,
- scheduler_config=None,
- lr_g_factor=1.0,
- remap=None,
- sane_index_shape=False, # tell vector quantizer to return indices as bhw
- use_ema=False
- ):
- super().__init__()
- self.embed_dim = embed_dim
- self.n_embed = n_embed
- self.image_key = image_key
- self.encoder = Encoder(**ddconfig)
- self.decoder = Decoder(**ddconfig)
- self.loss = instantiate_from_config(lossconfig)
- self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
- remap=remap,
- sane_index_shape=sane_index_shape)
- self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
- self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
- if colorize_nlabels is not None:
- assert type(colorize_nlabels)==int
- self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
- if monitor is not None:
- self.monitor = monitor
- self.batch_resize_range = batch_resize_range
- if self.batch_resize_range is not None:
- print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.")
-
- self.use_ema = use_ema
- if self.use_ema:
- self.model_ema = LitEma(self)
- print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
-
- if ckpt_path is not None:
- self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
- self.scheduler_config = scheduler_config
- self.lr_g_factor = lr_g_factor
-
- @contextmanager
- def ema_scope(self, context=None):
- if self.use_ema:
- self.model_ema.store(self.parameters())
- self.model_ema.copy_to(self)
- if context is not None:
- print(f"{context}: Switched to EMA weights")
- try:
- yield None
- finally:
- if self.use_ema:
- self.model_ema.restore(self.parameters())
- if context is not None:
- print(f"{context}: Restored training weights")
-
- def init_from_ckpt(self, path, ignore_keys=list()):
- sd = torch.load(path, map_location="cpu")["state_dict"]
- keys = list(sd.keys())
- for k in keys:
- for ik in ignore_keys:
- if k.startswith(ik):
- print("Deleting key {} from state_dict.".format(k))
- del sd[k]
- missing, unexpected = self.load_state_dict(sd, strict=False)
- print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
- if len(missing) > 0:
- print(f"Missing Keys: {missing}")
- print(f"Unexpected Keys: {unexpected}")
-
- def on_train_batch_end(self, *args, **kwargs):
- if self.use_ema:
- self.model_ema(self)
-
- def encode(self, x):
- h = self.encoder(x)
- h = self.quant_conv(h)
- quant, emb_loss, info = self.quantize(h)
- return quant, emb_loss, info
-
- def encode_to_prequant(self, x):
- h = self.encoder(x)
- h = self.quant_conv(h)
- return h
-
- def decode(self, quant):
- quant = self.post_quant_conv(quant)
- dec = self.decoder(quant)
- return dec
-
- def decode_code(self, code_b):
- quant_b = self.quantize.embed_code(code_b)
- dec = self.decode(quant_b)
- return dec
-
- def forward(self, input, return_pred_indices=False):
- quant, diff, (_,_,ind) = self.encode(input)
- dec = self.decode(quant)
- if return_pred_indices:
- return dec, diff, ind
- return dec, diff
-
- def get_input(self, batch, k):
- x = batch[k]
- if len(x.shape) == 3:
- x = x[..., None]
- x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
- if self.batch_resize_range is not None:
- lower_size = self.batch_resize_range[0]
- upper_size = self.batch_resize_range[1]
- if self.global_step <= 4:
- # do the first few batches with max size to avoid later oom
- new_resize = upper_size
- else:
- new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))
- if new_resize != x.shape[2]:
- x = F.interpolate(x, size=new_resize, mode="bicubic")
- x = x.detach()
- return x
-
- def training_step(self, batch, batch_idx, optimizer_idx):
- # https://github.com/pytorch/pytorch/issues/37142
- # try not to fool the heuristics
- x = self.get_input(batch, self.image_key)
- xrec, qloss, ind = self(x, return_pred_indices=True)
-
- if optimizer_idx == 0:
- # autoencode
- aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train",
- predicted_indices=ind)
-
- self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
- return aeloss
-
- if optimizer_idx == 1:
- # discriminator
- discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train")
- self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
- return discloss
-
- def validation_step(self, batch, batch_idx):
- log_dict = self._validation_step(batch, batch_idx)
- with self.ema_scope():
- log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
- return log_dict
-
- def _validation_step(self, batch, batch_idx, suffix=""):
- x = self.get_input(batch, self.image_key)
- xrec, qloss, ind = self(x, return_pred_indices=True)
- aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,
- self.global_step,
- last_layer=self.get_last_layer(),
- split="val"+suffix,
- predicted_indices=ind
- )
-
- discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,
- self.global_step,
- last_layer=self.get_last_layer(),
- split="val"+suffix,
- predicted_indices=ind
- )
- rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
- self.log(f"val{suffix}/rec_loss", rec_loss,
- prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
- self.log(f"val{suffix}/aeloss", aeloss,
- prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
- if version.parse(pl.__version__) >= version.parse('1.4.0'):
- del log_dict_ae[f"val{suffix}/rec_loss"]
- self.log_dict(log_dict_ae)
- self.log_dict(log_dict_disc)
- return self.log_dict
-
- def configure_optimizers(self):
- lr_d = self.learning_rate
- lr_g = self.lr_g_factor*self.learning_rate
- print("lr_d", lr_d)
- print("lr_g", lr_g)
- opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
- list(self.decoder.parameters())+
- list(self.quantize.parameters())+
- list(self.quant_conv.parameters())+
- list(self.post_quant_conv.parameters()),
- lr=lr_g, betas=(0.5, 0.9))
- opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
- lr=lr_d, betas=(0.5, 0.9))
-
- if self.scheduler_config is not None:
- scheduler = instantiate_from_config(self.scheduler_config)
-
- print("Setting up LambdaLR scheduler...")
- scheduler = [
- {
- 'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
- 'interval': 'step',
- 'frequency': 1
- },
- {
- 'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
- 'interval': 'step',
- 'frequency': 1
- },
- ]
- return [opt_ae, opt_disc], scheduler
- return [opt_ae, opt_disc], []
-
- def get_last_layer(self):
- return self.decoder.conv_out.weight
-
- def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
- log = dict()
- x = self.get_input(batch, self.image_key)
- x = x.to(self.device)
- if only_inputs:
- log["inputs"] = x
- return log
- xrec, _ = self(x)
- if x.shape[1] > 3:
- # colorize with random projection
- assert xrec.shape[1] > 3
- x = self.to_rgb(x)
- xrec = self.to_rgb(xrec)
- log["inputs"] = x
- log["reconstructions"] = xrec
- if plot_ema:
- with self.ema_scope():
- xrec_ema, _ = self(x)
- if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
- log["reconstructions_ema"] = xrec_ema
- return log
-
- def to_rgb(self, x):
- assert self.image_key == "segmentation"
- if not hasattr(self, "colorize"):
- self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
- x = F.conv2d(x, weight=self.colorize)
- x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
- return x
-
-
-class VQModelInterface(VQModel):
- def __init__(self, embed_dim, *args, **kwargs):
- super().__init__(embed_dim=embed_dim, *args, **kwargs)
- self.embed_dim = embed_dim
-
- def encode(self, x):
- h = self.encoder(x)
- h = self.quant_conv(h)
- return h
-
- def decode(self, h, force_not_quantize=False):
- # also go through quantization layer
- if not force_not_quantize:
- quant, emb_loss, info = self.quantize(h)
- else:
- quant = h
- quant = self.post_quant_conv(quant)
- dec = self.decoder(quant)
- return dec
-
-
-class AutoencoderKL(pl.LightningModule):
- def __init__(self,
- ddconfig,
- lossconfig,
- embed_dim,
- ckpt_path=None,
- ignore_keys=[],
- image_key="image",
- colorize_nlabels=None,
- monitor=None,
- ):
- super().__init__()
- self.image_key = image_key
- self.encoder = Encoder(**ddconfig)
- self.decoder = Decoder(**ddconfig)
- self.loss = instantiate_from_config(lossconfig)
- assert ddconfig["double_z"]
- self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
- self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
- self.embed_dim = embed_dim
- if colorize_nlabels is not None:
- assert type(colorize_nlabels)==int
- self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
- if monitor is not None:
- self.monitor = monitor
- if ckpt_path is not None:
- self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
-
- def init_from_ckpt(self, path, ignore_keys=list()):
- sd = torch.load(path, map_location="cpu")["state_dict"]
- keys = list(sd.keys())
- for k in keys:
- for ik in ignore_keys:
- if k.startswith(ik):
- print("Deleting key {} from state_dict.".format(k))
- del sd[k]
- self.load_state_dict(sd, strict=False)
- print(f"Restored from {path}")
-
- def encode(self, x):
- h = self.encoder(x)
- moments = self.quant_conv(h)
- posterior = DiagonalGaussianDistribution(moments)
- return posterior
-
- def decode(self, z):
- z = self.post_quant_conv(z)
- dec = self.decoder(z)
- return dec
-
- def forward(self, input, sample_posterior=True):
- posterior = self.encode(input)
- if sample_posterior:
- z = posterior.sample()
- else:
- z = posterior.mode()
- dec = self.decode(z)
- return dec, posterior
-
- def get_input(self, batch, k):
- x = batch[k]
- if len(x.shape) == 3:
- x = x[..., None]
- x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
- return x
-
- def training_step(self, batch, batch_idx, optimizer_idx):
- inputs = self.get_input(batch, self.image_key)
- reconstructions, posterior = self(inputs)
-
- if optimizer_idx == 0:
- # train encoder+decoder+logvar
- aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train")
- self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
- self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
- return aeloss
-
- if optimizer_idx == 1:
- # train the discriminator
- discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train")
-
- self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
- self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
- return discloss
-
- def validation_step(self, batch, batch_idx):
- inputs = self.get_input(batch, self.image_key)
- reconstructions, posterior = self(inputs)
- aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
- last_layer=self.get_last_layer(), split="val")
-
- discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
- last_layer=self.get_last_layer(), split="val")
-
- self.log("val/rec_loss", log_dict_ae["val/rec_loss"])
- self.log_dict(log_dict_ae)
- self.log_dict(log_dict_disc)
- return self.log_dict
-
- def configure_optimizers(self):
- lr = self.learning_rate
- opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
- list(self.decoder.parameters())+
- list(self.quant_conv.parameters())+
- list(self.post_quant_conv.parameters()),
- lr=lr, betas=(0.5, 0.9))
- opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
- lr=lr, betas=(0.5, 0.9))
- return [opt_ae, opt_disc], []
-
- def get_last_layer(self):
- return self.decoder.conv_out.weight
-
- @torch.no_grad()
- def log_images(self, batch, only_inputs=False, **kwargs):
- log = dict()
- x = self.get_input(batch, self.image_key)
- x = x.to(self.device)
- if not only_inputs:
- xrec, posterior = self(x)
- if x.shape[1] > 3:
- # colorize with random projection
- assert xrec.shape[1] > 3
- x = self.to_rgb(x)
- xrec = self.to_rgb(xrec)
- log["samples"] = self.decode(torch.randn_like(posterior.sample()))
- log["reconstructions"] = xrec
- log["inputs"] = x
- return log
-
- def to_rgb(self, x):
- assert self.image_key == "segmentation"
- if not hasattr(self, "colorize"):
- self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
- x = F.conv2d(x, weight=self.colorize)
- x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
- return x
-
-
-class IdentityFirstStage(torch.nn.Module):
- def __init__(self, *args, vq_interface=False, **kwargs):
- self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff
- super().__init__()
-
- def encode(self, x, *args, **kwargs):
- return x
-
- def decode(self, x, *args, **kwargs):
- return x
-
- def quantize(self, x, *args, **kwargs):
- if self.vq_interface:
- return x, None, [None, None, None]
- return x
-
- def forward(self, x, *args, **kwargs):
- return x
diff --git a/spaces/mygyasir/genious_bgremover/carvekit/ml/arch/fba_matting/layers_WS.py b/spaces/mygyasir/genious_bgremover/carvekit/ml/arch/fba_matting/layers_WS.py
deleted file mode 100644
index 51085989c4f090d4dc5f599be3c550d16ec0b2e7..0000000000000000000000000000000000000000
--- a/spaces/mygyasir/genious_bgremover/carvekit/ml/arch/fba_matting/layers_WS.py
+++ /dev/null
@@ -1,57 +0,0 @@
-"""
-Modified by Nikita Selin (OPHoperHPO)[https://github.com/OPHoperHPO].
-Source url: https://github.com/MarcoForte/FBA_Matting
-License: MIT License
-"""
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-
-
-class Conv2d(nn.Conv2d):
- def __init__(
- self,
- in_channels,
- out_channels,
- kernel_size,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- bias=True,
- ):
- super(Conv2d, self).__init__(
- in_channels,
- out_channels,
- kernel_size,
- stride,
- padding,
- dilation,
- groups,
- bias,
- )
-
- def forward(self, x):
- # return super(Conv2d, self).forward(x)
- weight = self.weight
- weight_mean = (
- weight.mean(dim=1, keepdim=True)
- .mean(dim=2, keepdim=True)
- .mean(dim=3, keepdim=True)
- )
- weight = weight - weight_mean
- # std = (weight).view(weight.size(0), -1).std(dim=1).view(-1, 1, 1, 1) + 1e-5
- std = (
- torch.sqrt(torch.var(weight.view(weight.size(0), -1), dim=1) + 1e-12).view(
- -1, 1, 1, 1
- )
- + 1e-5
- )
- weight = weight / std.expand_as(weight)
- return F.conv2d(
- x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups
- )
-
-
-def BatchNorm2d(num_features):
- return nn.GroupNorm(num_channels=num_features, num_groups=32)
diff --git a/spaces/myrad01/Inpaint-Anything/third_party/lama/saicinpainting/training/modules/ffc.py b/spaces/myrad01/Inpaint-Anything/third_party/lama/saicinpainting/training/modules/ffc.py
deleted file mode 100644
index 2f8aeb1411fc1537916275fd3243706cc74b8d3c..0000000000000000000000000000000000000000
--- a/spaces/myrad01/Inpaint-Anything/third_party/lama/saicinpainting/training/modules/ffc.py
+++ /dev/null
@@ -1,433 +0,0 @@
-# Fast Fourier Convolution NeurIPS 2020
-# original implementation https://github.com/pkumivision/FFC/blob/main/model_zoo/ffc.py
-# paper https://proceedings.neurips.cc/paper/2020/file/2fd5d41ec6cfab47e32164d5624269b1-Paper.pdf
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from saicinpainting.training.modules.base import get_activation, BaseDiscriminator
-from saicinpainting.training.modules.spatial_transform import LearnableSpatialTransformWrapper
-from saicinpainting.training.modules.squeeze_excitation import SELayer
-from saicinpainting.utils import get_shape
-
-
-class FFCSE_block(nn.Module):
-
- def __init__(self, channels, ratio_g):
- super(FFCSE_block, self).__init__()
- in_cg = int(channels * ratio_g)
- in_cl = channels - in_cg
- r = 16
-
- self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
- self.conv1 = nn.Conv2d(channels, channels // r,
- kernel_size=1, bias=True)
- self.relu1 = nn.ReLU(inplace=True)
- self.conv_a2l = None if in_cl == 0 else nn.Conv2d(
- channels // r, in_cl, kernel_size=1, bias=True)
- self.conv_a2g = None if in_cg == 0 else nn.Conv2d(
- channels // r, in_cg, kernel_size=1, bias=True)
- self.sigmoid = nn.Sigmoid()
-
- def forward(self, x):
- x = x if type(x) is tuple else (x, 0)
- id_l, id_g = x
-
- x = id_l if type(id_g) is int else torch.cat([id_l, id_g], dim=1)
- x = self.avgpool(x)
- x = self.relu1(self.conv1(x))
-
- x_l = 0 if self.conv_a2l is None else id_l * \
- self.sigmoid(self.conv_a2l(x))
- x_g = 0 if self.conv_a2g is None else id_g * \
- self.sigmoid(self.conv_a2g(x))
- return x_l, x_g
-
-
-class FourierUnit(nn.Module):
-
- def __init__(self, in_channels, out_channels, groups=1, spatial_scale_factor=None, spatial_scale_mode='bilinear',
- spectral_pos_encoding=False, use_se=False, se_kwargs=None, ffc3d=False, fft_norm='ortho'):
- # bn_layer not used
- super(FourierUnit, self).__init__()
- self.groups = groups
-
- self.conv_layer = torch.nn.Conv2d(in_channels=in_channels * 2 + (2 if spectral_pos_encoding else 0),
- out_channels=out_channels * 2,
- kernel_size=1, stride=1, padding=0, groups=self.groups, bias=False)
- self.bn = torch.nn.BatchNorm2d(out_channels * 2)
- self.relu = torch.nn.ReLU(inplace=True)
-
- # squeeze and excitation block
- self.use_se = use_se
- if use_se:
- if se_kwargs is None:
- se_kwargs = {}
- self.se = SELayer(self.conv_layer.in_channels, **se_kwargs)
-
- self.spatial_scale_factor = spatial_scale_factor
- self.spatial_scale_mode = spatial_scale_mode
- self.spectral_pos_encoding = spectral_pos_encoding
- self.ffc3d = ffc3d
- self.fft_norm = fft_norm
-
- def forward(self, x):
- batch = x.shape[0]
-
- if self.spatial_scale_factor is not None:
- orig_size = x.shape[-2:]
- x = F.interpolate(x, scale_factor=self.spatial_scale_factor, mode=self.spatial_scale_mode, align_corners=False)
-
- r_size = x.size()
- # (batch, c, h, w/2+1, 2)
- fft_dim = (-3, -2, -1) if self.ffc3d else (-2, -1)
- ffted = torch.fft.rfftn(x, dim=fft_dim, norm=self.fft_norm)
- ffted = torch.stack((ffted.real, ffted.imag), dim=-1)
- ffted = ffted.permute(0, 1, 4, 2, 3).contiguous() # (batch, c, 2, h, w/2+1)
- ffted = ffted.view((batch, -1,) + ffted.size()[3:])
-
- if self.spectral_pos_encoding:
- height, width = ffted.shape[-2:]
- coords_vert = torch.linspace(0, 1, height)[None, None, :, None].expand(batch, 1, height, width).to(ffted)
- coords_hor = torch.linspace(0, 1, width)[None, None, None, :].expand(batch, 1, height, width).to(ffted)
- ffted = torch.cat((coords_vert, coords_hor, ffted), dim=1)
-
- if self.use_se:
- ffted = self.se(ffted)
-
- ffted = self.conv_layer(ffted) # (batch, c*2, h, w/2+1)
- ffted = self.relu(self.bn(ffted))
-
- ffted = ffted.view((batch, -1, 2,) + ffted.size()[2:]).permute(
- 0, 1, 3, 4, 2).contiguous() # (batch,c, t, h, w/2+1, 2)
- ffted = torch.complex(ffted[..., 0], ffted[..., 1])
-
- ifft_shape_slice = x.shape[-3:] if self.ffc3d else x.shape[-2:]
- output = torch.fft.irfftn(ffted, s=ifft_shape_slice, dim=fft_dim, norm=self.fft_norm)
-
- if self.spatial_scale_factor is not None:
- output = F.interpolate(output, size=orig_size, mode=self.spatial_scale_mode, align_corners=False)
-
- return output
-
-
-class SpectralTransform(nn.Module):
-
- def __init__(self, in_channels, out_channels, stride=1, groups=1, enable_lfu=True, **fu_kwargs):
- # bn_layer not used
- super(SpectralTransform, self).__init__()
- self.enable_lfu = enable_lfu
- if stride == 2:
- self.downsample = nn.AvgPool2d(kernel_size=(2, 2), stride=2)
- else:
- self.downsample = nn.Identity()
-
- self.stride = stride
- self.conv1 = nn.Sequential(
- nn.Conv2d(in_channels, out_channels //
- 2, kernel_size=1, groups=groups, bias=False),
- nn.BatchNorm2d(out_channels // 2),
- nn.ReLU(inplace=True)
- )
- self.fu = FourierUnit(
- out_channels // 2, out_channels // 2, groups, **fu_kwargs)
- if self.enable_lfu:
- self.lfu = FourierUnit(
- out_channels // 2, out_channels // 2, groups)
- self.conv2 = torch.nn.Conv2d(
- out_channels // 2, out_channels, kernel_size=1, groups=groups, bias=False)
-
- def forward(self, x):
-
- x = self.downsample(x)
- x = self.conv1(x)
- output = self.fu(x)
-
- if self.enable_lfu:
- n, c, h, w = x.shape
- split_no = 2
- split_s = h // split_no
- xs = torch.cat(torch.split(
- x[:, :c // 4], split_s, dim=-2), dim=1).contiguous()
- xs = torch.cat(torch.split(xs, split_s, dim=-1),
- dim=1).contiguous()
- xs = self.lfu(xs)
- xs = xs.repeat(1, 1, split_no, split_no).contiguous()
- else:
- xs = 0
-
- output = self.conv2(x + output + xs)
-
- return output
-
-
-class FFC(nn.Module):
-
- def __init__(self, in_channels, out_channels, kernel_size,
- ratio_gin, ratio_gout, stride=1, padding=0,
- dilation=1, groups=1, bias=False, enable_lfu=True,
- padding_type='reflect', gated=False, **spectral_kwargs):
- super(FFC, self).__init__()
-
- assert stride == 1 or stride == 2, "Stride should be 1 or 2."
- self.stride = stride
-
- in_cg = int(in_channels * ratio_gin)
- in_cl = in_channels - in_cg
- out_cg = int(out_channels * ratio_gout)
- out_cl = out_channels - out_cg
- #groups_g = 1 if groups == 1 else int(groups * ratio_gout)
- #groups_l = 1 if groups == 1 else groups - groups_g
-
- self.ratio_gin = ratio_gin
- self.ratio_gout = ratio_gout
- self.global_in_num = in_cg
-
- module = nn.Identity if in_cl == 0 or out_cl == 0 else nn.Conv2d
- self.convl2l = module(in_cl, out_cl, kernel_size,
- stride, padding, dilation, groups, bias, padding_mode=padding_type)
- module = nn.Identity if in_cl == 0 or out_cg == 0 else nn.Conv2d
- self.convl2g = module(in_cl, out_cg, kernel_size,
- stride, padding, dilation, groups, bias, padding_mode=padding_type)
- module = nn.Identity if in_cg == 0 or out_cl == 0 else nn.Conv2d
- self.convg2l = module(in_cg, out_cl, kernel_size,
- stride, padding, dilation, groups, bias, padding_mode=padding_type)
- module = nn.Identity if in_cg == 0 or out_cg == 0 else SpectralTransform
- self.convg2g = module(
- in_cg, out_cg, stride, 1 if groups == 1 else groups // 2, enable_lfu, **spectral_kwargs)
-
- self.gated = gated
- module = nn.Identity if in_cg == 0 or out_cl == 0 or not self.gated else nn.Conv2d
- self.gate = module(in_channels, 2, 1)
-
- def forward(self, x):
- x_l, x_g = x if type(x) is tuple else (x, 0)
- out_xl, out_xg = 0, 0
-
- if self.gated:
- total_input_parts = [x_l]
- if torch.is_tensor(x_g):
- total_input_parts.append(x_g)
- total_input = torch.cat(total_input_parts, dim=1)
-
- gates = torch.sigmoid(self.gate(total_input))
- g2l_gate, l2g_gate = gates.chunk(2, dim=1)
- else:
- g2l_gate, l2g_gate = 1, 1
-
- if self.ratio_gout != 1:
- out_xl = self.convl2l(x_l) + self.convg2l(x_g) * g2l_gate
- if self.ratio_gout != 0:
- out_xg = self.convl2g(x_l) * l2g_gate + self.convg2g(x_g)
-
- return out_xl, out_xg
-
-
-class FFC_BN_ACT(nn.Module):
-
- def __init__(self, in_channels, out_channels,
- kernel_size, ratio_gin, ratio_gout,
- stride=1, padding=0, dilation=1, groups=1, bias=False,
- norm_layer=nn.BatchNorm2d, activation_layer=nn.Identity,
- padding_type='reflect',
- enable_lfu=True, **kwargs):
- super(FFC_BN_ACT, self).__init__()
- self.ffc = FFC(in_channels, out_channels, kernel_size,
- ratio_gin, ratio_gout, stride, padding, dilation,
- groups, bias, enable_lfu, padding_type=padding_type, **kwargs)
- lnorm = nn.Identity if ratio_gout == 1 else norm_layer
- gnorm = nn.Identity if ratio_gout == 0 else norm_layer
- global_channels = int(out_channels * ratio_gout)
- self.bn_l = lnorm(out_channels - global_channels)
- self.bn_g = gnorm(global_channels)
-
- lact = nn.Identity if ratio_gout == 1 else activation_layer
- gact = nn.Identity if ratio_gout == 0 else activation_layer
- self.act_l = lact(inplace=True)
- self.act_g = gact(inplace=True)
-
- def forward(self, x):
- x_l, x_g = self.ffc(x)
- x_l = self.act_l(self.bn_l(x_l))
- x_g = self.act_g(self.bn_g(x_g))
- return x_l, x_g
-
-
-class FFCResnetBlock(nn.Module):
- def __init__(self, dim, padding_type, norm_layer, activation_layer=nn.ReLU, dilation=1,
- spatial_transform_kwargs=None, inline=False, **conv_kwargs):
- super().__init__()
- self.conv1 = FFC_BN_ACT(dim, dim, kernel_size=3, padding=dilation, dilation=dilation,
- norm_layer=norm_layer,
- activation_layer=activation_layer,
- padding_type=padding_type,
- **conv_kwargs)
- self.conv2 = FFC_BN_ACT(dim, dim, kernel_size=3, padding=dilation, dilation=dilation,
- norm_layer=norm_layer,
- activation_layer=activation_layer,
- padding_type=padding_type,
- **conv_kwargs)
- if spatial_transform_kwargs is not None:
- self.conv1 = LearnableSpatialTransformWrapper(self.conv1, **spatial_transform_kwargs)
- self.conv2 = LearnableSpatialTransformWrapper(self.conv2, **spatial_transform_kwargs)
- self.inline = inline
-
- def forward(self, x):
- if self.inline:
- x_l, x_g = x[:, :-self.conv1.ffc.global_in_num], x[:, -self.conv1.ffc.global_in_num:]
- else:
- x_l, x_g = x if type(x) is tuple else (x, 0)
-
- id_l, id_g = x_l, x_g
-
- x_l, x_g = self.conv1((x_l, x_g))
- x_l, x_g = self.conv2((x_l, x_g))
-
- x_l, x_g = id_l + x_l, id_g + x_g
- out = x_l, x_g
- if self.inline:
- out = torch.cat(out, dim=1)
- return out
-
-
-class ConcatTupleLayer(nn.Module):
- def forward(self, x):
- assert isinstance(x, tuple)
- x_l, x_g = x
- assert torch.is_tensor(x_l) or torch.is_tensor(x_g)
- if not torch.is_tensor(x_g):
- return x_l
- return torch.cat(x, dim=1)
-
-
-class FFCResNetGenerator(nn.Module):
- def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d,
- padding_type='reflect', activation_layer=nn.ReLU,
- up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True),
- init_conv_kwargs={}, downsample_conv_kwargs={}, resnet_conv_kwargs={},
- spatial_transform_layers=None, spatial_transform_kwargs={},
- add_out_act=True, max_features=1024, out_ffc=False, out_ffc_kwargs={}):
- assert (n_blocks >= 0)
- super().__init__()
-
- model = [nn.ReflectionPad2d(3),
- FFC_BN_ACT(input_nc, ngf, kernel_size=7, padding=0, norm_layer=norm_layer,
- activation_layer=activation_layer, **init_conv_kwargs)]
-
- ### downsample
- for i in range(n_downsampling):
- mult = 2 ** i
- if i == n_downsampling - 1:
- cur_conv_kwargs = dict(downsample_conv_kwargs)
- cur_conv_kwargs['ratio_gout'] = resnet_conv_kwargs.get('ratio_gin', 0)
- else:
- cur_conv_kwargs = downsample_conv_kwargs
- model += [FFC_BN_ACT(min(max_features, ngf * mult),
- min(max_features, ngf * mult * 2),
- kernel_size=3, stride=2, padding=1,
- norm_layer=norm_layer,
- activation_layer=activation_layer,
- **cur_conv_kwargs)]
-
- mult = 2 ** n_downsampling
- feats_num_bottleneck = min(max_features, ngf * mult)
-
- ### resnet blocks
- for i in range(n_blocks):
- cur_resblock = FFCResnetBlock(feats_num_bottleneck, padding_type=padding_type, activation_layer=activation_layer,
- norm_layer=norm_layer, **resnet_conv_kwargs)
- if spatial_transform_layers is not None and i in spatial_transform_layers:
- cur_resblock = LearnableSpatialTransformWrapper(cur_resblock, **spatial_transform_kwargs)
- model += [cur_resblock]
-
- model += [ConcatTupleLayer()]
-
- ### upsample
- for i in range(n_downsampling):
- mult = 2 ** (n_downsampling - i)
- model += [nn.ConvTranspose2d(min(max_features, ngf * mult),
- min(max_features, int(ngf * mult / 2)),
- kernel_size=3, stride=2, padding=1, output_padding=1),
- up_norm_layer(min(max_features, int(ngf * mult / 2))),
- up_activation]
-
- if out_ffc:
- model += [FFCResnetBlock(ngf, padding_type=padding_type, activation_layer=activation_layer,
- norm_layer=norm_layer, inline=True, **out_ffc_kwargs)]
-
- model += [nn.ReflectionPad2d(3),
- nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
- if add_out_act:
- model.append(get_activation('tanh' if add_out_act is True else add_out_act))
- self.model = nn.Sequential(*model)
-
- def forward(self, input):
- return self.model(input)
-
-
-class FFCNLayerDiscriminator(BaseDiscriminator):
- def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, max_features=512,
- init_conv_kwargs={}, conv_kwargs={}):
- super().__init__()
- self.n_layers = n_layers
-
- def _act_ctor(inplace=True):
- return nn.LeakyReLU(negative_slope=0.2, inplace=inplace)
-
- kw = 3
- padw = int(np.ceil((kw-1.0)/2))
- sequence = [[FFC_BN_ACT(input_nc, ndf, kernel_size=kw, padding=padw, norm_layer=norm_layer,
- activation_layer=_act_ctor, **init_conv_kwargs)]]
-
- nf = ndf
- for n in range(1, n_layers):
- nf_prev = nf
- nf = min(nf * 2, max_features)
-
- cur_model = [
- FFC_BN_ACT(nf_prev, nf,
- kernel_size=kw, stride=2, padding=padw,
- norm_layer=norm_layer,
- activation_layer=_act_ctor,
- **conv_kwargs)
- ]
- sequence.append(cur_model)
-
- nf_prev = nf
- nf = min(nf * 2, 512)
-
- cur_model = [
- FFC_BN_ACT(nf_prev, nf,
- kernel_size=kw, stride=1, padding=padw,
- norm_layer=norm_layer,
- activation_layer=lambda *args, **kwargs: nn.LeakyReLU(*args, negative_slope=0.2, **kwargs),
- **conv_kwargs),
- ConcatTupleLayer()
- ]
- sequence.append(cur_model)
-
- sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]
-
- for n in range(len(sequence)):
- setattr(self, 'model'+str(n), nn.Sequential(*sequence[n]))
-
- def get_all_activations(self, x):
- res = [x]
- for n in range(self.n_layers + 2):
- model = getattr(self, 'model' + str(n))
- res.append(model(res[-1]))
- return res[1:]
-
- def forward(self, x):
- act = self.get_all_activations(x)
- feats = []
- for out in act[:-1]:
- if isinstance(out, tuple):
- if torch.is_tensor(out[1]):
- out = torch.cat(out, dim=1)
- else:
- out = out[0]
- feats.append(out)
- return act[-1], feats
diff --git a/spaces/nateraw/dockerplayground/start_server.sh b/spaces/nateraw/dockerplayground/start_server.sh
deleted file mode 100644
index 01ae830fd64c84e2f82d64836d3ef8e42e712361..0000000000000000000000000000000000000000
--- a/spaces/nateraw/dockerplayground/start_server.sh
+++ /dev/null
@@ -1,12 +0,0 @@
-#!/bin/bash
-echo "Starting Jupyter Lab with token $JUPYTER_TOKEN"
-
-jupyter-lab \
- --ip 0.0.0.0 \
- --port 7860 \
- --no-browser \
- --allow-root \
- --ServerApp.token="$JUPYTER_TOKEN" \
- --ServerApp.tornado_settings="{'headers': {'Content-Security-Policy': 'frame-ancestors *'}}" \
- --ServerApp.disable_check_xsrf=True \
- --ServerApp.cookie_options="{'SameSite': 'None', 'Secure': True}"
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Office 2013 Ita Preattivato Torrent.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Office 2013 Ita Preattivato Torrent.md
deleted file mode 100644
index 26d48a17e4cb8cc24d5c2333082e2d7ff0a5370e..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Office 2013 Ita Preattivato Torrent.md
+++ /dev/null
@@ -1,20 +0,0 @@
-How to Download and Install Office 2013 ITA Preattivato Torrent
-Office 2013 is a popular productivity suite that includes applications such as Word, Excel, PowerPoint, Outlook, and more. If you want to use Office 2013 in Italian, you can download and install a pre-activated version from a torrent site. However, you should be aware of the risks and legal issues involved in using pirated software.
-In this article, we will show you how to download and install Office 2013 ITA preattivato torrent safely and easily. Follow these steps:
-office 2013 ita preattivato torrent Download ✅ https://urlcod.com/2uI9O3
-Find a reliable torrent site that offers Office 2013 ITA preattivato torrent. You can use a search engine or a torrent aggregator to find one. Some examples are The Pirate Bay, Kickass Torrents, and RARBG.
-Download a torrent client that can handle magnet links. A torrent client is a software that allows you to download files from other users who are sharing them. Some examples are uTorrent, BitTorrent, and qBittorrent.
-Open the torrent site and search for Office 2013 ITA preattivato torrent. You should see a list of results with different file sizes and seeders. Seeders are users who have the complete file and are uploading it to others. Choose the one with the most seeders and the smallest file size.
-Click on the magnet link or the download button to start downloading the torrent file. A magnet link is a URL that contains information about the file, such as its name, size, and hash. A download button will download a small file that contains the same information.
-Open the torrent file with your torrent client. It will automatically connect to other users who have the file and start downloading it. You can monitor the progress and speed of the download in your torrent client.
-Once the download is complete, you will have a folder with the Office 2013 ITA preattivato files. You can open it and run the setup.exe file to install Office 2013 on your computer. You do not need to enter any product key or activation code as it is already pre-activated.
-
-Congratulations! You have successfully downloaded and installed Office 2013 ITA preattivato torrent on your computer. You can now enjoy using Office 2013 in Italian for free.
-However, you should also be aware of the potential risks and legal issues involved in using pirated software. Pirated software may contain viruses, malware, or spyware that can harm your computer or steal your personal information. Pirated software may also not receive updates or support from Microsoft, which can affect its performance and security. Pirated software may also violate the intellectual property rights of Microsoft and other software developers, which can result in legal consequences or fines.
-
-Therefore, we recommend that you use only genuine and licensed software from official sources. You can purchase Office 2013 from Microsoft's website or authorized resellers. You can also use alternative productivity suites that are free and legal, such as LibreOffice, OpenOffice, or Google Docs.
-We hope this article was helpful and informative. Thank you for reading!
81aa517590This is an abstractive text summarizer app using fine-tuned bart-large-cnn model. The abstractive approach involves rephrasing the complete document while capturing the complete meaning of the document. This type of summarization provides more human-like summary.
-
Note: For faster summaries input smaller texts. Current model supports context size of 1024 words, and anything longer is truncated
-
Sample text inputs are provided at the bottom!
-"""
-
-
-
-model_name = "nikhedward/bart-large-cnn-finetuned-multi-news"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-
-def auto_summarize(inp):
- inp = inp.replace('\n','')
- inp = tokenizer.encode(inp, return_tensors='pt', max_length=1024, truncation=True)
- summary_ids = model.generate(inp, num_beams=4, max_length=150, early_stopping=True, do_sample=True, top_k=50, top_p=0.95)
- summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
- return summary
-
-interface = gr.Interface(fn=auto_summarize, inputs=gr.inputs.Textbox(lines=10, label="Input Text"), description = desc, theme = "dark-peach",
-examples = sample_texts, title = title, outputs="text", css=".footer{display:none !important}")
-
-interface.launch()
-
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/configs/common/README.md b/spaces/nikitaPDL2023/assignment4/detectron2/configs/common/README.md
deleted file mode 100644
index 912cc29927542bfe4258d3208cf52d73cb0ea477..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/configs/common/README.md
+++ /dev/null
@@ -1,6 +0,0 @@
-This directory provides definitions for a few common models, dataloaders, scheduler,
-and optimizers that are often used in training.
-The definition of these objects are provided in the form of lazy instantiation:
-their arguments can be edited by users before constructing the objects.
-
-They can be imported, or loaded by `model_zoo.get_config` API in users' own configs.
diff --git a/spaces/nupurkmr9/concept-ablation/__init__.py b/spaces/nupurkmr9/concept-ablation/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/oliver2023/chatgpt-on-wechat/channel/wechat/wechaty_channel.py b/spaces/oliver2023/chatgpt-on-wechat/channel/wechat/wechaty_channel.py
deleted file mode 100644
index 65348bacc96c46f0e9b9eee38dc062de3805bf56..0000000000000000000000000000000000000000
--- a/spaces/oliver2023/chatgpt-on-wechat/channel/wechat/wechaty_channel.py
+++ /dev/null
@@ -1,125 +0,0 @@
-# encoding:utf-8
-
-"""
-wechaty channel
-Python Wechaty - https://github.com/wechaty/python-wechaty
-"""
-import base64
-import os
-import time
-import asyncio
-from bridge.context import Context
-from wechaty_puppet import FileBox
-from wechaty import Wechaty, Contact
-from wechaty.user import Message
-from bridge.reply import *
-from bridge.context import *
-from channel.chat_channel import ChatChannel
-from channel.wechat.wechaty_message import WechatyMessage
-from common.log import logger
-from common.singleton import singleton
-from config import conf
-try:
- from voice.audio_convert import any_to_sil
-except Exception as e:
- pass
-
-@singleton
-class WechatyChannel(ChatChannel):
- NOT_SUPPORT_REPLYTYPE = []
- def __init__(self):
- super().__init__()
-
- def startup(self):
- config = conf()
- token = config.get('wechaty_puppet_service_token')
- os.environ['WECHATY_PUPPET_SERVICE_TOKEN'] = token
- asyncio.run(self.main())
-
- async def main(self):
-
- loop = asyncio.get_event_loop()
- #将asyncio的loop传入处理线程
- self.handler_pool._initializer= lambda: asyncio.set_event_loop(loop)
- self.bot = Wechaty()
- self.bot.on('login', self.on_login)
- self.bot.on('message', self.on_message)
- await self.bot.start()
-
- async def on_login(self, contact: Contact):
- self.user_id = contact.contact_id
- self.name = contact.name
- logger.info('[WX] login user={}'.format(contact))
-
- # 统一的发送函数，每个Channel自行实现，根据reply的type字段发送不同类型的消息
- def send(self, reply: Reply, context: Context):
- receiver_id = context['receiver']
- loop = asyncio.get_event_loop()
- if context['isgroup']:
- receiver = asyncio.run_coroutine_threadsafe(self.bot.Room.find(receiver_id),loop).result()
- else:
- receiver = asyncio.run_coroutine_threadsafe(self.bot.Contact.find(receiver_id),loop).result()
- msg = None
- if reply.type == ReplyType.TEXT:
- msg = reply.content
- asyncio.run_coroutine_threadsafe(receiver.say(msg),loop).result()
- logger.info('[WX] sendMsg={}, receiver={}'.format(reply, receiver))
- elif reply.type == ReplyType.ERROR or reply.type == ReplyType.INFO:
- msg = reply.content
- asyncio.run_coroutine_threadsafe(receiver.say(msg),loop).result()
- logger.info('[WX] sendMsg={}, receiver={}'.format(reply, receiver))
- elif reply.type == ReplyType.VOICE:
- voiceLength = None
- file_path = reply.content
- sil_file = os.path.splitext(file_path)[0] + '.sil'
- voiceLength = int(any_to_sil(file_path, sil_file))
- if voiceLength >= 60000:
- voiceLength = 60000
- logger.info('[WX] voice too long, length={}, set to 60s'.format(voiceLength))
- # 发送语音
- t = int(time.time())
- msg = FileBox.from_file(sil_file, name=str(t) + '.sil')
- if voiceLength is not None:
- msg.metadata['voiceLength'] = voiceLength
- asyncio.run_coroutine_threadsafe(receiver.say(msg),loop).result()
- try:
- os.remove(file_path)
- if sil_file != file_path:
- os.remove(sil_file)
- except Exception as e:
- pass
- logger.info('[WX] sendVoice={}, receiver={}'.format(reply.content, receiver))
- elif reply.type == ReplyType.IMAGE_URL: # 从网络下载图片
- img_url = reply.content
- t = int(time.time())
- msg = FileBox.from_url(url=img_url, name=str(t) + '.png')
- asyncio.run_coroutine_threadsafe(receiver.say(msg),loop).result()
- logger.info('[WX] sendImage url={}, receiver={}'.format(img_url,receiver))
- elif reply.type == ReplyType.IMAGE: # 从文件读取图片
- image_storage = reply.content
- image_storage.seek(0)
- t = int(time.time())
- msg = FileBox.from_base64(base64.b64encode(image_storage.read()), str(t) + '.png')
- asyncio.run_coroutine_threadsafe(receiver.say(msg),loop).result()
- logger.info('[WX] sendImage, receiver={}'.format(receiver))
-
- async def on_message(self, msg: Message):
- """
- listen for message event
- """
- try:
- cmsg = await WechatyMessage(msg)
- except NotImplementedError as e:
- logger.debug('[WX] {}'.format(e))
- return
- except Exception as e:
- logger.exception('[WX] {}'.format(e))
- return
- logger.debug('[WX] message:{}'.format(cmsg))
- room = msg.room() # 获取消息来自的群聊. 如果消息不是来自群聊, 则返回None
- isgroup = room is not None
- ctype = cmsg.ctype
- context = self._compose_context(ctype, cmsg.content, isgroup=isgroup, msg=cmsg)
- if context:
- logger.info('[WX] receiveMsg={}, context={}'.format(cmsg, context))
- self.produce(context)
\ No newline at end of file
diff --git a/spaces/openaccess-ai-collective/manticore-13b-chat-pyg/README.md b/spaces/openaccess-ai-collective/manticore-13b-chat-pyg/README.md
deleted file mode 100644
index 0bf50653704091fd4cb182b46801624d8fd2b810..0000000000000000000000000000000000000000
--- a/spaces/openaccess-ai-collective/manticore-13b-chat-pyg/README.md
+++ /dev/null
@@ -1,18 +0,0 @@
----
-title: Manticore 13B Chat
-emoji: 🏃
-colorFrom: blue
-colorTo: gray
-sdk: gradio
-sdk_version: 3.29.0
-app_file: tabbed.py
-pinned: false
-duplicated_from: openaccess-ai-collective/ggml-ui
----
-
-# GGML UI Inference w/ HuggingFace Spaces
-
-- Fork this space to use your own GGML models. Simply update the [./config.yml](./config.yml)
-- Contribute at [https://github.com/OpenAccess-AI-Collective/ggml-webui](https://github.com/OpenAccess-AI-Collective/ggml-webui)
-
-Brought to you by [OpenAccess AI Collective](https://github.com/OpenAccess-AI-Collective)
\ No newline at end of file
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/ko/installation.md b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/ko/installation.md
deleted file mode 100644
index 4a9146a22620699a7faabb45844809be581a4d7a..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/ko/installation.md
+++ /dev/null
@@ -1,142 +0,0 @@
-
-
-# 설치
-
-사용하시는 라이브러리에 맞는 🤗 Diffusers를 설치하세요.
-
-🤗 Diffusers는 Python 3.8+, PyTorch 1.7.0+ 및 flax에서 테스트되었습니다. 사용중인 딥러닝 라이브러리에 대한 아래의 설치 안내를 따르세요.
-
-- [PyTorch 설치 안내](https://pytorch.org/get-started/locally/)
-- [Flax 설치 안내](https://flax.readthedocs.io/en/latest/)
-
-## pip를 이용한 설치
-
-[가상 환경](https://docs.python.org/3/library/venv.html)에 🤗 Diffusers를 설치해야 합니다.
-Python 가상 환경에 익숙하지 않은 경우 [가상환경 pip 설치 가이드](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/)를 살펴보세요.
-가상 환경을 사용하면 서로 다른 프로젝트를 더 쉽게 관리하고, 종속성간의 호환성 문제를 피할 수 있습니다.
-
-프로젝트 디렉토리에 가상 환경을 생성하는 것으로 시작하세요:
-
-```bash
-python -m venv .env
-```
-
-그리고 가상 환경을 활성화합니다:
-
-```bash
-source .env/bin/activate
-```
-
-이제 다음의 명령어로 🤗 Diffusers를 설치할 준비가 되었습니다:
-
-**PyTorch의 경우**
-
-```bash
-pip install diffusers["torch"]
-```
-
-**Flax의 경우**
-
-```bash
-pip install diffusers["flax"]
-```
-
-## 소스로부터 설치
-
-소스에서 `diffusers`를 설치하기 전에, `torch` 및 `accelerate`이 설치되어 있는지 확인하세요.
-
-`torch` 설치에 대해서는 [torch docs](https://pytorch.org/get-started/locally/#start-locally)를 참고하세요.
-
-다음과 같이 `accelerate`을 설치하세요.
-
-```bash
-pip install accelerate
-```
-
-다음 명령어를 사용하여 소스에서 🤗 Diffusers를 설치하세요:
-
-```bash
-pip install git+https://github.com/huggingface/diffusers
-```
-
-이 명령어는 최신 `stable` 버전이 아닌 최첨단 `main` 버전을 설치합니다.
-`main` 버전은 최신 개발 정보를 최신 상태로 유지하는 데 유용합니다.
-예를 들어 마지막 공식 릴리즈 이후 버그가 수정되었지만, 새 릴리즈가 아직 출시되지 않은 경우입니다.
-그러나 이는 `main` 버전이 항상 안정적이지 않을 수 있음을 의미합니다.
-우리는 `main` 버전이 지속적으로 작동하도록 노력하고 있으며, 대부분의 문제는 보통 몇 시간 또는 하루 안에 해결됩니다.
-문제가 발생하면 더 빨리 해결할 수 있도록 [Issue](https://github.com/huggingface/transformers/issues)를 열어주세요!
-
-
-## 편집가능한 설치
-
-다음을 수행하려면 편집가능한 설치가 필요합니다:
-
-* 소스 코드의 `main` 버전을 사용
-* 🤗 Diffusers에 기여 (코드의 변경 사항을 테스트하기 위해 필요)
-
-저장소를 복제하고 다음 명령어를 사용하여 🤗 Diffusers를 설치합니다:
-
-```bash
-git clone https://github.com/huggingface/diffusers.git
-cd diffusers
-```
-
-**PyTorch의 경우**
-
-```
-pip install -e ".[torch]"
-```
-
-**Flax의 경우**
-
-```
-pip install -e ".[flax]"
-```
-
-이러한 명령어들은 저장소를 복제한 폴더와 Python 라이브러리 경로를 연결합니다.
-Python은 이제 일반 라이브러리 경로에 더하여 복제한 폴더 내부를 살펴봅니다.
-예를들어 Python 패키지가 `~/anaconda3/envs/main/lib/python3.8/site-packages/`에 설치되어 있는 경우 Python은 복제한 폴더인 `~/diffusers/`도 검색합니다.
-
-
-
-라이브러리를 계속 사용하려면 `diffusers` 폴더를 유지해야 합니다.
-
-
-
-이제 다음 명령어를 사용하여 최신 버전의 🤗 Diffusers로 쉽게 업데이트할 수 있습니다:
-
-```bash
-cd ~/diffusers/
-git pull
-```
-
-이렇게 하면, 다음에 실행할 때 Python 환경이 🤗 Diffusers의 `main` 버전을 찾게 됩니다.
-
-## 텔레메트리 로깅에 대한 알림
-
-우리 라이브러리는 `from_pretrained()` 요청 중에 텔레메트리 정보를 원격으로 수집합니다.
-이 데이터에는 Diffusers 및 PyTorch/Flax의 버전, 요청된 모델 또는 파이프라인 클래스, 그리고 허브에서 호스팅되는 경우 사전학습된 체크포인트에 대한 경로를 포함합니다.
-이 사용 데이터는 문제를 디버깅하고 새로운 기능의 우선순위를 지정하는데 도움이 됩니다.
-텔레메트리는 HuggingFace 허브에서 모델과 파이프라인을 불러올 때만 전송되며, 로컬 사용 중에는 수집되지 않습니다.
-
-우리는 추가 정보를 공유하지 않기를 원하는 사람이 있다는 것을 이해하고 개인 정보를 존중하므로, 터미널에서 `DISABLE_TELEMETRY` 환경 변수를 설정하여 텔레메트리 수집을 비활성화할 수 있습니다.
-
-Linux/MacOS에서:
-```bash
-export DISABLE_TELEMETRY=YES
-```
-
-Windows에서:
-```bash
-set DISABLE_TELEMETRY=YES
-```
\ No newline at end of file
diff --git a/spaces/pakooo/Text2Image/README.md b/spaces/pakooo/Text2Image/README.md
deleted file mode 100644
index f326898bc24067a7c44b0e55bfa2ace562581751..0000000000000000000000000000000000000000
--- a/spaces/pakooo/Text2Image/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Text To Image
-emoji: 👀
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
-duplicated_from: yizhangliu/Text-to-Image
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/parkyzh/bingo/src/lib/utils.ts b/spaces/parkyzh/bingo/src/lib/utils.ts
deleted file mode 100644
index 07feedb34e356b1b3cf867872f32d47a96ae12fb..0000000000000000000000000000000000000000
--- a/spaces/parkyzh/bingo/src/lib/utils.ts
+++ /dev/null
@@ -1,138 +0,0 @@
-import { clsx, type ClassValue } from 'clsx'
-import { customAlphabet } from 'nanoid'
-import { twMerge } from 'tailwind-merge'
-
-export function cn(...inputs: ClassValue[]) {
- return twMerge(clsx(inputs))
-}
-
-export const nanoid = customAlphabet(
- '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz',
- 7
-) // 7-character random string
-
-export function createChunkDecoder() {
- const decoder = new TextDecoder()
- return function (chunk: Uint8Array | undefined): string {
- if (!chunk) return ''
- return decoder.decode(chunk, { stream: true })
- }
-}
-
-export function random (start: number, end: number) {
- return start + Math.ceil(Math.random() * (end - start))
-}
-
-export function randomIP() {
- return `11.${random(104, 107)}.${random(1, 255)}.${random(1, 255)}`
-}
-
-export function parseHeadersFromCurl(content: string) {
- const re = /-H '([^:]+):\s*([^']+)/mg
- const headers: HeadersInit = {}
- content = content.replaceAll('-H "', '-H \'').replaceAll('" ^', '\'\\').replaceAll('^\\^"', '"') // 将 cmd curl 转成 bash curl
- content.replace(re, (_: string, key: string, value: string) => {
- headers[key] = value
- return ''
- })
-
- return headers
-}
-
-export const ChunkKeys = ['BING_HEADER', 'BING_HEADER1', 'BING_HEADER2']
-export function encodeHeadersToCookie(content: string) {
- const base64Content = btoa(content)
- const contentChunks = base64Content.match(/.{1,4000}/g) || []
- return ChunkKeys.map((key, index) => `${key}=${contentChunks[index] ?? ''}`)
-}
-
-export function extraCurlFromCookie(cookies: Partial<{ [key: string]: string }>) {
- let base64Content = ''
- ChunkKeys.forEach((key) => {
- base64Content += (cookies[key] || '')
- })
- try {
- return atob(base64Content)
- } catch(e) {
- return ''
- }
-}
-
-export function extraHeadersFromCookie(cookies: Partial<{ [key: string]: string }>) {
- return parseHeadersFromCurl(extraCurlFromCookie(cookies))
-}
-
-export function formatDate(input: string | number | Date): string {
- const date = new Date(input)
- return date.toLocaleDateString('en-US', {
- month: 'long',
- day: 'numeric',
- year: 'numeric'
- })
-}
-
-export function parseCookie(cookie: string, cookieName: string) {
- const targetCookie = new RegExp(`(?:[; ]|^)${cookieName}=([^;]*)`).test(cookie) ? RegExp.$1 : cookie
- return targetCookie ? decodeURIComponent(targetCookie).trim() : cookie.indexOf('=') === -1 ? cookie.trim() : ''
-}
-
-export function parseCookies(cookie: string, cookieNames: string[]) {
- const cookies: { [key: string]: string } = {}
- cookieNames.forEach(cookieName => {
- cookies[cookieName] = parseCookie(cookie, cookieName)
- })
- return cookies
-}
-
-export const DEFAULT_UA = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/115.0.0.0 Safari/537.36 Edg/115.0.0.0'
-export const DEFAULT_IP = process.env.BING_IP || randomIP()
-
-export function parseUA(ua?: string, default_ua = DEFAULT_UA) {
- return / EDGE?/i.test(decodeURIComponent(ua || '')) ? decodeURIComponent(ua!.trim()) : default_ua
-}
-
-export function createHeaders(cookies: Partial<{ [key: string]: string }>, defaultHeaders?: Partial<{ [key: string]: string }>) {
- let {
- BING_COOKIE = process.env.BING_COOKIE,
- BING_UA = process.env.BING_UA,
- BING_IP = process.env.BING_IP,
- BING_HEADER = process.env.BING_HEADER,
- } = cookies
-
- if (BING_HEADER) {
- return extraHeadersFromCookie({
- BING_HEADER,
- ...cookies,
- })
- }
-
- const ua = parseUA(BING_UA)
-
- if (!BING_COOKIE) {
- BING_COOKIE = defaultHeaders?.IMAGE_BING_COOKIE || 'xxx' // hf 暂时不用 Cookie 也可以正常使用
- }
-
- const parsedCookie = parseCookie(BING_COOKIE, '_U')
- if (!parsedCookie) {
- throw new Error('Invalid Cookie')
- }
- return {
- 'x-forwarded-for': BING_IP || DEFAULT_IP,
- 'Accept-Encoding': 'gzip, deflate, br',
- 'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6',
- 'User-Agent': ua!,
- 'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
- cookie: `_U=${parsedCookie}` || '',
- }
-}
-
-export class WatchDog {
- private tid = 0
- watch(fn: Function, timeout = 2000) {
- clearTimeout(this.tid)
- this.tid = setTimeout(fn, timeout + Math.random() * 1000)
- }
- reset() {
- clearTimeout(this.tid)
- }
-}
diff --git a/spaces/paulengstler/interpretable-vertebral-fracture-diagnosis/netdissect/oldresnet152.py b/spaces/paulengstler/interpretable-vertebral-fracture-diagnosis/netdissect/oldresnet152.py
deleted file mode 100644
index f783c2c0c0b11a9bde635dac3090c38af448ab88..0000000000000000000000000000000000000000
--- a/spaces/paulengstler/interpretable-vertebral-fracture-diagnosis/netdissect/oldresnet152.py
+++ /dev/null
@@ -1,922 +0,0 @@
-
-import torch
-import torch.nn as nn
-
-from functools import reduce
-from torch.autograd import Variable
-
-def load_places_resnet152(weight_file):
- model = OldResNet152()
- state_dict = torch.load(weight_file)
- model.load_state_dict(state_dict)
- return model
-
-class LambdaBase(nn.Sequential):
- def __init__(self, fn, *args):
- super(LambdaBase, self).__init__(*args)
- self.lambda_func = fn
-
- def forward_prepare(self, input):
- output = []
- for module in self._modules.values():
- output.append(module(input))
- return output if output else input
-
-class Lambda(LambdaBase):
- def forward(self, input):
- return self.lambda_func(self.forward_prepare(input))
-
-class LambdaMap(LambdaBase):
- def forward(self, input):
- return list(map(self.lambda_func,self.forward_prepare(input)))
-
-class LambdaReduce(LambdaBase):
- def forward(self, input):
- return reduce(self.lambda_func,self.forward_prepare(input))
-
-
-class OldResNet152(nn.Sequential):
- def __init__(self):
- children = [
-# resnet152_places365 = nn.Sequential( # Sequential,
- nn.Conv2d(3,64,(7, 7),(2, 2),(3, 3),1,1,bias=False),
- nn.BatchNorm2d(64),
- nn.ReLU(),
- nn.MaxPool2d((3, 3),(2, 2),(1, 1)),
- nn.Sequential( # Sequential,
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(64,64,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(64),
- nn.ReLU(),
- nn.Conv2d(64,64,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(64),
- nn.ReLU(),
- nn.Conv2d(64,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- ),
- nn.Sequential( # Sequential,
- nn.Conv2d(64,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- ),
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(256,64,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(64),
- nn.ReLU(),
- nn.Conv2d(64,64,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(64),
- nn.ReLU(),
- nn.Conv2d(64,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(256,64,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(64),
- nn.ReLU(),
- nn.Conv2d(64,64,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(64),
- nn.ReLU(),
- nn.Conv2d(64,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- ),
- nn.Sequential( # Sequential,
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(256,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(128),
- nn.ReLU(),
- nn.Conv2d(128,128,(3, 3),(2, 2),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(128),
- nn.ReLU(),
- nn.Conv2d(128,512,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(512),
- ),
- nn.Sequential( # Sequential,
- nn.Conv2d(256,512,(1, 1),(2, 2),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(512),
- ),
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(512,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(128),
- nn.ReLU(),
- nn.Conv2d(128,128,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(128),
- nn.ReLU(),
- nn.Conv2d(128,512,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(512),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(512,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(128),
- nn.ReLU(),
- nn.Conv2d(128,128,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(128),
- nn.ReLU(),
- nn.Conv2d(128,512,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(512),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(512,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(128),
- nn.ReLU(),
- nn.Conv2d(128,128,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(128),
- nn.ReLU(),
- nn.Conv2d(128,512,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(512),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(512,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(128),
- nn.ReLU(),
- nn.Conv2d(128,128,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(128),
- nn.ReLU(),
- nn.Conv2d(128,512,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(512),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(512,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(128),
- nn.ReLU(),
- nn.Conv2d(128,128,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(128),
- nn.ReLU(),
- nn.Conv2d(128,512,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(512),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(512,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(128),
- nn.ReLU(),
- nn.Conv2d(128,128,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(128),
- nn.ReLU(),
- nn.Conv2d(128,512,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(512),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(512,128,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(128),
- nn.ReLU(),
- nn.Conv2d(128,128,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(128),
- nn.ReLU(),
- nn.Conv2d(128,512,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(512),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- ),
- nn.Sequential( # Sequential,
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(512,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(2, 2),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- nn.Sequential( # Sequential,
- nn.Conv2d(512,1024,(1, 1),(2, 2),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,256,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,256,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.Conv2d(256,1024,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(1024),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- ),
- nn.Sequential( # Sequential,
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,512,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(512),
- nn.ReLU(),
- nn.Conv2d(512,512,(3, 3),(2, 2),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(512),
- nn.ReLU(),
- nn.Conv2d(512,2048,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(2048),
- ),
- nn.Sequential( # Sequential,
- nn.Conv2d(1024,2048,(1, 1),(2, 2),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(2048),
- ),
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(2048,512,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(512),
- nn.ReLU(),
- nn.Conv2d(512,512,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(512),
- nn.ReLU(),
- nn.Conv2d(512,2048,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(2048),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- nn.Sequential( # Sequential,
- LambdaMap(lambda x: x, # ConcatTable,
- nn.Sequential( # Sequential,
- nn.Conv2d(2048,512,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(512),
- nn.ReLU(),
- nn.Conv2d(512,512,(3, 3),(1, 1),(1, 1),1,1,bias=False),
- nn.BatchNorm2d(512),
- nn.ReLU(),
- nn.Conv2d(512,2048,(1, 1),(1, 1),(0, 0),1,1,bias=False),
- nn.BatchNorm2d(2048),
- ),
- Lambda(lambda x: x), # Identity,
- ),
- LambdaReduce(lambda x,y: x+y), # CAddTable,
- nn.ReLU(),
- ),
- ),
- nn.AvgPool2d((7, 7),(1, 1)),
- Lambda(lambda x: x.view(x.size(0),-1)), # View,
- nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x )
- ,nn.Linear(2048,365)), # Linear,
- ]
-
- super(OldResNet152, self).__init__(*children)
diff --git a/spaces/penguin2023/vncs/start.sh b/spaces/penguin2023/vncs/start.sh
deleted file mode 100644
index d9b29a76d1e08e744c659939c871e8f69fad978d..0000000000000000000000000000000000000000
--- a/spaces/penguin2023/vncs/start.sh
+++ /dev/null
@@ -1,21 +0,0 @@
-#!/bin/bash
-# ls
-# # umask 0077 # use safe default permissions
-# mkdir -p "$HO/.vnc" # create config directory
-# chmod go-rwx "$HO/.vnc" # enforce safe permissions
-# ls -l
-tigervnc_path="/tigervnc-${tigervnc_version}.x86_64/usr/bin"
-
-# Start TigerVNC
-if [ ! -z $VNC_PASSWD ]; then
- ${tigervnc_path}/vncpasswd -f <<< "$VNC_PASSWD" > ".vnc/passwd"
- ${tigervnc_path}/vncserver -rfbport 5900 -geometry ${GEOMETRY} -depth ${DEPTH}
-else
- ${tigervnc_path}/vncpasswd -f <<< "" > ".vnc/passwd"
- ${tigervnc_path}/vncserver -rfbport 5900 -geometry ${GEOMETRY} -depth ${DEPTH} -SecurityTypes None
-fi
-
-cat /noVNC-${noVNC_version}/utils/launch.sh
-
-# Start noVNC
-/noVNC-${noVNC_version}/utils/launch.sh
\ No newline at end of file
diff --git a/spaces/philsark/clip-guided-diffusion-identity/README.md b/spaces/philsark/clip-guided-diffusion-identity/README.md
deleted file mode 100644
index c87d045351d4dd8f256b19497ce1ba28b9e36f8b..0000000000000000000000000000000000000000
--- a/spaces/philsark/clip-guided-diffusion-identity/README.md
+++ /dev/null
@@ -1,33 +0,0 @@
----
-title: Clip-Guided-Diffusion-Identity
-emoji: 📈
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/pikto/Elite-freegpt-webui/g4f/Provider/Providers/Liaobots.py b/spaces/pikto/Elite-freegpt-webui/g4f/Provider/Providers/Liaobots.py
deleted file mode 100644
index 985bf53ddfd3877db3c60aedee86db11ec0e7243..0000000000000000000000000000000000000000
--- a/spaces/pikto/Elite-freegpt-webui/g4f/Provider/Providers/Liaobots.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import os, uuid, requests
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://liaobots.com'
-model = ['gpt-4-0613']
-supports_stream = True
-needs_auth = True
-
-models = {
- 'gpt-4-0613': {
- "id":"gpt-4-0613",
- "name":"GPT-4",
- "maxLength":24000,
- "tokenLimit":8000
- }
-}
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-
- print(kwargs)
-
- headers = {
- 'authority': 'liaobots.com',
- 'content-type': 'application/json',
- 'origin': 'https://liaobots.com',
- 'referer': 'https://liaobots.com/',
- 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36',
- 'x-auth-code': 'P6cPPK6Z8JDG3'
- }
-
- json_data = {
- 'conversationId': str(uuid.uuid4()),
- 'model': models[model],
- 'authcode':"jrzVZMJiwN0NU",
- 'messages': messages,
- 'key': '',
- 'prompt': "You are ChatGPT, a large language model trained by OpenAI. Follow the user's instructions carefully. Respond using markdown.",
- }
-
- response = requests.post('https://liaobots.com/api/chat',
- headers=headers, json=json_data, stream=True)
-
- for token in response.iter_content(chunk_size=2046):
- yield (token.decode('cp1251'))
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
- '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
\ No newline at end of file
diff --git a/spaces/pixiou/bingo/README.md b/spaces/pixiou/bingo/README.md
deleted file mode 100644
index d65eafbc8431818f738e8e086455fa6159f101bb..0000000000000000000000000000000000000000
--- a/spaces/pixiou/bingo/README.md
+++ /dev/null
@@ -1,196 +0,0 @@
----
-title: bingo
-emoji: 📉
-colorFrom: red
-colorTo: red
-sdk: docker
-license: mit
-duplicated_from: hf4all/bingo
----
-
-
-
-# Bingo
-
-Bingo，一个让你呼吸顺畅 New Bing。
-
-高度还原 New Bing 网页版的主要操作，国内可用，兼容绝大多数微软 Bing AI 的功能，可自行部署使用。
-
-
-
-[](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[](https://github.com/weaigc/bingo/blob/main/license)
-
-
-
-## 演示站点
-
-https://bing.github1s.tk
-
-
-
-[](https://bing.github1s.tk)
-
-## 功能和特点
-
-- 完全基于 Next.js 重写，高度还原 New Bing Web 版 UI，使用体验和 Bing AI 基本一致。
-- 支持 Docker 构建，方便快捷地部署和访问。
-- Cookie 可全局配置，全局共享。
-- 支持持续语音对话
-
-## RoadMap
-
- - [x] 支持 wss 转发
- - [x] 支持一键部署
- - [x] 优化移动端展示
- - [x] 支持画图
- - [x] 支持语音输入(支持语音指令，目前仅支持 PC 版 Edge 及 Chrome 浏览器)
- - [x] 支持语音输出(需要手动开启)
- - [x] 支持图片输入
- - [x] 支持自定义域名
- - [ ] 支持历史记录
- - [ ] 适配深色模式
- - [ ] 支持内置提示词
- - [ ] 支持离线访问
- - [ ] 国际化翻译
-
-## 一键部署
-你也可以一键部署自己的 New Bing AI 到 🤗 HuggingFace 。
-
-### 部署到 Huggingface
-1. 点击此图标
-[](https://huggingface.co/login?next=%2Fspaces%2Fhf4all%2Fbingo%3Fduplicate%3Dtrue%26visibility%3Dpublic)，配置可以不改。
-
-2. 部署署完成后，点击“设置” 》“站点域名”，点一下，复制一下 HF 域名信息，然后分享给别人即可。
-
-> Huggingface 不支持绑定自己的域名，不过我们可以使用曲线救国的方式来达到这个目的
-> 1. 方式二，借助 Cloudflare Workers [部署Cloudflare Workers](#使用Cloudflare-Workers自定义域名)
-> 2. 方式一，借助 Github Pages 及 iframe [如何绑定域名](https://github.com/weaigc/bingo/issues/4)
-
-### 使用Cloudflare Workers自定义域名
-
-> 核心代码 [worker.js](./cloudflare/worker.js)
-
-- [注册 Cloudflare 账号](https://dash.cloudflare.com/sign-up)
-
-- 添加一个新的网站，需要你有自己的域名并且将域名`Name Server`托管给 Cloudflare 才行（更多信息可自行 Google)
-
-- 通过左侧菜单进入「Workers」，并点击「Create a Worker」。
-
-- 创建 Worker 服务，复制 [worker.js](./cloudflare/worker.js) 全部代码，粘贴至创建的服务中，根据注释进行改动，保存并部署。
-
-- 触发器 中自定义访问域名。
-
-### 部署其它平台
-
-
-由于其他平台目前遭到 New Bing 封杀，会遇到很多问题，不再做推荐，有需要的可以自行查看
-
-
-#### 部署到 Netlify
-[](https://app.netlify.com/start/deploy?repository=https://github.com/weaigc/bingo)
-
-#### 部署到 Vercel
-如果你是 Vercel 付费用户，可以点以下链接一键部署到 Vercel。免费版本有[接口超时限制](https://vercel.com/docs/concepts/limits/overview)，不推荐使用
-
-[](https://vercel.com/new/clone?demo-title=bingo&demo-description=bingo&demo-url=https%3A%2F%2Fbing.github1s.tk%2F&project-name=bingo&repository-name=bingo&repository-url=https%3A%2F%2Fgithub.com%2Fweaigc%2Fbingo&from=templates&skippable-integrations=1&env=BING_HEADER&envDescription=%E5%A6%82%E6%9E%9C%E4%B8%8D%E7%9F%A5%E9%81%93%E6%80%8E%E4%B9%88%E9%85%8D%E7%BD%AE%E8%AF%B7%E7%82%B9%E5%8F%B3%E4%BE%A7Learn+More&envLink=https%3A%2F%2Fgithub.com%2Fweaigc%2Fbingo%2Fblob%2Fmain%2F.env.example)
-
-#### 部署到 Render
-
-[](https://render.com/deploy?repo=https://github.com/weaigc/bingo)
-
-
-## 环境和依赖
-
-- Node.js >= 18
-- Bing AI 的[身份信息](#如何获取-BING_HEADER))
-
-## 安装和使用
-
-> 由于目前微软封杀比较严重，推荐优先使用 [部署 Huggingface](#部署到-huggingface) 。
-
-* 使用 Node 启动
-
-```bash
-git clone https://github.com/weaigc/bingo.git
-npm i # 推荐使用 pnpm i
-npm run build
-npm run start
-```
-
-* 使用 Docker 启动
-```bash
-docker pull weaigc/bingo
-docker run --rm -it -p 7860:7860 weaigc/bingo
-# 或者
-docker run --rm -it -e BING_HEADER=xxxx -p 7860:7860 weaigc/bingo
-```
-
-## 如何获取 BING_HEADER
-> 配置了 BING_HEADER 意味着你将自己的账号共享给所有使用此服务的人，如果不需要免登录画图的功能，不建议设置此变量
-
-打开 https://www.bing.com 并登录，然后访问 https://www.bing.com/turing/captcha/challenge ，通过人机校验，然后
-
-
-
-> 复制出来的内容应该如下所示。确认格式无误后，打开 https://effulgent-bubblegum-e2f5df.netlify.app/#dialog=%22settings%22 ，粘贴进去，点击“转成 BING_HEADER 并复制”，然后从剪切板粘贴即可得到。（你也可以先在网页上进行验证）
-
-以下是格式参考，需要注意的是，网页端保存的格式是以`curl`开头, 而服务端配置的 `BING_HEADER` 是 `base64` 格式，两者不能互通。
-
-正常格式/网页端保存的格式(格式仅供参考)
-
-```
-curl 'https://www.bing.com/turing/captcha/challenge' \
- -H 'authority: www.bing.com' \
- -H 'accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7' \
- -H 'accept-language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6' \
- -H 'cache-control: max-age=0' \
- -H 'cookie: MicrosoftApplicationsTelemetryDeviceId=3399c004-fd0e-48ec-bb92-d82a27b2bbd4; _EDGE_V=1; SRCHD=AF=NOFORM; SRCHUID=V=2&GUID=29EBDDA4E6674329ACCF1A0A423C3E98&dmnchg=1; _UR=QS=0&TQS=0; _HPVN=CS=eyJQbiI6eyJDbiI6MSwiU3QiOjAsIlFzIjowLCJQcm9kIjoiUCJ9LCJTYyI6eyJDbiI6MSwiU3QiOjAsIlFzIjowLCJQcm9kIjoiSCJ9LCJReiI6eyJDbiI6MSwiU3QiOjAsIlFzIjowLCJQcm9kIjoiVCJ9LCJBcCI6dHJ1ZSwiTXV0ZSI6dHJ1ZSwiTGFkIjoiMjAyMy0wNy0yNVQwMDowMDowMFoiLCJJb3RkIjowLCJHd2IiOjAsIkRmdCI6bnVsbCwiTXZzIjowLCJGbHQiOjAsIkltcCI6Mn0=; _RwBf=ilt=1&ihpd=1&ispd=0&rc=0&rb=0&gb=0&rg=200&pc=0&mtu=0&rbb=0&g=0&cid=&clo=0&v=1&l=2023-07-25T07:00:00.0000000Z&lft=0001-01-01T00:00:00.0000000&aof=0&o=2&p=&c=&t=0&s=0001-01-01T00:00:00.0000000+00:00&ts=2023-07-25T11:00:31.7111548+00:00&rwred=0&wls=&lka=0&lkt=0&TH=&dci=0; ANON=A=0043C6590EA808ED6E395059FFFFFFFF&E=1c8b&W=1; NAP=V=1.9&E=1c31&C=DnaMSbDN_4efZ_xXqBF3Daorjr53kYqYoaP8YHsupjmiXnysX7a37A&W=1; PPLState=1; KievRPSSecAuth=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; WLS=C=9df3f9d8518fae19&N=wen; WLID=pGY8HgWCu4p5XYCOk2oa0+DBdftkMUfmNIn8XtSjSTKsgv/Il7GUlYs0Jpjf/E12jZMgV7x44Dy3fXOgjjUoJx7Y/ClLrLhsk20THksJJoI=; _EDGE_S=F=1&SID=17CF6EE006426448213C7DB907436588&mkt=zh-CN; MUID=225621093D8A6C27301632413C0E6D08; MUIDB=225621093D8A6C27301632413C0E6D08; SUID=A; SNRHOP=I=&TS=; _U=nGyzKQruEsDwLiu65fZFIG6e12hf2lwTJmroW__k8joUJIKmG3OIjayXKGW9dCVR3sNhF76mEVxyW6yjUGPodOfjtSa3s3J_DxMOrEK1BqXCOBI9bC66spAIASV7prsYFlVAJz73jVNENp_tBubLHJy6EbT0BKRe4AjrYkH-9uMnmCKB8Zmyg; _SS=SID=17CF6EE006426448213C7DB907436588&R=0&RB=0&GB=0&RG=200&RP=0&PC=U531; SRCHS=PC=U531; USRLOC=HS=1&ELOC=LAT=22.501529693603516|LON=113.9263687133789|N=%E5%8D%97%E5%B1%B1%E5%8C%BA%EF%BC%8C%E5%B9%BF%E4%B8%9C%E7%9C%81|ELT=2|&CLOC=LAT=22.50153029046461|LON=113.92637070632928|A=733.4464586120832|TS=230726151034|SRC=W; SRCHUSR=DOB=20230725&T=1690384908000&POEX=W; ipv6=hit=1690388509974&t=6; SRCHHPGUSR=HV=1690384945&SRCHLANG=zh-Hans&PV=15.0.0&BRW=MW&BRH=MT&CW=410&CH=794&SCW=410&SCH=794&DPR=1.5&UTC=480&DM=0&WTS=63825879627&PRVCW=410&PRVCH=794&PR=1.5; cct=AjWIBYOoVP-Afq6gWwtx80If6yHn6iBuEVHA1XHdAKpny6Y_CVyi_MSyM94VyMWnjdYkkccVtm3czoIAtXUGQA; GC=AjWIBYOoVP-Afq6gWwtx80If6yHn6iBuEVHA1XHdAKpR3Y_D9Ytcks4Ht6XhadXk75dvhzP4YOUS0UmoEyqyxw' \
- -H 'dnt: 1' \
- -H 'sec-ch-ua: "Chromium";v="116", "Not)A;Brand";v="24", "Microsoft Edge";v="116"' \
- -H 'sec-ch-ua-arch: "x86"' \
- -H 'sec-ch-ua-bitness: "64"' \
- -H 'sec-ch-ua-full-version: "116.0.1938.29"' \
- -H 'sec-ch-ua-full-version-list: "Chromium";v="116.0.5845.42", "Not)A;Brand";v="24.0.0.0", "Microsoft Edge";v="116.0.1938.29"' \
- -H 'sec-ch-ua-mobile: ?0' \
- -H 'sec-ch-ua-model: ""' \
- -H 'sec-ch-ua-platform: "Windows"' \
- -H 'sec-ch-ua-platform-version: "15.0.0"' \
- -H 'sec-fetch-dest: document' \
- -H 'sec-fetch-mode: navigate' \
- -H 'sec-fetch-site: none' \
- -H 'sec-fetch-user: ?1' \
- -H 'sec-ms-gec: B3F47AD4A283CAB374C0451C46AAFD147C6A4DACAFF6A1C13F34B2C72B024494' \
- -H 'sec-ms-gec-version: 1-116.0.1938.29' \
- -H 'upgrade-insecure-requests: 1' \
- -H 'user-agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36 Edg/116.0.0.0' \
- -H 'x-client-data: eyIxIjoiMiIsIjEwIjoiXCJTMGg3R05HOTF2aDQ1TUZSUnZ5NHN2akRmMWdlaVJKenNxNlA3aU1WbnF3PVwiIiwiMiI6IjEiLCIzIjoiMSIsIjQiOiIyMTU4ODQ5NTM4MjY4OTM5NTA3IiwiNSI6IlwiSm9GUWpPTDk3OS9MbkRRZnlCd2N1M2FsOUN3eTZTQmdaMGNYMXBtOWVMZz1cIiIsIjYiOiJiZXRhIiwiNyI6IjE4MDM4ODYyNjQzNSIsIjkiOiJkZXNrdG9wIn0=' \
- -H 'x-edge-shopping-flag: 1' \
- --compressed
-```
-
-
-
-转成base64之后的格式(BING_HEADER只能使用 base64 之后的格式)
-
-```
-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
-```
-
-
-
-## 鸣谢
- - 感谢 [EdgeGPT](https://github.com/acheong08/EdgeGPT) 提供的代理 API 的方法。
- - 感谢 [Vercel AI](https://github.com/vercel-labs/ai-chatbot) 提供的基础脚手架和 [ChatHub](https://github.com/chathub-dev/chathub) [go-proxy-bingai](https://github.com/adams549659584/go-proxy-bingai) 提供的部分代码。
-
-
-## 答疑及交流
-
-.+?)-(?P.+?)
- (-(?Ppy\d\.\d+)
- (-(?P.+?))?
- )?.egg$""",
- re.VERBOSE,
-)
-
-
-class _bdist_wheel_tag(bdist_wheel):
- # allow the client to override the default generated wheel tag
- # The default bdist_wheel implementation uses python and abi tags
- # of the running python process. This is not suitable for
- # generating/repackaging prebuild binaries.
-
- full_tag_supplied = False
- full_tag = None # None or a (pytag, soabitag, plattag) triple
-
- def get_tag(self):
- if self.full_tag_supplied and self.full_tag is not None:
- return self.full_tag
- else:
- return bdist_wheel.get_tag(self)
-
-
-def egg2wheel(egg_path: str, dest_dir: str):
- filename = os.path.basename(egg_path)
- match = egg_info_re.match(filename)
- if not match:
- raise WheelError(f"Invalid egg file name: {filename}")
-
- egg_info = match.groupdict()
- dir = tempfile.mkdtemp(suffix="_e2w")
- if os.path.isfile(egg_path):
- # assume we have a bdist_egg otherwise
- with zipfile.ZipFile(egg_path) as egg:
- egg.extractall(dir)
- else:
- # support buildout-style installed eggs directories
- for pth in os.listdir(egg_path):
- src = os.path.join(egg_path, pth)
- if os.path.isfile(src):
- shutil.copy2(src, dir)
- else:
- shutil.copytree(src, os.path.join(dir, pth))
-
- pyver = egg_info["pyver"]
- if pyver:
- pyver = egg_info["pyver"] = pyver.replace(".", "")
-
- arch = (egg_info["arch"] or "any").replace(".", "_").replace("-", "_")
-
- # assume all binary eggs are for CPython
- abi = "cp" + pyver[2:] if arch != "any" else "none"
-
- root_is_purelib = egg_info["arch"] is None
- if root_is_purelib:
- bw = bdist_wheel(Distribution())
- else:
- bw = _bdist_wheel_tag(Distribution())
-
- bw.root_is_pure = root_is_purelib
- bw.python_tag = pyver
- bw.plat_name_supplied = True
- bw.plat_name = egg_info["arch"] or "any"
- if not root_is_purelib:
- bw.full_tag_supplied = True
- bw.full_tag = (pyver, abi, arch)
-
- dist_info_dir = os.path.join(dir, "{name}-{ver}.dist-info".format(**egg_info))
- bw.egg2dist(os.path.join(dir, "EGG-INFO"), dist_info_dir)
- bw.write_wheelfile(dist_info_dir, generator="egg2wheel")
- wheel_name = "{name}-{ver}-{pyver}-{}-{}.whl".format(abi, arch, **egg_info)
- with WheelFile(os.path.join(dest_dir, wheel_name), "w") as wf:
- wf.write_files(dir)
-
- shutil.rmtree(dir)
-
-
-def parse_wininst_info(wininfo_name, egginfo_name):
- """Extract metadata from filenames.
-
- Extracts the 4 metadataitems needed (name, version, pyversion, arch) from
- the installer filename and the name of the egg-info directory embedded in
- the zipfile (if any).
-
- The egginfo filename has the format::
-
- name-ver(-pyver)(-arch).egg-info
-
- The installer filename has the format::
-
- name-ver.arch(-pyver).exe
-
- Some things to note:
-
- 1. The installer filename is not definitive. An installer can be renamed
- and work perfectly well as an installer. So more reliable data should
- be used whenever possible.
- 2. The egg-info data should be preferred for the name and version, because
- these come straight from the distutils metadata, and are mandatory.
- 3. The pyver from the egg-info data should be ignored, as it is
- constructed from the version of Python used to build the installer,
- which is irrelevant - the installer filename is correct here (even to
- the point that when it's not there, any version is implied).
- 4. The architecture must be taken from the installer filename, as it is
- not included in the egg-info data.
- 5. Architecture-neutral installers still have an architecture because the
- installer format itself (being executable) is architecture-specific. We
- should therefore ignore the architecture if the content is pure-python.
- """
-
- egginfo = None
- if egginfo_name:
- egginfo = egg_info_re.search(egginfo_name)
- if not egginfo:
- raise ValueError(f"Egg info filename {egginfo_name} is not valid")
-
- # Parse the wininst filename
- # 1. Distribution name (up to the first '-')
- w_name, sep, rest = wininfo_name.partition("-")
- if not sep:
- raise ValueError(f"Installer filename {wininfo_name} is not valid")
-
- # Strip '.exe'
- rest = rest[:-4]
- # 2. Python version (from the last '-', must start with 'py')
- rest2, sep, w_pyver = rest.rpartition("-")
- if sep and w_pyver.startswith("py"):
- rest = rest2
- w_pyver = w_pyver.replace(".", "")
- else:
- # Not version specific - use py2.py3. While it is possible that
- # pure-Python code is not compatible with both Python 2 and 3, there
- # is no way of knowing from the wininst format, so we assume the best
- # here (the user can always manually rename the wheel to be more
- # restrictive if needed).
- w_pyver = "py2.py3"
- # 3. Version and architecture
- w_ver, sep, w_arch = rest.rpartition(".")
- if not sep:
- raise ValueError(f"Installer filename {wininfo_name} is not valid")
-
- if egginfo:
- w_name = egginfo.group("name")
- w_ver = egginfo.group("ver")
-
- return {"name": w_name, "ver": w_ver, "arch": w_arch, "pyver": w_pyver}
-
-
-def wininst2wheel(path, dest_dir):
- with zipfile.ZipFile(path) as bdw:
- # Search for egg-info in the archive
- egginfo_name = None
- for filename in bdw.namelist():
- if ".egg-info" in filename:
- egginfo_name = filename
- break
-
- info = parse_wininst_info(os.path.basename(path), egginfo_name)
-
- root_is_purelib = True
- for zipinfo in bdw.infolist():
- if zipinfo.filename.startswith("PLATLIB"):
- root_is_purelib = False
- break
- if root_is_purelib:
- paths = {"purelib": ""}
- else:
- paths = {"platlib": ""}
-
- dist_info = "{name}-{ver}".format(**info)
- datadir = "%s.data/" % dist_info
-
- # rewrite paths to trick ZipFile into extracting an egg
- # XXX grab wininst .ini - between .exe, padding, and first zip file.
- members = []
- egginfo_name = ""
- for zipinfo in bdw.infolist():
- key, basename = zipinfo.filename.split("/", 1)
- key = key.lower()
- basepath = paths.get(key, None)
- if basepath is None:
- basepath = datadir + key.lower() + "/"
- oldname = zipinfo.filename
- newname = basepath + basename
- zipinfo.filename = newname
- del bdw.NameToInfo[oldname]
- bdw.NameToInfo[newname] = zipinfo
- # Collect member names, but omit '' (from an entry like "PLATLIB/"
- if newname:
- members.append(newname)
- # Remember egg-info name for the egg2dist call below
- if not egginfo_name:
- if newname.endswith(".egg-info"):
- egginfo_name = newname
- elif ".egg-info/" in newname:
- egginfo_name, sep, _ = newname.rpartition("/")
- dir = tempfile.mkdtemp(suffix="_b2w")
- bdw.extractall(dir, members)
-
- # egg2wheel
- abi = "none"
- pyver = info["pyver"]
- arch = (info["arch"] or "any").replace(".", "_").replace("-", "_")
- # Wininst installers always have arch even if they are not
- # architecture-specific (because the format itself is).
- # So, assume the content is architecture-neutral if root is purelib.
- if root_is_purelib:
- arch = "any"
- # If the installer is architecture-specific, it's almost certainly also
- # CPython-specific.
- if arch != "any":
- pyver = pyver.replace("py", "cp")
- wheel_name = "-".join((dist_info, pyver, abi, arch))
- if root_is_purelib:
- bw = bdist_wheel(Distribution())
- else:
- bw = _bdist_wheel_tag(Distribution())
-
- bw.root_is_pure = root_is_purelib
- bw.python_tag = pyver
- bw.plat_name_supplied = True
- bw.plat_name = info["arch"] or "any"
-
- if not root_is_purelib:
- bw.full_tag_supplied = True
- bw.full_tag = (pyver, abi, arch)
-
- dist_info_dir = os.path.join(dir, "%s.dist-info" % dist_info)
- bw.egg2dist(os.path.join(dir, egginfo_name), dist_info_dir)
- bw.write_wheelfile(dist_info_dir, generator="wininst2wheel")
-
- wheel_path = os.path.join(dest_dir, wheel_name)
- with WheelFile(wheel_path, "w") as wf:
- wf.write_files(dir)
-
- shutil.rmtree(dir)
-
-
-def convert(files, dest_dir, verbose):
- for pat in files:
- for installer in iglob(pat):
- if os.path.splitext(installer)[1] == ".egg":
- conv = egg2wheel
- else:
- conv = wininst2wheel
-
- if verbose:
- print(f"{installer}... ", flush=True)
-
- conv(installer, dest_dir)
- if verbose:
- print("OK")
diff --git a/spaces/prerna9811/Chord/portaudio/src/hostapi/wasapi/mingw-include/structuredquery.h b/spaces/prerna9811/Chord/portaudio/src/hostapi/wasapi/mingw-include/structuredquery.h
deleted file mode 100644
index bca20a9adac790f1f46ca915c121beb01b07c0f6..0000000000000000000000000000000000000000
--- a/spaces/prerna9811/Chord/portaudio/src/hostapi/wasapi/mingw-include/structuredquery.h
+++ /dev/null
@@ -1,2478 +0,0 @@
-
-
-/* this ALWAYS GENERATED file contains the definitions for the interfaces */
-
-
- /* File created by MIDL compiler version 7.00.0499 */
-/* Compiler settings for structuredquery.idl:
- Oicf, W1, Zp8, env=Win32 (32b run)
- protocol : dce , ms_ext, c_ext, robust
- error checks: allocation ref bounds_check enum stub_data
- VC __declspec() decoration level:
- __declspec(uuid()), __declspec(selectany), __declspec(novtable)
- DECLSPEC_UUID(), MIDL_INTERFACE()
-*/
-//@@MIDL_FILE_HEADING( )
-
-#pragma warning( disable: 4049 ) /* more than 64k source lines */
-
-
-/* verify that the version is high enough to compile this file*/
-#ifndef __REQUIRED_RPCNDR_H_VERSION__
-#define __REQUIRED_RPCNDR_H_VERSION__ 475
-#endif
-
-/* verify that the version is high enough to compile this file*/
-#ifndef __REQUIRED_RPCSAL_H_VERSION__
-#define __REQUIRED_RPCSAL_H_VERSION__ 100
-#endif
-
-#include "rpc.h"
-#include "rpcndr.h"
-
-#ifndef __RPCNDR_H_VERSION__
-#error this stub requires an updated version of
-#endif // __RPCNDR_H_VERSION__
-
-#ifndef COM_NO_WINDOWS_H
-#include "windows.h"
-#include "ole2.h"
-#endif /*COM_NO_WINDOWS_H*/
-
-#ifndef __structuredquery_h__
-#define __structuredquery_h__
-
-#if defined(_MSC_VER) && (_MSC_VER >= 1020)
-#pragma once
-#endif
-
-/* Forward Declarations */
-
-#ifndef __IQueryParser_FWD_DEFINED__
-#define __IQueryParser_FWD_DEFINED__
-typedef interface IQueryParser IQueryParser;
-#endif /* __IQueryParser_FWD_DEFINED__ */
-
-
-#ifndef __IConditionFactory_FWD_DEFINED__
-#define __IConditionFactory_FWD_DEFINED__
-typedef interface IConditionFactory IConditionFactory;
-#endif /* __IConditionFactory_FWD_DEFINED__ */
-
-
-#ifndef __IQuerySolution_FWD_DEFINED__
-#define __IQuerySolution_FWD_DEFINED__
-typedef interface IQuerySolution IQuerySolution;
-#endif /* __IQuerySolution_FWD_DEFINED__ */
-
-
-#ifndef __ICondition_FWD_DEFINED__
-#define __ICondition_FWD_DEFINED__
-typedef interface ICondition ICondition;
-#endif /* __ICondition_FWD_DEFINED__ */
-
-
-#ifndef __IConditionGenerator_FWD_DEFINED__
-#define __IConditionGenerator_FWD_DEFINED__
-typedef interface IConditionGenerator IConditionGenerator;
-#endif /* __IConditionGenerator_FWD_DEFINED__ */
-
-
-#ifndef __IRichChunk_FWD_DEFINED__
-#define __IRichChunk_FWD_DEFINED__
-typedef interface IRichChunk IRichChunk;
-#endif /* __IRichChunk_FWD_DEFINED__ */
-
-
-#ifndef __IInterval_FWD_DEFINED__
-#define __IInterval_FWD_DEFINED__
-typedef interface IInterval IInterval;
-#endif /* __IInterval_FWD_DEFINED__ */
-
-
-#ifndef __IMetaData_FWD_DEFINED__
-#define __IMetaData_FWD_DEFINED__
-typedef interface IMetaData IMetaData;
-#endif /* __IMetaData_FWD_DEFINED__ */
-
-
-#ifndef __IEntity_FWD_DEFINED__
-#define __IEntity_FWD_DEFINED__
-typedef interface IEntity IEntity;
-#endif /* __IEntity_FWD_DEFINED__ */
-
-
-#ifndef __IRelationship_FWD_DEFINED__
-#define __IRelationship_FWD_DEFINED__
-typedef interface IRelationship IRelationship;
-#endif /* __IRelationship_FWD_DEFINED__ */
-
-
-#ifndef __INamedEntity_FWD_DEFINED__
-#define __INamedEntity_FWD_DEFINED__
-typedef interface INamedEntity INamedEntity;
-#endif /* __INamedEntity_FWD_DEFINED__ */
-
-
-#ifndef __ISchemaProvider_FWD_DEFINED__
-#define __ISchemaProvider_FWD_DEFINED__
-typedef interface ISchemaProvider ISchemaProvider;
-#endif /* __ISchemaProvider_FWD_DEFINED__ */
-
-
-#ifndef __ITokenCollection_FWD_DEFINED__
-#define __ITokenCollection_FWD_DEFINED__
-typedef interface ITokenCollection ITokenCollection;
-#endif /* __ITokenCollection_FWD_DEFINED__ */
-
-
-#ifndef __INamedEntityCollector_FWD_DEFINED__
-#define __INamedEntityCollector_FWD_DEFINED__
-typedef interface INamedEntityCollector INamedEntityCollector;
-#endif /* __INamedEntityCollector_FWD_DEFINED__ */
-
-
-#ifndef __ISchemaLocalizerSupport_FWD_DEFINED__
-#define __ISchemaLocalizerSupport_FWD_DEFINED__
-typedef interface ISchemaLocalizerSupport ISchemaLocalizerSupport;
-#endif /* __ISchemaLocalizerSupport_FWD_DEFINED__ */
-
-
-#ifndef __IQueryParserManager_FWD_DEFINED__
-#define __IQueryParserManager_FWD_DEFINED__
-typedef interface IQueryParserManager IQueryParserManager;
-#endif /* __IQueryParserManager_FWD_DEFINED__ */
-
-
-#ifndef __QueryParser_FWD_DEFINED__
-#define __QueryParser_FWD_DEFINED__
-
-#ifdef __cplusplus
-typedef class QueryParser QueryParser;
-#else
-typedef struct QueryParser QueryParser;
-#endif /* __cplusplus */
-
-#endif /* __QueryParser_FWD_DEFINED__ */
-
-
-#ifndef __NegationCondition_FWD_DEFINED__
-#define __NegationCondition_FWD_DEFINED__
-
-#ifdef __cplusplus
-typedef class NegationCondition NegationCondition;
-#else
-typedef struct NegationCondition NegationCondition;
-#endif /* __cplusplus */
-
-#endif /* __NegationCondition_FWD_DEFINED__ */
-
-
-#ifndef __CompoundCondition_FWD_DEFINED__
-#define __CompoundCondition_FWD_DEFINED__
-
-#ifdef __cplusplus
-typedef class CompoundCondition CompoundCondition;
-#else
-typedef struct CompoundCondition CompoundCondition;
-#endif /* __cplusplus */
-
-#endif /* __CompoundCondition_FWD_DEFINED__ */
-
-
-#ifndef __LeafCondition_FWD_DEFINED__
-#define __LeafCondition_FWD_DEFINED__
-
-#ifdef __cplusplus
-typedef class LeafCondition LeafCondition;
-#else
-typedef struct LeafCondition LeafCondition;
-#endif /* __cplusplus */
-
-#endif /* __LeafCondition_FWD_DEFINED__ */
-
-
-#ifndef __ConditionFactory_FWD_DEFINED__
-#define __ConditionFactory_FWD_DEFINED__
-
-#ifdef __cplusplus
-typedef class ConditionFactory ConditionFactory;
-#else
-typedef struct ConditionFactory ConditionFactory;
-#endif /* __cplusplus */
-
-#endif /* __ConditionFactory_FWD_DEFINED__ */
-
-
-#ifndef __Interval_FWD_DEFINED__
-#define __Interval_FWD_DEFINED__
-
-#ifdef __cplusplus
-typedef class Interval Interval;
-#else
-typedef struct Interval Interval;
-#endif /* __cplusplus */
-
-#endif /* __Interval_FWD_DEFINED__ */
-
-
-#ifndef __QueryParserManager_FWD_DEFINED__
-#define __QueryParserManager_FWD_DEFINED__
-
-#ifdef __cplusplus
-typedef class QueryParserManager QueryParserManager;
-#else
-typedef struct QueryParserManager QueryParserManager;
-#endif /* __cplusplus */
-
-#endif /* __QueryParserManager_FWD_DEFINED__ */
-
-
-/* header files for imported files */
-#include "oaidl.h"
-#include "ocidl.h"
-#include "propidl.h"
-
-#ifdef __cplusplus
-extern "C"{
-#endif
-
-
-/* interface __MIDL_itf_structuredquery_0000_0000 */
-/* [local] */
-
-
-
-
-
-
-
-
-
-
-
-typedef /* [v1_enum] */
-enum tagCONDITION_TYPE
- { CT_AND_CONDITION = 0,
- CT_OR_CONDITION = ( CT_AND_CONDITION + 1 ) ,
- CT_NOT_CONDITION = ( CT_OR_CONDITION + 1 ) ,
- CT_LEAF_CONDITION = ( CT_NOT_CONDITION + 1 )
- } CONDITION_TYPE;
-
-typedef /* [v1_enum] */
-enum tagCONDITION_OPERATION
- { COP_IMPLICIT = 0,
- COP_EQUAL = ( COP_IMPLICIT + 1 ) ,
- COP_NOTEQUAL = ( COP_EQUAL + 1 ) ,
- COP_LESSTHAN = ( COP_NOTEQUAL + 1 ) ,
- COP_GREATERTHAN = ( COP_LESSTHAN + 1 ) ,
- COP_LESSTHANOREQUAL = ( COP_GREATERTHAN + 1 ) ,
- COP_GREATERTHANOREQUAL = ( COP_LESSTHANOREQUAL + 1 ) ,
- COP_VALUE_STARTSWITH = ( COP_GREATERTHANOREQUAL + 1 ) ,
- COP_VALUE_ENDSWITH = ( COP_VALUE_STARTSWITH + 1 ) ,
- COP_VALUE_CONTAINS = ( COP_VALUE_ENDSWITH + 1 ) ,
- COP_VALUE_NOTCONTAINS = ( COP_VALUE_CONTAINS + 1 ) ,
- COP_DOSWILDCARDS = ( COP_VALUE_NOTCONTAINS + 1 ) ,
- COP_WORD_EQUAL = ( COP_DOSWILDCARDS + 1 ) ,
- COP_WORD_STARTSWITH = ( COP_WORD_EQUAL + 1 ) ,
- COP_APPLICATION_SPECIFIC = ( COP_WORD_STARTSWITH + 1 )
- } CONDITION_OPERATION;
-
-typedef /* [v1_enum] */
-enum tagSTRUCTURED_QUERY_SINGLE_OPTION
- { SQSO_SCHEMA = 0,
- SQSO_LOCALE_WORD_BREAKING = ( SQSO_SCHEMA + 1 ) ,
- SQSO_WORD_BREAKER = ( SQSO_LOCALE_WORD_BREAKING + 1 ) ,
- SQSO_NATURAL_SYNTAX = ( SQSO_WORD_BREAKER + 1 ) ,
- SQSO_AUTOMATIC_WILDCARD = ( SQSO_NATURAL_SYNTAX + 1 ) ,
- SQSO_TRACE_LEVEL = ( SQSO_AUTOMATIC_WILDCARD + 1 ) ,
- SQSO_LANGUAGE_KEYWORDS = ( SQSO_TRACE_LEVEL + 1 )
- } STRUCTURED_QUERY_SINGLE_OPTION;
-
-typedef /* [v1_enum] */
-enum tagSTRUCTURED_QUERY_MULTIOPTION
- { SQMO_VIRTUAL_PROPERTY = 0,
- SQMO_DEFAULT_PROPERTY = ( SQMO_VIRTUAL_PROPERTY + 1 ) ,
- SQMO_GENERATOR_FOR_TYPE = ( SQMO_DEFAULT_PROPERTY + 1 )
- } STRUCTURED_QUERY_MULTIOPTION;
-
-typedef /* [v1_enum] */
-enum tagSTRUCTURED_QUERY_PARSE_ERROR
- { SQPE_NONE = 0,
- SQPE_EXTRA_OPENING_PARENTHESIS = ( SQPE_NONE + 1 ) ,
- SQPE_EXTRA_CLOSING_PARENTHESIS = ( SQPE_EXTRA_OPENING_PARENTHESIS + 1 ) ,
- SQPE_IGNORED_MODIFIER = ( SQPE_EXTRA_CLOSING_PARENTHESIS + 1 ) ,
- SQPE_IGNORED_CONNECTOR = ( SQPE_IGNORED_MODIFIER + 1 ) ,
- SQPE_IGNORED_KEYWORD = ( SQPE_IGNORED_CONNECTOR + 1 ) ,
- SQPE_UNHANDLED = ( SQPE_IGNORED_KEYWORD + 1 )
- } STRUCTURED_QUERY_PARSE_ERROR;
-
-/* [v1_enum] */
-enum tagSTRUCTURED_QUERY_RESOLVE_OPTION
- { SQRO_DONT_RESOLVE_DATETIME = 0x1,
- SQRO_ALWAYS_ONE_INTERVAL = 0x2,
- SQRO_DONT_SIMPLIFY_CONDITION_TREES = 0x4,
- SQRO_DONT_MAP_RELATIONS = 0x8,
- SQRO_DONT_RESOLVE_RANGES = 0x10,
- SQRO_DONT_REMOVE_UNRESTRICTED_KEYWORDS = 0x20,
- SQRO_DONT_SPLIT_WORDS = 0x40,
- SQRO_IGNORE_PHRASE_ORDER = 0x80
- } ;
-typedef int STRUCTURED_QUERY_RESOLVE_OPTION;
-
-typedef /* [v1_enum] */
-enum tagINTERVAL_LIMIT_KIND
- { ILK_EXPLICIT_INCLUDED = 0,
- ILK_EXPLICIT_EXCLUDED = ( ILK_EXPLICIT_INCLUDED + 1 ) ,
- ILK_NEGATIVE_INFINITY = ( ILK_EXPLICIT_EXCLUDED + 1 ) ,
- ILK_POSITIVE_INFINITY = ( ILK_NEGATIVE_INFINITY + 1 )
- } INTERVAL_LIMIT_KIND;
-
-typedef /* [v1_enum] */
-enum tagQUERY_PARSER_MANAGER_OPTION
- { QPMO_SCHEMA_BINARY_NAME = 0,
- QPMO_PRELOCALIZED_SCHEMA_BINARY_PATH = ( QPMO_SCHEMA_BINARY_NAME + 1 ) ,
- QPMO_UNLOCALIZED_SCHEMA_BINARY_PATH = ( QPMO_PRELOCALIZED_SCHEMA_BINARY_PATH + 1 ) ,
- QPMO_LOCALIZED_SCHEMA_BINARY_PATH = ( QPMO_UNLOCALIZED_SCHEMA_BINARY_PATH + 1 ) ,
- QPMO_APPEND_LCID_TO_LOCALIZED_PATH = ( QPMO_LOCALIZED_SCHEMA_BINARY_PATH + 1 ) ,
- QPMO_LOCALIZER_SUPPORT = ( QPMO_APPEND_LCID_TO_LOCALIZED_PATH + 1 )
- } QUERY_PARSER_MANAGER_OPTION;
-
-
-
-extern RPC_IF_HANDLE __MIDL_itf_structuredquery_0000_0000_v0_0_c_ifspec;
-extern RPC_IF_HANDLE __MIDL_itf_structuredquery_0000_0000_v0_0_s_ifspec;
-
-#ifndef __IQueryParser_INTERFACE_DEFINED__
-#define __IQueryParser_INTERFACE_DEFINED__
-
-/* interface IQueryParser */
-/* [unique][uuid][object] */
-
-
-EXTERN_C const IID IID_IQueryParser;
-
-#if defined(__cplusplus) && !defined(CINTERFACE)
-
- MIDL_INTERFACE("2EBDEE67-3505-43f8-9946-EA44ABC8E5B0")
- IQueryParser : public IUnknown
- {
- public:
- virtual HRESULT STDMETHODCALLTYPE Parse(
- /* [in] */ __RPC__in LPCWSTR pszInputString,
- /* [in] */ __RPC__in_opt IEnumUnknown *pCustomProperties,
- /* [retval][out] */ __RPC__deref_out_opt IQuerySolution **ppSolution) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE SetOption(
- /* [in] */ STRUCTURED_QUERY_SINGLE_OPTION option,
- /* [in] */ __RPC__in const PROPVARIANT *pOptionValue) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE GetOption(
- /* [in] */ STRUCTURED_QUERY_SINGLE_OPTION option,
- /* [retval][out] */ __RPC__out PROPVARIANT *pOptionValue) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE SetMultiOption(
- /* [in] */ STRUCTURED_QUERY_MULTIOPTION option,
- /* [in] */ __RPC__in LPCWSTR pszOptionKey,
- /* [in] */ __RPC__in const PROPVARIANT *pOptionValue) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE GetSchemaProvider(
- /* [retval][out] */ __RPC__deref_out_opt ISchemaProvider **ppSchemaProvider) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE RestateToString(
- /* [in] */ __RPC__in_opt ICondition *pCondition,
- /* [in] */ BOOL fUseEnglish,
- /* [out] */ __RPC__deref_out_opt LPWSTR *ppszQueryString) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE ParsePropertyValue(
- /* [in] */ __RPC__in LPCWSTR pszPropertyName,
- /* [in] */ __RPC__in LPCWSTR pszInputString,
- /* [retval][out] */ __RPC__deref_out_opt IQuerySolution **ppSolution) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE RestatePropertyValueToString(
- /* [in] */ __RPC__in_opt ICondition *pCondition,
- /* [in] */ BOOL fUseEnglish,
- /* [out] */ __RPC__deref_out_opt LPWSTR *ppszPropertyName,
- /* [out] */ __RPC__deref_out_opt LPWSTR *ppszQueryString) = 0;
-
- };
-
-#else /* C style interface */
-
- typedef struct IQueryParserVtbl
- {
- BEGIN_INTERFACE
-
- HRESULT ( STDMETHODCALLTYPE *QueryInterface )(
- IQueryParser * This,
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][out] */
- __RPC__deref_out void **ppvObject);
-
- ULONG ( STDMETHODCALLTYPE *AddRef )(
- IQueryParser * This);
-
- ULONG ( STDMETHODCALLTYPE *Release )(
- IQueryParser * This);
-
- HRESULT ( STDMETHODCALLTYPE *Parse )(
- IQueryParser * This,
- /* [in] */ __RPC__in LPCWSTR pszInputString,
- /* [in] */ __RPC__in_opt IEnumUnknown *pCustomProperties,
- /* [retval][out] */ __RPC__deref_out_opt IQuerySolution **ppSolution);
-
- HRESULT ( STDMETHODCALLTYPE *SetOption )(
- IQueryParser * This,
- /* [in] */ STRUCTURED_QUERY_SINGLE_OPTION option,
- /* [in] */ __RPC__in const PROPVARIANT *pOptionValue);
-
- HRESULT ( STDMETHODCALLTYPE *GetOption )(
- IQueryParser * This,
- /* [in] */ STRUCTURED_QUERY_SINGLE_OPTION option,
- /* [retval][out] */ __RPC__out PROPVARIANT *pOptionValue);
-
- HRESULT ( STDMETHODCALLTYPE *SetMultiOption )(
- IQueryParser * This,
- /* [in] */ STRUCTURED_QUERY_MULTIOPTION option,
- /* [in] */ __RPC__in LPCWSTR pszOptionKey,
- /* [in] */ __RPC__in const PROPVARIANT *pOptionValue);
-
- HRESULT ( STDMETHODCALLTYPE *GetSchemaProvider )(
- IQueryParser * This,
- /* [retval][out] */ __RPC__deref_out_opt ISchemaProvider **ppSchemaProvider);
-
- HRESULT ( STDMETHODCALLTYPE *RestateToString )(
- IQueryParser * This,
- /* [in] */ __RPC__in_opt ICondition *pCondition,
- /* [in] */ BOOL fUseEnglish,
- /* [out] */ __RPC__deref_out_opt LPWSTR *ppszQueryString);
-
- HRESULT ( STDMETHODCALLTYPE *ParsePropertyValue )(
- IQueryParser * This,
- /* [in] */ __RPC__in LPCWSTR pszPropertyName,
- /* [in] */ __RPC__in LPCWSTR pszInputString,
- /* [retval][out] */ __RPC__deref_out_opt IQuerySolution **ppSolution);
-
- HRESULT ( STDMETHODCALLTYPE *RestatePropertyValueToString )(
- IQueryParser * This,
- /* [in] */ __RPC__in_opt ICondition *pCondition,
- /* [in] */ BOOL fUseEnglish,
- /* [out] */ __RPC__deref_out_opt LPWSTR *ppszPropertyName,
- /* [out] */ __RPC__deref_out_opt LPWSTR *ppszQueryString);
-
- END_INTERFACE
- } IQueryParserVtbl;
-
- interface IQueryParser
- {
- CONST_VTBL struct IQueryParserVtbl *lpVtbl;
- };
-
-
-
-#ifdef COBJMACROS
-
-
-#define IQueryParser_QueryInterface(This,riid,ppvObject) \
- ( (This)->lpVtbl -> QueryInterface(This,riid,ppvObject) )
-
-#define IQueryParser_AddRef(This) \
- ( (This)->lpVtbl -> AddRef(This) )
-
-#define IQueryParser_Release(This) \
- ( (This)->lpVtbl -> Release(This) )
-
-
-#define IQueryParser_Parse(This,pszInputString,pCustomProperties,ppSolution) \
- ( (This)->lpVtbl -> Parse(This,pszInputString,pCustomProperties,ppSolution) )
-
-#define IQueryParser_SetOption(This,option,pOptionValue) \
- ( (This)->lpVtbl -> SetOption(This,option,pOptionValue) )
-
-#define IQueryParser_GetOption(This,option,pOptionValue) \
- ( (This)->lpVtbl -> GetOption(This,option,pOptionValue) )
-
-#define IQueryParser_SetMultiOption(This,option,pszOptionKey,pOptionValue) \
- ( (This)->lpVtbl -> SetMultiOption(This,option,pszOptionKey,pOptionValue) )
-
-#define IQueryParser_GetSchemaProvider(This,ppSchemaProvider) \
- ( (This)->lpVtbl -> GetSchemaProvider(This,ppSchemaProvider) )
-
-#define IQueryParser_RestateToString(This,pCondition,fUseEnglish,ppszQueryString) \
- ( (This)->lpVtbl -> RestateToString(This,pCondition,fUseEnglish,ppszQueryString) )
-
-#define IQueryParser_ParsePropertyValue(This,pszPropertyName,pszInputString,ppSolution) \
- ( (This)->lpVtbl -> ParsePropertyValue(This,pszPropertyName,pszInputString,ppSolution) )
-
-#define IQueryParser_RestatePropertyValueToString(This,pCondition,fUseEnglish,ppszPropertyName,ppszQueryString) \
- ( (This)->lpVtbl -> RestatePropertyValueToString(This,pCondition,fUseEnglish,ppszPropertyName,ppszQueryString) )
-
-#endif /* COBJMACROS */
-
-
-#endif /* C style interface */
-
-
-
-
-#endif /* __IQueryParser_INTERFACE_DEFINED__ */
-
-
-#ifndef __IConditionFactory_INTERFACE_DEFINED__
-#define __IConditionFactory_INTERFACE_DEFINED__
-
-/* interface IConditionFactory */
-/* [unique][uuid][object] */
-
-
-EXTERN_C const IID IID_IConditionFactory;
-
-#if defined(__cplusplus) && !defined(CINTERFACE)
-
- MIDL_INTERFACE("A5EFE073-B16F-474f-9F3E-9F8B497A3E08")
- IConditionFactory : public IUnknown
- {
- public:
- virtual HRESULT STDMETHODCALLTYPE MakeNot(
- /* [in] */ __RPC__in_opt ICondition *pSubCondition,
- /* [in] */ BOOL simplify,
- /* [retval][out] */ __RPC__deref_out_opt ICondition **ppResultQuery) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE MakeAndOr(
- /* [in] */ CONDITION_TYPE nodeType,
- /* [in] */ __RPC__in_opt IEnumUnknown *pSubConditions,
- /* [in] */ BOOL simplify,
- /* [retval][out] */ __RPC__deref_out_opt ICondition **ppResultQuery) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE MakeLeaf(
- /* [unique][in] */ __RPC__in_opt LPCWSTR pszPropertyName,
- /* [in] */ CONDITION_OPERATION op,
- /* [unique][in] */ __RPC__in_opt LPCWSTR pszValueType,
- /* [in] */ __RPC__in const PROPVARIANT *pValue,
- /* [in] */ __RPC__in_opt IRichChunk *pPropertyNameTerm,
- /* [in] */ __RPC__in_opt IRichChunk *pOperationTerm,
- /* [in] */ __RPC__in_opt IRichChunk *pValueTerm,
- /* [in] */ BOOL expand,
- /* [retval][out] */ __RPC__deref_out_opt ICondition **ppResultQuery) = 0;
-
- virtual /* [local] */ HRESULT STDMETHODCALLTYPE Resolve(
- /* [in] */
- __in ICondition *pConditionTree,
- /* [in] */
- __in STRUCTURED_QUERY_RESOLVE_OPTION sqro,
- /* [ref][in] */
- __in_opt const SYSTEMTIME *pstReferenceTime,
- /* [retval][out] */
- __out ICondition **ppResolvedConditionTree) = 0;
-
- };
-
-#else /* C style interface */
-
- typedef struct IConditionFactoryVtbl
- {
- BEGIN_INTERFACE
-
- HRESULT ( STDMETHODCALLTYPE *QueryInterface )(
- IConditionFactory * This,
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][out] */
- __RPC__deref_out void **ppvObject);
-
- ULONG ( STDMETHODCALLTYPE *AddRef )(
- IConditionFactory * This);
-
- ULONG ( STDMETHODCALLTYPE *Release )(
- IConditionFactory * This);
-
- HRESULT ( STDMETHODCALLTYPE *MakeNot )(
- IConditionFactory * This,
- /* [in] */ __RPC__in_opt ICondition *pSubCondition,
- /* [in] */ BOOL simplify,
- /* [retval][out] */ __RPC__deref_out_opt ICondition **ppResultQuery);
-
- HRESULT ( STDMETHODCALLTYPE *MakeAndOr )(
- IConditionFactory * This,
- /* [in] */ CONDITION_TYPE nodeType,
- /* [in] */ __RPC__in_opt IEnumUnknown *pSubConditions,
- /* [in] */ BOOL simplify,
- /* [retval][out] */ __RPC__deref_out_opt ICondition **ppResultQuery);
-
- HRESULT ( STDMETHODCALLTYPE *MakeLeaf )(
- IConditionFactory * This,
- /* [unique][in] */ __RPC__in_opt LPCWSTR pszPropertyName,
- /* [in] */ CONDITION_OPERATION op,
- /* [unique][in] */ __RPC__in_opt LPCWSTR pszValueType,
- /* [in] */ __RPC__in const PROPVARIANT *pValue,
- /* [in] */ __RPC__in_opt IRichChunk *pPropertyNameTerm,
- /* [in] */ __RPC__in_opt IRichChunk *pOperationTerm,
- /* [in] */ __RPC__in_opt IRichChunk *pValueTerm,
- /* [in] */ BOOL expand,
- /* [retval][out] */ __RPC__deref_out_opt ICondition **ppResultQuery);
-
- /* [local] */ HRESULT ( STDMETHODCALLTYPE *Resolve )(
- IConditionFactory * This,
- /* [in] */
- __in ICondition *pConditionTree,
- /* [in] */
- __in STRUCTURED_QUERY_RESOLVE_OPTION sqro,
- /* [ref][in] */
- __in_opt const SYSTEMTIME *pstReferenceTime,
- /* [retval][out] */
- __out ICondition **ppResolvedConditionTree);
-
- END_INTERFACE
- } IConditionFactoryVtbl;
-
- interface IConditionFactory
- {
- CONST_VTBL struct IConditionFactoryVtbl *lpVtbl;
- };
-
-
-
-#ifdef COBJMACROS
-
-
-#define IConditionFactory_QueryInterface(This,riid,ppvObject) \
- ( (This)->lpVtbl -> QueryInterface(This,riid,ppvObject) )
-
-#define IConditionFactory_AddRef(This) \
- ( (This)->lpVtbl -> AddRef(This) )
-
-#define IConditionFactory_Release(This) \
- ( (This)->lpVtbl -> Release(This) )
-
-
-#define IConditionFactory_MakeNot(This,pSubCondition,simplify,ppResultQuery) \
- ( (This)->lpVtbl -> MakeNot(This,pSubCondition,simplify,ppResultQuery) )
-
-#define IConditionFactory_MakeAndOr(This,nodeType,pSubConditions,simplify,ppResultQuery) \
- ( (This)->lpVtbl -> MakeAndOr(This,nodeType,pSubConditions,simplify,ppResultQuery) )
-
-#define IConditionFactory_MakeLeaf(This,pszPropertyName,op,pszValueType,pValue,pPropertyNameTerm,pOperationTerm,pValueTerm,expand,ppResultQuery) \
- ( (This)->lpVtbl -> MakeLeaf(This,pszPropertyName,op,pszValueType,pValue,pPropertyNameTerm,pOperationTerm,pValueTerm,expand,ppResultQuery) )
-
-#define IConditionFactory_Resolve(This,pConditionTree,sqro,pstReferenceTime,ppResolvedConditionTree) \
- ( (This)->lpVtbl -> Resolve(This,pConditionTree,sqro,pstReferenceTime,ppResolvedConditionTree) )
-
-#endif /* COBJMACROS */
-
-
-#endif /* C style interface */
-
-
-
-
-#endif /* __IConditionFactory_INTERFACE_DEFINED__ */
-
-
-#ifndef __IQuerySolution_INTERFACE_DEFINED__
-#define __IQuerySolution_INTERFACE_DEFINED__
-
-/* interface IQuerySolution */
-/* [unique][uuid][object] */
-
-
-EXTERN_C const IID IID_IQuerySolution;
-
-#if defined(__cplusplus) && !defined(CINTERFACE)
-
- MIDL_INTERFACE("D6EBC66B-8921-4193-AFDD-A1789FB7FF57")
- IQuerySolution : public IConditionFactory
- {
- public:
- virtual /* [local] */ HRESULT STDMETHODCALLTYPE GetQuery(
- /* [out] */
- __out_opt ICondition **ppQueryNode,
- /* [out] */
- __out_opt IEntity **ppMainType) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE GetErrors(
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][retval][out] */ __RPC__deref_out_opt void **ppParseErrors) = 0;
-
- virtual /* [local] */ HRESULT STDMETHODCALLTYPE GetLexicalData(
- /* [out] */
- __deref_opt_out LPWSTR *ppszInputString,
- /* [out] */
- __out_opt ITokenCollection **ppTokens,
- /* [out] */
- __out_opt LCID *pLocale,
- /* [out] */
- __out_opt IUnknown **ppWordBreaker) = 0;
-
- };
-
-#else /* C style interface */
-
- typedef struct IQuerySolutionVtbl
- {
- BEGIN_INTERFACE
-
- HRESULT ( STDMETHODCALLTYPE *QueryInterface )(
- IQuerySolution * This,
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][out] */
- __RPC__deref_out void **ppvObject);
-
- ULONG ( STDMETHODCALLTYPE *AddRef )(
- IQuerySolution * This);
-
- ULONG ( STDMETHODCALLTYPE *Release )(
- IQuerySolution * This);
-
- HRESULT ( STDMETHODCALLTYPE *MakeNot )(
- IQuerySolution * This,
- /* [in] */ __RPC__in_opt ICondition *pSubCondition,
- /* [in] */ BOOL simplify,
- /* [retval][out] */ __RPC__deref_out_opt ICondition **ppResultQuery);
-
- HRESULT ( STDMETHODCALLTYPE *MakeAndOr )(
- IQuerySolution * This,
- /* [in] */ CONDITION_TYPE nodeType,
- /* [in] */ __RPC__in_opt IEnumUnknown *pSubConditions,
- /* [in] */ BOOL simplify,
- /* [retval][out] */ __RPC__deref_out_opt ICondition **ppResultQuery);
-
- HRESULT ( STDMETHODCALLTYPE *MakeLeaf )(
- IQuerySolution * This,
- /* [unique][in] */ __RPC__in_opt LPCWSTR pszPropertyName,
- /* [in] */ CONDITION_OPERATION op,
- /* [unique][in] */ __RPC__in_opt LPCWSTR pszValueType,
- /* [in] */ __RPC__in const PROPVARIANT *pValue,
- /* [in] */ __RPC__in_opt IRichChunk *pPropertyNameTerm,
- /* [in] */ __RPC__in_opt IRichChunk *pOperationTerm,
- /* [in] */ __RPC__in_opt IRichChunk *pValueTerm,
- /* [in] */ BOOL expand,
- /* [retval][out] */ __RPC__deref_out_opt ICondition **ppResultQuery);
-
- /* [local] */ HRESULT ( STDMETHODCALLTYPE *Resolve )(
- IQuerySolution * This,
- /* [in] */
- __in ICondition *pConditionTree,
- /* [in] */
- __in STRUCTURED_QUERY_RESOLVE_OPTION sqro,
- /* [ref][in] */
- __in_opt const SYSTEMTIME *pstReferenceTime,
- /* [retval][out] */
- __out ICondition **ppResolvedConditionTree);
-
- /* [local] */ HRESULT ( STDMETHODCALLTYPE *GetQuery )(
- IQuerySolution * This,
- /* [out] */
- __out_opt ICondition **ppQueryNode,
- /* [out] */
- __out_opt IEntity **ppMainType);
-
- HRESULT ( STDMETHODCALLTYPE *GetErrors )(
- IQuerySolution * This,
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][retval][out] */ __RPC__deref_out_opt void **ppParseErrors);
-
- /* [local] */ HRESULT ( STDMETHODCALLTYPE *GetLexicalData )(
- IQuerySolution * This,
- /* [out] */
- __deref_opt_out LPWSTR *ppszInputString,
- /* [out] */
- __out_opt ITokenCollection **ppTokens,
- /* [out] */
- __out_opt LCID *pLocale,
- /* [out] */
- __out_opt IUnknown **ppWordBreaker);
-
- END_INTERFACE
- } IQuerySolutionVtbl;
-
- interface IQuerySolution
- {
- CONST_VTBL struct IQuerySolutionVtbl *lpVtbl;
- };
-
-
-
-#ifdef COBJMACROS
-
-
-#define IQuerySolution_QueryInterface(This,riid,ppvObject) \
- ( (This)->lpVtbl -> QueryInterface(This,riid,ppvObject) )
-
-#define IQuerySolution_AddRef(This) \
- ( (This)->lpVtbl -> AddRef(This) )
-
-#define IQuerySolution_Release(This) \
- ( (This)->lpVtbl -> Release(This) )
-
-
-#define IQuerySolution_MakeNot(This,pSubCondition,simplify,ppResultQuery) \
- ( (This)->lpVtbl -> MakeNot(This,pSubCondition,simplify,ppResultQuery) )
-
-#define IQuerySolution_MakeAndOr(This,nodeType,pSubConditions,simplify,ppResultQuery) \
- ( (This)->lpVtbl -> MakeAndOr(This,nodeType,pSubConditions,simplify,ppResultQuery) )
-
-#define IQuerySolution_MakeLeaf(This,pszPropertyName,op,pszValueType,pValue,pPropertyNameTerm,pOperationTerm,pValueTerm,expand,ppResultQuery) \
- ( (This)->lpVtbl -> MakeLeaf(This,pszPropertyName,op,pszValueType,pValue,pPropertyNameTerm,pOperationTerm,pValueTerm,expand,ppResultQuery) )
-
-#define IQuerySolution_Resolve(This,pConditionTree,sqro,pstReferenceTime,ppResolvedConditionTree) \
- ( (This)->lpVtbl -> Resolve(This,pConditionTree,sqro,pstReferenceTime,ppResolvedConditionTree) )
-
-
-#define IQuerySolution_GetQuery(This,ppQueryNode,ppMainType) \
- ( (This)->lpVtbl -> GetQuery(This,ppQueryNode,ppMainType) )
-
-#define IQuerySolution_GetErrors(This,riid,ppParseErrors) \
- ( (This)->lpVtbl -> GetErrors(This,riid,ppParseErrors) )
-
-#define IQuerySolution_GetLexicalData(This,ppszInputString,ppTokens,pLocale,ppWordBreaker) \
- ( (This)->lpVtbl -> GetLexicalData(This,ppszInputString,ppTokens,pLocale,ppWordBreaker) )
-
-#endif /* COBJMACROS */
-
-
-#endif /* C style interface */
-
-
-
-
-#endif /* __IQuerySolution_INTERFACE_DEFINED__ */
-
-
-#ifndef __ICondition_INTERFACE_DEFINED__
-#define __ICondition_INTERFACE_DEFINED__
-
-/* interface ICondition */
-/* [unique][uuid][object] */
-
-
-EXTERN_C const IID IID_ICondition;
-
-#if defined(__cplusplus) && !defined(CINTERFACE)
-
- MIDL_INTERFACE("0FC988D4-C935-4b97-A973-46282EA175C8")
- ICondition : public IPersistStream
- {
- public:
- virtual HRESULT STDMETHODCALLTYPE GetConditionType(
- /* [retval][out] */ __RPC__out CONDITION_TYPE *pNodeType) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE GetSubConditions(
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][retval][out] */ __RPC__deref_out_opt void **ppv) = 0;
-
- virtual /* [local] */ HRESULT STDMETHODCALLTYPE GetComparisonInfo(
- /* [out] */
- __deref_opt_out LPWSTR *ppszPropertyName,
- /* [out] */
- __out_opt CONDITION_OPERATION *pOperation,
- /* [out] */
- __out_opt PROPVARIANT *pValue) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE GetValueType(
- /* [retval][out] */ __RPC__deref_out_opt LPWSTR *ppszValueTypeName) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE GetValueNormalization(
- /* [retval][out] */ __RPC__deref_out_opt LPWSTR *ppszNormalization) = 0;
-
- virtual /* [local] */ HRESULT STDMETHODCALLTYPE GetInputTerms(
- /* [out] */
- __out_opt IRichChunk **ppPropertyTerm,
- /* [out] */
- __out_opt IRichChunk **ppOperationTerm,
- /* [out] */
- __out_opt IRichChunk **ppValueTerm) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE Clone(
- /* [retval][out] */ __RPC__deref_out_opt ICondition **ppc) = 0;
-
- };
-
-#else /* C style interface */
-
- typedef struct IConditionVtbl
- {
- BEGIN_INTERFACE
-
- HRESULT ( STDMETHODCALLTYPE *QueryInterface )(
- ICondition * This,
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][out] */
- __RPC__deref_out void **ppvObject);
-
- ULONG ( STDMETHODCALLTYPE *AddRef )(
- ICondition * This);
-
- ULONG ( STDMETHODCALLTYPE *Release )(
- ICondition * This);
-
- HRESULT ( STDMETHODCALLTYPE *GetClassID )(
- ICondition * This,
- /* [out] */ __RPC__out CLSID *pClassID);
-
- HRESULT ( STDMETHODCALLTYPE *IsDirty )(
- ICondition * This);
-
- HRESULT ( STDMETHODCALLTYPE *Load )(
- ICondition * This,
- /* [unique][in] */ __RPC__in_opt IStream *pStm);
-
- HRESULT ( STDMETHODCALLTYPE *Save )(
- ICondition * This,
- /* [unique][in] */ __RPC__in_opt IStream *pStm,
- /* [in] */ BOOL fClearDirty);
-
- HRESULT ( STDMETHODCALLTYPE *GetSizeMax )(
- ICondition * This,
- /* [out] */ __RPC__out ULARGE_INTEGER *pcbSize);
-
- HRESULT ( STDMETHODCALLTYPE *GetConditionType )(
- ICondition * This,
- /* [retval][out] */ __RPC__out CONDITION_TYPE *pNodeType);
-
- HRESULT ( STDMETHODCALLTYPE *GetSubConditions )(
- ICondition * This,
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][retval][out] */ __RPC__deref_out_opt void **ppv);
-
- /* [local] */ HRESULT ( STDMETHODCALLTYPE *GetComparisonInfo )(
- ICondition * This,
- /* [out] */
- __deref_opt_out LPWSTR *ppszPropertyName,
- /* [out] */
- __out_opt CONDITION_OPERATION *pOperation,
- /* [out] */
- __out_opt PROPVARIANT *pValue);
-
- HRESULT ( STDMETHODCALLTYPE *GetValueType )(
- ICondition * This,
- /* [retval][out] */ __RPC__deref_out_opt LPWSTR *ppszValueTypeName);
-
- HRESULT ( STDMETHODCALLTYPE *GetValueNormalization )(
- ICondition * This,
- /* [retval][out] */ __RPC__deref_out_opt LPWSTR *ppszNormalization);
-
- /* [local] */ HRESULT ( STDMETHODCALLTYPE *GetInputTerms )(
- ICondition * This,
- /* [out] */
- __out_opt IRichChunk **ppPropertyTerm,
- /* [out] */
- __out_opt IRichChunk **ppOperationTerm,
- /* [out] */
- __out_opt IRichChunk **ppValueTerm);
-
- HRESULT ( STDMETHODCALLTYPE *Clone )(
- ICondition * This,
- /* [retval][out] */ __RPC__deref_out_opt ICondition **ppc);
-
- END_INTERFACE
- } IConditionVtbl;
-
- interface ICondition
- {
- CONST_VTBL struct IConditionVtbl *lpVtbl;
- };
-
-
-
-#ifdef COBJMACROS
-
-
-#define ICondition_QueryInterface(This,riid,ppvObject) \
- ( (This)->lpVtbl -> QueryInterface(This,riid,ppvObject) )
-
-#define ICondition_AddRef(This) \
- ( (This)->lpVtbl -> AddRef(This) )
-
-#define ICondition_Release(This) \
- ( (This)->lpVtbl -> Release(This) )
-
-
-#define ICondition_GetClassID(This,pClassID) \
- ( (This)->lpVtbl -> GetClassID(This,pClassID) )
-
-
-#define ICondition_IsDirty(This) \
- ( (This)->lpVtbl -> IsDirty(This) )
-
-#define ICondition_Load(This,pStm) \
- ( (This)->lpVtbl -> Load(This,pStm) )
-
-#define ICondition_Save(This,pStm,fClearDirty) \
- ( (This)->lpVtbl -> Save(This,pStm,fClearDirty) )
-
-#define ICondition_GetSizeMax(This,pcbSize) \
- ( (This)->lpVtbl -> GetSizeMax(This,pcbSize) )
-
-
-#define ICondition_GetConditionType(This,pNodeType) \
- ( (This)->lpVtbl -> GetConditionType(This,pNodeType) )
-
-#define ICondition_GetSubConditions(This,riid,ppv) \
- ( (This)->lpVtbl -> GetSubConditions(This,riid,ppv) )
-
-#define ICondition_GetComparisonInfo(This,ppszPropertyName,pOperation,pValue) \
- ( (This)->lpVtbl -> GetComparisonInfo(This,ppszPropertyName,pOperation,pValue) )
-
-#define ICondition_GetValueType(This,ppszValueTypeName) \
- ( (This)->lpVtbl -> GetValueType(This,ppszValueTypeName) )
-
-#define ICondition_GetValueNormalization(This,ppszNormalization) \
- ( (This)->lpVtbl -> GetValueNormalization(This,ppszNormalization) )
-
-#define ICondition_GetInputTerms(This,ppPropertyTerm,ppOperationTerm,ppValueTerm) \
- ( (This)->lpVtbl -> GetInputTerms(This,ppPropertyTerm,ppOperationTerm,ppValueTerm) )
-
-#define ICondition_Clone(This,ppc) \
- ( (This)->lpVtbl -> Clone(This,ppc) )
-
-#endif /* COBJMACROS */
-
-
-#endif /* C style interface */
-
-
-
-
-#endif /* __ICondition_INTERFACE_DEFINED__ */
-
-
-#ifndef __IConditionGenerator_INTERFACE_DEFINED__
-#define __IConditionGenerator_INTERFACE_DEFINED__
-
-/* interface IConditionGenerator */
-/* [unique][uuid][object] */
-
-
-EXTERN_C const IID IID_IConditionGenerator;
-
-#if defined(__cplusplus) && !defined(CINTERFACE)
-
- MIDL_INTERFACE("92D2CC58-4386-45a3-B98C-7E0CE64A4117")
- IConditionGenerator : public IUnknown
- {
- public:
- virtual HRESULT STDMETHODCALLTYPE Initialize(
- /* [in] */ __RPC__in_opt ISchemaProvider *pSchemaProvider) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE RecognizeNamedEntities(
- /* [in] */ __RPC__in LPCWSTR pszInputString,
- /* [in] */ LCID lcid,
- /* [in] */ __RPC__in_opt ITokenCollection *pTokenCollection,
- /* [out][in] */ __RPC__inout_opt INamedEntityCollector *pNamedEntities) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE GenerateForLeaf(
- /* [in] */ __RPC__in_opt IConditionFactory *pConditionFactory,
- /* [unique][in] */ __RPC__in_opt LPCWSTR pszPropertyName,
- /* [in] */ CONDITION_OPERATION op,
- /* [unique][in] */ __RPC__in_opt LPCWSTR pszValueType,
- /* [in] */ __RPC__in LPCWSTR pszValue,
- /* [unique][in] */ __RPC__in_opt LPCWSTR pszValue2,
- /* [in] */ __RPC__in_opt IRichChunk *pPropertyNameTerm,
- /* [in] */ __RPC__in_opt IRichChunk *pOperationTerm,
- /* [in] */ __RPC__in_opt IRichChunk *pValueTerm,
- /* [in] */ BOOL automaticWildcard,
- /* [out] */ __RPC__out BOOL *pNoStringQuery,
- /* [retval][out] */ __RPC__deref_out_opt ICondition **ppQueryExpression) = 0;
-
- virtual /* [local] */ HRESULT STDMETHODCALLTYPE DefaultPhrase(
- /* [unique][in] */ LPCWSTR pszValueType,
- /* [in] */ const PROPVARIANT *ppropvar,
- /* [in] */ BOOL fUseEnglish,
- /* [retval][out] */
- __deref_opt_out LPWSTR *ppszPhrase) = 0;
-
- };
-
-#else /* C style interface */
-
- typedef struct IConditionGeneratorVtbl
- {
- BEGIN_INTERFACE
-
- HRESULT ( STDMETHODCALLTYPE *QueryInterface )(
- IConditionGenerator * This,
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][out] */
- __RPC__deref_out void **ppvObject);
-
- ULONG ( STDMETHODCALLTYPE *AddRef )(
- IConditionGenerator * This);
-
- ULONG ( STDMETHODCALLTYPE *Release )(
- IConditionGenerator * This);
-
- HRESULT ( STDMETHODCALLTYPE *Initialize )(
- IConditionGenerator * This,
- /* [in] */ __RPC__in_opt ISchemaProvider *pSchemaProvider);
-
- HRESULT ( STDMETHODCALLTYPE *RecognizeNamedEntities )(
- IConditionGenerator * This,
- /* [in] */ __RPC__in LPCWSTR pszInputString,
- /* [in] */ LCID lcid,
- /* [in] */ __RPC__in_opt ITokenCollection *pTokenCollection,
- /* [out][in] */ __RPC__inout_opt INamedEntityCollector *pNamedEntities);
-
- HRESULT ( STDMETHODCALLTYPE *GenerateForLeaf )(
- IConditionGenerator * This,
- /* [in] */ __RPC__in_opt IConditionFactory *pConditionFactory,
- /* [unique][in] */ __RPC__in_opt LPCWSTR pszPropertyName,
- /* [in] */ CONDITION_OPERATION op,
- /* [unique][in] */ __RPC__in_opt LPCWSTR pszValueType,
- /* [in] */ __RPC__in LPCWSTR pszValue,
- /* [unique][in] */ __RPC__in_opt LPCWSTR pszValue2,
- /* [in] */ __RPC__in_opt IRichChunk *pPropertyNameTerm,
- /* [in] */ __RPC__in_opt IRichChunk *pOperationTerm,
- /* [in] */ __RPC__in_opt IRichChunk *pValueTerm,
- /* [in] */ BOOL automaticWildcard,
- /* [out] */ __RPC__out BOOL *pNoStringQuery,
- /* [retval][out] */ __RPC__deref_out_opt ICondition **ppQueryExpression);
-
- /* [local] */ HRESULT ( STDMETHODCALLTYPE *DefaultPhrase )(
- IConditionGenerator * This,
- /* [unique][in] */ LPCWSTR pszValueType,
- /* [in] */ const PROPVARIANT *ppropvar,
- /* [in] */ BOOL fUseEnglish,
- /* [retval][out] */
- __deref_opt_out LPWSTR *ppszPhrase);
-
- END_INTERFACE
- } IConditionGeneratorVtbl;
-
- interface IConditionGenerator
- {
- CONST_VTBL struct IConditionGeneratorVtbl *lpVtbl;
- };
-
-
-
-#ifdef COBJMACROS
-
-
-#define IConditionGenerator_QueryInterface(This,riid,ppvObject) \
- ( (This)->lpVtbl -> QueryInterface(This,riid,ppvObject) )
-
-#define IConditionGenerator_AddRef(This) \
- ( (This)->lpVtbl -> AddRef(This) )
-
-#define IConditionGenerator_Release(This) \
- ( (This)->lpVtbl -> Release(This) )
-
-
-#define IConditionGenerator_Initialize(This,pSchemaProvider) \
- ( (This)->lpVtbl -> Initialize(This,pSchemaProvider) )
-
-#define IConditionGenerator_RecognizeNamedEntities(This,pszInputString,lcid,pTokenCollection,pNamedEntities) \
- ( (This)->lpVtbl -> RecognizeNamedEntities(This,pszInputString,lcid,pTokenCollection,pNamedEntities) )
-
-#define IConditionGenerator_GenerateForLeaf(This,pConditionFactory,pszPropertyName,op,pszValueType,pszValue,pszValue2,pPropertyNameTerm,pOperationTerm,pValueTerm,automaticWildcard,pNoStringQuery,ppQueryExpression) \
- ( (This)->lpVtbl -> GenerateForLeaf(This,pConditionFactory,pszPropertyName,op,pszValueType,pszValue,pszValue2,pPropertyNameTerm,pOperationTerm,pValueTerm,automaticWildcard,pNoStringQuery,ppQueryExpression) )
-
-#define IConditionGenerator_DefaultPhrase(This,pszValueType,ppropvar,fUseEnglish,ppszPhrase) \
- ( (This)->lpVtbl -> DefaultPhrase(This,pszValueType,ppropvar,fUseEnglish,ppszPhrase) )
-
-#endif /* COBJMACROS */
-
-
-#endif /* C style interface */
-
-
-
-
-#endif /* __IConditionGenerator_INTERFACE_DEFINED__ */
-
-
-#ifndef __IRichChunk_INTERFACE_DEFINED__
-#define __IRichChunk_INTERFACE_DEFINED__
-
-/* interface IRichChunk */
-/* [unique][uuid][object] */
-
-
-EXTERN_C const IID IID_IRichChunk;
-
-#if defined(__cplusplus) && !defined(CINTERFACE)
-
- MIDL_INTERFACE("4FDEF69C-DBC9-454e-9910-B34F3C64B510")
- IRichChunk : public IUnknown
- {
- public:
- virtual /* [local] */ HRESULT STDMETHODCALLTYPE GetData(
- /* [out] */
- __out_opt ULONG *pFirstPos,
- /* [out] */
- __out_opt ULONG *pLength,
- /* [out] */
- __deref_opt_out LPWSTR *ppsz,
- /* [out] */
- __out_opt PROPVARIANT *pValue) = 0;
-
- };
-
-#else /* C style interface */
-
- typedef struct IRichChunkVtbl
- {
- BEGIN_INTERFACE
-
- HRESULT ( STDMETHODCALLTYPE *QueryInterface )(
- IRichChunk * This,
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][out] */
- __RPC__deref_out void **ppvObject);
-
- ULONG ( STDMETHODCALLTYPE *AddRef )(
- IRichChunk * This);
-
- ULONG ( STDMETHODCALLTYPE *Release )(
- IRichChunk * This);
-
- /* [local] */ HRESULT ( STDMETHODCALLTYPE *GetData )(
- IRichChunk * This,
- /* [out] */
- __out_opt ULONG *pFirstPos,
- /* [out] */
- __out_opt ULONG *pLength,
- /* [out] */
- __deref_opt_out LPWSTR *ppsz,
- /* [out] */
- __out_opt PROPVARIANT *pValue);
-
- END_INTERFACE
- } IRichChunkVtbl;
-
- interface IRichChunk
- {
- CONST_VTBL struct IRichChunkVtbl *lpVtbl;
- };
-
-
-
-#ifdef COBJMACROS
-
-
-#define IRichChunk_QueryInterface(This,riid,ppvObject) \
- ( (This)->lpVtbl -> QueryInterface(This,riid,ppvObject) )
-
-#define IRichChunk_AddRef(This) \
- ( (This)->lpVtbl -> AddRef(This) )
-
-#define IRichChunk_Release(This) \
- ( (This)->lpVtbl -> Release(This) )
-
-
-#define IRichChunk_GetData(This,pFirstPos,pLength,ppsz,pValue) \
- ( (This)->lpVtbl -> GetData(This,pFirstPos,pLength,ppsz,pValue) )
-
-#endif /* COBJMACROS */
-
-
-#endif /* C style interface */
-
-
-
-
-#endif /* __IRichChunk_INTERFACE_DEFINED__ */
-
-
-#ifndef __IInterval_INTERFACE_DEFINED__
-#define __IInterval_INTERFACE_DEFINED__
-
-/* interface IInterval */
-/* [unique][uuid][object] */
-
-
-EXTERN_C const IID IID_IInterval;
-
-#if defined(__cplusplus) && !defined(CINTERFACE)
-
- MIDL_INTERFACE("6BF0A714-3C18-430b-8B5D-83B1C234D3DB")
- IInterval : public IUnknown
- {
- public:
- virtual HRESULT STDMETHODCALLTYPE GetLimits(
- /* [out] */ __RPC__out INTERVAL_LIMIT_KIND *pilkLower,
- /* [out] */ __RPC__out PROPVARIANT *ppropvarLower,
- /* [out] */ __RPC__out INTERVAL_LIMIT_KIND *pilkUpper,
- /* [out] */ __RPC__out PROPVARIANT *ppropvarUpper) = 0;
-
- };
-
-#else /* C style interface */
-
- typedef struct IIntervalVtbl
- {
- BEGIN_INTERFACE
-
- HRESULT ( STDMETHODCALLTYPE *QueryInterface )(
- IInterval * This,
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][out] */
- __RPC__deref_out void **ppvObject);
-
- ULONG ( STDMETHODCALLTYPE *AddRef )(
- IInterval * This);
-
- ULONG ( STDMETHODCALLTYPE *Release )(
- IInterval * This);
-
- HRESULT ( STDMETHODCALLTYPE *GetLimits )(
- IInterval * This,
- /* [out] */ __RPC__out INTERVAL_LIMIT_KIND *pilkLower,
- /* [out] */ __RPC__out PROPVARIANT *ppropvarLower,
- /* [out] */ __RPC__out INTERVAL_LIMIT_KIND *pilkUpper,
- /* [out] */ __RPC__out PROPVARIANT *ppropvarUpper);
-
- END_INTERFACE
- } IIntervalVtbl;
-
- interface IInterval
- {
- CONST_VTBL struct IIntervalVtbl *lpVtbl;
- };
-
-
-
-#ifdef COBJMACROS
-
-
-#define IInterval_QueryInterface(This,riid,ppvObject) \
- ( (This)->lpVtbl -> QueryInterface(This,riid,ppvObject) )
-
-#define IInterval_AddRef(This) \
- ( (This)->lpVtbl -> AddRef(This) )
-
-#define IInterval_Release(This) \
- ( (This)->lpVtbl -> Release(This) )
-
-
-#define IInterval_GetLimits(This,pilkLower,ppropvarLower,pilkUpper,ppropvarUpper) \
- ( (This)->lpVtbl -> GetLimits(This,pilkLower,ppropvarLower,pilkUpper,ppropvarUpper) )
-
-#endif /* COBJMACROS */
-
-
-#endif /* C style interface */
-
-
-
-
-#endif /* __IInterval_INTERFACE_DEFINED__ */
-
-
-#ifndef __IMetaData_INTERFACE_DEFINED__
-#define __IMetaData_INTERFACE_DEFINED__
-
-/* interface IMetaData */
-/* [unique][uuid][object][helpstring] */
-
-
-EXTERN_C const IID IID_IMetaData;
-
-#if defined(__cplusplus) && !defined(CINTERFACE)
-
- MIDL_INTERFACE("780102B0-C43B-4876-BC7B-5E9BA5C88794")
- IMetaData : public IUnknown
- {
- public:
- virtual /* [local] */ HRESULT STDMETHODCALLTYPE GetData(
- /* [out] */
- __deref_opt_out LPWSTR *ppszKey,
- /* [out] */
- __deref_opt_out LPWSTR *ppszValue) = 0;
-
- };
-
-#else /* C style interface */
-
- typedef struct IMetaDataVtbl
- {
- BEGIN_INTERFACE
-
- HRESULT ( STDMETHODCALLTYPE *QueryInterface )(
- IMetaData * This,
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][out] */
- __RPC__deref_out void **ppvObject);
-
- ULONG ( STDMETHODCALLTYPE *AddRef )(
- IMetaData * This);
-
- ULONG ( STDMETHODCALLTYPE *Release )(
- IMetaData * This);
-
- /* [local] */ HRESULT ( STDMETHODCALLTYPE *GetData )(
- IMetaData * This,
- /* [out] */
- __deref_opt_out LPWSTR *ppszKey,
- /* [out] */
- __deref_opt_out LPWSTR *ppszValue);
-
- END_INTERFACE
- } IMetaDataVtbl;
-
- interface IMetaData
- {
- CONST_VTBL struct IMetaDataVtbl *lpVtbl;
- };
-
-
-
-#ifdef COBJMACROS
-
-
-#define IMetaData_QueryInterface(This,riid,ppvObject) \
- ( (This)->lpVtbl -> QueryInterface(This,riid,ppvObject) )
-
-#define IMetaData_AddRef(This) \
- ( (This)->lpVtbl -> AddRef(This) )
-
-#define IMetaData_Release(This) \
- ( (This)->lpVtbl -> Release(This) )
-
-
-#define IMetaData_GetData(This,ppszKey,ppszValue) \
- ( (This)->lpVtbl -> GetData(This,ppszKey,ppszValue) )
-
-#endif /* COBJMACROS */
-
-
-#endif /* C style interface */
-
-
-
-
-#endif /* __IMetaData_INTERFACE_DEFINED__ */
-
-
-/* interface __MIDL_itf_structuredquery_0000_0008 */
-/* [local] */
-
-
-
-
-extern RPC_IF_HANDLE __MIDL_itf_structuredquery_0000_0008_v0_0_c_ifspec;
-extern RPC_IF_HANDLE __MIDL_itf_structuredquery_0000_0008_v0_0_s_ifspec;
-
-#ifndef __IEntity_INTERFACE_DEFINED__
-#define __IEntity_INTERFACE_DEFINED__
-
-/* interface IEntity */
-/* [unique][object][uuid][helpstring] */
-
-
-EXTERN_C const IID IID_IEntity;
-
-#if defined(__cplusplus) && !defined(CINTERFACE)
-
- MIDL_INTERFACE("24264891-E80B-4fd3-B7CE-4FF2FAE8931F")
- IEntity : public IUnknown
- {
- public:
- virtual /* [local] */ HRESULT STDMETHODCALLTYPE Name(
- /* [retval][out] */
- __deref_opt_out LPWSTR *ppszName) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE Base(
- /* [retval][out] */ __RPC__deref_out_opt IEntity **pBaseEntity) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE Relationships(
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][retval][out] */ __RPC__deref_out_opt void **pRelationships) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE GetRelationship(
- /* [in] */ __RPC__in LPCWSTR pszRelationName,
- /* [retval][out] */ __RPC__deref_out_opt IRelationship **pRelationship) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE MetaData(
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][retval][out] */ __RPC__deref_out_opt void **pMetaData) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE NamedEntities(
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][retval][out] */ __RPC__deref_out_opt void **pNamedEntities) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE GetNamedEntity(
- /* [in] */ __RPC__in LPCWSTR pszValue,
- /* [retval][out] */ __RPC__deref_out_opt INamedEntity **ppNamedEntity) = 0;
-
- virtual /* [local] */ HRESULT STDMETHODCALLTYPE DefaultPhrase(
- /* [retval][out] */
- __deref_opt_out LPWSTR *ppszPhrase) = 0;
-
- };
-
-#else /* C style interface */
-
- typedef struct IEntityVtbl
- {
- BEGIN_INTERFACE
-
- HRESULT ( STDMETHODCALLTYPE *QueryInterface )(
- IEntity * This,
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][out] */
- __RPC__deref_out void **ppvObject);
-
- ULONG ( STDMETHODCALLTYPE *AddRef )(
- IEntity * This);
-
- ULONG ( STDMETHODCALLTYPE *Release )(
- IEntity * This);
-
- /* [local] */ HRESULT ( STDMETHODCALLTYPE *Name )(
- IEntity * This,
- /* [retval][out] */
- __deref_opt_out LPWSTR *ppszName);
-
- HRESULT ( STDMETHODCALLTYPE *Base )(
- IEntity * This,
- /* [retval][out] */ __RPC__deref_out_opt IEntity **pBaseEntity);
-
- HRESULT ( STDMETHODCALLTYPE *Relationships )(
- IEntity * This,
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][retval][out] */ __RPC__deref_out_opt void **pRelationships);
-
- HRESULT ( STDMETHODCALLTYPE *GetRelationship )(
- IEntity * This,
- /* [in] */ __RPC__in LPCWSTR pszRelationName,
- /* [retval][out] */ __RPC__deref_out_opt IRelationship **pRelationship);
-
- HRESULT ( STDMETHODCALLTYPE *MetaData )(
- IEntity * This,
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][retval][out] */ __RPC__deref_out_opt void **pMetaData);
-
- HRESULT ( STDMETHODCALLTYPE *NamedEntities )(
- IEntity * This,
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][retval][out] */ __RPC__deref_out_opt void **pNamedEntities);
-
- HRESULT ( STDMETHODCALLTYPE *GetNamedEntity )(
- IEntity * This,
- /* [in] */ __RPC__in LPCWSTR pszValue,
- /* [retval][out] */ __RPC__deref_out_opt INamedEntity **ppNamedEntity);
-
- /* [local] */ HRESULT ( STDMETHODCALLTYPE *DefaultPhrase )(
- IEntity * This,
- /* [retval][out] */
- __deref_opt_out LPWSTR *ppszPhrase);
-
- END_INTERFACE
- } IEntityVtbl;
-
- interface IEntity
- {
- CONST_VTBL struct IEntityVtbl *lpVtbl;
- };
-
-
-
-#ifdef COBJMACROS
-
-
-#define IEntity_QueryInterface(This,riid,ppvObject) \
- ( (This)->lpVtbl -> QueryInterface(This,riid,ppvObject) )
-
-#define IEntity_AddRef(This) \
- ( (This)->lpVtbl -> AddRef(This) )
-
-#define IEntity_Release(This) \
- ( (This)->lpVtbl -> Release(This) )
-
-
-#define IEntity_Name(This,ppszName) \
- ( (This)->lpVtbl -> Name(This,ppszName) )
-
-#define IEntity_Base(This,pBaseEntity) \
- ( (This)->lpVtbl -> Base(This,pBaseEntity) )
-
-#define IEntity_Relationships(This,riid,pRelationships) \
- ( (This)->lpVtbl -> Relationships(This,riid,pRelationships) )
-
-#define IEntity_GetRelationship(This,pszRelationName,pRelationship) \
- ( (This)->lpVtbl -> GetRelationship(This,pszRelationName,pRelationship) )
-
-#define IEntity_MetaData(This,riid,pMetaData) \
- ( (This)->lpVtbl -> MetaData(This,riid,pMetaData) )
-
-#define IEntity_NamedEntities(This,riid,pNamedEntities) \
- ( (This)->lpVtbl -> NamedEntities(This,riid,pNamedEntities) )
-
-#define IEntity_GetNamedEntity(This,pszValue,ppNamedEntity) \
- ( (This)->lpVtbl -> GetNamedEntity(This,pszValue,ppNamedEntity) )
-
-#define IEntity_DefaultPhrase(This,ppszPhrase) \
- ( (This)->lpVtbl -> DefaultPhrase(This,ppszPhrase) )
-
-#endif /* COBJMACROS */
-
-
-#endif /* C style interface */
-
-
-
-
-#endif /* __IEntity_INTERFACE_DEFINED__ */
-
-
-#ifndef __IRelationship_INTERFACE_DEFINED__
-#define __IRelationship_INTERFACE_DEFINED__
-
-/* interface IRelationship */
-/* [unique][object][uuid][helpstring] */
-
-
-EXTERN_C const IID IID_IRelationship;
-
-#if defined(__cplusplus) && !defined(CINTERFACE)
-
- MIDL_INTERFACE("2769280B-5108-498c-9C7F-A51239B63147")
- IRelationship : public IUnknown
- {
- public:
- virtual /* [local] */ HRESULT STDMETHODCALLTYPE Name(
- /* [retval][out] */
- __deref_opt_out LPWSTR *ppszName) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE IsReal(
- /* [retval][out] */ __RPC__out BOOL *pIsReal) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE Destination(
- /* [retval][out] */ __RPC__deref_out_opt IEntity **pDestinationEntity) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE MetaData(
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][retval][out] */ __RPC__deref_out_opt void **pMetaData) = 0;
-
- virtual /* [local] */ HRESULT STDMETHODCALLTYPE DefaultPhrase(
- /* [retval][out] */
- __deref_opt_out LPWSTR *ppszPhrase) = 0;
-
- };
-
-#else /* C style interface */
-
- typedef struct IRelationshipVtbl
- {
- BEGIN_INTERFACE
-
- HRESULT ( STDMETHODCALLTYPE *QueryInterface )(
- IRelationship * This,
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][out] */
- __RPC__deref_out void **ppvObject);
-
- ULONG ( STDMETHODCALLTYPE *AddRef )(
- IRelationship * This);
-
- ULONG ( STDMETHODCALLTYPE *Release )(
- IRelationship * This);
-
- /* [local] */ HRESULT ( STDMETHODCALLTYPE *Name )(
- IRelationship * This,
- /* [retval][out] */
- __deref_opt_out LPWSTR *ppszName);
-
- HRESULT ( STDMETHODCALLTYPE *IsReal )(
- IRelationship * This,
- /* [retval][out] */ __RPC__out BOOL *pIsReal);
-
- HRESULT ( STDMETHODCALLTYPE *Destination )(
- IRelationship * This,
- /* [retval][out] */ __RPC__deref_out_opt IEntity **pDestinationEntity);
-
- HRESULT ( STDMETHODCALLTYPE *MetaData )(
- IRelationship * This,
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][retval][out] */ __RPC__deref_out_opt void **pMetaData);
-
- /* [local] */ HRESULT ( STDMETHODCALLTYPE *DefaultPhrase )(
- IRelationship * This,
- /* [retval][out] */
- __deref_opt_out LPWSTR *ppszPhrase);
-
- END_INTERFACE
- } IRelationshipVtbl;
-
- interface IRelationship
- {
- CONST_VTBL struct IRelationshipVtbl *lpVtbl;
- };
-
-
-
-#ifdef COBJMACROS
-
-
-#define IRelationship_QueryInterface(This,riid,ppvObject) \
- ( (This)->lpVtbl -> QueryInterface(This,riid,ppvObject) )
-
-#define IRelationship_AddRef(This) \
- ( (This)->lpVtbl -> AddRef(This) )
-
-#define IRelationship_Release(This) \
- ( (This)->lpVtbl -> Release(This) )
-
-
-#define IRelationship_Name(This,ppszName) \
- ( (This)->lpVtbl -> Name(This,ppszName) )
-
-#define IRelationship_IsReal(This,pIsReal) \
- ( (This)->lpVtbl -> IsReal(This,pIsReal) )
-
-#define IRelationship_Destination(This,pDestinationEntity) \
- ( (This)->lpVtbl -> Destination(This,pDestinationEntity) )
-
-#define IRelationship_MetaData(This,riid,pMetaData) \
- ( (This)->lpVtbl -> MetaData(This,riid,pMetaData) )
-
-#define IRelationship_DefaultPhrase(This,ppszPhrase) \
- ( (This)->lpVtbl -> DefaultPhrase(This,ppszPhrase) )
-
-#endif /* COBJMACROS */
-
-
-#endif /* C style interface */
-
-
-
-
-#endif /* __IRelationship_INTERFACE_DEFINED__ */
-
-
-#ifndef __INamedEntity_INTERFACE_DEFINED__
-#define __INamedEntity_INTERFACE_DEFINED__
-
-/* interface INamedEntity */
-/* [unique][uuid][object][helpstring] */
-
-
-EXTERN_C const IID IID_INamedEntity;
-
-#if defined(__cplusplus) && !defined(CINTERFACE)
-
- MIDL_INTERFACE("ABDBD0B1-7D54-49fb-AB5C-BFF4130004CD")
- INamedEntity : public IUnknown
- {
- public:
- virtual HRESULT STDMETHODCALLTYPE GetValue(
- /* [retval][out] */ __RPC__deref_out_opt LPWSTR *ppszValue) = 0;
-
- virtual /* [local] */ HRESULT STDMETHODCALLTYPE DefaultPhrase(
- /* [retval][out] */
- __deref_opt_out LPWSTR *ppszPhrase) = 0;
-
- };
-
-#else /* C style interface */
-
- typedef struct INamedEntityVtbl
- {
- BEGIN_INTERFACE
-
- HRESULT ( STDMETHODCALLTYPE *QueryInterface )(
- INamedEntity * This,
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][out] */
- __RPC__deref_out void **ppvObject);
-
- ULONG ( STDMETHODCALLTYPE *AddRef )(
- INamedEntity * This);
-
- ULONG ( STDMETHODCALLTYPE *Release )(
- INamedEntity * This);
-
- HRESULT ( STDMETHODCALLTYPE *GetValue )(
- INamedEntity * This,
- /* [retval][out] */ __RPC__deref_out_opt LPWSTR *ppszValue);
-
- /* [local] */ HRESULT ( STDMETHODCALLTYPE *DefaultPhrase )(
- INamedEntity * This,
- /* [retval][out] */
- __deref_opt_out LPWSTR *ppszPhrase);
-
- END_INTERFACE
- } INamedEntityVtbl;
-
- interface INamedEntity
- {
- CONST_VTBL struct INamedEntityVtbl *lpVtbl;
- };
-
-
-
-#ifdef COBJMACROS
-
-
-#define INamedEntity_QueryInterface(This,riid,ppvObject) \
- ( (This)->lpVtbl -> QueryInterface(This,riid,ppvObject) )
-
-#define INamedEntity_AddRef(This) \
- ( (This)->lpVtbl -> AddRef(This) )
-
-#define INamedEntity_Release(This) \
- ( (This)->lpVtbl -> Release(This) )
-
-
-#define INamedEntity_GetValue(This,ppszValue) \
- ( (This)->lpVtbl -> GetValue(This,ppszValue) )
-
-#define INamedEntity_DefaultPhrase(This,ppszPhrase) \
- ( (This)->lpVtbl -> DefaultPhrase(This,ppszPhrase) )
-
-#endif /* COBJMACROS */
-
-
-#endif /* C style interface */
-
-
-
-
-#endif /* __INamedEntity_INTERFACE_DEFINED__ */
-
-
-#ifndef __ISchemaProvider_INTERFACE_DEFINED__
-#define __ISchemaProvider_INTERFACE_DEFINED__
-
-/* interface ISchemaProvider */
-/* [unique][object][uuid][helpstring] */
-
-
-EXTERN_C const IID IID_ISchemaProvider;
-
-#if defined(__cplusplus) && !defined(CINTERFACE)
-
- MIDL_INTERFACE("8CF89BCB-394C-49b2-AE28-A59DD4ED7F68")
- ISchemaProvider : public IUnknown
- {
- public:
- virtual HRESULT STDMETHODCALLTYPE Entities(
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][retval][out] */ __RPC__deref_out_opt void **pEntities) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE RootEntity(
- /* [retval][out] */ __RPC__deref_out_opt IEntity **pRootEntity) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE GetEntity(
- /* [in] */ __RPC__in LPCWSTR pszEntityName,
- /* [retval][out] */ __RPC__deref_out_opt IEntity **pEntity) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE MetaData(
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][retval][out] */ __RPC__deref_out_opt void **pMetaData) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE Localize(
- /* [in] */ LCID lcid,
- /* [in] */ __RPC__in_opt ISchemaLocalizerSupport *pSchemaLocalizerSupport) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE SaveBinary(
- /* [in] */ __RPC__in LPCWSTR pszSchemaBinaryPath) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE LookupAuthoredNamedEntity(
- /* [in] */ __RPC__in_opt IEntity *pEntity,
- /* [in] */ __RPC__in LPCWSTR pszInputString,
- /* [in] */ __RPC__in_opt ITokenCollection *pTokenCollection,
- /* [in] */ ULONG cTokensBegin,
- /* [out] */ __RPC__out ULONG *pcTokensLength,
- /* [out] */ __RPC__deref_out_opt LPWSTR *ppszValue) = 0;
-
- };
-
-#else /* C style interface */
-
- typedef struct ISchemaProviderVtbl
- {
- BEGIN_INTERFACE
-
- HRESULT ( STDMETHODCALLTYPE *QueryInterface )(
- ISchemaProvider * This,
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][out] */
- __RPC__deref_out void **ppvObject);
-
- ULONG ( STDMETHODCALLTYPE *AddRef )(
- ISchemaProvider * This);
-
- ULONG ( STDMETHODCALLTYPE *Release )(
- ISchemaProvider * This);
-
- HRESULT ( STDMETHODCALLTYPE *Entities )(
- ISchemaProvider * This,
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][retval][out] */ __RPC__deref_out_opt void **pEntities);
-
- HRESULT ( STDMETHODCALLTYPE *RootEntity )(
- ISchemaProvider * This,
- /* [retval][out] */ __RPC__deref_out_opt IEntity **pRootEntity);
-
- HRESULT ( STDMETHODCALLTYPE *GetEntity )(
- ISchemaProvider * This,
- /* [in] */ __RPC__in LPCWSTR pszEntityName,
- /* [retval][out] */ __RPC__deref_out_opt IEntity **pEntity);
-
- HRESULT ( STDMETHODCALLTYPE *MetaData )(
- ISchemaProvider * This,
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][retval][out] */ __RPC__deref_out_opt void **pMetaData);
-
- HRESULT ( STDMETHODCALLTYPE *Localize )(
- ISchemaProvider * This,
- /* [in] */ LCID lcid,
- /* [in] */ __RPC__in_opt ISchemaLocalizerSupport *pSchemaLocalizerSupport);
-
- HRESULT ( STDMETHODCALLTYPE *SaveBinary )(
- ISchemaProvider * This,
- /* [in] */ __RPC__in LPCWSTR pszSchemaBinaryPath);
-
- HRESULT ( STDMETHODCALLTYPE *LookupAuthoredNamedEntity )(
- ISchemaProvider * This,
- /* [in] */ __RPC__in_opt IEntity *pEntity,
- /* [in] */ __RPC__in LPCWSTR pszInputString,
- /* [in] */ __RPC__in_opt ITokenCollection *pTokenCollection,
- /* [in] */ ULONG cTokensBegin,
- /* [out] */ __RPC__out ULONG *pcTokensLength,
- /* [out] */ __RPC__deref_out_opt LPWSTR *ppszValue);
-
- END_INTERFACE
- } ISchemaProviderVtbl;
-
- interface ISchemaProvider
- {
- CONST_VTBL struct ISchemaProviderVtbl *lpVtbl;
- };
-
-
-
-#ifdef COBJMACROS
-
-
-#define ISchemaProvider_QueryInterface(This,riid,ppvObject) \
- ( (This)->lpVtbl -> QueryInterface(This,riid,ppvObject) )
-
-#define ISchemaProvider_AddRef(This) \
- ( (This)->lpVtbl -> AddRef(This) )
-
-#define ISchemaProvider_Release(This) \
- ( (This)->lpVtbl -> Release(This) )
-
-
-#define ISchemaProvider_Entities(This,riid,pEntities) \
- ( (This)->lpVtbl -> Entities(This,riid,pEntities) )
-
-#define ISchemaProvider_RootEntity(This,pRootEntity) \
- ( (This)->lpVtbl -> RootEntity(This,pRootEntity) )
-
-#define ISchemaProvider_GetEntity(This,pszEntityName,pEntity) \
- ( (This)->lpVtbl -> GetEntity(This,pszEntityName,pEntity) )
-
-#define ISchemaProvider_MetaData(This,riid,pMetaData) \
- ( (This)->lpVtbl -> MetaData(This,riid,pMetaData) )
-
-#define ISchemaProvider_Localize(This,lcid,pSchemaLocalizerSupport) \
- ( (This)->lpVtbl -> Localize(This,lcid,pSchemaLocalizerSupport) )
-
-#define ISchemaProvider_SaveBinary(This,pszSchemaBinaryPath) \
- ( (This)->lpVtbl -> SaveBinary(This,pszSchemaBinaryPath) )
-
-#define ISchemaProvider_LookupAuthoredNamedEntity(This,pEntity,pszInputString,pTokenCollection,cTokensBegin,pcTokensLength,ppszValue) \
- ( (This)->lpVtbl -> LookupAuthoredNamedEntity(This,pEntity,pszInputString,pTokenCollection,cTokensBegin,pcTokensLength,ppszValue) )
-
-#endif /* COBJMACROS */
-
-
-#endif /* C style interface */
-
-
-
-
-#endif /* __ISchemaProvider_INTERFACE_DEFINED__ */
-
-
-#ifndef __ITokenCollection_INTERFACE_DEFINED__
-#define __ITokenCollection_INTERFACE_DEFINED__
-
-/* interface ITokenCollection */
-/* [unique][object][uuid][helpstring] */
-
-
-EXTERN_C const IID IID_ITokenCollection;
-
-#if defined(__cplusplus) && !defined(CINTERFACE)
-
- MIDL_INTERFACE("22D8B4F2-F577-4adb-A335-C2AE88416FAB")
- ITokenCollection : public IUnknown
- {
- public:
- virtual HRESULT STDMETHODCALLTYPE NumberOfTokens(
- __RPC__in ULONG *pCount) = 0;
-
- virtual /* [local] */ HRESULT STDMETHODCALLTYPE GetToken(
- /* [in] */ ULONG i,
- /* [out] */
- __out_opt ULONG *pBegin,
- /* [out] */
- __out_opt ULONG *pLength,
- /* [out] */
- __deref_opt_out LPWSTR *ppsz) = 0;
-
- };
-
-#else /* C style interface */
-
- typedef struct ITokenCollectionVtbl
- {
- BEGIN_INTERFACE
-
- HRESULT ( STDMETHODCALLTYPE *QueryInterface )(
- ITokenCollection * This,
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][out] */
- __RPC__deref_out void **ppvObject);
-
- ULONG ( STDMETHODCALLTYPE *AddRef )(
- ITokenCollection * This);
-
- ULONG ( STDMETHODCALLTYPE *Release )(
- ITokenCollection * This);
-
- HRESULT ( STDMETHODCALLTYPE *NumberOfTokens )(
- ITokenCollection * This,
- __RPC__in ULONG *pCount);
-
- /* [local] */ HRESULT ( STDMETHODCALLTYPE *GetToken )(
- ITokenCollection * This,
- /* [in] */ ULONG i,
- /* [out] */
- __out_opt ULONG *pBegin,
- /* [out] */
- __out_opt ULONG *pLength,
- /* [out] */
- __deref_opt_out LPWSTR *ppsz);
-
- END_INTERFACE
- } ITokenCollectionVtbl;
-
- interface ITokenCollection
- {
- CONST_VTBL struct ITokenCollectionVtbl *lpVtbl;
- };
-
-
-
-#ifdef COBJMACROS
-
-
-#define ITokenCollection_QueryInterface(This,riid,ppvObject) \
- ( (This)->lpVtbl -> QueryInterface(This,riid,ppvObject) )
-
-#define ITokenCollection_AddRef(This) \
- ( (This)->lpVtbl -> AddRef(This) )
-
-#define ITokenCollection_Release(This) \
- ( (This)->lpVtbl -> Release(This) )
-
-
-#define ITokenCollection_NumberOfTokens(This,pCount) \
- ( (This)->lpVtbl -> NumberOfTokens(This,pCount) )
-
-#define ITokenCollection_GetToken(This,i,pBegin,pLength,ppsz) \
- ( (This)->lpVtbl -> GetToken(This,i,pBegin,pLength,ppsz) )
-
-#endif /* COBJMACROS */
-
-
-#endif /* C style interface */
-
-
-
-
-#endif /* __ITokenCollection_INTERFACE_DEFINED__ */
-
-
-/* interface __MIDL_itf_structuredquery_0000_0013 */
-/* [local] */
-
-typedef /* [public][public][v1_enum] */
-enum __MIDL___MIDL_itf_structuredquery_0000_0013_0001
- { NEC_LOW = 0,
- NEC_MEDIUM = ( NEC_LOW + 1 ) ,
- NEC_HIGH = ( NEC_MEDIUM + 1 )
- } NAMED_ENTITY_CERTAINTY;
-
-
-
-extern RPC_IF_HANDLE __MIDL_itf_structuredquery_0000_0013_v0_0_c_ifspec;
-extern RPC_IF_HANDLE __MIDL_itf_structuredquery_0000_0013_v0_0_s_ifspec;
-
-#ifndef __INamedEntityCollector_INTERFACE_DEFINED__
-#define __INamedEntityCollector_INTERFACE_DEFINED__
-
-/* interface INamedEntityCollector */
-/* [unique][object][uuid][helpstring] */
-
-
-EXTERN_C const IID IID_INamedEntityCollector;
-
-#if defined(__cplusplus) && !defined(CINTERFACE)
-
- MIDL_INTERFACE("AF2440F6-8AFC-47d0-9A7F-396A0ACFB43D")
- INamedEntityCollector : public IUnknown
- {
- public:
- virtual HRESULT STDMETHODCALLTYPE Add(
- /* [in] */ ULONG beginSpan,
- /* [in] */ ULONG endSpan,
- /* [in] */ ULONG beginActual,
- /* [in] */ ULONG endActual,
- /* [in] */ __RPC__in_opt IEntity *pType,
- /* [in] */ __RPC__in LPCWSTR pszValue,
- /* [in] */ NAMED_ENTITY_CERTAINTY certainty) = 0;
-
- };
-
-#else /* C style interface */
-
- typedef struct INamedEntityCollectorVtbl
- {
- BEGIN_INTERFACE
-
- HRESULT ( STDMETHODCALLTYPE *QueryInterface )(
- INamedEntityCollector * This,
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][out] */
- __RPC__deref_out void **ppvObject);
-
- ULONG ( STDMETHODCALLTYPE *AddRef )(
- INamedEntityCollector * This);
-
- ULONG ( STDMETHODCALLTYPE *Release )(
- INamedEntityCollector * This);
-
- HRESULT ( STDMETHODCALLTYPE *Add )(
- INamedEntityCollector * This,
- /* [in] */ ULONG beginSpan,
- /* [in] */ ULONG endSpan,
- /* [in] */ ULONG beginActual,
- /* [in] */ ULONG endActual,
- /* [in] */ __RPC__in_opt IEntity *pType,
- /* [in] */ __RPC__in LPCWSTR pszValue,
- /* [in] */ NAMED_ENTITY_CERTAINTY certainty);
-
- END_INTERFACE
- } INamedEntityCollectorVtbl;
-
- interface INamedEntityCollector
- {
- CONST_VTBL struct INamedEntityCollectorVtbl *lpVtbl;
- };
-
-
-
-#ifdef COBJMACROS
-
-
-#define INamedEntityCollector_QueryInterface(This,riid,ppvObject) \
- ( (This)->lpVtbl -> QueryInterface(This,riid,ppvObject) )
-
-#define INamedEntityCollector_AddRef(This) \
- ( (This)->lpVtbl -> AddRef(This) )
-
-#define INamedEntityCollector_Release(This) \
- ( (This)->lpVtbl -> Release(This) )
-
-
-#define INamedEntityCollector_Add(This,beginSpan,endSpan,beginActual,endActual,pType,pszValue,certainty) \
- ( (This)->lpVtbl -> Add(This,beginSpan,endSpan,beginActual,endActual,pType,pszValue,certainty) )
-
-#endif /* COBJMACROS */
-
-
-#endif /* C style interface */
-
-
-
-
-#endif /* __INamedEntityCollector_INTERFACE_DEFINED__ */
-
-
-#ifndef __ISchemaLocalizerSupport_INTERFACE_DEFINED__
-#define __ISchemaLocalizerSupport_INTERFACE_DEFINED__
-
-/* interface ISchemaLocalizerSupport */
-/* [unique][object][uuid] */
-
-
-EXTERN_C const IID IID_ISchemaLocalizerSupport;
-
-#if defined(__cplusplus) && !defined(CINTERFACE)
-
- MIDL_INTERFACE("CA3FDCA2-BFBE-4eed-90D7-0CAEF0A1BDA1")
- ISchemaLocalizerSupport : public IUnknown
- {
- public:
- virtual HRESULT STDMETHODCALLTYPE Localize(
- /* [in] */ __RPC__in LPCWSTR pszGlobalString,
- /* [retval][out] */ __RPC__deref_out_opt LPWSTR *ppszLocalString) = 0;
-
- };
-
-#else /* C style interface */
-
- typedef struct ISchemaLocalizerSupportVtbl
- {
- BEGIN_INTERFACE
-
- HRESULT ( STDMETHODCALLTYPE *QueryInterface )(
- ISchemaLocalizerSupport * This,
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][out] */
- __RPC__deref_out void **ppvObject);
-
- ULONG ( STDMETHODCALLTYPE *AddRef )(
- ISchemaLocalizerSupport * This);
-
- ULONG ( STDMETHODCALLTYPE *Release )(
- ISchemaLocalizerSupport * This);
-
- HRESULT ( STDMETHODCALLTYPE *Localize )(
- ISchemaLocalizerSupport * This,
- /* [in] */ __RPC__in LPCWSTR pszGlobalString,
- /* [retval][out] */ __RPC__deref_out_opt LPWSTR *ppszLocalString);
-
- END_INTERFACE
- } ISchemaLocalizerSupportVtbl;
-
- interface ISchemaLocalizerSupport
- {
- CONST_VTBL struct ISchemaLocalizerSupportVtbl *lpVtbl;
- };
-
-
-
-#ifdef COBJMACROS
-
-
-#define ISchemaLocalizerSupport_QueryInterface(This,riid,ppvObject) \
- ( (This)->lpVtbl -> QueryInterface(This,riid,ppvObject) )
-
-#define ISchemaLocalizerSupport_AddRef(This) \
- ( (This)->lpVtbl -> AddRef(This) )
-
-#define ISchemaLocalizerSupport_Release(This) \
- ( (This)->lpVtbl -> Release(This) )
-
-
-#define ISchemaLocalizerSupport_Localize(This,pszGlobalString,ppszLocalString) \
- ( (This)->lpVtbl -> Localize(This,pszGlobalString,ppszLocalString) )
-
-#endif /* COBJMACROS */
-
-
-#endif /* C style interface */
-
-
-
-
-#endif /* __ISchemaLocalizerSupport_INTERFACE_DEFINED__ */
-
-
-#ifndef __IQueryParserManager_INTERFACE_DEFINED__
-#define __IQueryParserManager_INTERFACE_DEFINED__
-
-/* interface IQueryParserManager */
-/* [unique][object][uuid] */
-
-
-EXTERN_C const IID IID_IQueryParserManager;
-
-#if defined(__cplusplus) && !defined(CINTERFACE)
-
- MIDL_INTERFACE("A879E3C4-AF77-44fb-8F37-EBD1487CF920")
- IQueryParserManager : public IUnknown
- {
- public:
- virtual HRESULT STDMETHODCALLTYPE CreateLoadedParser(
- /* [in] */ __RPC__in LPCWSTR pszCatalog,
- /* [in] */ LANGID langidForKeywords,
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][retval][out] */ __RPC__deref_out_opt void **ppQueryParser) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE InitializeOptions(
- /* [in] */ BOOL fUnderstandNQS,
- /* [in] */ BOOL fAutoWildCard,
- /* [in] */ __RPC__in_opt IQueryParser *pQueryParser) = 0;
-
- virtual HRESULT STDMETHODCALLTYPE SetOption(
- /* [in] */ QUERY_PARSER_MANAGER_OPTION option,
- /* [in] */ __RPC__in const PROPVARIANT *pOptionValue) = 0;
-
- };
-
-#else /* C style interface */
-
- typedef struct IQueryParserManagerVtbl
- {
- BEGIN_INTERFACE
-
- HRESULT ( STDMETHODCALLTYPE *QueryInterface )(
- IQueryParserManager * This,
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][out] */
- __RPC__deref_out void **ppvObject);
-
- ULONG ( STDMETHODCALLTYPE *AddRef )(
- IQueryParserManager * This);
-
- ULONG ( STDMETHODCALLTYPE *Release )(
- IQueryParserManager * This);
-
- HRESULT ( STDMETHODCALLTYPE *CreateLoadedParser )(
- IQueryParserManager * This,
- /* [in] */ __RPC__in LPCWSTR pszCatalog,
- /* [in] */ LANGID langidForKeywords,
- /* [in] */ __RPC__in REFIID riid,
- /* [iid_is][retval][out] */ __RPC__deref_out_opt void **ppQueryParser);
-
- HRESULT ( STDMETHODCALLTYPE *InitializeOptions )(
- IQueryParserManager * This,
- /* [in] */ BOOL fUnderstandNQS,
- /* [in] */ BOOL fAutoWildCard,
- /* [in] */ __RPC__in_opt IQueryParser *pQueryParser);
-
- HRESULT ( STDMETHODCALLTYPE *SetOption )(
- IQueryParserManager * This,
- /* [in] */ QUERY_PARSER_MANAGER_OPTION option,
- /* [in] */ __RPC__in const PROPVARIANT *pOptionValue);
-
- END_INTERFACE
- } IQueryParserManagerVtbl;
-
- interface IQueryParserManager
- {
- CONST_VTBL struct IQueryParserManagerVtbl *lpVtbl;
- };
-
-
-
-#ifdef COBJMACROS
-
-
-#define IQueryParserManager_QueryInterface(This,riid,ppvObject) \
- ( (This)->lpVtbl -> QueryInterface(This,riid,ppvObject) )
-
-#define IQueryParserManager_AddRef(This) \
- ( (This)->lpVtbl -> AddRef(This) )
-
-#define IQueryParserManager_Release(This) \
- ( (This)->lpVtbl -> Release(This) )
-
-
-#define IQueryParserManager_CreateLoadedParser(This,pszCatalog,langidForKeywords,riid,ppQueryParser) \
- ( (This)->lpVtbl -> CreateLoadedParser(This,pszCatalog,langidForKeywords,riid,ppQueryParser) )
-
-#define IQueryParserManager_InitializeOptions(This,fUnderstandNQS,fAutoWildCard,pQueryParser) \
- ( (This)->lpVtbl -> InitializeOptions(This,fUnderstandNQS,fAutoWildCard,pQueryParser) )
-
-#define IQueryParserManager_SetOption(This,option,pOptionValue) \
- ( (This)->lpVtbl -> SetOption(This,option,pOptionValue) )
-
-#endif /* COBJMACROS */
-
-
-#endif /* C style interface */
-
-
-
-
-#endif /* __IQueryParserManager_INTERFACE_DEFINED__ */
-
-
-
-#ifndef __StructuredQuery1_LIBRARY_DEFINED__
-#define __StructuredQuery1_LIBRARY_DEFINED__
-
-/* library StructuredQuery1 */
-/* [version][uuid] */
-
-
-EXTERN_C const IID LIBID_StructuredQuery1;
-
-EXTERN_C const CLSID CLSID_QueryParser;
-
-#ifdef __cplusplus
-
-class DECLSPEC_UUID("B72F8FD8-0FAB-4dd9-BDBF-245A6CE1485B")
-QueryParser;
-#endif
-
-EXTERN_C const CLSID CLSID_NegationCondition;
-
-#ifdef __cplusplus
-
-class DECLSPEC_UUID("8DE9C74C-605A-4acd-BEE3-2B222AA2D23D")
-NegationCondition;
-#endif
-
-EXTERN_C const CLSID CLSID_CompoundCondition;
-
-#ifdef __cplusplus
-
-class DECLSPEC_UUID("116F8D13-101E-4fa5-84D4-FF8279381935")
-CompoundCondition;
-#endif
-
-EXTERN_C const CLSID CLSID_LeafCondition;
-
-#ifdef __cplusplus
-
-class DECLSPEC_UUID("52F15C89-5A17-48e1-BBCD-46A3F89C7CC2")
-LeafCondition;
-#endif
-
-EXTERN_C const CLSID CLSID_ConditionFactory;
-
-#ifdef __cplusplus
-
-class DECLSPEC_UUID("E03E85B0-7BE3-4000-BA98-6C13DE9FA486")
-ConditionFactory;
-#endif
-
-EXTERN_C const CLSID CLSID_Interval;
-
-#ifdef __cplusplus
-
-class DECLSPEC_UUID("D957171F-4BF9-4de2-BCD5-C70A7CA55836")
-Interval;
-#endif
-
-EXTERN_C const CLSID CLSID_QueryParserManager;
-
-#ifdef __cplusplus
-
-class DECLSPEC_UUID("5088B39A-29B4-4d9d-8245-4EE289222F66")
-QueryParserManager;
-#endif
-#endif /* __StructuredQuery1_LIBRARY_DEFINED__ */
-
-/* Additional Prototypes for ALL interfaces */
-
-unsigned long __RPC_USER BSTR_UserSize( unsigned long *, unsigned long , BSTR * );
-unsigned char * __RPC_USER BSTR_UserMarshal( unsigned long *, unsigned char *, BSTR * );
-unsigned char * __RPC_USER BSTR_UserUnmarshal(unsigned long *, unsigned char *, BSTR * );
-void __RPC_USER BSTR_UserFree( unsigned long *, BSTR * );
-
-unsigned long __RPC_USER LPSAFEARRAY_UserSize( unsigned long *, unsigned long , LPSAFEARRAY * );
-unsigned char * __RPC_USER LPSAFEARRAY_UserMarshal( unsigned long *, unsigned char *, LPSAFEARRAY * );
-unsigned char * __RPC_USER LPSAFEARRAY_UserUnmarshal(unsigned long *, unsigned char *, LPSAFEARRAY * );
-void __RPC_USER LPSAFEARRAY_UserFree( unsigned long *, LPSAFEARRAY * );
-
-/* end of Additional Prototypes */
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif
-
-
-
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/cffLib/specializer.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/cffLib/specializer.py
deleted file mode 100644
index 3d28c82dc77b8b8b764bcf76d401265903db1a64..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/cffLib/specializer.py
+++ /dev/null
@@ -1,850 +0,0 @@
-# -*- coding: utf-8 -*-
-
-"""T2CharString operator specializer and generalizer.
-
-PostScript glyph drawing operations can be expressed in multiple different
-ways. For example, as well as the ``lineto`` operator, there is also a
-``hlineto`` operator which draws a horizontal line, removing the need to
-specify a ``dx`` coordinate, and a ``vlineto`` operator which draws a
-vertical line, removing the need to specify a ``dy`` coordinate. As well
-as decompiling :class:`fontTools.misc.psCharStrings.T2CharString` objects
-into lists of operations, this module allows for conversion between general
-and specific forms of the operation.
-
-"""
-
-from fontTools.cffLib import maxStackLimit
-
-
-def stringToProgram(string):
- if isinstance(string, str):
- string = string.split()
- program = []
- for token in string:
- try:
- token = int(token)
- except ValueError:
- try:
- token = float(token)
- except ValueError:
- pass
- program.append(token)
- return program
-
-
-def programToString(program):
- return " ".join(str(x) for x in program)
-
-
-def programToCommands(program, getNumRegions=None):
- """Takes a T2CharString program list and returns list of commands.
- Each command is a two-tuple of commandname,arg-list. The commandname might
- be empty string if no commandname shall be emitted (used for glyph width,
- hintmask/cntrmask argument, as well as stray arguments at the end of the
- program (¯\_(ツ)_/¯).
- 'getNumRegions' may be None, or a callable object. It must return the
- number of regions. 'getNumRegions' takes a single argument, vsindex. If
- the vsindex argument is None, getNumRegions returns the default number
- of regions for the charstring, else it returns the numRegions for
- the vsindex.
- The Charstring may or may not start with a width value. If the first
- non-blend operator has an odd number of arguments, then the first argument is
- a width, and is popped off. This is complicated with blend operators, as
- there may be more than one before the first hint or moveto operator, and each
- one reduces several arguments to just one list argument. We have to sum the
- number of arguments that are not part of the blend arguments, and all the
- 'numBlends' values. We could instead have said that by definition, if there
- is a blend operator, there is no width value, since CFF2 Charstrings don't
- have width values. I discussed this with Behdad, and we are allowing for an
- initial width value in this case because developers may assemble a CFF2
- charstring from CFF Charstrings, which could have width values.
- """
-
- seenWidthOp = False
- vsIndex = None
- lenBlendStack = 0
- lastBlendIndex = 0
- commands = []
- stack = []
- it = iter(program)
-
- for token in it:
- if not isinstance(token, str):
- stack.append(token)
- continue
-
- if token == "blend":
- assert getNumRegions is not None
- numSourceFonts = 1 + getNumRegions(vsIndex)
- # replace the blend op args on the stack with a single list
- # containing all the blend op args.
- numBlends = stack[-1]
- numBlendArgs = numBlends * numSourceFonts + 1
- # replace first blend op by a list of the blend ops.
- stack[-numBlendArgs:] = [stack[-numBlendArgs:]]
- lenBlendStack += numBlends + len(stack) - 1
- lastBlendIndex = len(stack)
- # if a blend op exists, this is or will be a CFF2 charstring.
- continue
-
- elif token == "vsindex":
- vsIndex = stack[-1]
- assert type(vsIndex) is int
-
- elif (not seenWidthOp) and token in {
- "hstem",
- "hstemhm",
- "vstem",
- "vstemhm",
- "cntrmask",
- "hintmask",
- "hmoveto",
- "vmoveto",
- "rmoveto",
- "endchar",
- }:
- seenWidthOp = True
- parity = token in {"hmoveto", "vmoveto"}
- if lenBlendStack:
- # lenBlendStack has the number of args represented by the last blend
- # arg and all the preceding args. We need to now add the number of
- # args following the last blend arg.
- numArgs = lenBlendStack + len(stack[lastBlendIndex:])
- else:
- numArgs = len(stack)
- if numArgs and (numArgs % 2) ^ parity:
- width = stack.pop(0)
- commands.append(("", [width]))
-
- if token in {"hintmask", "cntrmask"}:
- if stack:
- commands.append(("", stack))
- commands.append((token, []))
- commands.append(("", [next(it)]))
- else:
- commands.append((token, stack))
- stack = []
- if stack:
- commands.append(("", stack))
- return commands
-
-
-def _flattenBlendArgs(args):
- token_list = []
- for arg in args:
- if isinstance(arg, list):
- token_list.extend(arg)
- token_list.append("blend")
- else:
- token_list.append(arg)
- return token_list
-
-
-def commandsToProgram(commands):
- """Takes a commands list as returned by programToCommands() and converts
- it back to a T2CharString program list."""
- program = []
- for op, args in commands:
- if any(isinstance(arg, list) for arg in args):
- args = _flattenBlendArgs(args)
- program.extend(args)
- if op:
- program.append(op)
- return program
-
-
-def _everyN(el, n):
- """Group the list el into groups of size n"""
- if len(el) % n != 0:
- raise ValueError(el)
- for i in range(0, len(el), n):
- yield el[i : i + n]
-
-
-class _GeneralizerDecombinerCommandsMap(object):
- @staticmethod
- def rmoveto(args):
- if len(args) != 2:
- raise ValueError(args)
- yield ("rmoveto", args)
-
- @staticmethod
- def hmoveto(args):
- if len(args) != 1:
- raise ValueError(args)
- yield ("rmoveto", [args[0], 0])
-
- @staticmethod
- def vmoveto(args):
- if len(args) != 1:
- raise ValueError(args)
- yield ("rmoveto", [0, args[0]])
-
- @staticmethod
- def rlineto(args):
- if not args:
- raise ValueError(args)
- for args in _everyN(args, 2):
- yield ("rlineto", args)
-
- @staticmethod
- def hlineto(args):
- if not args:
- raise ValueError(args)
- it = iter(args)
- try:
- while True:
- yield ("rlineto", [next(it), 0])
- yield ("rlineto", [0, next(it)])
- except StopIteration:
- pass
-
- @staticmethod
- def vlineto(args):
- if not args:
- raise ValueError(args)
- it = iter(args)
- try:
- while True:
- yield ("rlineto", [0, next(it)])
- yield ("rlineto", [next(it), 0])
- except StopIteration:
- pass
-
- @staticmethod
- def rrcurveto(args):
- if not args:
- raise ValueError(args)
- for args in _everyN(args, 6):
- yield ("rrcurveto", args)
-
- @staticmethod
- def hhcurveto(args):
- if len(args) < 4 or len(args) % 4 > 1:
- raise ValueError(args)
- if len(args) % 2 == 1:
- yield ("rrcurveto", [args[1], args[0], args[2], args[3], args[4], 0])
- args = args[5:]
- for args in _everyN(args, 4):
- yield ("rrcurveto", [args[0], 0, args[1], args[2], args[3], 0])
-
- @staticmethod
- def vvcurveto(args):
- if len(args) < 4 or len(args) % 4 > 1:
- raise ValueError(args)
- if len(args) % 2 == 1:
- yield ("rrcurveto", [args[0], args[1], args[2], args[3], 0, args[4]])
- args = args[5:]
- for args in _everyN(args, 4):
- yield ("rrcurveto", [0, args[0], args[1], args[2], 0, args[3]])
-
- @staticmethod
- def hvcurveto(args):
- if len(args) < 4 or len(args) % 8 not in {0, 1, 4, 5}:
- raise ValueError(args)
- last_args = None
- if len(args) % 2 == 1:
- lastStraight = len(args) % 8 == 5
- args, last_args = args[:-5], args[-5:]
- it = _everyN(args, 4)
- try:
- while True:
- args = next(it)
- yield ("rrcurveto", [args[0], 0, args[1], args[2], 0, args[3]])
- args = next(it)
- yield ("rrcurveto", [0, args[0], args[1], args[2], args[3], 0])
- except StopIteration:
- pass
- if last_args:
- args = last_args
- if lastStraight:
- yield ("rrcurveto", [args[0], 0, args[1], args[2], args[4], args[3]])
- else:
- yield ("rrcurveto", [0, args[0], args[1], args[2], args[3], args[4]])
-
- @staticmethod
- def vhcurveto(args):
- if len(args) < 4 or len(args) % 8 not in {0, 1, 4, 5}:
- raise ValueError(args)
- last_args = None
- if len(args) % 2 == 1:
- lastStraight = len(args) % 8 == 5
- args, last_args = args[:-5], args[-5:]
- it = _everyN(args, 4)
- try:
- while True:
- args = next(it)
- yield ("rrcurveto", [0, args[0], args[1], args[2], args[3], 0])
- args = next(it)
- yield ("rrcurveto", [args[0], 0, args[1], args[2], 0, args[3]])
- except StopIteration:
- pass
- if last_args:
- args = last_args
- if lastStraight:
- yield ("rrcurveto", [0, args[0], args[1], args[2], args[3], args[4]])
- else:
- yield ("rrcurveto", [args[0], 0, args[1], args[2], args[4], args[3]])
-
- @staticmethod
- def rcurveline(args):
- if len(args) < 8 or len(args) % 6 != 2:
- raise ValueError(args)
- args, last_args = args[:-2], args[-2:]
- for args in _everyN(args, 6):
- yield ("rrcurveto", args)
- yield ("rlineto", last_args)
-
- @staticmethod
- def rlinecurve(args):
- if len(args) < 8 or len(args) % 2 != 0:
- raise ValueError(args)
- args, last_args = args[:-6], args[-6:]
- for args in _everyN(args, 2):
- yield ("rlineto", args)
- yield ("rrcurveto", last_args)
-
-
-def _convertBlendOpToArgs(blendList):
- # args is list of blend op args. Since we are supporting
- # recursive blend op calls, some of these args may also
- # be a list of blend op args, and need to be converted before
- # we convert the current list.
- if any([isinstance(arg, list) for arg in blendList]):
- args = [
- i
- for e in blendList
- for i in (_convertBlendOpToArgs(e) if isinstance(e, list) else [e])
- ]
- else:
- args = blendList
-
- # We now know that blendList contains a blend op argument list, even if
- # some of the args are lists that each contain a blend op argument list.
- # Convert from:
- # [default font arg sequence x0,...,xn] + [delta tuple for x0] + ... + [delta tuple for xn]
- # to:
- # [ [x0] + [delta tuple for x0],
- # ...,
- # [xn] + [delta tuple for xn] ]
- numBlends = args[-1]
- # Can't use args.pop() when the args are being used in a nested list
- # comprehension. See calling context
- args = args[:-1]
-
- numRegions = len(args) // numBlends - 1
- if not (numBlends * (numRegions + 1) == len(args)):
- raise ValueError(blendList)
-
- defaultArgs = [[arg] for arg in args[:numBlends]]
- deltaArgs = args[numBlends:]
- numDeltaValues = len(deltaArgs)
- deltaList = [
- deltaArgs[i : i + numRegions] for i in range(0, numDeltaValues, numRegions)
- ]
- blend_args = [a + b + [1] for a, b in zip(defaultArgs, deltaList)]
- return blend_args
-
-
-def generalizeCommands(commands, ignoreErrors=False):
- result = []
- mapping = _GeneralizerDecombinerCommandsMap
- for op, args in commands:
- # First, generalize any blend args in the arg list.
- if any([isinstance(arg, list) for arg in args]):
- try:
- args = [
- n
- for arg in args
- for n in (
- _convertBlendOpToArgs(arg) if isinstance(arg, list) else [arg]
- )
- ]
- except ValueError:
- if ignoreErrors:
- # Store op as data, such that consumers of commands do not have to
- # deal with incorrect number of arguments.
- result.append(("", args))
- result.append(("", [op]))
- else:
- raise
-
- func = getattr(mapping, op, None)
- if not func:
- result.append((op, args))
- continue
- try:
- for command in func(args):
- result.append(command)
- except ValueError:
- if ignoreErrors:
- # Store op as data, such that consumers of commands do not have to
- # deal with incorrect number of arguments.
- result.append(("", args))
- result.append(("", [op]))
- else:
- raise
- return result
-
-
-def generalizeProgram(program, getNumRegions=None, **kwargs):
- return commandsToProgram(
- generalizeCommands(programToCommands(program, getNumRegions), **kwargs)
- )
-
-
-def _categorizeVector(v):
- """
- Takes X,Y vector v and returns one of r, h, v, or 0 depending on which
- of X and/or Y are zero, plus tuple of nonzero ones. If both are zero,
- it returns a single zero still.
-
- >>> _categorizeVector((0,0))
- ('0', (0,))
- >>> _categorizeVector((1,0))
- ('h', (1,))
- >>> _categorizeVector((0,2))
- ('v', (2,))
- >>> _categorizeVector((1,2))
- ('r', (1, 2))
- """
- if not v[0]:
- if not v[1]:
- return "0", v[:1]
- else:
- return "v", v[1:]
- else:
- if not v[1]:
- return "h", v[:1]
- else:
- return "r", v
-
-
-def _mergeCategories(a, b):
- if a == "0":
- return b
- if b == "0":
- return a
- if a == b:
- return a
- return None
-
-
-def _negateCategory(a):
- if a == "h":
- return "v"
- if a == "v":
- return "h"
- assert a in "0r"
- return a
-
-
-def _convertToBlendCmds(args):
- # return a list of blend commands, and
- # the remaining non-blended args, if any.
- num_args = len(args)
- stack_use = 0
- new_args = []
- i = 0
- while i < num_args:
- arg = args[i]
- if not isinstance(arg, list):
- new_args.append(arg)
- i += 1
- stack_use += 1
- else:
- prev_stack_use = stack_use
- # The arg is a tuple of blend values.
- # These are each (master 0,delta 1..delta n, 1)
- # Combine as many successive tuples as we can,
- # up to the max stack limit.
- num_sources = len(arg) - 1
- blendlist = [arg]
- i += 1
- stack_use += 1 + num_sources # 1 for the num_blends arg
- while (i < num_args) and isinstance(args[i], list):
- blendlist.append(args[i])
- i += 1
- stack_use += num_sources
- if stack_use + num_sources > maxStackLimit:
- # if we are here, max stack is the CFF2 max stack.
- # I use the CFF2 max stack limit here rather than
- # the 'maxstack' chosen by the client, as the default
- # maxstack may have been used unintentionally. For all
- # the other operators, this just produces a little less
- # optimization, but here it puts a hard (and low) limit
- # on the number of source fonts that can be used.
- break
- # blendList now contains as many single blend tuples as can be
- # combined without exceeding the CFF2 stack limit.
- num_blends = len(blendlist)
- # append the 'num_blends' default font values
- blend_args = []
- for arg in blendlist:
- blend_args.append(arg[0])
- for arg in blendlist:
- assert arg[-1] == 1
- blend_args.extend(arg[1:-1])
- blend_args.append(num_blends)
- new_args.append(blend_args)
- stack_use = prev_stack_use + num_blends
-
- return new_args
-
-
-def _addArgs(a, b):
- if isinstance(b, list):
- if isinstance(a, list):
- if len(a) != len(b) or a[-1] != b[-1]:
- raise ValueError()
- return [_addArgs(va, vb) for va, vb in zip(a[:-1], b[:-1])] + [a[-1]]
- else:
- a, b = b, a
- if isinstance(a, list):
- assert a[-1] == 1
- return [_addArgs(a[0], b)] + a[1:]
- return a + b
-
-
-def specializeCommands(
- commands,
- ignoreErrors=False,
- generalizeFirst=True,
- preserveTopology=False,
- maxstack=48,
-):
-
- # We perform several rounds of optimizations. They are carefully ordered and are:
- #
- # 0. Generalize commands.
- # This ensures that they are in our expected simple form, with each line/curve only
- # having arguments for one segment, and using the generic form (rlineto/rrcurveto).
- # If caller is sure the input is in this form, they can turn off generalization to
- # save time.
- #
- # 1. Combine successive rmoveto operations.
- #
- # 2. Specialize rmoveto/rlineto/rrcurveto operators into horizontal/vertical variants.
- # We specialize into some, made-up, variants as well, which simplifies following
- # passes.
- #
- # 3. Merge or delete redundant operations, to the extent requested.
- # OpenType spec declares point numbers in CFF undefined. As such, we happily
- # change topology. If client relies on point numbers (in GPOS anchors, or for
- # hinting purposes(what?)) they can turn this off.
- #
- # 4. Peephole optimization to revert back some of the h/v variants back into their
- # original "relative" operator (rline/rrcurveto) if that saves a byte.
- #
- # 5. Combine adjacent operators when possible, minding not to go over max stack size.
- #
- # 6. Resolve any remaining made-up operators into real operators.
- #
- # I have convinced myself that this produces optimal bytecode (except for, possibly
- # one byte each time maxstack size prohibits combining.) YMMV, but you'd be wrong. :-)
- # A dynamic-programming approach can do the same but would be significantly slower.
- #
- # 7. For any args which are blend lists, convert them to a blend command.
-
- # 0. Generalize commands.
- if generalizeFirst:
- commands = generalizeCommands(commands, ignoreErrors=ignoreErrors)
- else:
- commands = list(commands) # Make copy since we modify in-place later.
-
- # 1. Combine successive rmoveto operations.
- for i in range(len(commands) - 1, 0, -1):
- if "rmoveto" == commands[i][0] == commands[i - 1][0]:
- v1, v2 = commands[i - 1][1], commands[i][1]
- commands[i - 1] = ("rmoveto", [v1[0] + v2[0], v1[1] + v2[1]])
- del commands[i]
-
- # 2. Specialize rmoveto/rlineto/rrcurveto operators into horizontal/vertical variants.
- #
- # We, in fact, specialize into more, made-up, variants that special-case when both
- # X and Y components are zero. This simplifies the following optimization passes.
- # This case is rare, but OCD does not let me skip it.
- #
- # After this round, we will have four variants that use the following mnemonics:
- #
- # - 'r' for relative, ie. non-zero X and non-zero Y,
- # - 'h' for horizontal, ie. zero X and non-zero Y,
- # - 'v' for vertical, ie. non-zero X and zero Y,
- # - '0' for zeros, ie. zero X and zero Y.
- #
- # The '0' pseudo-operators are not part of the spec, but help simplify the following
- # optimization rounds. We resolve them at the end. So, after this, we will have four
- # moveto and four lineto variants:
- #
- # - 0moveto, 0lineto
- # - hmoveto, hlineto
- # - vmoveto, vlineto
- # - rmoveto, rlineto
- #
- # and sixteen curveto variants. For example, a '0hcurveto' operator means a curve
- # dx0,dy0,dx1,dy1,dx2,dy2,dx3,dy3 where dx0, dx1, and dy3 are zero but not dx3.
- # An 'rvcurveto' means dx3 is zero but not dx0,dy0,dy3.
- #
- # There are nine different variants of curves without the '0'. Those nine map exactly
- # to the existing curve variants in the spec: rrcurveto, and the four variants hhcurveto,
- # vvcurveto, hvcurveto, and vhcurveto each cover two cases, one with an odd number of
- # arguments and one without. Eg. an hhcurveto with an extra argument (odd number of
- # arguments) is in fact an rhcurveto. The operators in the spec are designed such that
- # all four of rhcurveto, rvcurveto, hrcurveto, and vrcurveto are encodable for one curve.
- #
- # Of the curve types with '0', the 00curveto is equivalent to a lineto variant. The rest
- # of the curve types with a 0 need to be encoded as a h or v variant. Ie. a '0' can be
- # thought of a "don't care" and can be used as either an 'h' or a 'v'. As such, we always
- # encode a number 0 as argument when we use a '0' variant. Later on, we can just substitute
- # the '0' with either 'h' or 'v' and it works.
- #
- # When we get to curve splines however, things become more complicated... XXX finish this.
- # There's one more complexity with splines. If one side of the spline is not horizontal or
- # vertical (or zero), ie. if it's 'r', then it limits which spline types we can encode.
- # Only hhcurveto and vvcurveto operators can encode a spline starting with 'r', and
- # only hvcurveto and vhcurveto operators can encode a spline ending with 'r'.
- # This limits our merge opportunities later.
- #
- for i in range(len(commands)):
- op, args = commands[i]
-
- if op in {"rmoveto", "rlineto"}:
- c, args = _categorizeVector(args)
- commands[i] = c + op[1:], args
- continue
-
- if op == "rrcurveto":
- c1, args1 = _categorizeVector(args[:2])
- c2, args2 = _categorizeVector(args[-2:])
- commands[i] = c1 + c2 + "curveto", args1 + args[2:4] + args2
- continue
-
- # 3. Merge or delete redundant operations, to the extent requested.
- #
- # TODO
- # A 0moveto that comes before all other path operations can be removed.
- # though I find conflicting evidence for this.
- #
- # TODO
- # "If hstem and vstem hints are both declared at the beginning of a
- # CharString, and this sequence is followed directly by the hintmask or
- # cntrmask operators, then the vstem hint operator (or, if applicable,
- # the vstemhm operator) need not be included."
- #
- # "The sequence and form of a CFF2 CharString program may be represented as:
- # {hs* vs* cm* hm* mt subpath}? {mt subpath}*"
- #
- # https://www.microsoft.com/typography/otspec/cff2charstr.htm#section3.1
- #
- # For Type2 CharStrings the sequence is:
- # w? {hs* vs* cm* hm* mt subpath}? {mt subpath}* endchar"
-
- # Some other redundancies change topology (point numbers).
- if not preserveTopology:
- for i in range(len(commands) - 1, -1, -1):
- op, args = commands[i]
-
- # A 00curveto is demoted to a (specialized) lineto.
- if op == "00curveto":
- assert len(args) == 4
- c, args = _categorizeVector(args[1:3])
- op = c + "lineto"
- commands[i] = op, args
- # and then...
-
- # A 0lineto can be deleted.
- if op == "0lineto":
- del commands[i]
- continue
-
- # Merge adjacent hlineto's and vlineto's.
- # In CFF2 charstrings from variable fonts, each
- # arg item may be a list of blendable values, one from
- # each source font.
- if i and op in {"hlineto", "vlineto"} and (op == commands[i - 1][0]):
- _, other_args = commands[i - 1]
- assert len(args) == 1 and len(other_args) == 1
- try:
- new_args = [_addArgs(args[0], other_args[0])]
- except ValueError:
- continue
- commands[i - 1] = (op, new_args)
- del commands[i]
- continue
-
- # 4. Peephole optimization to revert back some of the h/v variants back into their
- # original "relative" operator (rline/rrcurveto) if that saves a byte.
- for i in range(1, len(commands) - 1):
- op, args = commands[i]
- prv, nxt = commands[i - 1][0], commands[i + 1][0]
-
- if op in {"0lineto", "hlineto", "vlineto"} and prv == nxt == "rlineto":
- assert len(args) == 1
- args = [0, args[0]] if op[0] == "v" else [args[0], 0]
- commands[i] = ("rlineto", args)
- continue
-
- if op[2:] == "curveto" and len(args) == 5 and prv == nxt == "rrcurveto":
- assert (op[0] == "r") ^ (op[1] == "r")
- if op[0] == "v":
- pos = 0
- elif op[0] != "r":
- pos = 1
- elif op[1] == "v":
- pos = 4
- else:
- pos = 5
- # Insert, while maintaining the type of args (can be tuple or list).
- args = args[:pos] + type(args)((0,)) + args[pos:]
- commands[i] = ("rrcurveto", args)
- continue
-
- # 5. Combine adjacent operators when possible, minding not to go over max stack size.
- for i in range(len(commands) - 1, 0, -1):
- op1, args1 = commands[i - 1]
- op2, args2 = commands[i]
- new_op = None
-
- # Merge logic...
- if {op1, op2} <= {"rlineto", "rrcurveto"}:
- if op1 == op2:
- new_op = op1
- else:
- if op2 == "rrcurveto" and len(args2) == 6:
- new_op = "rlinecurve"
- elif len(args2) == 2:
- new_op = "rcurveline"
-
- elif (op1, op2) in {("rlineto", "rlinecurve"), ("rrcurveto", "rcurveline")}:
- new_op = op2
-
- elif {op1, op2} == {"vlineto", "hlineto"}:
- new_op = op1
-
- elif "curveto" == op1[2:] == op2[2:]:
- d0, d1 = op1[:2]
- d2, d3 = op2[:2]
-
- if d1 == "r" or d2 == "r" or d0 == d3 == "r":
- continue
-
- d = _mergeCategories(d1, d2)
- if d is None:
- continue
- if d0 == "r":
- d = _mergeCategories(d, d3)
- if d is None:
- continue
- new_op = "r" + d + "curveto"
- elif d3 == "r":
- d0 = _mergeCategories(d0, _negateCategory(d))
- if d0 is None:
- continue
- new_op = d0 + "r" + "curveto"
- else:
- d0 = _mergeCategories(d0, d3)
- if d0 is None:
- continue
- new_op = d0 + d + "curveto"
-
- # Make sure the stack depth does not exceed (maxstack - 1), so
- # that subroutinizer can insert subroutine calls at any point.
- if new_op and len(args1) + len(args2) < maxstack:
- commands[i - 1] = (new_op, args1 + args2)
- del commands[i]
-
- # 6. Resolve any remaining made-up operators into real operators.
- for i in range(len(commands)):
- op, args = commands[i]
-
- if op in {"0moveto", "0lineto"}:
- commands[i] = "h" + op[1:], args
- continue
-
- if op[2:] == "curveto" and op[:2] not in {"rr", "hh", "vv", "vh", "hv"}:
- op0, op1 = op[:2]
- if (op0 == "r") ^ (op1 == "r"):
- assert len(args) % 2 == 1
- if op0 == "0":
- op0 = "h"
- if op1 == "0":
- op1 = "h"
- if op0 == "r":
- op0 = op1
- if op1 == "r":
- op1 = _negateCategory(op0)
- assert {op0, op1} <= {"h", "v"}, (op0, op1)
-
- if len(args) % 2:
- if op0 != op1: # vhcurveto / hvcurveto
- if (op0 == "h") ^ (len(args) % 8 == 1):
- # Swap last two args order
- args = args[:-2] + args[-1:] + args[-2:-1]
- else: # hhcurveto / vvcurveto
- if op0 == "h": # hhcurveto
- # Swap first two args order
- args = args[1:2] + args[:1] + args[2:]
-
- commands[i] = op0 + op1 + "curveto", args
- continue
-
- # 7. For any series of args which are blend lists, convert the series to a single blend arg.
- for i in range(len(commands)):
- op, args = commands[i]
- if any(isinstance(arg, list) for arg in args):
- commands[i] = op, _convertToBlendCmds(args)
-
- return commands
-
-
-def specializeProgram(program, getNumRegions=None, **kwargs):
- return commandsToProgram(
- specializeCommands(programToCommands(program, getNumRegions), **kwargs)
- )
-
-
-if __name__ == "__main__":
- import sys
-
- if len(sys.argv) == 1:
- import doctest
-
- sys.exit(doctest.testmod().failed)
-
- import argparse
-
- parser = argparse.ArgumentParser(
- "fonttools cffLib.specialer",
- description="CFF CharString generalizer/specializer",
- )
- parser.add_argument("program", metavar="command", nargs="*", help="Commands.")
- parser.add_argument(
- "--num-regions",
- metavar="NumRegions",
- nargs="*",
- default=None,
- help="Number of variable-font regions for blend opertaions.",
- )
-
- options = parser.parse_args(sys.argv[1:])
-
- getNumRegions = (
- None
- if options.num_regions is None
- else lambda vsIndex: int(options.num_regions[0 if vsIndex is None else vsIndex])
- )
-
- program = stringToProgram(options.program)
- print("Program:")
- print(programToString(program))
- commands = programToCommands(program, getNumRegions)
- print("Commands:")
- print(commands)
- program2 = commandsToProgram(commands)
- print("Program from commands:")
- print(programToString(program2))
- assert program == program2
- print("Generalized program:")
- print(programToString(generalizeProgram(program, getNumRegions)))
- print("Specialized program:")
- print(programToString(specializeProgram(program, getNumRegions)))
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-fef9d5f8.js b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-fef9d5f8.js
deleted file mode 100644
index 0672f2fb4a6a2f22671672ffbd003d906fd75ea0..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-fef9d5f8.js
+++ /dev/null
@@ -1,104 +0,0 @@
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e,t;return{c(){e=nn("svg"),t=nn("path"),x(t,"stroke","currentColor"),x(t,"stroke-width","1.5"),x(t,"stroke-linecap","round"),x(t,"stroke-linejoin","round"),x(t,"d","M21.044 5.704a.6.6 0 0 1 .956.483v11.626a.6.6 0 0 1-.956.483l-7.889-5.813a.6.6 0 0 1 0-.966l7.89-5.813ZM10.044 5.704a.6.6 0 0 1 .956.483v11.626a.6.6 0 0 1-.956.483l-7.888-5.813a.6.6 0 0 1 0-.966l7.888-5.813Z"),x(e,"xmlns","http://www.w3.org/2000/svg"),x(e,"width","24px"),x(e,"height","24px"),x(e,"fill","currentColor"),x(e,"stroke-width","1.5"),x(e,"viewBox","0 0 24 24"),x(e,"color","currentColor")},m(n,i){Ci(n,e,i),wi(e,t)},p:Rt,i:Rt,o:Rt,d(n){n&&yi(e)}}}class Si extends bi{constructor(e){super(),ki(this,e,null,Ri,Ei,{})}}const{SvelteComponent:Di,append:Mi,attr:ee,detach:Li,init:Ai,insert:Pi,noop:St,safe_not_equal:Ti,svg_element:on}=window.__gradio__svelte__internal;function Wi(o){let e,t;return{c(){e=on("svg"),t=on("path"),ee(t,"stroke","currentColor"),ee(t,"stroke-width","1.5"),ee(t,"stroke-linecap","round"),ee(t,"stroke-linejoin","round"),ee(t,"d","M2.956 5.704A.6.6 0 0 0 2 6.187v11.626a.6.6 0 0 0 .956.483l7.889-5.813a.6.6 0 0 0 0-.966l-7.89-5.813ZM13.956 5.704a.6.6 0 0 0-.956.483v11.626a.6.6 0 0 0 .956.483l7.889-5.813a.6.6 0 0 0 0-.966l-7.89-5.813Z"),ee(e,"xmlns","http://www.w3.org/2000/svg"),ee(e,"width","24px"),ee(e,"height","24px"),ee(e,"fill","currentColor"),ee(e,"stroke-width","1.5"),ee(e,"viewBox","0 0 24 24"),ee(e,"color","currentColor")},m(n,i){Pi(n,e,i),Mi(e,t)},p:St,i:St,o:St,d(n){n&&Li(e)}}}class zi extends Di{constructor(e){super(),Ai(this,e,null,Wi,Ti,{})}}const{SvelteComponent:Bi,append:rt,attr:H,detach:Oi,init:Ii,insert:Ni,noop:Dt,safe_not_equal:Hi,svg_element:Je}=window.__gradio__svelte__internal;function qi(o){let e,t,n,i,s;return{c(){e=Je("svg"),t=Je("path"),n=Je("path"),i=Je("line"),s=Je("line"),H(t,"d","M12 1a3 3 0 0 0-3 3v8a3 3 0 0 0 6 0V4a3 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0||e.disconnect(),this.bufferNode=this.audioContext.createBufferSource(),this.bufferNode.buffer=this.buffer,this.bufferNode.connect(this.gainNode),this.playedDuration>=this.duration&&(this.playedDuration=0),this.bufferNode.start(this.audioContext.currentTime,this.playedDuration),this.playStartTime=this.audioContext.currentTime,this.bufferNode.onended=()=>{this.currentTime>=this.duration&&(this.pause(),this.emit("ended"))})}_pause(){var e;this.paused||(this.paused=!0,(e=this.bufferNode)===null||e===void 0||e.stop(),this.playedDuration+=this.audioContext.currentTime-this.playStartTime)}play(){return Pt(this,void 0,void 0,function*(){this._play(),this.emit("play")})}pause(){this._pause(),this.emit("pause")}setSinkId(e){return Pt(this,void 0,void 0,function*(){return this.audioContext.setSinkId(e)})}get playbackRate(){var e,t;return(t=(e=this.bufferNode)===null||e===void 0?void 0:e.playbackRate.value)!==null&&t!==void 0?t:1}set playbackRate(e){this.bufferNode&&(this.bufferNode.playbackRate.value=e)}get currentTime(){return this.paused?this.playedDuration:this.playedDuration+this.audioContext.currentTime-this.playStartTime}set currentTime(e){this.emit("seeking"),this.paused?this.playedDuration=e:(this._pause(),this.playedDuration=e,this._play()),this.emit("timeupdate")}get duration(){var e;return((e=this.buffer)===null||e===void 0?void 0:e.duration)||0}get volume(){return this.gainNode.gain.value}set volume(e){this.gainNode.gain.value=e,this.emit("volumechange")}get muted(){return this._muted}set muted(e){this._muted!==e&&(this._muted=e,this._muted?this.gainNode.disconnect():this.gainNode.connect(this.audioContext.destination))}getGainNode(){return this.gainNode}}var Pe=globalThis&&globalThis.__awaiter||function(o,e,t,n){function i(s){return s instanceof t?s:new t(function(a){a(s)})}return new(t||(t=Promise))(function(s,a){function u(r){try{d(n.next(r))}catch(c){a(c)}}function l(r){try{d(n.throw(r))}catch(c){a(c)}}function d(r){r.done?s(r.value):i(r.value).then(u,l)}d((n=n.apply(o,e||[])).next())})};const oo={waveColor:"#999",progressColor:"#555",cursorWidth:1,minPxPerSec:0,fillParent:!0,interact:!0,dragToSeek:!1,autoScroll:!0,autoCenter:!0,sampleRate:8e3};class He extends eo{static create(e){return new He(e)}constructor(e){const t=e.media||(e.backend==="WebAudio"?new io:void 0);super({media:t,mediaControls:e.mediaControls,autoplay:e.autoplay,playbackRate:e.audioRate}),this.plugins=[],this.decodedData=null,this.subscriptions=[],this.mediaSubscriptions=[],this.options=Object.assign({},oo,e),this.timer=new no;const n=t?void 0:this.getMediaElement();this.renderer=new bt(this.options,n),this.initPlayerEvents(),this.initRendererEvents(),this.initTimerEvents(),this.initPlugins();const i=this.options.url||this.getSrc();i?this.load(i,this.options.peaks,this.options.duration):this.options.peaks&&this.options.duration&&this.loadPredecoded()}initTimerEvents(){this.subscriptions.push(this.timer.on("tick",()=>{const e=this.getCurrentTime();this.renderer.renderProgress(e/this.getDuration(),!0),this.emit("timeupdate",e),this.emit("audioprocess",e)}))}initPlayerEvents(){this.mediaSubscriptions.push(this.onMediaEvent("timeupdate",()=>{const e=this.getCurrentTime();this.renderer.renderProgress(e/this.getDuration(),this.isPlaying()),this.emit("timeupdate",e)}),this.onMediaEvent("play",()=>{this.emit("play"),this.timer.start()}),this.onMediaEvent("pause",()=>{this.emit("pause"),this.timer.stop()}),this.onMediaEvent("emptied",()=>{this.timer.stop()}),this.onMediaEvent("ended",()=>{this.emit("finish")}),this.onMediaEvent("seeking",()=>{this.emit("seeking",this.getCurrentTime())}))}initRendererEvents(){this.subscriptions.push(this.renderer.on("click",(e,t)=>{this.options.interact&&(this.seekTo(e),this.emit("interaction",e*this.getDuration()),this.emit("click",e,t))}),this.renderer.on("dblclick",(e,t)=>{this.emit("dblclick",e,t)}),this.renderer.on("scroll",(e,t)=>{const n=this.getDuration();this.emit("scroll",e*n,t*n)}),this.renderer.on("render",()=>{this.emit("redraw")}));{let e;this.subscriptions.push(this.renderer.on("drag",t=>{this.options.interact&&(this.renderer.renderProgress(t),clearTimeout(e),e=setTimeout(()=>{this.seekTo(t)},this.isPlaying()?0:200),this.emit("interaction",t*this.getDuration()),this.emit("drag",t))}))}}initPlugins(){var e;!((e=this.options.plugins)===null||e===void 0)&&e.length&&this.options.plugins.forEach(t=>{this.registerPlugin(t)})}unsubscribePlayerEvents(){this.mediaSubscriptions.forEach(e=>e()),this.mediaSubscriptions=[]}setOptions(e){this.options=Object.assign({},this.options,e),this.renderer.setOptions(this.options),e.audioRate&&this.setPlaybackRate(e.audioRate),e.mediaControls!=null&&(this.getMediaElement().controls=e.mediaControls)}registerPlugin(e){return e.init(this),this.plugins.push(e),this.subscriptions.push(e.once("destroy",()=>{this.plugins=this.plugins.filter(t=>t!==e)})),e}getWrapper(){return this.renderer.getWrapper()}getScroll(){return this.renderer.getScroll()}getActivePlugins(){return this.plugins}loadPredecoded(){return Pe(this,void 0,void 0,function*(){this.options.peaks&&this.options.duration&&(this.decodedData=At.createBuffer(this.options.peaks,this.options.duration),yield Promise.resolve(),this.renderDecoded())})}renderDecoded(){return Pe(this,void 0,void 0,function*(){this.decodedData&&(this.emit("decode",this.getDuration()),this.renderer.render(this.decodedData))})}loadAudio(e,t,n,i){return Pe(this,void 0,void 0,function*(){if(this.emit("load",e),!this.options.media&&this.isPlaying()&&this.pause(),this.decodedData=null,!t&&!n){const s=a=>this.emit("loading",a);t=yield xi.fetchBlob(e,s,this.options.fetchParams)}if(this.setSrc(e,t),i=(yield Promise.resolve(i||this.getDuration()))||(yield new Promise(s=>{this.onceMediaEvent("loadedmetadata",()=>s(this.getDuration()))}))||(yield Promise.resolve(0)),n)this.decodedData=At.createBuffer(n,i);else if(t){const s=yield t.arrayBuffer();this.decodedData=yield At.decode(s,this.options.sampleRate)}this.renderDecoded(),this.emit("ready",this.getDuration())})}load(e,t,n){return Pe(this,void 0,void 0,function*(){yield this.loadAudio(e,void 0,t,n)})}loadBlob(e,t,n){return Pe(this,void 0,void 0,function*(){yield this.loadAudio("blob",e,t,n)})}zoom(e){if(!this.decodedData)throw new Error("No audio loaded");this.renderer.zoom(e),this.emit("zoom",e)}getDecodedData(){return this.decodedData}exportPeaks({channels:e=2,maxLength:t=8e3,precision:n=1e4}={}){if(!this.decodedData)throw new Error("The audio has not been decoded yet");const i=Math.min(e,this.decodedData.numberOfChannels),s=[];for(let a=0;ae.destroy()),this.subscriptions.forEach(e=>e()),this.unsubscribePlayerEvents(),this.timer.destroy(),this.renderer.destroy(),super.destroy()}}let In=class{constructor(){this.listeners={},this.on=this.addEventListener,this.un=this.removeEventListener}addEventListener(e,t,n){if(this.listeners[e]||(this.listeners[e]=new Set),this.listeners[e].add(t),n?.once){const i=()=>{this.removeEventListener(e,i),this.removeEventListener(e,t)};return this.addEventListener(e,i),i}return()=>this.removeEventListener(e,t)}removeEventListener(e,t){var n;(n=this.listeners[e])===null||n===void 0||n.delete(t)}once(e,t){return this.on(e,t,{once:!0})}unAll(){this.listeners={}}emit(e,...t){this.listeners[e]&&this.listeners[e].forEach(n=>n(...t))}},so=class extends In{constructor(e){super(),this.subscriptions=[],this.options=e}onInit(){}init(e){this.wavesurfer=e,this.onInit()}destroy(){this.emit("destroy"),this.subscriptions.forEach(e=>e())}};function ut(o,e,t,n,i=5){let s=()=>{};if(!o)return s;const a=u=>{if(u.button===2)return;u.preventDefault(),u.stopPropagation(),o.style.touchAction="none";let l=u.clientX,d=u.clientY,r=!1;const c=m=>{m.preventDefault(),m.stopPropagation();const _=m.clientX,g=m.clientY;if(r||Math.abs(_-l)>=i||Math.abs(g-d)>=i){const{left:v,top:y}=o.getBoundingClientRect();r||(r=!0,t?.(l-v,d-y)),e(_-l,g-d,_-v,g-y),l=_,d=g}},h=m=>{r&&(m.preventDefault(),m.stopPropagation())},f=()=>{o.style.touchAction="",r&&n?.(),s()};document.addEventListener("pointermove",c),document.addEventListener("pointerup",f),document.addEventListener("pointerleave",f),document.addEventListener("click",h,!0),s=()=>{document.removeEventListener("pointermove",c),document.removeEventListener("pointerup",f),document.removeEventListener("pointerleave",f),setTimeout(()=>{document.removeEventListener("click",h,!0)},10)}};return o.addEventListener("pointerdown",a),()=>{s(),o.removeEventListener("pointerdown",a)}}class rn extends In{constructor(e,t,n=0){var i,s,a,u,l,d,r;super(),this.totalDuration=t,this.numberOfChannels=n,this.minLength=0,this.maxLength=1/0,this.id=e.id||`region-${Math.random().toString(32).slice(2)}`,this.start=this.clampPosition(e.start),this.end=this.clampPosition((i=e.end)!==null&&i!==void 0?i:e.start),this.drag=(s=e.drag)===null||s===void 0||s,this.resize=(a=e.resize)===null||a===void 0||a,this.color=(u=e.color)!==null&&u!==void 0?u:"rgba(0, 0, 0, 0.1)",this.minLength=(l=e.minLength)!==null&&l!==void 0?l:this.minLength,this.maxLength=(d=e.maxLength)!==null&&d!==void 0?d:this.maxLength,this.channelIdx=(r=e.channelIdx)!==null&&r!==void 0?r:-1,this.element=this.initElement(),this.setContent(e.content),this.setPart(),this.renderPosition(),this.initMouseEvents()}clampPosition(e){return Math.max(0,Math.min(this.totalDuration,e))}setPart(){const e=this.start===this.end;this.element.setAttribute("part",`${e?"marker":"region"} ${this.id}`)}addResizeHandles(e){const t=document.createElement("div");t.setAttribute("data-resize","left"),t.setAttribute("style",`
- position: absolute;
- z-index: 2;
- width: 6px;
- height: 100%;
- top: 0;
- left: 0;
- border-left: 2px solid rgba(0, 0, 0, 0.5);
- border-radius: 2px 0 0 2px;
- cursor: ew-resize;
- word-break: keep-all;
- `),t.setAttribute("part","region-handle region-handle-left");const n=t.cloneNode();n.setAttribute("data-resize","right"),n.style.left="",n.style.right="0",n.style.borderRight=n.style.borderLeft,n.style.borderLeft="",n.style.borderRadius="0 2px 2px 0",n.setAttribute("part","region-handle region-handle-right"),e.appendChild(t),e.appendChild(n),ut(t,i=>this.onResize(i,"start"),()=>null,()=>this.onEndResizing(),1),ut(n,i=>this.onResize(i,"end"),()=>null,()=>this.onEndResizing(),1)}removeResizeHandles(e){const t=e.querySelector('[data-resize="left"]'),n=e.querySelector('[data-resize="right"]');t&&e.removeChild(t),n&&e.removeChild(n)}initElement(){const e=document.createElement("div"),t=this.start===this.end;let n=0,i=100;return this.channelIdx>=0&&this.channelIdxthis.emit("click",t)),e.addEventListener("mouseenter",t=>this.emit("over",t)),e.addEventListener("mouseleave",t=>this.emit("leave",t)),e.addEventListener("dblclick",t=>this.emit("dblclick",t)),ut(e,t=>this.onMove(t),()=>this.onStartMoving(),()=>this.onEndMoving()))}onStartMoving(){this.drag&&(this.element.style.cursor="grabbing")}onEndMoving(){this.drag&&(this.element.style.cursor="grab",this.emit("update-end"))}_onUpdate(e,t){if(!this.element.parentElement)return;const n=e/this.element.parentElement.clientWidth*this.totalDuration,i=t&&t!=="start"?this.start:this.start+n,s=t&&t!=="end"?this.end:this.end+n,a=s-i;i>=0&&s<=this.totalDuration&&i<=s&&a>=this.minLength&&a<=this.maxLength&&(this.start=i,this.end=s,this.renderPosition(),this.emit("update"))}onMove(e){this.drag&&this._onUpdate(e)}onResize(e,t){this.resize&&this._onUpdate(e,t)}onEndResizing(){this.resize&&this.emit("update-end")}_setTotalDuration(e){this.totalDuration=e,this.renderPosition()}play(){this.emit("play")}setContent(e){var t;if((t=this.content)===null||t===void 0||t.remove(),e){if(typeof e=="string"){this.content=document.createElement("div");const n=this.start===this.end;this.content.style.padding=`0.2em ${n?.2:.4}em`,this.content.textContent=e}else this.content=e;this.content.setAttribute("part","region-content"),this.element.appendChild(this.content)}else this.content=void 0}setOptions(e){var t,n;if(e.color&&(this.color=e.color,this.element.style.backgroundColor=this.color),e.drag!==void 0&&(this.drag=e.drag,this.element.style.cursor=this.drag?"grab":"default"),e.start!==void 0||e.end!==void 0){const i=this.start===this.end;this.start=this.clampPosition((t=e.start)!==null&&t!==void 0?t:this.start),this.end=this.clampPosition((n=e.end)!==null&&n!==void 0?n:i?this.start:this.end),this.renderPosition(),this.setPart()}if(e.content&&this.setContent(e.content),e.id&&(this.id=e.id,this.setPart()),e.resize!==void 0&&e.resize!==this.resize){const i=this.start===this.end;this.resize=e.resize,this.resize&&!i?this.addResizeHandles(this.element):this.removeResizeHandles(this.element)}}remove(){this.emit("remove"),this.element.remove(),this.element=null}}let ro=class Nn extends so{constructor(e){super(e),this.regions=[],this.regionsContainer=this.initRegionsContainer()}static create(e){return new Nn(e)}onInit(){if(!this.wavesurfer)throw Error("WaveSurfer is not initialized");this.wavesurfer.getWrapper().appendChild(this.regionsContainer);let e=[];this.subscriptions.push(this.wavesurfer.on("timeupdate",t=>{const n=this.regions.filter(i=>i.start<=t&&i.end>=t);n.forEach(i=>{e.includes(i)||this.emit("region-in",i)}),e.forEach(i=>{n.includes(i)||this.emit("region-out",i)}),e=n}))}initRegionsContainer(){const e=document.createElement("div");return e.setAttribute("style",`
- position: absolute;
- top: 0;
- left: 0;
- width: 100%;
- height: 100%;
- z-index: 3;
- pointer-events: none;
- `),e}getRegions(){return this.regions}avoidOverlapping(e){if(!e.content)return;const t=e.content,n=t.getBoundingClientRect().left,i=e.element.scrollWidth,s=this.regions.filter(a=>{if(a===e||!a.content)return!1;const u=a.content.getBoundingClientRect().left,l=a.element.scrollWidth;return n{var u;return((u=a.content)===null||u===void 0?void 0:u.getBoundingClientRect().height)||0}).reduce((a,u)=>a+u,0);t.style.marginTop=`${s}px`}saveRegion(e){this.regionsContainer.appendChild(e.element),this.avoidOverlapping(e),this.regions.push(e);const t=[e.on("update-end",()=>{this.avoidOverlapping(e),this.emit("region-updated",e)}),e.on("play",()=>{var n,i;(n=this.wavesurfer)===null||n===void 0||n.play(),(i=this.wavesurfer)===null||i===void 0||i.setTime(e.start)}),e.on("click",n=>{this.emit("region-clicked",e,n)}),e.on("dblclick",n=>{this.emit("region-double-clicked",e,n)}),e.once("remove",()=>{t.forEach(n=>n()),this.regions=this.regions.filter(n=>n!==e)})];this.subscriptions.push(...t),this.emit("region-created",e)}addRegion(e){var t,n;if(!this.wavesurfer)throw Error("WaveSurfer is not initialized");const i=this.wavesurfer.getDuration(),s=(n=(t=this.wavesurfer)===null||t===void 0?void 0:t.getDecodedData())===null||n===void 0?void 0:n.numberOfChannels,a=new rn(e,i,s);return i?this.saveRegion(a):this.subscriptions.push(this.wavesurfer.once("ready",u=>{a._setTotalDuration(u),this.saveRegion(a)})),a}enableDragSelection(e){var t,n;const i=(n=(t=this.wavesurfer)===null||t===void 0?void 0:t.getWrapper())===null||n===void 0?void 0:n.querySelector("div");if(!i)return()=>{};let s=null,a=0;return ut(i,(u,l,d)=>{s&&s._onUpdate(u,d>a?"end":"start")},u=>{var l,d;if(a=u,!this.wavesurfer)return;const r=this.wavesurfer.getDuration(),c=(d=(l=this.wavesurfer)===null||l===void 0?void 0:l.getDecodedData())===null||d===void 0?void 0:d.numberOfChannels,h=this.wavesurfer.getWrapper().clientWidth,f=u/h*r,m=(u+5)/h*r;s=new rn(Object.assign(Object.assign({},e),{start:f,end:m}),r,c),this.regionsContainer.appendChild(s.element)},()=>{s&&(this.saveRegion(s),s=null)})}clearRegions(){this.regions.forEach(e=>e.remove())}destroy(){this.clearRegions(),super.destroy()}};function ao(o){const e=o.numberOfChannels,t=o.length*e*2+44,n=new ArrayBuffer(t),i=new DataView(n);let s=0;const a=function(u,l,d){for(let r=0;r{const n=new AudioContext,i=o.numberOfChannels,s=o.sampleRate;let a=o.length,u=0;e&&t&&(u=Math.round(e*s),a=Math.round(t*s)-u);const l=n.createBuffer(i,a,s);for(let d=0;d{o&&o.skip(e)},ze=(o,e)=>o/100*(e||5);const{SvelteComponent:lo,append:J,attr:O,check_outros:dt,create_component:qe,destroy_component:Ue,detach:Ce,element:te,empty:uo,group_outros:ct,init:co,insert:Ee,listen:be,mount_component:je,noop:xe,run_all:Hn,safe_not_equal:ho,set_data:fo,space:Te,text:an,transition_in:Y,transition_out:K}=window.__gradio__svelte__internal;function mo(o){let e,t;return e=new ui({}),{c(){qe(e.$$.fragment)},m(n,i){je(e,n,i),t=!0},i(n){t||(Y(e.$$.fragment,n),t=!0)},o(n){K(e.$$.fragment,n),t=!1},d(n){Ue(e,n)}}}function _o(o){let e,t;return e=new On({}),{c(){qe(e.$$.fragment)},m(n,i){je(e,n,i),t=!0},i(n){t||(Y(e.$$.fragment,n),t=!0)},o(n){K(e.$$.fragment,n),t=!1},d(n){Ue(e,n)}}}function ln(o){let e,t,n,i,s;return t=new ci({}),{c(){e=te("button"),qe(t.$$.fragment),O(e,"class","action icon svelte-k0z87h"),O(e,"aria-label","Reset 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min_width(e){this.$$set({min_width:e}),N()}get loading_status(){return this.$$.ctx[1]}set loading_status(e){this.$$set({loading_status:e}),N()}get autoplay(){return this.$$.ctx[13]}set autoplay(e){this.$$set({autoplay:e}),N()}get show_download_button(){return this.$$.ctx[14]}set show_download_button(e){this.$$set({show_download_button:e}),N()}get show_share_button(){return this.$$.ctx[15]}set show_share_button(e){this.$$set({show_share_button:e}),N()}get waveform_options(){return this.$$.ctx[25]}set waveform_options(e){this.$$set({waveform_options:e}),N()}get pending(){return this.$$.ctx[16]}set pending(e){this.$$set({pending:e}),N()}get streaming(){return this.$$.ctx[17]}set streaming(e){this.$$set({streaming:e}),N()}get gradio(){return this.$$.ctx[18]}set gradio(e){this.$$set({gradio:e}),N()}}const Or=pr;export{Hr as BaseExample,xs as BaseInteractiveAudio,Fn as BasePlayer,Xo as BaseStaticAudio,Or as default};
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diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-f8aef4a9.js b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-f8aef4a9.js
deleted file mode 100644
index d14a477d9c67e7e54080d77d31801ae86690612b..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-f8aef4a9.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{L as e,S as m,S as p}from"./Index-c74a8b7c.js";import{T as i}from"./Blocks-f0dbd8c3.js";import"./index-50ad4c77.js";import"./svelte/svelte.js";import"./Button-8eeccca1.js";export{e as Loader,m as StatusTracker,i as Toast,p as default};
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diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/huggingface_hub/commands/lfs.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/huggingface_hub/commands/lfs.py
deleted file mode 100644
index a40951c2a3b6a139786203dc09d28714e7194782..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/huggingface_hub/commands/lfs.py
+++ /dev/null
@@ -1,202 +0,0 @@
-"""
-Implementation of a custom transfer agent for the transfer type "multipart" for
-git-lfs.
-
-Inspired by:
-github.com/cbartz/git-lfs-swift-transfer-agent/blob/master/git_lfs_swift_transfer.py
-
-Spec is: github.com/git-lfs/git-lfs/blob/master/docs/custom-transfers.md
-
-
-To launch debugger while developing:
-
-``` [lfs "customtransfer.multipart"]
-path = /path/to/huggingface_hub/.env/bin/python args = -m debugpy --listen 5678
---wait-for-client
-/path/to/huggingface_hub/src/huggingface_hub/commands/huggingface_cli.py
-lfs-multipart-upload ```"""
-
-import json
-import os
-import subprocess
-import sys
-from argparse import _SubParsersAction
-from typing import Dict, List, Optional
-
-from huggingface_hub.commands import BaseHuggingfaceCLICommand
-from huggingface_hub.lfs import LFS_MULTIPART_UPLOAD_COMMAND, SliceFileObj
-
-from ..utils import get_session, hf_raise_for_status, logging
-
-
-logger = logging.get_logger(__name__)
-
-
-class LfsCommands(BaseHuggingfaceCLICommand):
- """
- Implementation of a custom transfer agent for the transfer type "multipart"
- for git-lfs. This lets users upload large files >5GB 🔥. Spec for LFS custom
- transfer agent is:
- https://github.com/git-lfs/git-lfs/blob/master/docs/custom-transfers.md
-
- This introduces two commands to the CLI:
-
- 1. $ huggingface-cli lfs-enable-largefiles
-
- This should be executed once for each model repo that contains a model file
- >5GB. It's documented in the error message you get if you just try to git
- push a 5GB file without having enabled it before.
-
- 2. $ huggingface-cli lfs-multipart-upload
-
- This command is called by lfs directly and is not meant to be called by the
- user.
- """
-
- @staticmethod
- def register_subcommand(parser: _SubParsersAction):
- enable_parser = parser.add_parser(
- "lfs-enable-largefiles",
- help="Configure your repository to enable upload of files > 5GB.",
- )
- enable_parser.add_argument("path", type=str, help="Local path to repository you want to configure.")
- enable_parser.set_defaults(func=lambda args: LfsEnableCommand(args))
-
- upload_parser = parser.add_parser(
- LFS_MULTIPART_UPLOAD_COMMAND,
- help="Command will get called by git-lfs, do not call it directly.",
- )
- upload_parser.set_defaults(func=lambda args: LfsUploadCommand(args))
-
-
-class LfsEnableCommand:
- def __init__(self, args):
- self.args = args
-
- def run(self):
- local_path = os.path.abspath(self.args.path)
- if not os.path.isdir(local_path):
- print("This does not look like a valid git repo.")
- exit(1)
- subprocess.run(
- "git config lfs.customtransfer.multipart.path huggingface-cli".split(),
- check=True,
- cwd=local_path,
- )
- subprocess.run(
- f"git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}".split(),
- check=True,
- cwd=local_path,
- )
- print("Local repo set up for largefiles")
-
-
-def write_msg(msg: Dict):
- """Write out the message in Line delimited JSON."""
- msg_str = json.dumps(msg) + "\n"
- sys.stdout.write(msg_str)
- sys.stdout.flush()
-
-
-def read_msg() -> Optional[Dict]:
- """Read Line delimited JSON from stdin."""
- msg = json.loads(sys.stdin.readline().strip())
-
- if "terminate" in (msg.get("type"), msg.get("event")):
- # terminate message received
- return None
-
- if msg.get("event") not in ("download", "upload"):
- logger.critical("Received unexpected message")
- sys.exit(1)
-
- return msg
-
-
-class LfsUploadCommand:
- def __init__(self, args) -> None:
- self.args = args
-
- def run(self) -> None:
- # Immediately after invoking a custom transfer process, git-lfs
- # sends initiation data to the process over stdin.
- # This tells the process useful information about the configuration.
- init_msg = json.loads(sys.stdin.readline().strip())
- if not (init_msg.get("event") == "init" and init_msg.get("operation") == "upload"):
- write_msg({"error": {"code": 32, "message": "Wrong lfs init operation"}})
- sys.exit(1)
-
- # The transfer process should use the information it needs from the
- # initiation structure, and also perform any one-off setup tasks it
- # needs to do. It should then respond on stdout with a simple empty
- # confirmation structure, as follows:
- write_msg({})
-
- # After the initiation exchange, git-lfs will send any number of
- # transfer requests to the stdin of the transfer process, in a serial sequence.
- while True:
- msg = read_msg()
- if msg is None:
- # When all transfers have been processed, git-lfs will send
- # a terminate event to the stdin of the transfer process.
- # On receiving this message the transfer process should
- # clean up and terminate. No response is expected.
- sys.exit(0)
-
- oid = msg["oid"]
- filepath = msg["path"]
- completion_url = msg["action"]["href"]
- header = msg["action"]["header"]
- chunk_size = int(header.pop("chunk_size"))
- presigned_urls: List[str] = list(header.values())
-
- # Send a "started" progress event to allow other workers to start.
- # Otherwise they're delayed until first "progress" event is reported,
- # i.e. after the first 5GB by default (!)
- write_msg(
- {
- "event": "progress",
- "oid": oid,
- "bytesSoFar": 1,
- "bytesSinceLast": 0,
- }
- )
-
- parts = []
- with open(filepath, "rb") as file:
- for i, presigned_url in enumerate(presigned_urls):
- with SliceFileObj(
- file,
- seek_from=i * chunk_size,
- read_limit=chunk_size,
- ) as data:
- r = get_session().put(presigned_url, data=data)
- hf_raise_for_status(r)
- parts.append(
- {
- "etag": r.headers.get("etag"),
- "partNumber": i + 1,
- }
- )
- # In order to support progress reporting while data is uploading / downloading,
- # the transfer process should post messages to stdout
- write_msg(
- {
- "event": "progress",
- "oid": oid,
- "bytesSoFar": (i + 1) * chunk_size,
- "bytesSinceLast": chunk_size,
- }
- )
- # Not precise but that's ok.
-
- r = get_session().post(
- completion_url,
- json={
- "oid": oid,
- "parts": parts,
- },
- )
- hf_raise_for_status(r)
-
- write_msg({"event": "complete", "oid": oid})
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/tests/data/generate_umath_validation_data.cpp b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/tests/data/generate_umath_validation_data.cpp
deleted file mode 100644
index 575eec1188275064169e7ed533535617fc849d55..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/tests/data/generate_umath_validation_data.cpp
+++ /dev/null
@@ -1,170 +0,0 @@
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-
-struct ufunc {
- std::string name;
- double (*f32func)(double);
- long double (*f64func)(long double);
- float f32ulp;
- float f64ulp;
-};
-
-template
-T
-RandomFloat(T a, T b)
-{
- T random = ((T)rand()) / (T)RAND_MAX;
- T diff = b - a;
- T r = random * diff;
- return a + r;
-}
-
-template
-void
-append_random_array(std::vector &arr, T min, T max, size_t N)
-{
- for (size_t ii = 0; ii < N; ++ii)
- arr.emplace_back(RandomFloat(min, max));
-}
-
-template
-std::vector
-computeTrueVal(const std::vector &in, T2 (*mathfunc)(T2))
-{
- std::vector out;
- for (T1 elem : in) {
- T2 elem_d = (T2)elem;
- T1 out_elem = (T1)mathfunc(elem_d);
- out.emplace_back(out_elem);
- }
- return out;
-}
-
-/*
- * FP range:
- * [-inf, -maxflt, -1., -minflt, -minden, 0., minden, minflt, 1., maxflt, inf]
- */
-
-#define MINDEN std::numeric_limits::denorm_min()
-#define MINFLT std::numeric_limits::min()
-#define MAXFLT std::numeric_limits::max()
-#define INF std::numeric_limits::infinity()
-#define qNAN std::numeric_limits::quiet_NaN()
-#define sNAN std::numeric_limits::signaling_NaN()
-
-template
-std::vector
-generate_input_vector(std::string func)
-{
- std::vector input = {MINDEN, -MINDEN, MINFLT, -MINFLT, MAXFLT,
- -MAXFLT, INF, -INF, qNAN, sNAN,
- -1.0, 1.0, 0.0, -0.0};
-
- // [-1.0, 1.0]
- if ((func == "arcsin") || (func == "arccos") || (func == "arctanh")) {
- append_random_array(input, -1.0, 1.0, 700);
- }
- // (0.0, INF]
- else if ((func == "log2") || (func == "log10")) {
- append_random_array(input, 0.0, 1.0, 200);
- append_random_array(input, MINDEN, MINFLT, 200);
- append_random_array(input, MINFLT, 1.0, 200);
- append_random_array(input, 1.0, MAXFLT, 200);
- }
- // (-1.0, INF]
- else if (func == "log1p") {
- append_random_array(input, -1.0, 1.0, 200);
- append_random_array(input, -MINFLT, -MINDEN, 100);
- append_random_array(input, -1.0, -MINFLT, 100);
- append_random_array(input, MINDEN, MINFLT, 100);
- append_random_array(input, MINFLT, 1.0, 100);
- append_random_array(input, 1.0, MAXFLT, 100);
- }
- // [1.0, INF]
- else if (func == "arccosh") {
- append_random_array(input, 1.0, 2.0, 400);
- append_random_array(input, 2.0, MAXFLT, 300);
- }
- // [-INF, INF]
- else {
- append_random_array(input, -1.0, 1.0, 100);
- append_random_array(input, MINDEN, MINFLT, 100);
- append_random_array(input, -MINFLT, -MINDEN, 100);
- append_random_array(input, MINFLT, 1.0, 100);
- append_random_array(input, -1.0, -MINFLT, 100);
- append_random_array(input, 1.0, MAXFLT, 100);
- append_random_array(input, -MAXFLT, -100.0, 100);
- }
-
- std::random_shuffle(input.begin(), input.end());
- return input;
-}
-
-int
-main()
-{
- srand(42);
- std::vector umathfunc = {
- {"sin", sin, sin, 1.49, 1.00},
- {"cos", cos, cos, 1.49, 1.00},
- {"tan", tan, tan, 3.91, 3.93},
- {"arcsin", asin, asin, 3.12, 2.55},
- {"arccos", acos, acos, 2.1, 1.67},
- {"arctan", atan, atan, 2.3, 2.52},
- {"sinh", sinh, sinh, 1.55, 1.89},
- {"cosh", cosh, cosh, 2.48, 1.97},
- {"tanh", tanh, tanh, 1.38, 1.19},
- {"arcsinh", asinh, asinh, 1.01, 1.48},
- {"arccosh", acosh, acosh, 1.16, 1.05},
- {"arctanh", atanh, atanh, 1.45, 1.46},
- {"cbrt", cbrt, cbrt, 1.94, 1.82},
- //{"exp",exp,exp,3.76,1.53},
- {"exp2", exp2, exp2, 1.01, 1.04},
- {"expm1", expm1, expm1, 2.62, 2.1},
- //{"log",log,log,1.84,1.67},
- {"log10", log10, log10, 3.5, 1.92},
- {"log1p", log1p, log1p, 1.96, 1.93},
- {"log2", log2, log2, 2.12, 1.84},
- };
-
- for (int ii = 0; ii < umathfunc.size(); ++ii) {
- // ignore sin/cos
- if ((umathfunc[ii].name != "sin") && (umathfunc[ii].name != "cos")) {
- std::string fileName =
- "umath-validation-set-" + umathfunc[ii].name + ".csv";
- std::ofstream txtOut;
- txtOut.open(fileName, std::ofstream::trunc);
- txtOut << "dtype,input,output,ulperrortol" << std::endl;
-
- // Single Precision
- auto f32in = generate_input_vector(umathfunc[ii].name);
- auto f32out = computeTrueVal(f32in,
- umathfunc[ii].f32func);
- for (int jj = 0; jj < f32in.size(); ++jj) {
- txtOut << "np.float32" << std::hex << ",0x"
- << *reinterpret_cast(&f32in[jj]) << ",0x"
- << *reinterpret_cast(&f32out[jj]) << ","
- << ceil(umathfunc[ii].f32ulp) << std::endl;
- }
-
- // Double Precision
- auto f64in = generate_input_vector(umathfunc[ii].name);
- auto f64out = computeTrueVal(
- f64in, umathfunc[ii].f64func);
- for (int jj = 0; jj < f64in.size(); ++jj) {
- txtOut << "np.float64" << std::hex << ",0x"
- << *reinterpret_cast(&f64in[jj]) << ",0x"
- << *reinterpret_cast(&f64out[jj]) << ","
- << ceil(umathfunc[ii].f64ulp) << std::endl;
- }
- txtOut.close();
- }
- }
- return 0;
-}
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/core/indexes/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/core/indexes/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/frame/methods/test_dropna.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/frame/methods/test_dropna.py
deleted file mode 100644
index 7899b4aeac3fdef6548f3aadf76ff7718418f089..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/frame/methods/test_dropna.py
+++ /dev/null
@@ -1,285 +0,0 @@
-import datetime
-
-import dateutil
-import numpy as np
-import pytest
-
-import pandas as pd
-from pandas import (
- DataFrame,
- Series,
-)
-import pandas._testing as tm
-
-
-class TestDataFrameMissingData:
- def test_dropEmptyRows(self, float_frame):
- N = len(float_frame.index)
- mat = np.random.default_rng(2).standard_normal(N)
- mat[:5] = np.nan
-
- frame = DataFrame({"foo": mat}, index=float_frame.index)
- original = Series(mat, index=float_frame.index, name="foo")
- expected = original.dropna()
- inplace_frame1, inplace_frame2 = frame.copy(), frame.copy()
-
- smaller_frame = frame.dropna(how="all")
- # check that original was preserved
- tm.assert_series_equal(frame["foo"], original)
- return_value = inplace_frame1.dropna(how="all", inplace=True)
- tm.assert_series_equal(smaller_frame["foo"], expected)
- tm.assert_series_equal(inplace_frame1["foo"], expected)
- assert return_value is None
-
- smaller_frame = frame.dropna(how="all", subset=["foo"])
- return_value = inplace_frame2.dropna(how="all", subset=["foo"], inplace=True)
- tm.assert_series_equal(smaller_frame["foo"], expected)
- tm.assert_series_equal(inplace_frame2["foo"], expected)
- assert return_value is None
-
- def test_dropIncompleteRows(self, float_frame):
- N = len(float_frame.index)
- mat = np.random.default_rng(2).standard_normal(N)
- mat[:5] = np.nan
-
- frame = DataFrame({"foo": mat}, index=float_frame.index)
- frame["bar"] = 5
- original = Series(mat, index=float_frame.index, name="foo")
- inp_frame1, inp_frame2 = frame.copy(), frame.copy()
-
- smaller_frame = frame.dropna()
- tm.assert_series_equal(frame["foo"], original)
- return_value = inp_frame1.dropna(inplace=True)
-
- exp = Series(mat[5:], index=float_frame.index[5:], name="foo")
- tm.assert_series_equal(smaller_frame["foo"], exp)
- tm.assert_series_equal(inp_frame1["foo"], exp)
- assert return_value is None
-
- samesize_frame = frame.dropna(subset=["bar"])
- tm.assert_series_equal(frame["foo"], original)
- assert (frame["bar"] == 5).all()
- return_value = inp_frame2.dropna(subset=["bar"], inplace=True)
- tm.assert_index_equal(samesize_frame.index, float_frame.index)
- tm.assert_index_equal(inp_frame2.index, float_frame.index)
- assert return_value is None
-
- def test_dropna(self):
- df = DataFrame(np.random.default_rng(2).standard_normal((6, 4)))
- df.iloc[:2, 2] = np.nan
-
- dropped = df.dropna(axis=1)
- expected = df.loc[:, [0, 1, 3]]
- inp = df.copy()
- return_value = inp.dropna(axis=1, inplace=True)
- tm.assert_frame_equal(dropped, expected)
- tm.assert_frame_equal(inp, expected)
- assert return_value is None
-
- dropped = df.dropna(axis=0)
- expected = df.loc[list(range(2, 6))]
- inp = df.copy()
- return_value = inp.dropna(axis=0, inplace=True)
- tm.assert_frame_equal(dropped, expected)
- tm.assert_frame_equal(inp, expected)
- assert return_value is None
-
- # threshold
- dropped = df.dropna(axis=1, thresh=5)
- expected = df.loc[:, [0, 1, 3]]
- inp = df.copy()
- return_value = inp.dropna(axis=1, thresh=5, inplace=True)
- tm.assert_frame_equal(dropped, expected)
- tm.assert_frame_equal(inp, expected)
- assert return_value is None
-
- dropped = df.dropna(axis=0, thresh=4)
- expected = df.loc[range(2, 6)]
- inp = df.copy()
- return_value = inp.dropna(axis=0, thresh=4, inplace=True)
- tm.assert_frame_equal(dropped, expected)
- tm.assert_frame_equal(inp, expected)
- assert return_value is None
-
- dropped = df.dropna(axis=1, thresh=4)
- tm.assert_frame_equal(dropped, df)
-
- dropped = df.dropna(axis=1, thresh=3)
- tm.assert_frame_equal(dropped, df)
-
- # subset
- dropped = df.dropna(axis=0, subset=[0, 1, 3])
- inp = df.copy()
- return_value = inp.dropna(axis=0, subset=[0, 1, 3], inplace=True)
- tm.assert_frame_equal(dropped, df)
- tm.assert_frame_equal(inp, df)
- assert return_value is None
-
- # all
- dropped = df.dropna(axis=1, how="all")
- tm.assert_frame_equal(dropped, df)
-
- df[2] = np.nan
- dropped = df.dropna(axis=1, how="all")
- expected = df.loc[:, [0, 1, 3]]
- tm.assert_frame_equal(dropped, expected)
-
- # bad input
- msg = "No axis named 3 for object type DataFrame"
- with pytest.raises(ValueError, match=msg):
- df.dropna(axis=3)
-
- def test_drop_and_dropna_caching(self):
- # tst that cacher updates
- original = Series([1, 2, np.nan], name="A")
- expected = Series([1, 2], dtype=original.dtype, name="A")
- df = DataFrame({"A": original.values.copy()})
- df2 = df.copy()
- df["A"].dropna()
- tm.assert_series_equal(df["A"], original)
-
- ser = df["A"]
- return_value = ser.dropna(inplace=True)
- tm.assert_series_equal(ser, expected)
- tm.assert_series_equal(df["A"], original)
- assert return_value is None
-
- df2["A"].drop([1])
- tm.assert_series_equal(df2["A"], original)
-
- ser = df2["A"]
- return_value = ser.drop([1], inplace=True)
- tm.assert_series_equal(ser, original.drop([1]))
- tm.assert_series_equal(df2["A"], original)
- assert return_value is None
-
- def test_dropna_corner(self, float_frame):
- # bad input
- msg = "invalid how option: foo"
- with pytest.raises(ValueError, match=msg):
- float_frame.dropna(how="foo")
- # non-existent column - 8303
- with pytest.raises(KeyError, match=r"^\['X'\]$"):
- float_frame.dropna(subset=["A", "X"])
-
- def test_dropna_multiple_axes(self):
- df = DataFrame(
- [
- [1, np.nan, 2, 3],
- [4, np.nan, 5, 6],
- [np.nan, np.nan, np.nan, np.nan],
- [7, np.nan, 8, 9],
- ]
- )
-
- # GH20987
- with pytest.raises(TypeError, match="supplying multiple axes"):
- df.dropna(how="all", axis=[0, 1])
- with pytest.raises(TypeError, match="supplying multiple axes"):
- df.dropna(how="all", axis=(0, 1))
-
- inp = df.copy()
- with pytest.raises(TypeError, match="supplying multiple axes"):
- inp.dropna(how="all", axis=(0, 1), inplace=True)
-
- def test_dropna_tz_aware_datetime(self):
- # GH13407
- df = DataFrame()
- dt1 = datetime.datetime(2015, 1, 1, tzinfo=dateutil.tz.tzutc())
- dt2 = datetime.datetime(2015, 2, 2, tzinfo=dateutil.tz.tzutc())
- df["Time"] = [dt1]
- result = df.dropna(axis=0)
- expected = DataFrame({"Time": [dt1]})
- tm.assert_frame_equal(result, expected)
-
- # Ex2
- df = DataFrame({"Time": [dt1, None, np.nan, dt2]})
- result = df.dropna(axis=0)
- expected = DataFrame([dt1, dt2], columns=["Time"], index=[0, 3])
- tm.assert_frame_equal(result, expected)
-
- def test_dropna_categorical_interval_index(self):
- # GH 25087
- ii = pd.IntervalIndex.from_breaks([0, 2.78, 3.14, 6.28])
- ci = pd.CategoricalIndex(ii)
- df = DataFrame({"A": list("abc")}, index=ci)
-
- expected = df
- result = df.dropna()
- tm.assert_frame_equal(result, expected)
-
- def test_dropna_with_duplicate_columns(self):
- df = DataFrame(
- {
- "A": np.random.default_rng(2).standard_normal(5),
- "B": np.random.default_rng(2).standard_normal(5),
- "C": np.random.default_rng(2).standard_normal(5),
- "D": ["a", "b", "c", "d", "e"],
- }
- )
- df.iloc[2, [0, 1, 2]] = np.nan
- df.iloc[0, 0] = np.nan
- df.iloc[1, 1] = np.nan
- df.iloc[:, 3] = np.nan
- expected = df.dropna(subset=["A", "B", "C"], how="all")
- expected.columns = ["A", "A", "B", "C"]
-
- df.columns = ["A", "A", "B", "C"]
-
- result = df.dropna(subset=["A", "C"], how="all")
- tm.assert_frame_equal(result, expected)
-
- def test_set_single_column_subset(self):
- # GH 41021
- df = DataFrame({"A": [1, 2, 3], "B": list("abc"), "C": [4, np.nan, 5]})
- expected = DataFrame(
- {"A": [1, 3], "B": list("ac"), "C": [4.0, 5.0]}, index=[0, 2]
- )
- result = df.dropna(subset="C")
- tm.assert_frame_equal(result, expected)
-
- def test_single_column_not_present_in_axis(self):
- # GH 41021
- df = DataFrame({"A": [1, 2, 3]})
-
- # Column not present
- with pytest.raises(KeyError, match="['D']"):
- df.dropna(subset="D", axis=0)
-
- def test_subset_is_nparray(self):
- # GH 41021
- df = DataFrame({"A": [1, 2, np.nan], "B": list("abc"), "C": [4, np.nan, 5]})
- expected = DataFrame({"A": [1.0], "B": ["a"], "C": [4.0]})
- result = df.dropna(subset=np.array(["A", "C"]))
- tm.assert_frame_equal(result, expected)
-
- def test_no_nans_in_frame(self, axis):
- # GH#41965
- df = DataFrame([[1, 2], [3, 4]], columns=pd.RangeIndex(0, 2))
- expected = df.copy()
- result = df.dropna(axis=axis)
- tm.assert_frame_equal(result, expected, check_index_type=True)
-
- def test_how_thresh_param_incompatible(self):
- # GH46575
- df = DataFrame([1, 2, pd.NA])
- msg = "You cannot set both the how and thresh arguments at the same time"
- with pytest.raises(TypeError, match=msg):
- df.dropna(how="all", thresh=2)
-
- with pytest.raises(TypeError, match=msg):
- df.dropna(how="any", thresh=2)
-
- with pytest.raises(TypeError, match=msg):
- df.dropna(how=None, thresh=None)
-
- @pytest.mark.parametrize("val", [1, 1.5])
- def test_dropna_ignore_index(self, val):
- # GH#31725
- df = DataFrame({"a": [1, 2, val]}, index=[3, 2, 1])
- result = df.dropna(ignore_index=True)
- expected = DataFrame({"a": [1, 2, val]})
- tm.assert_frame_equal(result, expected)
-
- df.dropna(ignore_index=True, inplace=True)
- tm.assert_frame_equal(df, expected)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/scalar/interval/test_interval.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/scalar/interval/test_interval.py
deleted file mode 100644
index 192aaacbac2b56c6f9bbd3970cd0c4a210ddf035..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/scalar/interval/test_interval.py
+++ /dev/null
@@ -1,279 +0,0 @@
-import numpy as np
-import pytest
-
-from pandas import (
- Interval,
- Period,
- Timedelta,
- Timestamp,
-)
-import pandas._testing as tm
-import pandas.core.common as com
-
-
-@pytest.fixture
-def interval():
- return Interval(0, 1)
-
-
-class TestInterval:
- def test_properties(self, interval):
- assert interval.closed == "right"
- assert interval.left == 0
- assert interval.right == 1
- assert interval.mid == 0.5
-
- def test_repr(self, interval):
- assert repr(interval) == "Interval(0, 1, closed='right')"
- assert str(interval) == "(0, 1]"
-
- interval_left = Interval(0, 1, closed="left")
- assert repr(interval_left) == "Interval(0, 1, closed='left')"
- assert str(interval_left) == "[0, 1)"
-
- def test_contains(self, interval):
- assert 0.5 in interval
- assert 1 in interval
- assert 0 not in interval
-
- interval_both = Interval(0, 1, "both")
- assert 0 in interval_both
- assert 1 in interval_both
-
- interval_neither = Interval(0, 1, closed="neither")
- assert 0 not in interval_neither
- assert 0.5 in interval_neither
- assert 1 not in interval_neither
-
- def test_equal(self):
- assert Interval(0, 1) == Interval(0, 1, closed="right")
- assert Interval(0, 1) != Interval(0, 1, closed="left")
- assert Interval(0, 1) != 0
-
- def test_comparison(self):
- msg = (
- "'<' not supported between instances of "
- "'pandas._libs.interval.Interval' and 'int'"
- )
- with pytest.raises(TypeError, match=msg):
- Interval(0, 1) < 2
-
- assert Interval(0, 1) < Interval(1, 2)
- assert Interval(0, 1) < Interval(0, 2)
- assert Interval(0, 1) < Interval(0.5, 1.5)
- assert Interval(0, 1) <= Interval(0, 1)
- assert Interval(0, 1) > Interval(-1, 2)
- assert Interval(0, 1) >= Interval(0, 1)
-
- def test_hash(self, interval):
- # should not raise
- hash(interval)
-
- @pytest.mark.parametrize(
- "left, right, expected",
- [
- (0, 5, 5),
- (-2, 5.5, 7.5),
- (10, 10, 0),
- (10, np.inf, np.inf),
- (-np.inf, -5, np.inf),
- (-np.inf, np.inf, np.inf),
- (Timedelta("0 days"), Timedelta("5 days"), Timedelta("5 days")),
- (Timedelta("10 days"), Timedelta("10 days"), Timedelta("0 days")),
- (Timedelta("1H10min"), Timedelta("5H5min"), Timedelta("3H55min")),
- (Timedelta("5S"), Timedelta("1H"), Timedelta("59min55S")),
- ],
- )
- def test_length(self, left, right, expected):
- # GH 18789
- iv = Interval(left, right)
- result = iv.length
- assert result == expected
-
- @pytest.mark.parametrize(
- "left, right, expected",
- [
- ("2017-01-01", "2017-01-06", "5 days"),
- ("2017-01-01", "2017-01-01 12:00:00", "12 hours"),
- ("2017-01-01 12:00", "2017-01-01 12:00:00", "0 days"),
- ("2017-01-01 12:01", "2017-01-05 17:31:00", "4 days 5 hours 30 min"),
- ],
- )
- @pytest.mark.parametrize("tz", (None, "UTC", "CET", "US/Eastern"))
- def test_length_timestamp(self, tz, left, right, expected):
- # GH 18789
- iv = Interval(Timestamp(left, tz=tz), Timestamp(right, tz=tz))
- result = iv.length
- expected = Timedelta(expected)
- assert result == expected
-
- @pytest.mark.parametrize(
- "left, right",
- [
- (0, 1),
- (Timedelta("0 days"), Timedelta("1 day")),
- (Timestamp("2018-01-01"), Timestamp("2018-01-02")),
- (
- Timestamp("2018-01-01", tz="US/Eastern"),
- Timestamp("2018-01-02", tz="US/Eastern"),
- ),
- ],
- )
- def test_is_empty(self, left, right, closed):
- # GH27219
- # non-empty always return False
- iv = Interval(left, right, closed)
- assert iv.is_empty is False
-
- # same endpoint is empty except when closed='both' (contains one point)
- iv = Interval(left, left, closed)
- result = iv.is_empty
- expected = closed != "both"
- assert result is expected
-
- @pytest.mark.parametrize(
- "left, right",
- [
- ("a", "z"),
- (("a", "b"), ("c", "d")),
- (list("AB"), list("ab")),
- (Interval(0, 1), Interval(1, 2)),
- (Period("2018Q1", freq="Q"), Period("2018Q1", freq="Q")),
- ],
- )
- def test_construct_errors(self, left, right):
- # GH 23013
- msg = "Only numeric, Timestamp and Timedelta endpoints are allowed"
- with pytest.raises(ValueError, match=msg):
- Interval(left, right)
-
- def test_math_add(self, closed):
- interval = Interval(0, 1, closed=closed)
- expected = Interval(1, 2, closed=closed)
-
- result = interval + 1
- assert result == expected
-
- result = 1 + interval
- assert result == expected
-
- result = interval
- result += 1
- assert result == expected
-
- msg = r"unsupported operand type\(s\) for \+"
- with pytest.raises(TypeError, match=msg):
- interval + interval
-
- with pytest.raises(TypeError, match=msg):
- interval + "foo"
-
- def test_math_sub(self, closed):
- interval = Interval(0, 1, closed=closed)
- expected = Interval(-1, 0, closed=closed)
-
- result = interval - 1
- assert result == expected
-
- result = interval
- result -= 1
- assert result == expected
-
- msg = r"unsupported operand type\(s\) for -"
- with pytest.raises(TypeError, match=msg):
- interval - interval
-
- with pytest.raises(TypeError, match=msg):
- interval - "foo"
-
- def test_math_mult(self, closed):
- interval = Interval(0, 1, closed=closed)
- expected = Interval(0, 2, closed=closed)
-
- result = interval * 2
- assert result == expected
-
- result = 2 * interval
- assert result == expected
-
- result = interval
- result *= 2
- assert result == expected
-
- msg = r"unsupported operand type\(s\) for \*"
- with pytest.raises(TypeError, match=msg):
- interval * interval
-
- msg = r"can\'t multiply sequence by non-int"
- with pytest.raises(TypeError, match=msg):
- interval * "foo"
-
- def test_math_div(self, closed):
- interval = Interval(0, 1, closed=closed)
- expected = Interval(0, 0.5, closed=closed)
-
- result = interval / 2.0
- assert result == expected
-
- result = interval
- result /= 2.0
- assert result == expected
-
- msg = r"unsupported operand type\(s\) for /"
- with pytest.raises(TypeError, match=msg):
- interval / interval
-
- with pytest.raises(TypeError, match=msg):
- interval / "foo"
-
- def test_math_floordiv(self, closed):
- interval = Interval(1, 2, closed=closed)
- expected = Interval(0, 1, closed=closed)
-
- result = interval // 2
- assert result == expected
-
- result = interval
- result //= 2
- assert result == expected
-
- msg = r"unsupported operand type\(s\) for //"
- with pytest.raises(TypeError, match=msg):
- interval // interval
-
- with pytest.raises(TypeError, match=msg):
- interval // "foo"
-
- def test_constructor_errors(self):
- msg = "invalid option for 'closed': foo"
- with pytest.raises(ValueError, match=msg):
- Interval(0, 1, closed="foo")
-
- msg = "left side of interval must be <= right side"
- with pytest.raises(ValueError, match=msg):
- Interval(1, 0)
-
- @pytest.mark.parametrize(
- "tz_left, tz_right", [(None, "UTC"), ("UTC", None), ("UTC", "US/Eastern")]
- )
- def test_constructor_errors_tz(self, tz_left, tz_right):
- # GH 18538
- left = Timestamp("2017-01-01", tz=tz_left)
- right = Timestamp("2017-01-02", tz=tz_right)
-
- if com.any_none(tz_left, tz_right):
- error = TypeError
- msg = "Cannot compare tz-naive and tz-aware timestamps"
- else:
- error = ValueError
- msg = "left and right must have the same time zone"
- with pytest.raises(error, match=msg):
- Interval(left, right)
-
- def test_equality_comparison_broadcasts_over_array(self):
- # https://github.com/pandas-dev/pandas/issues/35931
- interval = Interval(0, 1)
- arr = np.array([interval, interval])
- result = interval == arr
- expected = np.array([True, True])
- tm.assert_numpy_array_equal(result, expected)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/toolz/tests/test_dicttoolz.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/toolz/tests/test_dicttoolz.py
deleted file mode 100644
index d45cd6cf0c89c04ad78e2bd10e590fcb720b0b63..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/toolz/tests/test_dicttoolz.py
+++ /dev/null
@@ -1,270 +0,0 @@
-from collections import defaultdict as _defaultdict
-from collections.abc import Mapping
-import os
-from toolz.dicttoolz import (merge, merge_with, valmap, keymap, update_in,
- assoc, dissoc, keyfilter, valfilter, itemmap,
- itemfilter, assoc_in)
-from toolz.functoolz import identity
-from toolz.utils import raises
-
-
-def inc(x):
- return x + 1
-
-
-def iseven(i):
- return i % 2 == 0
-
-
-class TestDict(object):
- """Test typical usage: dict inputs, no factory keyword.
-
- Class attributes:
- D: callable that inputs a dict and creates or returns a MutableMapping
- kw: kwargs dict to specify "factory" keyword (if applicable)
- """
- D = dict
- kw = {}
-
- def test_merge(self):
- D, kw = self.D, self.kw
- assert merge(D({1: 1, 2: 2}), D({3: 4}), **kw) == D({1: 1, 2: 2, 3: 4})
-
- def test_merge_iterable_arg(self):
- D, kw = self.D, self.kw
- assert merge([D({1: 1, 2: 2}), D({3: 4})], **kw) == D({1: 1, 2: 2, 3: 4})
-
- def test_merge_with(self):
- D, kw = self.D, self.kw
- dicts = D({1: 1, 2: 2}), D({1: 10, 2: 20})
- assert merge_with(sum, *dicts, **kw) == D({1: 11, 2: 22})
- assert merge_with(tuple, *dicts, **kw) == D({1: (1, 10), 2: (2, 20)})
-
- dicts = D({1: 1, 2: 2, 3: 3}), D({1: 10, 2: 20})
- assert merge_with(sum, *dicts, **kw) == D({1: 11, 2: 22, 3: 3})
- assert merge_with(tuple, *dicts, **kw) == D({1: (1, 10), 2: (2, 20), 3: (3,)})
-
- assert not merge_with(sum)
-
- def test_merge_with_iterable_arg(self):
- D, kw = self.D, self.kw
- dicts = D({1: 1, 2: 2}), D({1: 10, 2: 20})
- assert merge_with(sum, *dicts, **kw) == D({1: 11, 2: 22})
- assert merge_with(sum, dicts, **kw) == D({1: 11, 2: 22})
- assert merge_with(sum, iter(dicts), **kw) == D({1: 11, 2: 22})
-
- def test_valmap(self):
- D, kw = self.D, self.kw
- assert valmap(inc, D({1: 1, 2: 2}), **kw) == D({1: 2, 2: 3})
-
- def test_keymap(self):
- D, kw = self.D, self.kw
- assert keymap(inc, D({1: 1, 2: 2}), **kw) == D({2: 1, 3: 2})
-
- def test_itemmap(self):
- D, kw = self.D, self.kw
- assert itemmap(reversed, D({1: 2, 2: 4}), **kw) == D({2: 1, 4: 2})
-
- def test_valfilter(self):
- D, kw = self.D, self.kw
- assert valfilter(iseven, D({1: 2, 2: 3}), **kw) == D({1: 2})
-
- def test_keyfilter(self):
- D, kw = self.D, self.kw
- assert keyfilter(iseven, D({1: 2, 2: 3}), **kw) == D({2: 3})
-
- def test_itemfilter(self):
- D, kw = self.D, self.kw
- assert itemfilter(lambda item: iseven(item[0]), D({1: 2, 2: 3}), **kw) == D({2: 3})
- assert itemfilter(lambda item: iseven(item[1]), D({1: 2, 2: 3}), **kw) == D({1: 2})
-
- def test_assoc(self):
- D, kw = self.D, self.kw
- assert assoc(D({}), "a", 1, **kw) == D({"a": 1})
- assert assoc(D({"a": 1}), "a", 3, **kw) == D({"a": 3})
- assert assoc(D({"a": 1}), "b", 3, **kw) == D({"a": 1, "b": 3})
-
- # Verify immutability:
- d = D({'x': 1})
- oldd = d
- assoc(d, 'x', 2, **kw)
- assert d is oldd
-
- def test_dissoc(self):
- D, kw = self.D, self.kw
- assert dissoc(D({"a": 1}), "a", **kw) == D({})
- assert dissoc(D({"a": 1, "b": 2}), "a", **kw) == D({"b": 2})
- assert dissoc(D({"a": 1, "b": 2}), "b", **kw) == D({"a": 1})
- assert dissoc(D({"a": 1, "b": 2}), "a", "b", **kw) == D({})
- assert dissoc(D({"a": 1}), "a", **kw) == dissoc(dissoc(D({"a": 1}), "a", **kw), "a", **kw)
-
- # Verify immutability:
- d = D({'x': 1})
- oldd = d
- d2 = dissoc(d, 'x', **kw)
- assert d is oldd
- assert d2 is not oldd
-
- def test_assoc_in(self):
- D, kw = self.D, self.kw
- assert assoc_in(D({"a": 1}), ["a"], 2, **kw) == D({"a": 2})
- assert (assoc_in(D({"a": D({"b": 1})}), ["a", "b"], 2, **kw) ==
- D({"a": D({"b": 2})}))
- assert assoc_in(D({}), ["a", "b"], 1, **kw) == D({"a": D({"b": 1})})
-
- # Verify immutability:
- d = D({'x': 1})
- oldd = d
- d2 = assoc_in(d, ['x'], 2, **kw)
- assert d is oldd
- assert d2 is not oldd
-
- def test_update_in(self):
- D, kw = self.D, self.kw
- assert update_in(D({"a": 0}), ["a"], inc, **kw) == D({"a": 1})
- assert update_in(D({"a": 0, "b": 1}), ["b"], str, **kw) == D({"a": 0, "b": "1"})
- assert (update_in(D({"t": 1, "v": D({"a": 0})}), ["v", "a"], inc, **kw) ==
- D({"t": 1, "v": D({"a": 1})}))
- # Handle one missing key.
- assert update_in(D({}), ["z"], str, None, **kw) == D({"z": "None"})
- assert update_in(D({}), ["z"], inc, 0, **kw) == D({"z": 1})
- assert update_in(D({}), ["z"], lambda x: x+"ar", default="b", **kw) == D({"z": "bar"})
- # Same semantics as Clojure for multiple missing keys, ie. recursively
- # create nested empty dictionaries to the depth specified by the
- # keys with the innermost value set to f(default).
- assert update_in(D({}), [0, 1], inc, default=-1, **kw) == D({0: D({1: 0})})
- assert update_in(D({}), [0, 1], str, default=100, **kw) == D({0: D({1: "100"})})
- assert (update_in(D({"foo": "bar", 1: 50}), ["d", 1, 0], str, 20, **kw) ==
- D({"foo": "bar", 1: 50, "d": D({1: D({0: "20"})})}))
- # Verify immutability:
- d = D({'x': 1})
- oldd = d
- update_in(d, ['x'], inc, **kw)
- assert d is oldd
-
- def test_factory(self):
- D, kw = self.D, self.kw
- assert merge(defaultdict(int, D({1: 2})), D({2: 3})) == {1: 2, 2: 3}
- assert (merge(defaultdict(int, D({1: 2})), D({2: 3}),
- factory=lambda: defaultdict(int)) ==
- defaultdict(int, D({1: 2, 2: 3})))
- assert not (merge(defaultdict(int, D({1: 2})), D({2: 3}),
- factory=lambda: defaultdict(int)) == {1: 2, 2: 3})
- assert raises(TypeError, lambda: merge(D({1: 2}), D({2: 3}), factoryy=dict))
-
-
-class defaultdict(_defaultdict):
- def __eq__(self, other):
- return (super(defaultdict, self).__eq__(other) and
- isinstance(other, _defaultdict) and
- self.default_factory == other.default_factory)
-
-
-class TestDefaultDict(TestDict):
- """Test defaultdict as input and factory
-
- Class attributes:
- D: callable that inputs a dict and creates or returns a MutableMapping
- kw: kwargs dict to specify "factory" keyword (if applicable)
- """
- @staticmethod
- def D(dict_):
- return defaultdict(int, dict_)
-
- kw = {'factory': lambda: defaultdict(int)}
-
-
-class CustomMapping(object):
- """Define methods of the MutableMapping protocol required by dicttoolz"""
- def __init__(self, *args, **kwargs):
- self._d = dict(*args, **kwargs)
-
- def __getitem__(self, key):
- return self._d[key]
-
- def __setitem__(self, key, val):
- self._d[key] = val
-
- def __delitem__(self, key):
- del self._d[key]
-
- def __iter__(self):
- return iter(self._d)
-
- def __len__(self):
- return len(self._d)
-
- def __contains__(self, key):
- return key in self._d
-
- def __eq__(self, other):
- return isinstance(other, CustomMapping) and self._d == other._d
-
- def __ne__(self, other):
- return not isinstance(other, CustomMapping) or self._d != other._d
-
- def keys(self):
- return self._d.keys()
-
- def values(self):
- return self._d.values()
-
- def items(self):
- return self._d.items()
-
- def update(self, *args, **kwargs):
- self._d.update(*args, **kwargs)
-
- # Unused methods that are part of the MutableMapping protocol
- #def get(self, key, *args):
- # return self._d.get(key, *args)
-
- #def pop(self, key, *args):
- # return self._d.pop(key, *args)
-
- #def popitem(self, key):
- # return self._d.popitem()
-
- #def clear(self):
- # self._d.clear()
-
- #def setdefault(self, key, *args):
- # return self._d.setdefault(self, key, *args)
-
-
-class TestCustomMapping(TestDict):
- """Test CustomMapping as input and factory
-
- Class attributes:
- D: callable that inputs a dict and creates or returns a MutableMapping
- kw: kwargs dict to specify "factory" keyword (if applicable)
- """
- D = CustomMapping
- kw = {'factory': lambda: CustomMapping()}
-
-
-def test_environ():
- # See: https://github.com/pytoolz/cytoolz/issues/127
- assert keymap(identity, os.environ) == os.environ
- assert valmap(identity, os.environ) == os.environ
- assert itemmap(identity, os.environ) == os.environ
-
-
-def test_merge_with_non_dict_mappings():
- class Foo(Mapping):
- def __init__(self, d):
- self.d = d
-
- def __iter__(self):
- return iter(self.d)
-
- def __getitem__(self, key):
- return self.d[key]
-
- def __len__(self):
- return len(self.d)
-
- d = Foo({1: 1})
-
- assert merge(d) is d or merge(d) == {1: 1}
- assert merge_with(sum, d) == {1: 1}
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/tzdata/zoneinfo/Chile/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/tzdata/zoneinfo/Chile/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/pyInter/Liyuu_sovits4/preprocess_flist_config.py b/spaces/pyInter/Liyuu_sovits4/preprocess_flist_config.py
deleted file mode 100644
index 6e3dd0bd9390a509c282bbde4ff2631ac94404e4..0000000000000000000000000000000000000000
--- a/spaces/pyInter/Liyuu_sovits4/preprocess_flist_config.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import os
-import argparse
-import re
-
-from tqdm import tqdm
-from random import shuffle
-import json
-
-config_template = json.load(open("configs/config.json"))
-
-pattern = re.compile(r'^[\.a-zA-Z0-9_\/]+$')
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument("--train_list", type=str, default="./filelists/train.txt", help="path to train list")
- parser.add_argument("--val_list", type=str, default="./filelists/val.txt", help="path to val list")
- parser.add_argument("--test_list", type=str, default="./filelists/test.txt", help="path to test list")
- parser.add_argument("--source_dir", type=str, default="./dataset/44k", help="path to source dir")
- args = parser.parse_args()
-
- train = []
- val = []
- test = []
- idx = 0
- spk_dict = {}
- spk_id = 0
- for speaker in tqdm(os.listdir(args.source_dir)):
- spk_dict[speaker] = spk_id
- spk_id += 1
- wavs = ["/".join([args.source_dir, speaker, i]) for i in os.listdir(os.path.join(args.source_dir, speaker))]
- for wavpath in wavs:
- if not pattern.match(wavpath):
- print(f"warning：文件名{wavpath}中包含非字母数字下划线，可能会导致错误。（也可能不会）")
- if len(wavs) < 10:
- print(f"warning：{speaker}数据集数量小于10条，请补充数据")
- wavs = [i for i in wavs if i.endswith("wav")]
- shuffle(wavs)
- train += wavs[2:-2]
- val += wavs[:2]
- test += wavs[-2:]
-
- shuffle(train)
- shuffle(val)
- shuffle(test)
-
- print("Writing", args.train_list)
- with open(args.train_list, "w") as f:
- for fname in tqdm(train):
- wavpath = fname
- f.write(wavpath + "\n")
-
- print("Writing", args.val_list)
- with open(args.val_list, "w") as f:
- for fname in tqdm(val):
- wavpath = fname
- f.write(wavpath + "\n")
-
- print("Writing", args.test_list)
- with open(args.test_list, "w") as f:
- for fname in tqdm(test):
- wavpath = fname
- f.write(wavpath + "\n")
-
- config_template["spk"] = spk_dict
- print("Writing configs/config.json")
- with open("configs/config.json", "w") as f:
- json.dump(config_template, f, indent=2)
diff --git a/spaces/pycoming/bingo/src/components/chat-list.tsx b/spaces/pycoming/bingo/src/components/chat-list.tsx
deleted file mode 100644
index 624a78ef0d7be0f1192cf02a81e2e9cf214cb193..0000000000000000000000000000000000000000
--- a/spaces/pycoming/bingo/src/components/chat-list.tsx
+++ /dev/null
@@ -1,28 +0,0 @@
-import React from 'react'
-
-import { Separator } from '@/components/ui/separator'
-import { ChatMessage } from '@/components/chat-message'
-import { ChatMessageModel } from '@/lib/bots/bing/types'
-
-export interface ChatList {
- messages: ChatMessageModel[]
-}
-
-export function ChatList({ messages }: ChatList) {
- if (!messages.length) {
- return null
- }
-
- return (
-
- {messages.map((message, index) => (
-
-
- ))}
-
- )
-}
diff --git a/spaces/qingxu98/gpt-academic/crazy_functions/test_project/python/dqn/dqn.py b/spaces/qingxu98/gpt-academic/crazy_functions/test_project/python/dqn/dqn.py
deleted file mode 100644
index 6cea64d39baa7ff4c1e549869aaa4b0ae17779a9..0000000000000000000000000000000000000000
--- a/spaces/qingxu98/gpt-academic/crazy_functions/test_project/python/dqn/dqn.py
+++ /dev/null
@@ -1,245 +0,0 @@
-from typing import Any, Dict, List, Optional, Tuple, Type, Union
-
-import gym
-import numpy as np
-import torch as th
-from torch.nn import functional as F
-
-from stable_baselines3.common import logger
-from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm
-from stable_baselines3.common.preprocessing import maybe_transpose
-from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
-from stable_baselines3.common.utils import get_linear_fn, is_vectorized_observation, polyak_update
-from stable_baselines3.dqn.policies import DQNPolicy
-
-
-class DQN(OffPolicyAlgorithm):
- """
- Deep Q-Network (DQN)
-
- Paper: https://arxiv.org/abs/1312.5602, https://www.nature.com/articles/nature14236
- Default hyperparameters are taken from the nature paper,
- except for the optimizer and learning rate that were taken from Stable Baselines defaults.
-
- :param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)
- :param env: The environment to learn from (if registered in Gym, can be str)
- :param learning_rate: The learning rate, it can be a function
- of the current progress remaining (from 1 to 0)
- :param buffer_size: size of the replay buffer
- :param learning_starts: how many steps of the model to collect transitions for before learning starts
- :param batch_size: Minibatch size for each gradient update
- :param tau: the soft update coefficient ("Polyak update", between 0 and 1) default 1 for hard update
- :param gamma: the discount factor
- :param train_freq: Update the model every ``train_freq`` steps. Alternatively pass a tuple of frequency and unit
- like ``(5, "step")`` or ``(2, "episode")``.
- :param gradient_steps: How many gradient steps to do after each rollout (see ``train_freq``)
- Set to ``-1`` means to do as many gradient steps as steps done in the environment
- during the rollout.
- :param optimize_memory_usage: Enable a memory efficient variant of the replay buffer
- at a cost of more complexity.
- See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195
- :param target_update_interval: update the target network every ``target_update_interval``
- environment steps.
- :param exploration_fraction: fraction of entire training period over which the exploration rate is reduced
- :param exploration_initial_eps: initial value of random action probability
- :param exploration_final_eps: final value of random action probability
- :param max_grad_norm: The maximum value for the gradient clipping
- :param tensorboard_log: the log location for tensorboard (if None, no logging)
- :param create_eval_env: Whether to create a second environment that will be
- used for evaluating the agent periodically. (Only available when passing string for the environment)
- :param policy_kwargs: additional arguments to be passed to the policy on creation
- :param verbose: the verbosity level: 0 no output, 1 info, 2 debug
- :param seed: Seed for the pseudo random generators
- :param device: Device (cpu, cuda, ...) on which the code should be run.
- Setting it to auto, the code will be run on the GPU if possible.
- :param _init_setup_model: Whether or not to build the network at the creation of the instance
- """
-
- def __init__(
- self,
- policy: Union[str, Type[DQNPolicy]],
- env: Union[GymEnv, str],
- learning_rate: Union[float, Schedule] = 1e-4,
- buffer_size: int = 1000000,
- learning_starts: int = 50000,
- batch_size: Optional[int] = 32,
- tau: float = 1.0,
- gamma: float = 0.99,
- train_freq: Union[int, Tuple[int, str]] = 4,
- gradient_steps: int = 1,
- optimize_memory_usage: bool = False,
- target_update_interval: int = 10000,
- exploration_fraction: float = 0.1,
- exploration_initial_eps: float = 1.0,
- exploration_final_eps: float = 0.05,
- max_grad_norm: float = 10,
- tensorboard_log: Optional[str] = None,
- create_eval_env: bool = False,
- policy_kwargs: Optional[Dict[str, Any]] = None,
- verbose: int = 0,
- seed: Optional[int] = None,
- device: Union[th.device, str] = "auto",
- _init_setup_model: bool = True,
- ):
-
- super(DQN, self).__init__(
- policy,
- env,
- DQNPolicy,
- learning_rate,
- buffer_size,
- learning_starts,
- batch_size,
- tau,
- gamma,
- train_freq,
- gradient_steps,
- action_noise=None, # No action noise
- policy_kwargs=policy_kwargs,
- tensorboard_log=tensorboard_log,
- verbose=verbose,
- device=device,
- create_eval_env=create_eval_env,
- seed=seed,
- sde_support=False,
- optimize_memory_usage=optimize_memory_usage,
- supported_action_spaces=(gym.spaces.Discrete,),
- )
-
- self.exploration_initial_eps = exploration_initial_eps
- self.exploration_final_eps = exploration_final_eps
- self.exploration_fraction = exploration_fraction
- self.target_update_interval = target_update_interval
- self.max_grad_norm = max_grad_norm
- # "epsilon" for the epsilon-greedy exploration
- self.exploration_rate = 0.0
- # Linear schedule will be defined in `_setup_model()`
- self.exploration_schedule = None
- self.q_net, self.q_net_target = None, None
-
- if _init_setup_model:
- self._setup_model()
-
- def _setup_model(self) -> None:
- super(DQN, self)._setup_model()
- self._create_aliases()
- self.exploration_schedule = get_linear_fn(
- self.exploration_initial_eps, self.exploration_final_eps, self.exploration_fraction
- )
-
- def _create_aliases(self) -> None:
- self.q_net = self.policy.q_net
- self.q_net_target = self.policy.q_net_target
-
- def _on_step(self) -> None:
- """
- Update the exploration rate and target network if needed.
- This method is called in ``collect_rollouts()`` after each step in the environment.
- """
- if self.num_timesteps % self.target_update_interval == 0:
- polyak_update(self.q_net.parameters(), self.q_net_target.parameters(), self.tau)
-
- self.exploration_rate = self.exploration_schedule(self._current_progress_remaining)
- logger.record("rollout/exploration rate", self.exploration_rate)
-
- def train(self, gradient_steps: int, batch_size: int = 100) -> None:
- # Update learning rate according to schedule
- self._update_learning_rate(self.policy.optimizer)
-
- losses = []
- for _ in range(gradient_steps):
- # Sample replay buffer
- replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)
-
- with th.no_grad():
- # Compute the next Q-values using the target network
- next_q_values = self.q_net_target(replay_data.next_observations)
- # Follow greedy policy: use the one with the highest value
- next_q_values, _ = next_q_values.max(dim=1)
- # Avoid potential broadcast issue
- next_q_values = next_q_values.reshape(-1, 1)
- # 1-step TD target
- target_q_values = replay_data.rewards + (1 - replay_data.dones) * self.gamma * next_q_values
-
- # Get current Q-values estimates
- current_q_values = self.q_net(replay_data.observations)
-
- # Retrieve the q-values for the actions from the replay buffer
- current_q_values = th.gather(current_q_values, dim=1, index=replay_data.actions.long())
-
- # Compute Huber loss (less sensitive to outliers)
- loss = F.smooth_l1_loss(current_q_values, target_q_values)
- losses.append(loss.item())
-
- # Optimize the policy
- self.policy.optimizer.zero_grad()
- loss.backward()
- # Clip gradient norm
- th.nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm)
- self.policy.optimizer.step()
-
- # Increase update counter
- self._n_updates += gradient_steps
-
- logger.record("train/n_updates", self._n_updates, exclude="tensorboard")
- logger.record("train/loss", np.mean(losses))
-
- def predict(
- self,
- observation: np.ndarray,
- state: Optional[np.ndarray] = None,
- mask: Optional[np.ndarray] = None,
- deterministic: bool = False,
- ) -> Tuple[np.ndarray, Optional[np.ndarray]]:
- """
- Overrides the base_class predict function to include epsilon-greedy exploration.
-
- :param observation: the input observation
- :param state: The last states (can be None, used in recurrent policies)
- :param mask: The last masks (can be None, used in recurrent policies)
- :param deterministic: Whether or not to return deterministic actions.
- :return: the model's action and the next state
- (used in recurrent policies)
- """
- if not deterministic and np.random.rand() < self.exploration_rate:
- if is_vectorized_observation(maybe_transpose(observation, self.observation_space), self.observation_space):
- n_batch = observation.shape[0]
- action = np.array([self.action_space.sample() for _ in range(n_batch)])
- else:
- action = np.array(self.action_space.sample())
- else:
- action, state = self.policy.predict(observation, state, mask, deterministic)
- return action, state
-
- def learn(
- self,
- total_timesteps: int,
- callback: MaybeCallback = None,
- log_interval: int = 4,
- eval_env: Optional[GymEnv] = None,
- eval_freq: int = -1,
- n_eval_episodes: int = 5,
- tb_log_name: str = "DQN",
- eval_log_path: Optional[str] = None,
- reset_num_timesteps: bool = True,
- ) -> OffPolicyAlgorithm:
-
- return super(DQN, self).learn(
- total_timesteps=total_timesteps,
- callback=callback,
- log_interval=log_interval,
- eval_env=eval_env,
- eval_freq=eval_freq,
- n_eval_episodes=n_eval_episodes,
- tb_log_name=tb_log_name,
- eval_log_path=eval_log_path,
- reset_num_timesteps=reset_num_timesteps,
- )
-
- def _excluded_save_params(self) -> List[str]:
- return super(DQN, self)._excluded_save_params() + ["q_net", "q_net_target"]
-
- def _get_torch_save_params(self) -> Tuple[List[str], List[str]]:
- state_dicts = ["policy", "policy.optimizer"]
-
- return state_dicts, []
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/CCleaner 5.0 Serial Key Licence Full Version Free Download.md b/spaces/quidiaMuxgu/Expedit-SAM/CCleaner 5.0 Serial Key Licence Full Version Free Download.md
deleted file mode 100644
index 0848ae6d53a9491a7289ac51ed5739aac8d9a5a7..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/CCleaner 5.0 Serial Key Licence Full Version Free Download.md
+++ /dev/null
@@ -1,12 +0,0 @@
-CCleaner 5.0 Serial Key Licence Full version Free Download Download ✑ https://geags.com/2uCsnN
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Cricket Batting Tips In Tamil Pdf 26.md b/spaces/quidiaMuxgu/Expedit-SAM/Cricket Batting Tips In Tamil Pdf 26.md
deleted file mode 100644
index b5a05bff7b0a9e5fddc3b121a90fc503d54200f3..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Cricket Batting Tips In Tamil Pdf 26.md
+++ /dev/null
@@ -1,9 +0,0 @@
-Cricket Batting Tips In Tamil Pdf 26 Download ⇔ https://geags.com/2uCrjo
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Histologia Basica Junqueira E Carneiro 11 Ed.md b/spaces/quidiaMuxgu/Expedit-SAM/Histologia Basica Junqueira E Carneiro 11 Ed.md
deleted file mode 100644
index f1b9754ddab66648a52ef16df11bc8528875e9c9..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Histologia Basica Junqueira E Carneiro 11 Ed.md
+++ /dev/null
@@ -1,100 +0,0 @@
-Histologia Basica Junqueira e Carneiro 11 ed: Um Livro Essencial para o Estudo da Histologia
-
-Histologia é a ciência que estuda a estrutura, a composição e as funções dos tecidos do corpo humano. A histologia é fundamental para o entendimento da fisiologia, da patologia e da medicina em geral. Para estudar a histologia, é preciso ter um bom livro que apresente os conceitos, as imagens e os exemplos de forma clara, didática e atualizada.
-histologia basica junqueira e carneiro 11 ed Download — https://geags.com/2uCqfV
Um dos livros mais usados e recomendados pelos estudantes e professores de histologia é o Histologia Basica Junqueira e Carneiro 11 ed. Esse livro é uma obra clássica que foi lançada pela primeira vez em 1972 e que já teve mais de 20 edições em vários idiomas. O livro foi escrito pelos renomados professores Luiz Carlos Uchôa Junqueira e José Carneiro, que foram pioneiros no ensino e na pesquisa em histologia no Brasil.
-
-O que você vai encontrar no Histologia Basica Junqueira e Carneiro 11 ed?
-
-O Histologia Basica Junqueira e Carneiro 11 ed é um livro que aborda todos os aspectos da histologia humana, desde os conceitos básicos até os mais avançados. O livro é dividido em quatro partes:
-
-
-A primeira parte trata dos métodos de estudo da histologia, como a microscopia óptica, a microscopia eletrônica, a imunohistoquímica, a citometria de fluxo, entre outros.
-A segunda parte trata dos tecidos básicos do corpo humano, como o tecido epitelial, o tecido conjuntivo, o tecido muscular, o tecido nervoso e o tecido hematopoiético.
-A terceira parte trata dos sistemas orgânicos do corpo humano, como o sistema cardiovascular, o sistema respiratório, o sistema digestório, o sistema urinário, o sistema endócrino, o sistema reprodutor, entre outros.
-A quarta parte trata da embriologia humana, desde a fecundação até o nascimento.
-
-
-O livro conta com mais de 800 páginas de texto e mais de 1000 ilustrações de alta qualidade, incluindo fotos coloridas de microscopia óptica e eletrônica, esquemas explicativos, tabelas comparativas e quadros resumos. O livro também conta com um atlas de histologia com mais de 200 lâminas histológicas comentadas.
-
-Quais são os diferenciais do Histologia Basica Junqueira e Carneiro 11 ed?
-
-O Histologia Basica Junqueira e Carneiro 11 ed é um livro que se destaca por vários motivos. Alguns deles são:
-
-
-É um livro atualizado que acompanha as novas descobertas e as novas tecnologias da área da histologia.
-É um livro completo que abrange todos os temas relevantes da histologia humana.
-É um livro didático que explica os conceitos de forma clara, objetiva e com exemplos práticos.
-É um livro ilustrado que apresenta as imagens de forma nítida, colorida e com legendas detalhadas.
-É um livro interativo que oferece recursos adicionais online, como vídeos, animações, exercícios, testes e casos clínicos.
-
-
-Como adquirir o Histologia Basica Junqueira e Carneiro 11 ed?
-
-Se você quer adquirir o Histologia Basica Junqueira e Carneiro 11 ed, você pode fazer isso de várias formas. Você pode comprar o livro impresso em livrarias físicas ou online. Você pode comprar o livro digital em formato PDF ou ePub em plataformas digitais. Você pode baixar o livro gratuitamente em sites confiáveis ou em redes sociais. Você pode acessar o livro online em sites oficiais ou em bibliotecas virtuais.
-
-
-O importante é que você tenha acesso a esse livro que é uma referência na área da histologia e que vai te ajudar a aprender e a se aprofundar nesse assunto tão fascinante. O Histologia Basica Junqueira e Carneiro 11 ed é um livro que vale a pena ter na sua estante ou no seu dispositivo.
-
-Conclusão
-
-Em conclusão, o Histologia Basica Junqueira e Carneiro 11 ed é um livro essencial para o estudo da histologia humana. Ele é um livro atualizado, completo, didático, ilustrado e interativo que aborda todos os aspectos da histologia desde os métodos de estudo até a embriologia. Ele é um livro que foi escrito por professores renomados que foram pioneiros no ensino e na pesquisa em histologia no Brasil. Ele é um livro que está disponível em vários formatos e em vários meios para facilitar o seu acesso. Se você quer aprender mais sobre a histologia humana, não deixe de adquirir o Histologia Basica Junqueira e Carneiro 11 ed.
-How to see histological images with Histologia Basica Junqueira e Carneiro 11 ed?
-
-One of the best features of Histologia Basica Junqueira e Carneiro 11 ed is that it provides you with a lot of histological images that illustrate the structure and function of the different tissues and organs of the human body. These images are taken from microscopes and show the details and characteristics of the cells, tissues, and organs at different magnifications and resolutions.
-
-To see histological images with Histologia Basica Junqueira e Carneiro 11 ed, you have several options. You can:
-
-
-Look at the images that are printed in the book. The book has more than 1000 illustrations that are arranged in a logical and sequential order according to the topics and chapters of the book.
-Look at the images that are included in the atlas of histology. The book comes with an atlas of histology that has more than 200 histological slides that are commented and explained by the authors.
-Look at the images that are available online. The book has a website that offers you access to more than 500 histological images that are interactive and zoomable. You can also find videos, animations, exercises, tests, and cases online.
-Look at the images that are provided by other sources. You can also find histological images from other books, websites, databases, or applications that are related to histology or anatomy.
-
-
-By seeing histological images with Histologia Basica Junqueira e Carneiro 11 ed, you can improve your understanding and appreciation of histology. You can also compare and contrast different tissues and organs and learn how they work together to maintain the health and function of the human body.
-What are some tips or tricks for interpreting histological images?
-
-Interpreting histological images is not an easy task. It requires a lot of knowledge, skill, and practice. However, there are some tips or tricks that can help you improve your ability to interpret histological images. Here are some of them:
-
-
-Know the basics of histology. Before you try to interpret histological images, you need to have a solid foundation of the basic concepts and principles of histology. You need to know the types, functions, and characteristics of the different cells, tissues, and organs of the human body.
-Know the methods of histology. You also need to know how histological images are obtained and processed. You need to know the types, advantages, and disadvantages of the different methods of histology, such as microtomy, staining, fixation, embedding, etc.
-Know the tools of histology. You also need to know how to use the tools that are available for viewing and analyzing histological images. You need to know how to use microscopes, cameras, computers, software, etc.
-Know the patterns of histology. You also need to know how to recognize and identify the patterns that are common in histological images. You need to know how to distinguish between normal and abnormal tissues and organs, how to classify tumors and lesions, how to correlate structure and function, etc.
-Know the sources of histology. You also need to know where to find reliable and relevant sources of information and reference for histological images. You need to know how to use books, websites, databases, journals, etc.
-
-
-By following these tips or tricks, you can improve your ability to interpret histological images and gain more insight and understanding of histology.
-What are some challenges or limitations of interpreting histological images?
-
-Interpreting histological images is not a simple or straightforward task. It involves a lot of challenges and limitations that can affect the accuracy and reliability of your interpretation. Here are some of them:
-
-
-The quality of histological images depends on many factors, such as the preparation, fixation, staining, cutting, mounting, and preservation of the tissue samples, as well as the type, resolution, and calibration of the microscope and camera used to capture the images.
-The interpretation of histological images requires a lot of experience and expertise, as well as a good knowledge of anatomy, physiology, pathology, and histology. It also requires a lot of attention and concentration, as well as a good eye for detail and pattern recognition.
-The interpretation of histological images can be influenced by subjective factors, such as personal bias, preference, expectation, or emotion. It can also be influenced by external factors, such as peer pressure, time pressure, or ethical issues.
-The interpretation of histological images can be affected by errors or mistakes, such as misidentification, misclassification, misdiagnosis, or miscommunication. It can also be affected by uncertainties or ambiguities, such as variations, anomalies, artifacts, or inconsistencies.
-The interpretation of histological images can be challenged or disputed by other sources of information or evidence, such as clinical data, laboratory tests, genetic tests, or imaging techniques.
-
-
-By being aware of these challenges and limitations, you can improve your ability to interpret histological images and avoid potential pitfalls and problems.
-What are some solutions or strategies for overcoming these challenges or limitations?
-
-Despite the challenges and limitations of interpreting histological images, there are some solutions or strategies that can help you overcome them and improve your interpretation skills. Here are some of them:
-
-
-Improve the quality of histological images by following the best practices and standards for preparing, processing, and preserving tissue samples, as well as using high-quality and well-calibrated equipment and software.
-Improve your knowledge and expertise in histology by studying the theory and practice of histology from reliable and updated sources, such as books, websites, journals, etc. You can also consult with experts or peers who have more experience or knowledge in histology.
-Improve your objectivity and accuracy in histology by avoiding or minimizing subjective or external factors that can influence your interpretation, such as bias, preference, expectation, emotion, pressure, or ethics. You can also use tools or methods that can help you reduce errors or uncertainties, such as checklists, guidelines, algorithms, etc.
-Improve your validation and verification in histology by comparing or contrasting your interpretation with other sources of information or evidence that can support or challenge your interpretation, such as clinical data, laboratory tests, genetic tests, or imaging techniques. You can also seek feedback or review from other experts or peers who can provide you with constructive criticism or suggestions.
-
-
-By applying these solutions or strategies, you can overcome the challenges and limitations of interpreting histological images and enhance your interpretation skills and confidence.
-Conclusion
-
-In conclusion, histologia basica junqueira e carneiro 11 ed is a book that is essential for the study of human histology. It is a book that is updated, complete, didactic, illustrated, and interactive that covers all aspects of histology from the methods of study to embryology. It is a book that was written by renowned professors who were pioneers in teaching and research in histology in Brazil. It is a book that is available in various formats and media to facilitate your access. If you want to learn more about human histology, do not miss the opportunity to acquire histologia basica junqueira e carneiro 11 ed.
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Battle Los Angeles PC Activation Code Where to Find and Redeem It.md b/spaces/raedeXanto/academic-chatgpt-beta/Battle Los Angeles PC Activation Code Where to Find and Redeem It.md
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-Battle Los Angeles PC Activation Code: What You Need to Know
- If you are a fan of first-person shooter games, you might have heard of Battle Los Angeles , a game based on the 2011 movie of the same name. In this game, you play as a US Marine who fights against an alien invasion in Los Angeles. The game features realistic graphics, intense combat, and various weapons and vehicles to use.
- However, before you can enjoy this game on your PC, you need to have an activation code that verifies your purchase and allows you to install and play the game. Without an activation code, you won't be able to access the game at all.
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So, how do you get an activation code for Battle Los Angeles? And what are the risks and challenges of using one? In this article, we will answer these questions and provide you with some methods to find or generate an activation code for this game.
- Methods to Find or Generate an Activation Code
- There are three main methods that you can use to get an activation code for Battle Los Angeles. Each method has its own advantages and disadvantages, so you should choose the one that suits your needs and preferences.
- Method 1: Buy the game from an official source
- The most straightforward and reliable way to get an activation code is to buy the game from an official source, such as Steam, Amazon, or Konami (the publisher of the game). When you buy the game from these sources, you will receive an activation code along with your purchase confirmation. You can then use this code to install and play the game on your PC.
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- The advantage of this method is that you will get a legitimate and valid code that works without any issues. You will also support the developers and publishers of the game, who deserve to be compensated for their work. Additionally, you will be able to access updates, patches, and online features of the game without any problems.
- The disadvantage of this method is that you will have to pay for the game, which might not be affordable or convenient for some people. The price of the game varies depending on the source and region, but it usually ranges from $10 to $20. You will also need a stable internet connection and enough disk space to download and install the game.
- Method 2: Use a key generator tool
- Another way to get an activation code is to use a key generator tool, which is a software program that creates random codes for various games and software. You can find many key generator tools online, such as KeyGenNinja, SerialBay, or AllKeygensDownload. These tools claim to generate working codes for Battle Los Angeles and other games.
- The advantage of this method is that you don't have to pay anything for the code, as these tools are usually free or require a small donation. You also don't need an internet connection or disk space to use these tools, as they run offline and are usually small in size.
- The disadvantage of this method is that it is very risky and unreliable. Most of these tools are illegal and violate the terms and conditions of the game. Using them can result in legal issues and penalties, such as fines or lawsuits. Moreover, many of these tools are infected with malware and viruses that can harm your PC and compromise your personal data. Furthermore, many of these codes are invalid or expired, meaning that they won't work or will stop working after a while.
- Method 3: Search online for free codes
- The third way to get an activation code is to search online for free codes that other people have shared or posted. You can find many websites, forums, blogs, videos, or social media posts that offer free codes for Battle Los Angeles and other games. Some examples are ChapterCheats, YouTube, or PDFDrive. These sources claim to provide working codes for Battle Los Angeles that you can use without paying anything.
- The advantage of this method is that you don't have to pay anything for the code, as these sources are free and accessible. You also don't need any software or tool to use these codes, as they are already generated and ready to use.
- The disadvantage of this method is that it is also very risky and unreliable. Like the previous method, most of these sources are illegal and violate the terms and conditions of the game. Using them can result in legal issues and penalties, such as fines or lawsuits. Moreover, many of these sources are infected with malware and viruses that can harm your PC and compromise your personal data. Furthermore, many of these codes are invalid or expired, meaning that they won't work or will stop working after a while.
- Risks and Challenges of Using an Activation Code
- As you can see from the above methods, getting an activation code for Battle Los Angeles is not easy or safe. There are many risks and challenges involved in using an activation code for this game. Here are some of them:
- Risk 1: Legal issues and penalties
- Using an activation code that is not obtained from an official source is considered piracy and theft. It violates the intellectual property rights of the developers and publishers of the game. It also breaches the end-user license agreement (EULA) that you agree to when you install the game. By using an illegal activation code, you are exposing yourself to legal issues and penalties, such as fines or lawsuits. You could also face criminal charges or imprisonment in some countries.
- Risk 2: Malware and viruses
- Using an activation code that is obtained from an untrusted source is dangerous for your PC and personal data. Many sources that offer free or generated codes are infected with malware and viruses that can harm your PC and compromise your personal data. These malware and viruses can steal your identity, passwords, bank accounts, credit cards, or other sensitive information. They can also damage your files, programs, system settings, or hardware components. They can also make your PC slow, unstable, or unusable.
- Risk 3: Invalid or expired codes
- or because they are detected and blocked by the game's security system. When this happens, you won't be able to install or play the game at all. You will also waste your time and effort trying to find or generate another code that works.
- Conclusion
- In conclusion, getting an activation code for Battle Los Angeles is not a simple or safe task. There are three main methods that you can use to get an activation code: buying the game from an official source, using a key generator tool, or searching online for free codes. However, each method has its own risks and challenges, such as legal issues and penalties, malware and viruses, or invalid or expired codes. Therefore, you should be careful and cautious when using an activation code for this game.
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-The best and safest way to get an activation code is to buy the game from an official source, such as Steam, Amazon, or Konami. This way, you will get a legitimate and valid code that works without any issues. You will also support the developers and publishers of the game, who deserve to be compensated for their work. Additionally, you will be able to access updates, patches, and online features of the game without any problems.
-If you decide to use a key generator tool or search online for free codes, make sure that you use a trusted and reputable source that has positive reviews and feedback from other users. You should also scan the source and the code with a reliable antivirus program before using them. You should also backup your PC and personal data before installing or playing the game.
-If you encounter any problems or errors with the activation code or the game, you should contact the customer support of the game or the source that provided you with the code. They might be able to help you solve the issue or provide you with a replacement code.
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- We hope that this article has helped you understand what you need to know about Battle Los Angeles PC activation code. If you have any questions or comments, please feel free to leave them below. Thank you for reading and have fun playing!
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- Here are some frequently asked questions about Battle Los Angeles PC activation code:
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-What is Battle Los Angeles? Why do I need an activation code for Battle Los Angeles? How do I get an activation code for Battle Los Angeles? What are the risks and challenges of using an activation code for Battle Los Angeles? What are some tips and recommendations for using an activation code for Battle Los Angeles?
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- Conclusion
-Charisma University is an online course that teaches you how to improve your social skills and become more charismatic. It is created by Charlie Houpert, the co-founder of Charisma on Command, a YouTube channel and a book on charisma. It is a 6-week step-by-step program that covers everything from making a great first impression, to building confidence, to mastering conversation skills, to telling captivating stories, to developing a magnetic presence, to leading and influencing others with charisma. It also includes daily action guides, worksheets, cheat sheets, and bonuses. It offers a 60-day money-back guarantee and lifetime access. You can enroll in Charisma University by visiting the official website and choosing your payment option. You can also watch some free videos and read some testimonials from previous students.
-If you want to take your charisma to the next level and enjoy more success and happiness in your personal and professional life, Charisma University might be the perfect course for you.
- FAQs
-What is charisma?
-Charisma is the ability to attract, influence, and inspire others with your personality and presence. It is a combination of confidence, charm, humor, warmth, authenticity, and other qualities that make people like you and want to follow you.
-Why is charisma important?
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-How long does it take to complete Charisma University?
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-How much does Charisma University cost?
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`_ for details.
-
- Args:
- in_channels (list[int]): Number of channels in the input feature map.
- feat_channels (int): Number of channels for feature.
- out_channels (int): Number of channels for output.
- num_things_classes (int): Number of things.
- num_stuff_classes (int): Number of stuff.
- num_queries (int): Number of query in Transformer.
- pixel_decoder (:obj:`mmcv.ConfigDict` | dict): Config for pixel
- decoder. Defaults to None.
- enforce_decoder_input_project (bool, optional): Whether to add a layer
- to change the embed_dim of tranformer encoder in pixel decoder to
- the embed_dim of transformer decoder. Defaults to False.
- transformer_decoder (:obj:`mmcv.ConfigDict` | dict): Config for
- transformer decoder. Defaults to None.
- positional_encoding (:obj:`mmcv.ConfigDict` | dict): Config for
- transformer decoder position encoding. Defaults to None.
- loss_cls (:obj:`mmcv.ConfigDict` | dict): Config of the classification
- loss. Defaults to `CrossEntropyLoss`.
- loss_mask (:obj:`mmcv.ConfigDict` | dict): Config of the mask loss.
- Defaults to `FocalLoss`.
- loss_dice (:obj:`mmcv.ConfigDict` | dict): Config of the dice loss.
- Defaults to `DiceLoss`.
- train_cfg (:obj:`mmcv.ConfigDict` | dict): Training config of
- Maskformer head.
- test_cfg (:obj:`mmcv.ConfigDict` | dict): Testing config of Maskformer
- head.
- init_cfg (dict or list[dict], optional): Initialization config dict.
- Defaults to None.
- """
-
- def __init__(self,
- in_channels,
- feat_channels,
- out_channels,
- num_things_classes=80,
- num_stuff_classes=53,
- num_queries=100,
- pixel_decoder=None,
- enforce_decoder_input_project=False,
- transformer_decoder=None,
- positional_encoding=None,
- loss_cls=dict(
- type='CrossEntropyLoss',
- use_sigmoid=False,
- loss_weight=1.0,
- class_weight=[1.0] * 133 + [0.1]),
- loss_mask=dict(
- type='FocalLoss',
- use_sigmoid=True,
- gamma=2.0,
- alpha=0.25,
- loss_weight=20.0),
- loss_dice=dict(
- type='DiceLoss',
- use_sigmoid=True,
- activate=True,
- naive_dice=True,
- loss_weight=1.0),
- train_cfg=None,
- test_cfg=None,
- init_cfg=None,
- **kwargs):
- super(AnchorFreeHead, self).__init__(init_cfg)
- self.num_things_classes = num_things_classes
- self.num_stuff_classes = num_stuff_classes
- self.num_classes = self.num_things_classes + self.num_stuff_classes
- self.num_queries = num_queries
-
- pixel_decoder.update(
- in_channels=in_channels,
- feat_channels=feat_channels,
- out_channels=out_channels)
- self.pixel_decoder = build_plugin_layer(pixel_decoder)[1]
- self.transformer_decoder = build_transformer_layer_sequence(
- transformer_decoder)
- self.decoder_embed_dims = self.transformer_decoder.embed_dims
- pixel_decoder_type = pixel_decoder.get('type')
- if pixel_decoder_type == 'PixelDecoder' and (
- self.decoder_embed_dims != in_channels[-1]
- or enforce_decoder_input_project):
- self.decoder_input_proj = Conv2d(
- in_channels[-1], self.decoder_embed_dims, kernel_size=1)
- else:
- self.decoder_input_proj = nn.Identity()
- self.decoder_pe = build_positional_encoding(positional_encoding)
- self.query_embed = nn.Embedding(self.num_queries, out_channels)
-
- self.cls_embed = nn.Linear(feat_channels, self.num_classes + 1)
- self.mask_embed = nn.Sequential(
- nn.Linear(feat_channels, feat_channels), nn.ReLU(inplace=True),
- nn.Linear(feat_channels, feat_channels), nn.ReLU(inplace=True),
- nn.Linear(feat_channels, out_channels))
-
- self.test_cfg = test_cfg
- self.train_cfg = train_cfg
- if train_cfg:
- self.assigner = build_assigner(train_cfg.get('assigner', None))
- self.sampler = build_sampler(
- train_cfg.get('sampler', None), context=self)
-
- self.class_weight = loss_cls.get('class_weight', None)
- self.loss_cls = build_loss(loss_cls)
- self.loss_mask = build_loss(loss_mask)
- self.loss_dice = build_loss(loss_dice)
-
- def init_weights(self):
- if isinstance(self.decoder_input_proj, Conv2d):
- caffe2_xavier_init(self.decoder_input_proj, bias=0)
-
- self.pixel_decoder.init_weights()
-
- for p in self.transformer_decoder.parameters():
- if p.dim() > 1:
- nn.init.xavier_uniform_(p)
-
- def preprocess_gt(self, gt_labels_list, gt_masks_list, gt_semantic_segs,
- img_metas):
- """Preprocess the ground truth for all images.
-
- Args:
- gt_labels_list (list[Tensor]): Each is ground truth
- labels of each bbox, with shape (num_gts, ).
- gt_masks_list (list[BitmapMasks]): Each is ground truth
- masks of each instances of a image, shape
- (num_gts, h, w).
- gt_semantic_seg (Tensor | None): Ground truth of semantic
- segmentation with the shape (batch_size, n, h, w).
- [0, num_thing_class - 1] means things,
- [num_thing_class, num_class-1] means stuff,
- 255 means VOID. It's None when training instance segmentation.
- img_metas (list[dict]): List of image meta information.
-
- Returns:
- tuple: a tuple containing the following targets.
- - labels (list[Tensor]): Ground truth class indices\
- for all images. Each with shape (n, ), n is the sum of\
- number of stuff type and number of instance in a image.
- - masks (list[Tensor]): Ground truth mask for each\
- image, each with shape (n, h, w).
- """
- num_things_list = [self.num_things_classes] * len(gt_labels_list)
- num_stuff_list = [self.num_stuff_classes] * len(gt_labels_list)
- if gt_semantic_segs is None:
- gt_semantic_segs = [None] * len(gt_labels_list)
-
- targets = multi_apply(preprocess_panoptic_gt, gt_labels_list,
- gt_masks_list, gt_semantic_segs, num_things_list,
- num_stuff_list, img_metas)
- labels, masks = targets
- return labels, masks
-
- def get_targets(self, cls_scores_list, mask_preds_list, gt_labels_list,
- gt_masks_list, img_metas):
- """Compute classification and mask targets for all images for a decoder
- layer.
-
- Args:
- cls_scores_list (list[Tensor]): Mask score logits from a single
- decoder layer for all images. Each with shape (num_queries,
- cls_out_channels).
- mask_preds_list (list[Tensor]): Mask logits from a single decoder
- layer for all images. Each with shape (num_queries, h, w).
- gt_labels_list (list[Tensor]): Ground truth class indices for all
- images. Each with shape (n, ), n is the sum of number of stuff
- type and number of instance in a image.
- gt_masks_list (list[Tensor]): Ground truth mask for each image,
- each with shape (n, h, w).
- img_metas (list[dict]): List of image meta information.
-
- Returns:
- tuple[list[Tensor]]: a tuple containing the following targets.
- - labels_list (list[Tensor]): Labels of all images.\
- Each with shape (num_queries, ).
- - label_weights_list (list[Tensor]): Label weights\
- of all images. Each with shape (num_queries, ).
- - mask_targets_list (list[Tensor]): Mask targets of\
- all images. Each with shape (num_queries, h, w).
- - mask_weights_list (list[Tensor]): Mask weights of\
- all images. Each with shape (num_queries, ).
- - num_total_pos (int): Number of positive samples in\
- all images.
- - num_total_neg (int): Number of negative samples in\
- all images.
- """
- (labels_list, label_weights_list, mask_targets_list, mask_weights_list,
- pos_inds_list,
- neg_inds_list) = multi_apply(self._get_target_single, cls_scores_list,
- mask_preds_list, gt_labels_list,
- gt_masks_list, img_metas)
-
- num_total_pos = sum((inds.numel() for inds in pos_inds_list))
- num_total_neg = sum((inds.numel() for inds in neg_inds_list))
- return (labels_list, label_weights_list, mask_targets_list,
- mask_weights_list, num_total_pos, num_total_neg)
-
- def _get_target_single(self, cls_score, mask_pred, gt_labels, gt_masks,
- img_metas):
- """Compute classification and mask targets for one image.
-
- Args:
- cls_score (Tensor): Mask score logits from a single decoder layer
- for one image. Shape (num_queries, cls_out_channels).
- mask_pred (Tensor): Mask logits for a single decoder layer for one
- image. Shape (num_queries, h, w).
- gt_labels (Tensor): Ground truth class indices for one image with
- shape (n, ). n is the sum of number of stuff type and number
- of instance in a image.
- gt_masks (Tensor): Ground truth mask for each image, each with
- shape (n, h, w).
- img_metas (dict): Image informtation.
-
- Returns:
- tuple[Tensor]: a tuple containing the following for one image.
- - labels (Tensor): Labels of each image.
- shape (num_queries, ).
- - label_weights (Tensor): Label weights of each image.
- shape (num_queries, ).
- - mask_targets (Tensor): Mask targets of each image.
- shape (num_queries, h, w).
- - mask_weights (Tensor): Mask weights of each image.
- shape (num_queries, ).
- - pos_inds (Tensor): Sampled positive indices for each image.
- - neg_inds (Tensor): Sampled negative indices for each image.
- """
- target_shape = mask_pred.shape[-2:]
- if gt_masks.shape[0] > 0:
- gt_masks_downsampled = F.interpolate(
- gt_masks.unsqueeze(1).float(), target_shape,
- mode='nearest').squeeze(1).long()
- else:
- gt_masks_downsampled = gt_masks
-
- # assign and sample
- assign_result = self.assigner.assign(cls_score, mask_pred, gt_labels,
- gt_masks_downsampled, img_metas)
- sampling_result = self.sampler.sample(assign_result, mask_pred,
- gt_masks)
- pos_inds = sampling_result.pos_inds
- neg_inds = sampling_result.neg_inds
-
- # label target
- labels = gt_labels.new_full((self.num_queries, ),
- self.num_classes,
- dtype=torch.long)
- labels[pos_inds] = gt_labels[sampling_result.pos_assigned_gt_inds]
- label_weights = gt_labels.new_ones(self.num_queries)
-
- # mask target
- mask_targets = gt_masks[sampling_result.pos_assigned_gt_inds]
- mask_weights = mask_pred.new_zeros((self.num_queries, ))
- mask_weights[pos_inds] = 1.0
-
- return (labels, label_weights, mask_targets, mask_weights, pos_inds,
- neg_inds)
-
- @force_fp32(apply_to=('all_cls_scores', 'all_mask_preds'))
- def loss(self, all_cls_scores, all_mask_preds, gt_labels_list,
- gt_masks_list, img_metas):
- """Loss function.
-
- Args:
- all_cls_scores (Tensor): Classification scores for all decoder
- layers with shape (num_decoder, batch_size, num_queries,
- cls_out_channels). Note `cls_out_channels` should includes
- background.
- all_mask_preds (Tensor): Mask scores for all decoder layers with
- shape (num_decoder, batch_size, num_queries, h, w).
- gt_labels_list (list[Tensor]): Ground truth class indices for each
- image with shape (n, ). n is the sum of number of stuff type
- and number of instance in a image.
- gt_masks_list (list[Tensor]): Ground truth mask for each image with
- shape (n, h, w).
- img_metas (list[dict]): List of image meta information.
-
- Returns:
- dict[str, Tensor]: A dictionary of loss components.
- """
- num_dec_layers = len(all_cls_scores)
- all_gt_labels_list = [gt_labels_list for _ in range(num_dec_layers)]
- all_gt_masks_list = [gt_masks_list for _ in range(num_dec_layers)]
- img_metas_list = [img_metas for _ in range(num_dec_layers)]
- losses_cls, losses_mask, losses_dice = multi_apply(
- self.loss_single, all_cls_scores, all_mask_preds,
- all_gt_labels_list, all_gt_masks_list, img_metas_list)
-
- loss_dict = dict()
- # loss from the last decoder layer
- loss_dict['loss_cls'] = losses_cls[-1]
- loss_dict['loss_mask'] = losses_mask[-1]
- loss_dict['loss_dice'] = losses_dice[-1]
- # loss from other decoder layers
- num_dec_layer = 0
- for loss_cls_i, loss_mask_i, loss_dice_i in zip(
- losses_cls[:-1], losses_mask[:-1], losses_dice[:-1]):
- loss_dict[f'd{num_dec_layer}.loss_cls'] = loss_cls_i
- loss_dict[f'd{num_dec_layer}.loss_mask'] = loss_mask_i
- loss_dict[f'd{num_dec_layer}.loss_dice'] = loss_dice_i
- num_dec_layer += 1
- return loss_dict
-
- def loss_single(self, cls_scores, mask_preds, gt_labels_list,
- gt_masks_list, img_metas):
- """Loss function for outputs from a single decoder layer.
-
- Args:
- cls_scores (Tensor): Mask score logits from a single decoder layer
- for all images. Shape (batch_size, num_queries,
- cls_out_channels). Note `cls_out_channels` should includes
- background.
- mask_preds (Tensor): Mask logits for a pixel decoder for all
- images. Shape (batch_size, num_queries, h, w).
- gt_labels_list (list[Tensor]): Ground truth class indices for each
- image, each with shape (n, ). n is the sum of number of stuff
- types and number of instances in a image.
- gt_masks_list (list[Tensor]): Ground truth mask for each image,
- each with shape (n, h, w).
- img_metas (list[dict]): List of image meta information.
-
- Returns:
- tuple[Tensor]: Loss components for outputs from a single decoder\
- layer.
- """
- num_imgs = cls_scores.size(0)
- cls_scores_list = [cls_scores[i] for i in range(num_imgs)]
- mask_preds_list = [mask_preds[i] for i in range(num_imgs)]
-
- (labels_list, label_weights_list, mask_targets_list, mask_weights_list,
- num_total_pos,
- num_total_neg) = self.get_targets(cls_scores_list, mask_preds_list,
- gt_labels_list, gt_masks_list,
- img_metas)
- # shape (batch_size, num_queries)
- labels = torch.stack(labels_list, dim=0)
- # shape (batch_size, num_queries)
- label_weights = torch.stack(label_weights_list, dim=0)
- # shape (num_total_gts, h, w)
- mask_targets = torch.cat(mask_targets_list, dim=0)
- # shape (batch_size, num_queries)
- mask_weights = torch.stack(mask_weights_list, dim=0)
-
- # classfication loss
- # shape (batch_size * num_queries, )
- cls_scores = cls_scores.flatten(0, 1)
- labels = labels.flatten(0, 1)
- label_weights = label_weights.flatten(0, 1)
-
- class_weight = cls_scores.new_tensor(self.class_weight)
- loss_cls = self.loss_cls(
- cls_scores,
- labels,
- label_weights,
- avg_factor=class_weight[labels].sum())
-
- num_total_masks = reduce_mean(cls_scores.new_tensor([num_total_pos]))
- num_total_masks = max(num_total_masks, 1)
-
- # extract positive ones
- # shape (batch_size, num_queries, h, w) -> (num_total_gts, h, w)
- mask_preds = mask_preds[mask_weights > 0]
- target_shape = mask_targets.shape[-2:]
-
- if mask_targets.shape[0] == 0:
- # zero match
- loss_dice = mask_preds.sum()
- loss_mask = mask_preds.sum()
- return loss_cls, loss_mask, loss_dice
-
- # upsample to shape of target
- # shape (num_total_gts, h, w)
- mask_preds = F.interpolate(
- mask_preds.unsqueeze(1),
- target_shape,
- mode='bilinear',
- align_corners=False).squeeze(1)
-
- # dice loss
- loss_dice = self.loss_dice(
- mask_preds, mask_targets, avg_factor=num_total_masks)
-
- # mask loss
- # FocalLoss support input of shape (n, num_class)
- h, w = mask_preds.shape[-2:]
- # shape (num_total_gts, h, w) -> (num_total_gts * h * w, 1)
- mask_preds = mask_preds.reshape(-1, 1)
- # shape (num_total_gts, h, w) -> (num_total_gts * h * w)
- mask_targets = mask_targets.reshape(-1)
- # target is (1 - mask_targets) !!!
- loss_mask = self.loss_mask(
- mask_preds, 1 - mask_targets, avg_factor=num_total_masks * h * w)
-
- return loss_cls, loss_mask, loss_dice
-
- def forward(self, feats, img_metas):
- """Forward function.
-
- Args:
- feats (list[Tensor]): Features from the upstream network, each
- is a 4D-tensor.
- img_metas (list[dict]): List of image information.
-
- Returns:
- tuple: a tuple contains two elements.
- - all_cls_scores (Tensor): Classification scores for each\
- scale level. Each is a 4D-tensor with shape\
- (num_decoder, batch_size, num_queries, cls_out_channels).\
- Note `cls_out_channels` should includes background.
- - all_mask_preds (Tensor): Mask scores for each decoder\
- layer. Each with shape (num_decoder, batch_size,\
- num_queries, h, w).
- """
- batch_size = len(img_metas)
- input_img_h, input_img_w = img_metas[0]['batch_input_shape']
- padding_mask = feats[-1].new_ones(
- (batch_size, input_img_h, input_img_w), dtype=torch.float32)
- for i in range(batch_size):
- img_h, img_w, _ = img_metas[i]['img_shape']
- padding_mask[i, :img_h, :img_w] = 0
- padding_mask = F.interpolate(
- padding_mask.unsqueeze(1),
- size=feats[-1].shape[-2:],
- mode='nearest').to(torch.bool).squeeze(1)
- # when backbone is swin, memory is output of last stage of swin.
- # when backbone is r50, memory is output of tranformer encoder.
- mask_features, memory = self.pixel_decoder(feats, img_metas)
- pos_embed = self.decoder_pe(padding_mask)
- memory = self.decoder_input_proj(memory)
- # shape (batch_size, c, h, w) -> (h*w, batch_size, c)
- memory = memory.flatten(2).permute(2, 0, 1)
- pos_embed = pos_embed.flatten(2).permute(2, 0, 1)
- # shape (batch_size, h * w)
- padding_mask = padding_mask.flatten(1)
- # shape = (num_queries, embed_dims)
- query_embed = self.query_embed.weight
- # shape = (num_queries, batch_size, embed_dims)
- query_embed = query_embed.unsqueeze(1).repeat(1, batch_size, 1)
- target = torch.zeros_like(query_embed)
- # shape (num_decoder, num_queries, batch_size, embed_dims)
- out_dec = self.transformer_decoder(
- query=target,
- key=memory,
- value=memory,
- key_pos=pos_embed,
- query_pos=query_embed,
- key_padding_mask=padding_mask)
- # shape (num_decoder, batch_size, num_queries, embed_dims)
- out_dec = out_dec.transpose(1, 2)
-
- # cls_scores
- all_cls_scores = self.cls_embed(out_dec)
-
- # mask_preds
- mask_embed = self.mask_embed(out_dec)
- all_mask_preds = torch.einsum('lbqc,bchw->lbqhw', mask_embed,
- mask_features)
-
- return all_cls_scores, all_mask_preds
-
- def forward_train(self,
- feats,
- img_metas,
- gt_bboxes,
- gt_labels,
- gt_masks,
- gt_semantic_seg,
- gt_bboxes_ignore=None):
- """Forward function for training mode.
-
- Args:
- feats (list[Tensor]): Multi-level features from the upstream
- network, each is a 4D-tensor.
- img_metas (list[Dict]): List of image information.
- gt_bboxes (list[Tensor]): Each element is ground truth bboxes of
- the image, shape (num_gts, 4). Not used here.
- gt_labels (list[Tensor]): Each element is ground truth labels of
- each box, shape (num_gts,).
- gt_masks (list[BitmapMasks]): Each element is masks of instances
- of a image, shape (num_gts, h, w).
- gt_semantic_seg (list[tensor] | None): Each element is the ground
- truth of semantic segmentation with the shape (N, H, W).
- [0, num_thing_class - 1] means things,
- [num_thing_class, num_class-1] means stuff,
- 255 means VOID. It's None when training instance segmentation.
- gt_bboxes_ignore (list[Tensor]): Ground truth bboxes to be
- ignored. Defaults to None.
-
- Returns:
- dict[str, Tensor]: a dictionary of loss components
- """
- # not consider ignoring bboxes
- assert gt_bboxes_ignore is None
-
- # forward
- all_cls_scores, all_mask_preds = self(feats, img_metas)
-
- # preprocess ground truth
- gt_labels, gt_masks = self.preprocess_gt(gt_labels, gt_masks,
- gt_semantic_seg, img_metas)
-
- # loss
- losses = self.loss(all_cls_scores, all_mask_preds, gt_labels, gt_masks,
- img_metas)
-
- return losses
-
- def simple_test(self, feats, img_metas, **kwargs):
- """Test without augmentaton.
-
- Args:
- feats (list[Tensor]): Multi-level features from the
- upstream network, each is a 4D-tensor.
- img_metas (list[dict]): List of image information.
-
- Returns:
- tuple: A tuple contains two tensors.
-
- - mask_cls_results (Tensor): Mask classification logits,\
- shape (batch_size, num_queries, cls_out_channels).
- Note `cls_out_channels` should includes background.
- - mask_pred_results (Tensor): Mask logits, shape \
- (batch_size, num_queries, h, w).
- """
- all_cls_scores, all_mask_preds = self(feats, img_metas)
- mask_cls_results = all_cls_scores[-1]
- mask_pred_results = all_mask_preds[-1]
-
- # upsample masks
- img_shape = img_metas[0]['batch_input_shape']
- mask_pred_results = F.interpolate(
- mask_pred_results,
- size=(img_shape[0], img_shape[1]),
- mode='bilinear',
- align_corners=False)
-
- return mask_cls_results, mask_pred_results
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/necks/__init__.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/necks/__init__.py
deleted file mode 100644
index 6f2fa823fb35fdd90c07065cc93238d08385ce8b..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/necks/__init__.py
+++ /dev/null
@@ -1,23 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .bfp import BFP
-from .channel_mapper import ChannelMapper
-from .ct_resnet_neck import CTResNetNeck
-from .dilated_encoder import DilatedEncoder
-from .dyhead import DyHead
-from .fpg import FPG
-from .fpn import FPN
-from .fpn_carafe import FPN_CARAFE
-from .hrfpn import HRFPN
-from .nas_fpn import NASFPN
-from .nasfcos_fpn import NASFCOS_FPN
-from .pafpn import PAFPN
-from .rfp import RFP
-from .ssd_neck import SSDNeck
-from .yolo_neck import YOLOV3Neck
-from .yolox_pafpn import YOLOXPAFPN
-
-__all__ = [
- 'FPN', 'BFP', 'ChannelMapper', 'HRFPN', 'NASFPN', 'FPN_CARAFE', 'PAFPN',
- 'NASFCOS_FPN', 'RFP', 'YOLOV3Neck', 'FPG', 'DilatedEncoder',
- 'CTResNetNeck', 'SSDNeck', 'YOLOXPAFPN', 'DyHead'
-]
diff --git a/spaces/ronvolutional/sk-node/app/README.md b/spaces/ronvolutional/sk-node/app/README.md
deleted file mode 100644
index 5c91169b0ca6508bb24301c957a9edea5abf2b01..0000000000000000000000000000000000000000
--- a/spaces/ronvolutional/sk-node/app/README.md
+++ /dev/null
@@ -1,38 +0,0 @@
-# create-svelte
-
-Everything you need to build a Svelte project, powered by [`create-svelte`](https://github.com/sveltejs/kit/tree/master/packages/create-svelte).
-
-## Creating a project
-
-If you're seeing this, you've probably already done this step. Congrats!
-
-```bash
-# create a new project in the current directory
-npm create svelte@latest
-
-# create a new project in my-app
-npm create svelte@latest my-app
-```
-
-## Developing
-
-Once you've created a project and installed dependencies with `npm install` (or `pnpm install` or `yarn`), start a development server:
-
-```bash
-npm run dev
-
-# or start the server and open the app in a new browser tab
-npm run dev -- --open
-```
-
-## Building
-
-To create a production version of your app:
-
-```bash
-npm run build
-```
-
-You can preview the production build with `npm run preview`.
-
-> To deploy your app, you may need to install an [adapter](https://kit.svelte.dev/docs/adapters) for your target environment.
diff --git a/spaces/ronvolutional/sk-node/app/src/app.html b/spaces/ronvolutional/sk-node/app/src/app.html
deleted file mode 100644
index b555f2469a3d6e76d93e70e96ed3fbcf9c470480..0000000000000000000000000000000000000000
--- a/spaces/ronvolutional/sk-node/app/src/app.html
+++ /dev/null
@@ -1,12 +0,0 @@
-
-
-
- %sveltekit.body%
-
-
diff --git a/spaces/ruslanmv/Clone-Your-Voice/vocoder/hparams.py b/spaces/ruslanmv/Clone-Your-Voice/vocoder/hparams.py
deleted file mode 100644
index c1de9f7dcc2926735b80a28ed1226ff1b5824753..0000000000000000000000000000000000000000
--- a/spaces/ruslanmv/Clone-Your-Voice/vocoder/hparams.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from synthesizer.hparams import hparams as _syn_hp
-
-
-# Audio settings------------------------------------------------------------------------
-# Match the values of the synthesizer
-sample_rate = _syn_hp.sample_rate
-n_fft = _syn_hp.n_fft
-num_mels = _syn_hp.num_mels
-hop_length = _syn_hp.hop_size
-win_length = _syn_hp.win_size
-fmin = _syn_hp.fmin
-min_level_db = _syn_hp.min_level_db
-ref_level_db = _syn_hp.ref_level_db
-mel_max_abs_value = _syn_hp.max_abs_value
-preemphasis = _syn_hp.preemphasis
-apply_preemphasis = _syn_hp.preemphasize
-
-bits = 9 # bit depth of signal
-mu_law = True # Recommended to suppress noise if using raw bits in hp.voc_mode
- # below
-
-
-# WAVERNN / VOCODER --------------------------------------------------------------------------------
-voc_mode = 'RAW' # either 'RAW' (softmax on raw bits) or 'MOL' (sample from
-# mixture of logistics)
-voc_upsample_factors = (5, 5, 8) # NB - this needs to correctly factorise hop_length
-voc_rnn_dims = 512
-voc_fc_dims = 512
-voc_compute_dims = 128
-voc_res_out_dims = 128
-voc_res_blocks = 10
-
-# Training
-voc_batch_size = 100
-voc_lr = 1e-4
-voc_gen_at_checkpoint = 5 # number of samples to generate at each checkpoint
-voc_pad = 2 # this will pad the input so that the resnet can 'see' wider
- # than input length
-voc_seq_len = hop_length * 5 # must be a multiple of hop_length
-
-# Generating / Synthesizing
-voc_gen_batched = True # very fast (realtime+) single utterance batched generation
-voc_target = 8000 # target number of samples to be generated in each batch entry
-voc_overlap = 400 # number of samples for crossfading between batches
diff --git a/spaces/sahshd/ChuanhuChatGPT/Dockerfile b/spaces/sahshd/ChuanhuChatGPT/Dockerfile
deleted file mode 100644
index 335c2dba28ba8c365de9306858462a59dea25f28..0000000000000000000000000000000000000000
--- a/spaces/sahshd/ChuanhuChatGPT/Dockerfile
+++ /dev/null
@@ -1,15 +0,0 @@
-FROM python:3.9 as builder
-RUN apt-get update && apt-get install -y build-essential
-COPY requirements.txt .
-COPY requirements_advanced.txt .
-RUN pip install --user -r requirements.txt
-# RUN pip install --user -r requirements_advanced.txt
-
-FROM python:3.9
-MAINTAINER iskoldt
-COPY --from=builder /root/.local /root/.local
-ENV PATH=/root/.local/bin:$PATH
-COPY . /app
-WORKDIR /app
-ENV dockerrun yes
-CMD ["python3", "-u", "ChuanhuChatbot.py", "2>&1", "|", "tee", "/var/log/application.log"]
diff --git a/spaces/samarthagarwal23/QuestionAnswering_on_annual_reports/app.py b/spaces/samarthagarwal23/QuestionAnswering_on_annual_reports/app.py
deleted file mode 100644
index 7163191e31b990134dcf6c397ddb71a88d30cdfc..0000000000000000000000000000000000000000
--- a/spaces/samarthagarwal23/QuestionAnswering_on_annual_reports/app.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import gradio as gr
-import os
-import numpy as np
-os.system("pip install pdfminer.six rank_bm25 torch transformers")
-
-from gradio.mix import Series
-#import re
-from rank_bm25 import BM25Okapi
-import string
-import torch
-from transformers import pipeline
-import pdfminer
-from pdfminer.high_level import extract_text
-
-len_doc = 500
-overlap = 15
-param_top_k_retriver = 15
-param_top_k_ranker = 3
-
-def read_pdf(file):
- text = extract_text(file.name)
- # Split text into smaller docs
- docs = []
-
- i = 0
- while i < len(text):
- docs.append(text[i:i+len_doc])
- i = i + len_doc - overlap
- return docs
-
- # We use BM25 as retriver which will do 1st round of candidate filtering based on word based matching
-
-def bm25_tokenizer(text):
- stop_w = ['a', 'the', 'am', 'is' , 'are', 'who', 'how', 'where', 'when', 'why', 'what']
- tokenized_doc = []
- for token in text.lower().split():
- token = token.strip(string.punctuation)
-
- if len(token) > 0 and token not in stop_w:
- tokenized_doc.append(token)
- return tokenized_doc
-
-def retrieval(query, top_k_retriver, docs, bm25_):
-
- bm25_scores = bm25_.get_scores(bm25_tokenizer(query))
- top_n = np.argsort(bm25_scores)[::-1][:top_k_retriver]
- bm25_hits = [{'corpus_id': idx,
- 'score': bm25_scores[idx],
- 'docs':docs[idx]} for idx in top_n if bm25_scores[idx] > 0]
- bm25_hits = sorted(bm25_hits, key=lambda x: x['score'], reverse=True)
-
- return bm25_hits
-
-def qa_ranker(query, docs_, top_k_ranker, qa_model):
- ans = []
- for doc in docs_:
- answer = qa_model(question = query,
- context = doc)
- answer['doc'] = doc
- ans.append(answer)
- return sorted(ans, key=lambda x: x['score'], reverse=True)[:top_k_ranker]
-
-def cstr(s, color='black'):
- return "{} ".format(color, s)
-def cstr_bold(s, color='black'):
- return "{} 21 jump street 720p yify 208 Download ☆☆☆☆☆ https://gohhs.com/2uEAtr
diff --git a/spaces/scedlatioru/img-to-music/example/IntroduccionALaPsicologiaRobertFeldmanPdf.md b/spaces/scedlatioru/img-to-music/example/IntroduccionALaPsicologiaRobertFeldmanPdf.md
deleted file mode 100644
index 3c2fc7b4b7b1333b6e926a10848140744793729f..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/IntroduccionALaPsicologiaRobertFeldmanPdf.md
+++ /dev/null
@@ -1,6 +0,0 @@
-IntroduccionALaPsicologiaRobertFeldmanPdf Download Zip 🆗 https://gohhs.com/2uEABa
diff --git a/spaces/scedlatioru/img-to-music/example/Microsoft Flight Simulator X CPY Crack High Quality Torrent Free Download [2020].md b/spaces/scedlatioru/img-to-music/example/Microsoft Flight Simulator X CPY Crack High Quality Torrent Free Download [2020].md
deleted file mode 100644
index ae180abcd2452bdcf410e74231dd882097790dad..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Microsoft Flight Simulator X CPY Crack High Quality Torrent Free Download [2020].md
+++ /dev/null
@@ -1,178 +0,0 @@
-
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diff --git a/spaces/scedlatioru/img-to-music/example/Zooskool Carmen Nubian Petlove.md b/spaces/scedlatioru/img-to-music/example/Zooskool Carmen Nubian Petlove.md
deleted file mode 100644
index 573e421bc432f55cbfde3890f8c4b92b0507b9cf..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Zooskool Carmen Nubian Petlove.md
+++ /dev/null
@@ -1,28 +0,0 @@
-Zooskool Carmen Nubian Petlove Download ✦ https://gohhs.com/2uEzoQ
diff --git a/spaces/sdeeas/ChuanhuChatGPT/modules/base_model.py b/spaces/sdeeas/ChuanhuChatGPT/modules/base_model.py
deleted file mode 100644
index 2b55623f6b0989f60d818be6e0e77f5948484b82..0000000000000000000000000000000000000000
--- a/spaces/sdeeas/ChuanhuChatGPT/modules/base_model.py
+++ /dev/null
@@ -1,561 +0,0 @@
-from __future__ import annotations
-from typing import TYPE_CHECKING, List
-
-import logging
-import json
-import commentjson as cjson
-import os
-import sys
-import requests
-import urllib3
-import traceback
-
-from tqdm import tqdm
-import colorama
-from duckduckgo_search import ddg
-import asyncio
-import aiohttp
-from enum import Enum
-
-from .presets import *
-from .llama_func import *
-from .utils import *
-from . import shared
-from .config import retrieve_proxy
-
-
-class ModelType(Enum):
- Unknown = -1
- OpenAI = 0
- ChatGLM = 1
- LLaMA = 2
- XMChat = 3
-
- @classmethod
- def get_type(cls, model_name: str):
- model_type = None
- model_name_lower = model_name.lower()
- if "gpt" in model_name_lower:
- model_type = ModelType.OpenAI
- elif "chatglm" in model_name_lower:
- model_type = ModelType.ChatGLM
- elif "llama" in model_name_lower or "alpaca" in model_name_lower:
- model_type = ModelType.LLaMA
- elif "xmchat" in model_name_lower:
- model_type = ModelType.XMChat
- else:
- model_type = ModelType.Unknown
- return model_type
-
-
-class BaseLLMModel:
- def __init__(
- self,
- model_name,
- system_prompt="",
- temperature=1.0,
- top_p=1.0,
- n_choices=1,
- stop=None,
- max_generation_token=None,
- presence_penalty=0,
- frequency_penalty=0,
- logit_bias=None,
- user="",
- ) -> None:
- self.history = []
- self.all_token_counts = []
- self.model_name = model_name
- self.model_type = ModelType.get_type(model_name)
- try:
- self.token_upper_limit = MODEL_TOKEN_LIMIT[model_name]
- except KeyError:
- self.token_upper_limit = DEFAULT_TOKEN_LIMIT
- self.interrupted = False
- self.system_prompt = system_prompt
- self.api_key = None
- self.need_api_key = False
- self.single_turn = False
-
- self.temperature = temperature
- self.top_p = top_p
- self.n_choices = n_choices
- self.stop_sequence = stop
- self.max_generation_token = None
- self.presence_penalty = presence_penalty
- self.frequency_penalty = frequency_penalty
- self.logit_bias = logit_bias
- self.user_identifier = user
-
- def get_answer_stream_iter(self):
- """stream predict, need to be implemented
- conversations are stored in self.history, with the most recent question, in OpenAI format
- should return a generator, each time give the next word (str) in the answer
- """
- logging.warning("stream predict not implemented, using at once predict instead")
- response, _ = self.get_answer_at_once()
- yield response
-
- def get_answer_at_once(self):
- """predict at once, need to be implemented
- conversations are stored in self.history, with the most recent question, in OpenAI format
- Should return:
- the answer (str)
- total token count (int)
- """
- logging.warning("at once predict not implemented, using stream predict instead")
- response_iter = self.get_answer_stream_iter()
- count = 0
- for response in response_iter:
- count += 1
- return response, sum(self.all_token_counts) + count
-
- def billing_info(self):
- """get billing infomation, inplement if needed"""
- logging.warning("billing info not implemented, using default")
- return BILLING_NOT_APPLICABLE_MSG
-
- def count_token(self, user_input):
- """get token count from input, implement if needed"""
- logging.warning("token count not implemented, using default")
- return len(user_input)
-
- def stream_next_chatbot(self, inputs, chatbot, fake_input=None, display_append=""):
- def get_return_value():
- return chatbot, status_text
-
- status_text = i18n("开始实时传输回答……")
- if fake_input:
- chatbot.append((fake_input, ""))
- else:
- chatbot.append((inputs, ""))
-
- user_token_count = self.count_token(inputs)
- self.all_token_counts.append(user_token_count)
- logging.debug(f"输入token计数: {user_token_count}")
-
- stream_iter = self.get_answer_stream_iter()
-
- for partial_text in stream_iter:
- chatbot[-1] = (chatbot[-1][0], partial_text + display_append)
- self.all_token_counts[-1] += 1
- status_text = self.token_message()
- yield get_return_value()
- if self.interrupted:
- self.recover()
- break
- self.history.append(construct_assistant(partial_text))
-
- def next_chatbot_at_once(self, inputs, chatbot, fake_input=None, display_append=""):
- if fake_input:
- chatbot.append((fake_input, ""))
- else:
- chatbot.append((inputs, ""))
- if fake_input is not None:
- user_token_count = self.count_token(fake_input)
- else:
- user_token_count = self.count_token(inputs)
- self.all_token_counts.append(user_token_count)
- ai_reply, total_token_count = self.get_answer_at_once()
- self.history.append(construct_assistant(ai_reply))
- if fake_input is not None:
- self.history[-2] = construct_user(fake_input)
- chatbot[-1] = (chatbot[-1][0], ai_reply + display_append)
- if fake_input is not None:
- self.all_token_counts[-1] += count_token(construct_assistant(ai_reply))
- else:
- self.all_token_counts[-1] = total_token_count - sum(self.all_token_counts)
- status_text = self.token_message()
- return chatbot, status_text
-
- def handle_file_upload(self, files, chatbot):
- """if the model accepts multi modal input, implement this function"""
- status = gr.Markdown.update()
- if files:
- construct_index(self.api_key, file_src=files)
- status = "索引构建完成"
- return gr.Files.update(), chatbot, status
-
- def prepare_inputs(self, real_inputs, use_websearch, files, reply_language, chatbot):
- fake_inputs = None
- display_append = []
- limited_context = False
- fake_inputs = real_inputs
- if files:
- from llama_index.indices.vector_store.base_query import GPTVectorStoreIndexQuery
- from llama_index.indices.query.schema import QueryBundle
- from langchain.embeddings.huggingface import HuggingFaceEmbeddings
- from langchain.chat_models import ChatOpenAI
- from llama_index import (
- GPTSimpleVectorIndex,
- ServiceContext,
- LangchainEmbedding,
- OpenAIEmbedding,
- )
- limited_context = True
- msg = "加载索引中……"
- logging.info(msg)
- # yield chatbot + [(inputs, "")], msg
- index = construct_index(self.api_key, file_src=files)
- assert index is not None, "获取索引失败"
- msg = "索引获取成功，生成回答中……"
- logging.info(msg)
- if local_embedding or self.model_type != ModelType.OpenAI:
- embed_model = LangchainEmbedding(HuggingFaceEmbeddings(model_name = "sentence-transformers/distiluse-base-multilingual-cased-v2"))
- else:
- embed_model = OpenAIEmbedding()
- # yield chatbot + [(inputs, "")], msg
- with retrieve_proxy():
- prompt_helper = PromptHelper(
- max_input_size=4096,
- num_output=5,
- max_chunk_overlap=20,
- chunk_size_limit=600,
- )
- from llama_index import ServiceContext
-
- service_context = ServiceContext.from_defaults(
- prompt_helper=prompt_helper, embed_model=embed_model
- )
- query_object = GPTVectorStoreIndexQuery(
- index.index_struct,
- service_context=service_context,
- similarity_top_k=5,
- vector_store=index._vector_store,
- docstore=index._docstore,
- )
- query_bundle = QueryBundle(real_inputs)
- nodes = query_object.retrieve(query_bundle)
- reference_results = [n.node.text for n in nodes]
- reference_results = add_source_numbers(reference_results, use_source=False)
- display_append = add_details(reference_results)
- display_append = "\n\n" + "".join(display_append)
- real_inputs = (
- replace_today(PROMPT_TEMPLATE)
- .replace("{query_str}", real_inputs)
- .replace("{context_str}", "\n\n".join(reference_results))
- .replace("{reply_language}", reply_language)
- )
- elif use_websearch:
- limited_context = True
- search_results = ddg(real_inputs, max_results=5)
- reference_results = []
- for idx, result in enumerate(search_results):
- logging.debug(f"搜索结果{idx + 1}：{result}")
- domain_name = urllib3.util.parse_url(result["href"]).host
- reference_results.append([result["body"], result["href"]])
- display_append.append(
- # f"{idx+1}. [{domain_name}]({result['href']})\n"
- f"{domain_name} \n\n" + "".join(display_append) + " "
- real_inputs = (
- replace_today(WEBSEARCH_PTOMPT_TEMPLATE)
- .replace("{query}", real_inputs)
- .replace("{web_results}", "\n\n".join(reference_results))
- .replace("{reply_language}", reply_language)
- )
- else:
- display_append = ""
- return limited_context, fake_inputs, display_append, real_inputs, chatbot
-
- def predict(
- self,
- inputs,
- chatbot,
- stream=False,
- use_websearch=False,
- files=None,
- reply_language="中文",
- should_check_token_count=True,
- ): # repetition_penalty, top_k
-
- status_text = "开始生成回答……"
- logging.info(
- "输入为：" + colorama.Fore.BLUE + f"{inputs}" + colorama.Style.RESET_ALL
- )
- if should_check_token_count:
- yield chatbot + [(inputs, "")], status_text
- if reply_language == "跟随问题语言（不稳定）":
- reply_language = "the same language as the question, such as English, 中文, 日本語, Español, Français, or Deutsch."
-
- limited_context, fake_inputs, display_append, inputs, chatbot = self.prepare_inputs(real_inputs=inputs, use_websearch=use_websearch, files=files, reply_language=reply_language, chatbot=chatbot)
- yield chatbot + [(fake_inputs, "")], status_text
-
- if (
- self.need_api_key and
- self.api_key is None
- and not shared.state.multi_api_key
- ):
- status_text = STANDARD_ERROR_MSG + NO_APIKEY_MSG
- logging.info(status_text)
- chatbot.append((inputs, ""))
- if len(self.history) == 0:
- self.history.append(construct_user(inputs))
- self.history.append("")
- self.all_token_counts.append(0)
- else:
- self.history[-2] = construct_user(inputs)
- yield chatbot + [(inputs, "")], status_text
- return
- elif len(inputs.strip()) == 0:
- status_text = STANDARD_ERROR_MSG + NO_INPUT_MSG
- logging.info(status_text)
- yield chatbot + [(inputs, "")], status_text
- return
-
- if self.single_turn:
- self.history = []
- self.all_token_counts = []
- self.history.append(construct_user(inputs))
-
- try:
- if stream:
- logging.debug("使用流式传输")
- iter = self.stream_next_chatbot(
- inputs,
- chatbot,
- fake_input=fake_inputs,
- display_append=display_append,
- )
- for chatbot, status_text in iter:
- yield chatbot, status_text
- else:
- logging.debug("不使用流式传输")
- chatbot, status_text = self.next_chatbot_at_once(
- inputs,
- chatbot,
- fake_input=fake_inputs,
- display_append=display_append,
- )
- yield chatbot, status_text
- except Exception as e:
- traceback.print_exc()
- status_text = STANDARD_ERROR_MSG + str(e)
- yield chatbot, status_text
-
- if len(self.history) > 1 and self.history[-1]["content"] != inputs:
- logging.info(
- "回答为："
- + colorama.Fore.BLUE
- + f"{self.history[-1]['content']}"
- + colorama.Style.RESET_ALL
- )
-
- if limited_context:
- # self.history = self.history[-4:]
- # self.all_token_counts = self.all_token_counts[-2:]
- self.history = []
- self.all_token_counts = []
-
- max_token = self.token_upper_limit - TOKEN_OFFSET
-
- if sum(self.all_token_counts) > max_token and should_check_token_count:
- count = 0
- while (
- sum(self.all_token_counts)
- > self.token_upper_limit * REDUCE_TOKEN_FACTOR
- and sum(self.all_token_counts) > 0
- ):
- count += 1
- del self.all_token_counts[0]
- del self.history[:2]
- logging.info(status_text)
- status_text = f"为了防止token超限，模型忘记了早期的 {count} 轮对话"
- yield chatbot, status_text
-
- def retry(
- self,
- chatbot,
- stream=False,
- use_websearch=False,
- files=None,
- reply_language="中文",
- ):
- logging.debug("重试中……")
- if len(self.history) > 0:
- inputs = self.history[-2]["content"]
- del self.history[-2:]
- self.all_token_counts.pop()
- elif len(chatbot) > 0:
- inputs = chatbot[-1][0]
- else:
- yield chatbot, f"{STANDARD_ERROR_MSG}上下文是空的"
- return
-
- iter = self.predict(
- inputs,
- chatbot,
- stream=stream,
- use_websearch=use_websearch,
- files=files,
- reply_language=reply_language,
- )
- for x in iter:
- yield x
- logging.debug("重试完毕")
-
- # def reduce_token_size(self, chatbot):
- # logging.info("开始减少token数量……")
- # chatbot, status_text = self.next_chatbot_at_once(
- # summarize_prompt,
- # chatbot
- # )
- # max_token_count = self.token_upper_limit * REDUCE_TOKEN_FACTOR
- # num_chat = find_n(self.all_token_counts, max_token_count)
- # logging.info(f"previous_token_count: {self.all_token_counts}, keeping {num_chat} chats")
- # chatbot = chatbot[:-1]
- # self.history = self.history[-2*num_chat:] if num_chat > 0 else []
- # self.all_token_counts = self.all_token_counts[-num_chat:] if num_chat > 0 else []
- # msg = f"保留了最近{num_chat}轮对话"
- # logging.info(msg)
- # logging.info("减少token数量完毕")
- # return chatbot, msg + "，" + self.token_message(self.all_token_counts if len(self.all_token_counts) > 0 else [0])
-
- def interrupt(self):
- self.interrupted = True
-
- def recover(self):
- self.interrupted = False
-
- def set_token_upper_limit(self, new_upper_limit):
- self.token_upper_limit = new_upper_limit
- print(f"token上限设置为{new_upper_limit}")
-
- def set_temperature(self, new_temperature):
- self.temperature = new_temperature
-
- def set_top_p(self, new_top_p):
- self.top_p = new_top_p
-
- def set_n_choices(self, new_n_choices):
- self.n_choices = new_n_choices
-
- def set_stop_sequence(self, new_stop_sequence: str):
- new_stop_sequence = new_stop_sequence.split(",")
- self.stop_sequence = new_stop_sequence
-
- def set_max_tokens(self, new_max_tokens):
- self.max_generation_token = new_max_tokens
-
- def set_presence_penalty(self, new_presence_penalty):
- self.presence_penalty = new_presence_penalty
-
- def set_frequency_penalty(self, new_frequency_penalty):
- self.frequency_penalty = new_frequency_penalty
-
- def set_logit_bias(self, logit_bias):
- logit_bias = logit_bias.split()
- bias_map = {}
- encoding = tiktoken.get_encoding("cl100k_base")
- for line in logit_bias:
- word, bias_amount = line.split(":")
- if word:
- for token in encoding.encode(word):
- bias_map[token] = float(bias_amount)
- self.logit_bias = bias_map
-
- def set_user_identifier(self, new_user_identifier):
- self.user_identifier = new_user_identifier
-
- def set_system_prompt(self, new_system_prompt):
- self.system_prompt = new_system_prompt
-
- def set_key(self, new_access_key):
- self.api_key = new_access_key.strip()
- msg = i18n("API密钥更改为了") + hide_middle_chars(self.api_key)
- logging.info(msg)
- return self.api_key, msg
-
- def set_single_turn(self, new_single_turn):
- self.single_turn = new_single_turn
-
- def reset(self):
- self.history = []
- self.all_token_counts = []
- self.interrupted = False
- return [], self.token_message([0])
-
- def delete_first_conversation(self):
- if self.history:
- del self.history[:2]
- del self.all_token_counts[0]
- return self.token_message()
-
- def delete_last_conversation(self, chatbot):
- if len(chatbot) > 0 and STANDARD_ERROR_MSG in chatbot[-1][1]:
- msg = "由于包含报错信息，只删除chatbot记录"
- chatbot.pop()
- return chatbot, self.history
- if len(self.history) > 0:
- self.history.pop()
- self.history.pop()
- if len(chatbot) > 0:
- msg = "删除了一组chatbot对话"
- chatbot.pop()
- if len(self.all_token_counts) > 0:
- msg = "删除了一组对话的token计数记录"
- self.all_token_counts.pop()
- msg = "删除了一组对话"
- return chatbot, msg
-
- def token_message(self, token_lst=None):
- if token_lst is None:
- token_lst = self.all_token_counts
- token_sum = 0
- for i in range(len(token_lst)):
- token_sum += sum(token_lst[: i + 1])
- return i18n("Token 计数: ") + f"{sum(token_lst)}" + i18n("，本次对话累计消耗了 ") + f"{token_sum} tokens"
-
- def save_chat_history(self, filename, chatbot, user_name):
- if filename == "":
- return
- if not filename.endswith(".json"):
- filename += ".json"
- return save_file(filename, self.system_prompt, self.history, chatbot, user_name)
-
- def export_markdown(self, filename, chatbot, user_name):
- if filename == "":
- return
- if not filename.endswith(".md"):
- filename += ".md"
- return save_file(filename, self.system_prompt, self.history, chatbot, user_name)
-
- def load_chat_history(self, filename, chatbot, user_name):
- logging.debug(f"{user_name} 加载对话历史中……")
- if type(filename) != str:
- filename = filename.name
- try:
- with open(os.path.join(HISTORY_DIR, user_name, filename), "r") as f:
- json_s = json.load(f)
- try:
- if type(json_s["history"][0]) == str:
- logging.info("历史记录格式为旧版，正在转换……")
- new_history = []
- for index, item in enumerate(json_s["history"]):
- if index % 2 == 0:
- new_history.append(construct_user(item))
- else:
- new_history.append(construct_assistant(item))
- json_s["history"] = new_history
- logging.info(new_history)
- except:
- # 没有对话历史
- pass
- logging.debug(f"{user_name} 加载对话历史完毕")
- self.history = json_s["history"]
- return filename, json_s["system"], json_s["chatbot"]
- except FileNotFoundError:
- logging.warning(f"{user_name} 没有找到对话历史文件，不执行任何操作")
- return filename, self.system_prompt, chatbot
-
- def like(self):
- """like the last response, implement if needed
- """
- return gr.update()
-
- def dislike(self):
- """dislike the last response, implement if needed
- """
- return gr.update()
diff --git a/spaces/segments-tobias/conex/espnet/asr/__init__.py b/spaces/segments-tobias/conex/espnet/asr/__init__.py
deleted file mode 100644
index b7f177368e62a5578b8706300e101f831a3972ac..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet/asr/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Initialize sub package."""
diff --git a/spaces/shgao/MDT/diffusion/timestep_sampler.py b/spaces/shgao/MDT/diffusion/timestep_sampler.py
deleted file mode 100644
index a3f369847677d8dbaaadb8297691b1be92cf189f..0000000000000000000000000000000000000000
--- a/spaces/shgao/MDT/diffusion/timestep_sampler.py
+++ /dev/null
@@ -1,150 +0,0 @@
-# Modified from OpenAI's diffusion repos
-# GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py
-# ADM: https://github.com/openai/guided-diffusion/blob/main/guided_diffusion
-# IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
-
-from abc import ABC, abstractmethod
-
-import numpy as np
-import torch as th
-import torch.distributed as dist
-
-
-def create_named_schedule_sampler(name, diffusion):
- """
- Create a ScheduleSampler from a library of pre-defined samplers.
- :param name: the name of the sampler.
- :param diffusion: the diffusion object to sample for.
- """
- if name == "uniform":
- return UniformSampler(diffusion)
- elif name == "loss-second-moment":
- return LossSecondMomentResampler(diffusion)
- else:
- raise NotImplementedError(f"unknown schedule sampler: {name}")
-
-
-class ScheduleSampler(ABC):
- """
- A distribution over timesteps in the diffusion process, intended to reduce
- variance of the objective.
- By default, samplers perform unbiased importance sampling, in which the
- objective's mean is unchanged.
- However, subclasses may override sample() to change how the resampled
- terms are reweighted, allowing for actual changes in the objective.
- """
-
- @abstractmethod
- def weights(self):
- """
- Get a numpy array of weights, one per diffusion step.
- The weights needn't be normalized, but must be positive.
- """
-
- def sample(self, batch_size, device):
- """
- Importance-sample timesteps for a batch.
- :param batch_size: the number of timesteps.
- :param device: the torch device to save to.
- :return: a tuple (timesteps, weights):
- - timesteps: a tensor of timestep indices.
- - weights: a tensor of weights to scale the resulting losses.
- """
- w = self.weights()
- p = w / np.sum(w)
- indices_np = np.random.choice(len(p), size=(batch_size,), p=p)
- indices = th.from_numpy(indices_np).long().to(device)
- weights_np = 1 / (len(p) * p[indices_np])
- weights = th.from_numpy(weights_np).float().to(device)
- return indices, weights
-
-
-class UniformSampler(ScheduleSampler):
- def __init__(self, diffusion):
- self.diffusion = diffusion
- self._weights = np.ones([diffusion.num_timesteps])
-
- def weights(self):
- return self._weights
-
-
-class LossAwareSampler(ScheduleSampler):
- def update_with_local_losses(self, local_ts, local_losses):
- """
- Update the reweighting using losses from a model.
- Call this method from each rank with a batch of timesteps and the
- corresponding losses for each of those timesteps.
- This method will perform synchronization to make sure all of the ranks
- maintain the exact same reweighting.
- :param local_ts: an integer Tensor of timesteps.
- :param local_losses: a 1D Tensor of losses.
- """
- batch_sizes = [
- th.tensor([0], dtype=th.int32, device=local_ts.device)
- for _ in range(dist.get_world_size())
- ]
- dist.all_gather(
- batch_sizes,
- th.tensor([len(local_ts)], dtype=th.int32, device=local_ts.device),
- )
-
- # Pad all_gather batches to be the maximum batch size.
- batch_sizes = [x.item() for x in batch_sizes]
- max_bs = max(batch_sizes)
-
- timestep_batches = [th.zeros(max_bs).to(local_ts) for bs in batch_sizes]
- loss_batches = [th.zeros(max_bs).to(local_losses) for bs in batch_sizes]
- dist.all_gather(timestep_batches, local_ts)
- dist.all_gather(loss_batches, local_losses)
- timesteps = [
- x.item() for y, bs in zip(timestep_batches, batch_sizes) for x in y[:bs]
- ]
- losses = [x.item() for y, bs in zip(loss_batches, batch_sizes) for x in y[:bs]]
- self.update_with_all_losses(timesteps, losses)
-
- @abstractmethod
- def update_with_all_losses(self, ts, losses):
- """
- Update the reweighting using losses from a model.
- Sub-classes should override this method to update the reweighting
- using losses from the model.
- This method directly updates the reweighting without synchronizing
- between workers. It is called by update_with_local_losses from all
- ranks with identical arguments. Thus, it should have deterministic
- behavior to maintain state across workers.
- :param ts: a list of int timesteps.
- :param losses: a list of float losses, one per timestep.
- """
-
-
-class LossSecondMomentResampler(LossAwareSampler):
- def __init__(self, diffusion, history_per_term=10, uniform_prob=0.001):
- self.diffusion = diffusion
- self.history_per_term = history_per_term
- self.uniform_prob = uniform_prob
- self._loss_history = np.zeros(
- [diffusion.num_timesteps, history_per_term], dtype=np.float64
- )
- self._loss_counts = np.zeros([diffusion.num_timesteps], dtype=np.int)
-
- def weights(self):
- if not self._warmed_up():
- return np.ones([self.diffusion.num_timesteps], dtype=np.float64)
- weights = np.sqrt(np.mean(self._loss_history ** 2, axis=-1))
- weights /= np.sum(weights)
- weights *= 1 - self.uniform_prob
- weights += self.uniform_prob / len(weights)
- return weights
-
- def update_with_all_losses(self, ts, losses):
- for t, loss in zip(ts, losses):
- if self._loss_counts[t] == self.history_per_term:
- # Shift out the oldest loss term.
- self._loss_history[t, :-1] = self._loss_history[t, 1:]
- self._loss_history[t, -1] = loss
- else:
- self._loss_history[t, self._loss_counts[t]] = loss
- self._loss_counts[t] += 1
-
- def _warmed_up(self):
- return (self._loss_counts == self.history_per_term).all()
diff --git a/spaces/shi-labs/FcF-Inpainting/training/losses/ade20k/segm_lib/utils/data/dataloader.py b/spaces/shi-labs/FcF-Inpainting/training/losses/ade20k/segm_lib/utils/data/dataloader.py
deleted file mode 100644
index 039b9ec3645b2a4626ff47c221e372f32a6ad339..0000000000000000000000000000000000000000
--- a/spaces/shi-labs/FcF-Inpainting/training/losses/ade20k/segm_lib/utils/data/dataloader.py
+++ /dev/null
@@ -1,425 +0,0 @@
-import torch
-import torch.multiprocessing as multiprocessing
-from torch._C import _set_worker_signal_handlers, \
- _remove_worker_pids, _error_if_any_worker_fails
-try:
- from torch._C import _set_worker_pids
-except:
- from torch._C import _update_worker_pids as _set_worker_pids
-from .sampler import SequentialSampler, RandomSampler, BatchSampler
-import signal
-import collections
-import re
-import sys
-import threading
-import traceback
-from torch._six import string_classes, int_classes
-import numpy as np
-
-if sys.version_info[0] == 2:
- import Queue as queue
-else:
- import queue
-
-
-class ExceptionWrapper(object):
- r"Wraps an exception plus traceback to communicate across threads"
-
- def __init__(self, exc_info):
- self.exc_type = exc_info[0]
- self.exc_msg = "".join(traceback.format_exception(*exc_info))
-
-
-_use_shared_memory = False
-"""Whether to use shared memory in default_collate"""
-
-
-def _worker_loop(dataset, index_queue, data_queue, collate_fn, seed, init_fn, worker_id):
- global _use_shared_memory
- _use_shared_memory = True
-
- # Intialize C side signal handlers for SIGBUS and SIGSEGV. Python signal
- # module's handlers are executed after Python returns from C low-level
- # handlers, likely when the same fatal signal happened again already.
- # https://docs.python.org/3/library/signal.html Sec. 18.8.1.1
- _set_worker_signal_handlers()
-
- torch.set_num_threads(1)
- torch.manual_seed(seed)
- np.random.seed(seed)
-
- if init_fn is not None:
- init_fn(worker_id)
-
- while True:
- r = index_queue.get()
- if r is None:
- break
- idx, batch_indices = r
- try:
- samples = collate_fn([dataset[i] for i in batch_indices])
- except Exception:
- data_queue.put((idx, ExceptionWrapper(sys.exc_info())))
- else:
- data_queue.put((idx, samples))
-
-
-def _worker_manager_loop(in_queue, out_queue, done_event, pin_memory, device_id):
- if pin_memory:
- torch.cuda.set_device(device_id)
-
- while True:
- try:
- r = in_queue.get()
- except Exception:
- if done_event.is_set():
- return
- raise
- if r is None:
- break
- if isinstance(r[1], ExceptionWrapper):
- out_queue.put(r)
- continue
- idx, batch = r
- try:
- if pin_memory:
- batch = pin_memory_batch(batch)
- except Exception:
- out_queue.put((idx, ExceptionWrapper(sys.exc_info())))
- else:
- out_queue.put((idx, batch))
-
-numpy_type_map = {
- 'float64': torch.DoubleTensor,
- 'float32': torch.FloatTensor,
- 'float16': torch.HalfTensor,
- 'int64': torch.LongTensor,
- 'int32': torch.IntTensor,
- 'int16': torch.ShortTensor,
- 'int8': torch.CharTensor,
- 'uint8': torch.ByteTensor,
-}
-
-
-def default_collate(batch):
- "Puts each data field into a tensor with outer dimension batch size"
-
- error_msg = "batch must contain tensors, numbers, dicts or lists; found {}"
- elem_type = type(batch[0])
- if torch.is_tensor(batch[0]):
- out = None
- if _use_shared_memory:
- # If we're in a background process, concatenate directly into a
- # shared memory tensor to avoid an extra copy
- numel = sum([x.numel() for x in batch])
- storage = batch[0].storage()._new_shared(numel)
- out = batch[0].new(storage)
- return torch.stack(batch, 0, out=out)
- elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
- and elem_type.__name__ != 'string_':
- elem = batch[0]
- if elem_type.__name__ == 'ndarray':
- # array of string classes and object
- if re.search('[SaUO]', elem.dtype.str) is not None:
- raise TypeError(error_msg.format(elem.dtype))
-
- return torch.stack([torch.from_numpy(b) for b in batch], 0)
- if elem.shape == (): # scalars
- py_type = float if elem.dtype.name.startswith('float') else int
- return numpy_type_map[elem.dtype.name](list(map(py_type, batch)))
- elif isinstance(batch[0], int_classes):
- return torch.LongTensor(batch)
- elif isinstance(batch[0], float):
- return torch.DoubleTensor(batch)
- elif isinstance(batch[0], string_classes):
- return batch
- elif isinstance(batch[0], collections.Mapping):
- return {key: default_collate([d[key] for d in batch]) for key in batch[0]}
- elif isinstance(batch[0], collections.Sequence):
- transposed = zip(*batch)
- return [default_collate(samples) for samples in transposed]
-
- raise TypeError((error_msg.format(type(batch[0]))))
-
-
-def pin_memory_batch(batch):
- if torch.is_tensor(batch):
- return batch.pin_memory()
- elif isinstance(batch, string_classes):
- return batch
- elif isinstance(batch, collections.Mapping):
- return {k: pin_memory_batch(sample) for k, sample in batch.items()}
- elif isinstance(batch, collections.Sequence):
- return [pin_memory_batch(sample) for sample in batch]
- else:
- return batch
-
-
-_SIGCHLD_handler_set = False
-"""Whether SIGCHLD handler is set for DataLoader worker failures. Only one
-handler needs to be set for all DataLoaders in a process."""
-
-
-def _set_SIGCHLD_handler():
- # Windows doesn't support SIGCHLD handler
- if sys.platform == 'win32':
- return
- # can't set signal in child threads
- if not isinstance(threading.current_thread(), threading._MainThread):
- return
- global _SIGCHLD_handler_set
- if _SIGCHLD_handler_set:
- return
- previous_handler = signal.getsignal(signal.SIGCHLD)
- if not callable(previous_handler):
- previous_handler = None
-
- def handler(signum, frame):
- # This following call uses `waitid` with WNOHANG from C side. Therefore,
- # Python can still get and update the process status successfully.
- _error_if_any_worker_fails()
- if previous_handler is not None:
- previous_handler(signum, frame)
-
- signal.signal(signal.SIGCHLD, handler)
- _SIGCHLD_handler_set = True
-
-
-class DataLoaderIter(object):
- "Iterates once over the DataLoader's dataset, as specified by the sampler"
-
- def __init__(self, loader):
- self.dataset = loader.dataset
- self.collate_fn = loader.collate_fn
- self.batch_sampler = loader.batch_sampler
- self.num_workers = loader.num_workers
- self.pin_memory = loader.pin_memory and torch.cuda.is_available()
- self.timeout = loader.timeout
- self.done_event = threading.Event()
-
- self.sample_iter = iter(self.batch_sampler)
-
- if self.num_workers > 0:
- self.worker_init_fn = loader.worker_init_fn
- self.index_queue = multiprocessing.SimpleQueue()
- self.worker_result_queue = multiprocessing.SimpleQueue()
- self.batches_outstanding = 0
- self.worker_pids_set = False
- self.shutdown = False
- self.send_idx = 0
- self.rcvd_idx = 0
- self.reorder_dict = {}
-
- base_seed = torch.LongTensor(1).random_(0, 2**31-1)[0]
- self.workers = [
- multiprocessing.Process(
- target=_worker_loop,
- args=(self.dataset, self.index_queue, self.worker_result_queue, self.collate_fn,
- base_seed + i, self.worker_init_fn, i))
- for i in range(self.num_workers)]
-
- if self.pin_memory or self.timeout > 0:
- self.data_queue = queue.Queue()
- if self.pin_memory:
- maybe_device_id = torch.cuda.current_device()
- else:
- # do not initialize cuda context if not necessary
- maybe_device_id = None
- self.worker_manager_thread = threading.Thread(
- target=_worker_manager_loop,
- args=(self.worker_result_queue, self.data_queue, self.done_event, self.pin_memory,
- maybe_device_id))
- self.worker_manager_thread.daemon = True
- self.worker_manager_thread.start()
- else:
- self.data_queue = self.worker_result_queue
-
- for w in self.workers:
- w.daemon = True # ensure that the worker exits on process exit
- w.start()
-
- _set_worker_pids(id(self), tuple(w.pid for w in self.workers))
- _set_SIGCHLD_handler()
- self.worker_pids_set = True
-
- # prime the prefetch loop
- for _ in range(2 * self.num_workers):
- self._put_indices()
-
- def __len__(self):
- return len(self.batch_sampler)
-
- def _get_batch(self):
- if self.timeout > 0:
- try:
- return self.data_queue.get(timeout=self.timeout)
- except queue.Empty:
- raise RuntimeError('DataLoader timed out after {} seconds'.format(self.timeout))
- else:
- return self.data_queue.get()
-
- def __next__(self):
- if self.num_workers == 0: # same-process loading
- indices = next(self.sample_iter) # may raise StopIteration
- batch = self.collate_fn([self.dataset[i] for i in indices])
- if self.pin_memory:
- batch = pin_memory_batch(batch)
- return batch
-
- # check if the next sample has already been generated
- if self.rcvd_idx in self.reorder_dict:
- batch = self.reorder_dict.pop(self.rcvd_idx)
- return self._process_next_batch(batch)
-
- if self.batches_outstanding == 0:
- self._shutdown_workers()
- raise StopIteration
-
- while True:
- assert (not self.shutdown and self.batches_outstanding > 0)
- idx, batch = self._get_batch()
- self.batches_outstanding -= 1
- if idx != self.rcvd_idx:
- # store out-of-order samples
- self.reorder_dict[idx] = batch
- continue
- return self._process_next_batch(batch)
-
- next = __next__ # Python 2 compatibility
-
- def __iter__(self):
- return self
-
- def _put_indices(self):
- assert self.batches_outstanding < 2 * self.num_workers
- indices = next(self.sample_iter, None)
- if indices is None:
- return
- self.index_queue.put((self.send_idx, indices))
- self.batches_outstanding += 1
- self.send_idx += 1
-
- def _process_next_batch(self, batch):
- self.rcvd_idx += 1
- self._put_indices()
- if isinstance(batch, ExceptionWrapper):
- raise batch.exc_type(batch.exc_msg)
- return batch
-
- def __getstate__(self):
- # TODO: add limited pickling support for sharing an iterator
- # across multiple threads for HOGWILD.
- # Probably the best way to do this is by moving the sample pushing
- # to a separate thread and then just sharing the data queue
- # but signalling the end is tricky without a non-blocking API
- raise NotImplementedError("DataLoaderIterator cannot be pickled")
-
- def _shutdown_workers(self):
- try:
- if not self.shutdown:
- self.shutdown = True
- self.done_event.set()
- # if worker_manager_thread is waiting to put
- while not self.data_queue.empty():
- self.data_queue.get()
- for _ in self.workers:
- self.index_queue.put(None)
- # done_event should be sufficient to exit worker_manager_thread,
- # but be safe here and put another None
- self.worker_result_queue.put(None)
- finally:
- # removes pids no matter what
- if self.worker_pids_set:
- _remove_worker_pids(id(self))
- self.worker_pids_set = False
-
- def __del__(self):
- if self.num_workers > 0:
- self._shutdown_workers()
-
-
-class DataLoader(object):
- """
- Data loader. Combines a dataset and a sampler, and provides
- single- or multi-process iterators over the dataset.
-
- Arguments:
- dataset (Dataset): dataset from which to load the data.
- batch_size (int, optional): how many samples per batch to load
- (default: 1).
- shuffle (bool, optional): set to ``True`` to have the data reshuffled
- at every epoch (default: False).
- sampler (Sampler, optional): defines the strategy to draw samples from
- the dataset. If specified, ``shuffle`` must be False.
- batch_sampler (Sampler, optional): like sampler, but returns a batch of
- indices at a time. Mutually exclusive with batch_size, shuffle,
- sampler, and drop_last.
- num_workers (int, optional): how many subprocesses to use for data
- loading. 0 means that the data will be loaded in the main process.
- (default: 0)
- collate_fn (callable, optional): merges a list of samples to form a mini-batch.
- pin_memory (bool, optional): If ``True``, the data loader will copy tensors
- into CUDA pinned memory before returning them.
- drop_last (bool, optional): set to ``True`` to drop the last incomplete batch,
- if the dataset size is not divisible by the batch size. If ``False`` and
- the size of dataset is not divisible by the batch size, then the last batch
- will be smaller. (default: False)
- timeout (numeric, optional): if positive, the timeout value for collecting a batch
- from workers. Should always be non-negative. (default: 0)
- worker_init_fn (callable, optional): If not None, this will be called on each
- worker subprocess with the worker id (an int in ``[0, num_workers - 1]``) as
- input, after seeding and before data loading. (default: None)
-
- .. note:: By default, each worker will have its PyTorch seed set to
- ``base_seed + worker_id``, where ``base_seed`` is a long generated
- by main process using its RNG. You may use ``torch.initial_seed()`` to access
- this value in :attr:`worker_init_fn`, which can be used to set other seeds
- (e.g. NumPy) before data loading.
-
- .. warning:: If ``spawn'' start method is used, :attr:`worker_init_fn` cannot be an
- unpicklable object, e.g., a lambda function.
- """
-
- def __init__(self, dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None,
- num_workers=0, collate_fn=default_collate, pin_memory=False, drop_last=False,
- timeout=0, worker_init_fn=None):
- self.dataset = dataset
- self.batch_size = batch_size
- self.num_workers = num_workers
- self.collate_fn = collate_fn
- self.pin_memory = pin_memory
- self.drop_last = drop_last
- self.timeout = timeout
- self.worker_init_fn = worker_init_fn
-
- if timeout < 0:
- raise ValueError('timeout option should be non-negative')
-
- if batch_sampler is not None:
- if batch_size > 1 or shuffle or sampler is not None or drop_last:
- raise ValueError('batch_sampler is mutually exclusive with '
- 'batch_size, shuffle, sampler, and drop_last')
-
- if sampler is not None and shuffle:
- raise ValueError('sampler is mutually exclusive with shuffle')
-
- if self.num_workers < 0:
- raise ValueError('num_workers cannot be negative; '
- 'use num_workers=0 to disable multiprocessing.')
-
- if batch_sampler is None:
- if sampler is None:
- if shuffle:
- sampler = RandomSampler(dataset)
- else:
- sampler = SequentialSampler(dataset)
- batch_sampler = BatchSampler(sampler, batch_size, drop_last)
-
- self.sampler = sampler
- self.batch_sampler = batch_sampler
-
- def __iter__(self):
- return DataLoaderIter(self)
-
- def __len__(self):
- return len(self.batch_sampler)
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Animal Revolt Battle Simulator The Ultimate Animal Combat Game for PC.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Animal Revolt Battle Simulator The Ultimate Animal Combat Game for PC.md
deleted file mode 100644
index 4b347817c8449c26331b90bf7649629370c39b5e..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Animal Revolt Battle Simulator The Ultimate Animal Combat Game for PC.md
+++ /dev/null
@@ -1,208 +0,0 @@
-Animal Revolt Battle Simulator: A Physics-Based Sandbox Game
-Do you love watching animals fight each other in a realistic and hilarious way? Do you want to create your own epic battles between different types of creatures, from dinosaurs to dragons, from sharks to goats, and even from godzilla to t-rex? Do you want to join the action yourself and blast away the enemy with some powerful guns? If you answered yes to any of these questions, then you should definitely check out Animal Revolt Battle Simulator, a physics-based sandbox game that lets you do all of these things and more!
-animal revolt battle simulator free download 2022 pc DOWNLOAD ⚹⚹⚹ https://ssurll.com/2uNSAs
What is Animal Revolt Battle Simulator?
-Animal Revolt Battle Simulator is a game developed by VDimension and published by VDimension and Yodo1 Ltd. It was released on Steam on April 15, 2022, and has received overwhelmingly positive reviews from players and critics alike. It is also available on Google Play and Nintendo Switch.
-The main features of the game
-Animal Revolt Battle Simulator is a game that offers you ultimate freedom and creativity in creating funny and chaotic battles between all sorts of ragdoll creatures. You can:
-
-Build your own maps or pick from a selection of ready-made ones.
-Place up to seven opposing armies made of different types of beasts and watch them tear each other apart in an epic battle!
-Create your own custom monsters by combining different body parts and weapons. You can attach as many body parts and weapons as you want, anywhere you want!
-Download a vast selection of custom monsters, maps, and buildings created by other players from the Steam Workshop. You can also upload your own creations for other people to try out.
-Test your tactical and strategic expertise in the campaign mode. Pick the right beasts, place them in the right place, and command them to defeat the enemy.
-Join the fight yourself in the first-person mode and blow the enemy away with some powerful guns!
-
-How to play Animal Revolt Battle Simulator?
-The game is very easy to play and has a simple interface. You can use your mouse to drag and drop units on the map, rotate them, scale them, clone them, delete them, etc. You can also use your keyboard to move around the map, zoom in and out, change the camera angle, etc. You can also use hotkeys to access different menus and options.
-animal revolt battle simulator pc download free full version
-Once you have placed your units on the map, you can press the start button to begin the battle. You can watch the battle unfold from different perspectives, such as top-down, side-view, or free camera. You can also pause, resume, slow down, or speed up the battle at any time. You can also switch to the first-person mode and join the battle yourself.
-The game has a physics-based engine that makes the battles realistic and hilarious. You can see the limbs bending, necks twisting, bodies flying around, blood splattering, etc. The game also has ragdoll effects that make the creatures flop around when they die or get hit. The game also has sound effects that add to the immersion and humor of the game.
-How to download Animal Revolt Battle Simulator for free on PC?
-If you want to download Animal Revolt Battle Simulator for free on your PC, you need to follow some steps. However, before you do that, you need to make sure that your PC meets the minimum system requirements for the game. Here are the system requirements for Animal Revolt Battle Simulator:
-The system requirements for the game
-
-
-Minimum
-Recommended
-
-
-OS: Windows 7 or newer
-OS: Windows 10
-
-
-Processor: Intel Core i5-2400 @ 3.1 GHz or AMD FX-6300 @ 3.5 GHz or equivalent
-Processor: Intel Core i7-4770 @ 3.4 GHz or AMD Ryzen 5 1600 @ 3.2 GHz or equivalent
-
-
-Memory: 8 GB RAM
-Memory: 16 GB RAM
-
-
-Graphics: NVIDIA GeForce GTX 670 or AMD R9 270 (2GB VRAM with Shader Model 5.0 or better)
-Graphics: NVIDIA GeForce GTX 970 or AMD R9 290X (4GB VRAM with Shader Model 5.0 or better)
-
-
-DirectX: Version 11
-DirectX: Version 11
-
-
-Storage: 4 GB available space
-Storage: 4 GB available space
-
-
-Sound Card: DirectX compatible sound card with latest drivers
-Sound Card: DirectX compatible sound card with latest drivers
-
-
- The steps to download and install the game
- If your PC meets the system requirements, you can follow these steps to download and install Animal Revolt Battle Simulator for free on your PC:
-
-Go to a trusted and reliable website that offers free downloads of PC games, such as [Ocean of Games] or [Steam Unlocked].
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-Read the description and instructions carefully and make sure you have enough space on your hard drive.
-Click on the download button and wait for the download to finish.
-Extract the zip file using WinRAR or 7-Zip and open the extracted folder.
-Run the setup.exe file as administrator and follow the installation steps.
-Copy the crack files from the crack folder and paste them into the game installation folder.
-Launch the game from the desktop shortcut or the game folder and enjoy!
- The benefits of downloading the game from a trusted source
- There are many benefits of downloading Animal Revolt Battle Simulator from a trusted source, such as:
-
- You can get the game for free without paying any money.
- You can get the latest version of the game with all the updates and bug fixes.
- You can get a safe and virus-free download without any malware or spyware.
- You can get a fast and smooth download without any interruptions or errors.
- You can get a full and complete game without any missing files or features.
- You can get a user-friendly and easy-to-use interface with clear instructions and support.
- You can get access to a large community of gamers who share their creations, feedback, and tips.
-
- How to create your own custom creatures and maps in Animal Revolt Battle Simulator?
- One of the most fun and creative aspects of Animal Revolt Battle Simulator is that you can create your own custom creatures and maps using the in-game tools. You can also download and use other players' creations from the Steam Workshop. Here is how you can do that:
- The unit creator tool
- The unit creator tool allows you to create your own custom monsters by combining different body parts and weapons. You can access this tool by clicking on the unit creator button on the main menu. You can then:
-
- Select a base body from a variety of animals, such as lions, bears, crocodiles, elephants, etc.
- Add different body parts, such as heads, legs, wings, tails, horns, etc., from different animals. You can also add human body parts, such as arms, hands, feet, etc.
- Add different weapons, such as swords, axes, guns, rockets, lasers, etc., to any body part. You can also add shields, armor, helmets, etc., for extra protection.
- Adjust the size, position, rotation, color, texture , and transparency of each body part and weapon. You can also use the sliders to change the mass, health, damage, speed, and range of each unit.
- Save your custom unit and give it a name and a description. You can also choose a category and a faction for your unit.
- Use your custom unit in the sandbox mode or the campaign mode. You can also upload your custom unit to the Steam Workshop for other players to download and use.
-
- The map editor tool
- The map editor tool allows you to create your own custom maps by placing different objects and terrain on a flat surface. You can access this tool by clicking on the map editor button on the main menu. You can then:
-
- Select a map size from small, medium, large, or huge.
- Select a terrain type from grass, sand, snow, water, lava, etc.
- Select a skybox from day, night, sunset, etc.
- Place different objects on the map, such as buildings, trees, rocks, bridges, fences, etc. You can also place animals and humans as static or dynamic objects.
- Adjust the size, position, rotation, color, texture, and transparency of each object. You can also use the sliders to change the gravity, wind, fog, etc., of the map.
- Save your custom map and give it a name and a description. You can also upload your custom map to the Steam Workshop for other players to download and use.
-
- The workshop and community creations
- The workshop is a feature that allows you to browse, download, and use other players' custom units and maps. You can access this feature by clicking on the workshop button on the main menu. You can then:
-
- Search for custom units and maps by name, category, rating, popularity, etc.
- View the details and screenshots of each custom unit and map. You can also read the comments and reviews from other players.
- Download the custom units and maps that you like and add them to your library.
- Use the custom units and maps in the sandbox mode or the campaign mode. You can also rate and review them after using them.
-
- How to join the battles yourself in Animal Revolt Battle Simulator?
- If you want to join the battles yourself and have some fun shooting at the enemy creatures, you can do that by switching to the first-person mode. Here is how you can do that:
- The first-person mode
- The first-person mode is a feature that allows you to control one of your units in the battle and see the action from their perspective. You can access this feature by pressing the F key on your keyboard during a battle. You can then:
-
-Move around using the WASD keys or the arrow keys.
-Aim using your mouse cursor.
-Shoot using the left mouse button.
-Reload using the R key.
-Switch weapons using the Q key or the mouse wheel.
-Crouch using the C key.
-Jump using the spacebar.
-
-The weapons and guns available
-In the first-person mode, you can use different weapons and guns to shoot at the enemy creatures. Some of these weapons are:
-
-Pistol: A basic handgun that has low damage but high accuracy and fire rate.
-Shotgun: A powerful shotgun that has high damage but low accuracy and fire rate.
-Rifle: A semi-automatic rifle that has medium damage and accuracy but high fire rate.
-Sniper: A long-range sniper rifle that has high damage and accuracy but low fire rate.
-Rocket Launcher: A devastating rocket launcher that has very high damage but low accuracy and fire rate.
-Laser Gun: A futuristic laser gun that has medium damage but high accuracy and fire rate.
-
-The tips and tricks for surviving the battles
-If you want to survive the battles in the first-person mode, you need to follow some tips and tricks. Here are some of them:
-
-Pick a weapon that suits your playstyle and situation. For example, use a shotgun for close-range combat or a sniper for long-range combat.
-Aim for the head or other weak spots of the enemy creatures to deal more damage and kill them faster.
-Reload your weapon before you run out of ammo or when you are in cover.
-Use cover to avoid getting hit by enemy fire or attacks. You can hide behind buildings , trees, rocks, fences, etc., and peek out to shoot.
-Keep moving and don't stay in one place for too long. The enemy creatures will chase you and attack you from different directions.
-Use the environment to your advantage. You can shoot explosive barrels, gas tanks, or vehicles to cause explosions and damage the enemy creatures.
-Watch your health and stamina bars. You can heal yourself by picking up health packs or eating food. You can also restore your stamina by resting or drinking water.
-Have fun and experiment with different combinations of units, weapons, and maps. You can create some hilarious and epic scenarios in the game!
-
- Conclusion
- Animal Revolt Battle Simulator is a physics-based sandbox game that lets you create and watch funny and chaotic battles between all sorts of ragdoll creatures. You can also create your own custom monsters and maps using the in-game tools or download other players' creations from the Steam Workshop. You can also join the battles yourself in the first-person mode and shoot at the enemy creatures with some powerful guns. The game is easy to play and has a simple interface, but it also offers a lot of freedom and creativity for you to enjoy. The game is also realistic and hilarious, thanks to its physics-based engine, ragdoll effects, sound effects, etc. The game is available for free on PC, as well as on Google Play and Nintendo Switch. If you are looking for a fun and entertaining game that will make you laugh and keep you hooked for hours, then you should definitely download Animal Revolt Battle Simulator today!
- FAQs
- Here are some frequently asked questions about Animal Revolt Battle Simulator:
-
-Q: How many units can I place on the map?
-Q: How many custom units can I create?
-Q: How many custom maps can I create?
-Q: How can I share my custom units and maps with other players?
-Q: How can I contact the developers of the game?
- 401be4b1e0 None:
- self._path_prefix = path_prefix
- self._coder = coder
- self._sizes = []
- self._ptrs = []
- self._data_len = 0
-
- def open(self):
- self._coder.to_file(vocab_file_path(self._path_prefix))
- self._data_file = open(indexed_dataset.data_file_path(self._path_prefix), "wb")
-
- def __enter__(self) -> "HuffmanMMapIndexedDatasetBuilder":
- self.open()
- return self
-
- def add_item(self, tokens: tp.List[str]) -> None:
- """
- add a list of tokens to the dataset, they will compressed with the
- provided coder before being written to file.
- """
- encoded = self._coder.encode(tokens)
- code_len = len(encoded)
- last_ptr = 0
- if len(self._ptrs) > 0:
- last_ptr = self._ptrs[-1]
- self._sizes.append(len(tokens))
- self._ptrs.append(last_ptr + code_len)
- self._data_len += code_len
- self._data_file.write(encoded)
-
- def append(self, other_dataset_path_prefix: str) -> None:
- """
- append an existing dataset.
- Beware, if it wasn't built with the same coder, you are in trouble.
- """
- other_index = HuffmanMMapIndex(
- indexed_dataset.index_file_path(other_dataset_path_prefix)
- )
- for (ptr, size) in other_index:
- self._ptrs.append(ptr + self._data_len)
- self._sizes.append(size)
-
- # Concatenate data
- with open(indexed_dataset.data_file_path(other_dataset_path_prefix), "rb") as f:
- shutil.copyfileobj(f, self._data_file)
-
- self._data_len += other_index.data_len
-
- def close(self):
- self._data_file.close()
- with HuffmanMMapIndex.writer(
- indexed_dataset.index_file_path(self._path_prefix), self._data_len
- ) as index:
- index.write(self._sizes, self._ptrs)
-
- def __exit__(self, exc_type, exc_val, exc_tb) -> None:
- self.close()
diff --git a/spaces/stable-diffusion-ai/upscaling/README.md b/spaces/stable-diffusion-ai/upscaling/README.md
deleted file mode 100644
index 230b13526e18ad81e52a1b27331ddbce7a2b7918..0000000000000000000000000000000000000000
--- a/spaces/stable-diffusion-ai/upscaling/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Stable Diffusion - Image Upscaling
-emoji: 🚀
-colorFrom: green
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.4.1
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: ai-art/upscaling
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Astrological Charts Pro 9.0.8 APK [Paid] [Full] EXCLUSIVE.md b/spaces/stomexserde/gpt4-ui/Examples/Astrological Charts Pro 9.0.8 APK [Paid] [Full] EXCLUSIVE.md
deleted file mode 100644
index 60d8eb6571e617732a4de054265d610b649b4ce9..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Astrological Charts Pro 9.0.8 APK [Paid] [Full] EXCLUSIVE.md
+++ /dev/null
@@ -1,21 +0,0 @@
-
-Astrological Charts Pro 9.0.8 APK [Paid] [Full]: A Professional Astrology App for Android
-If you are looking for a professional astrological program for Android, you might want to check out Astrological Charts Pro 9.0.8 APK [Paid] [Full]. This app is designed to provide you with 12 types of astrological charts, including planets, asteroids, fictitious points, lots, aspects, houses, and more. You can also access interpretations of natal and transit charts, synastry and composite charts, progressions and directions, returns and lunar phases, and harmonics.
-Astrological Charts Pro 9.0.8 APK [Paid] [Full] DOWNLOAD — https://urlgoal.com/2uI8lE
Astrological Charts Pro 9.0.8 APK [Paid] [Full] has a database of about 100000 places with specified time zones, so you don't have to worry about calculating the difference with GMT. You can also add new places if you want. The app calculates exact dates of triggering aspects, periods of aspects by orb, moments of sign changes, lunar phases, eclipses, void of course Moon, midpoints and planetary hours in the menu of the main page. You can choose between Tropical and Sidereal zodiac in the app.
-Astrological Charts Pro 9.0.8 APK [Paid] [Full] is not only a longitude calculator, but also provides data such as latitude, declination and parallels aspects for 10 planets. You can customize the orbs and the house systems according to your preference. There are 11 house systems and 22 types of aspect available in the app.
-If you want to download Astrological Charts Pro 9.0.8 APK [Paid] [Full], you can find it on Google Play Store[^1^], APKCombo[^2^], or Aptoide[^3^]. The app costs $19.99 and requires Android 4.4 or higher to run. It has a rating of 4.7 out of 5 stars on Google Play Store based on 566 reviews.
-
-Astrological Charts Pro 9.0.8 APK [Paid] [Full] is a must-have app for anyone who is interested in astrology and wants to have a professional tool at their fingertips. Whether you want to analyze your own chart, compare it with others, or explore different astrological techniques, this app will help you do it with ease and accuracy.
Here are some of the features and benefits of Astrological Charts Pro 9.0.8 APK [Paid] [Full] that you can enjoy:
-
-12 types of astrological charts : You can generate and view different types of charts, such as natal, transit, synastry, composite, progressions, directions, profections, returns, lunar phases, and harmonics. You can also switch between one radix chart and dual radix chart modes.13 asteroids and 23 fictitious points : You can include more factors in your analysis, such as Chiron, Ceres, Pallas, Juno, Vesta, Lilith, Vertex, Part of Fortune, and more. You can also add or remove any asteroid or point from the chart.Interpretations : You can read the meanings of natal planets in zodiac signs, in houses and in retrograde state, transit planets in natal houses, natal aspects, from transit to natal aspects, synastry aspects, natal Ascendent and houses in signs. The app also provides a Google search option for any interpretation that is not available.Customizable orbs and house systems : You can adjust the orbs for each aspect and planet according to your preference. You can also choose from 11 house systems, such as Placidus, Koch, Equal, Whole Sign, Campanus, Regiomontanus, Porphyry, Morinus, Alcabitius, Meridian and Axial Rotation System.Data accuracy and convenience : You can rely on the app's database of about 100000 places with specified time zones to calculate the charts correctly. You can also add new places if you need to. The app also shows you the exact dates of triggering aspects, periods of aspects by orb, moments of sign changes, lunar phases, eclipses, void of course Moon, midpoints and planetary hours in the menu of the main page.Tropical and Sidereal zodiac : You can switch between the two zodiac systems in the app. You can also choose from different ayanamsas for the Sidereal zodiac.Data export and import : You can save your charts as PDF files or images and share them with others. You can also import data from other astrological programs or websites using CSV files.
-Astrological Charts Pro 9.0.8 APK [Paid] [Full] is a comprehensive and professional astrological program for Android that will satisfy your astrological needs. Download it today and discover the secrets of the stars!
81aa517590Chick Corea's A Work in Progress: A Treasure of Musical Knowledge
-Chick Corea was one of the most influential and prolific jazz pianists and composers of the 20th and 21st centuries. He left behind a legacy of musical innovation and creativity that spans genres and generations. He also left behind a treasure of musical knowledge: his book A Work in Progress ... On Being a Musician.
-A Work in Progress is a collection of Chick Corea's notes and essays on various aspects of music and musicianship, such as creativity, practice, performance, communication, and aesthetics. He shares his insights and experiences from his long and successful career, as well as his personal philosophy and approach to music. He answers common questions that musicians face, such as:
-chick corea a work in progress pdf 24 Download ⚹⚹⚹ https://urlgoal.com/2uI89L
-What is the single most important element in making good music?
-How can one gain the ability to completely originate one's own music?
-How much time and effort should go into getting a single musical product?
-What's the best way to evaluate one's own live performance?
-What can one do about a "difficult" audience?
-Can others' opinions on your music serve some useful purpose?
-
-The book is not a typical instructional manual or textbook. It is more like a conversation with a master musician who generously shares his wisdom and advice. It is also a work in progress, as Chick Corea intended to add more chapters and answer more questions from his fans and students. The book is available in English and Spanish-language editions, exclusively at Chick's official store.
-One of the chapters that illustrates Chick Corea's musical mind is chapter 24, titled "Pulse and Time Flow". In this chapter, he explains how he imagines a pulse while laying phrases over it, how he creates tempo intentionally, how he uses different subdivisions of the beat to create rhythmic variety and interest, and how he relates to other musicians in a group setting. He also gives some practical exercises to improve one's sense of pulse and time flow.
-A Work in Progress by Chick Corea is a valuable resource for any musician who wants to learn from one of the greatest artists of our time. It is also a testament to Chick Corea's love for music and his dedication to sharing it with others. As he writes in the introduction:
-"I hope you can use some of it to your benefit and success in making music and being a musician."
-
-If you want to read A Work in Progress by Chick Corea, you can download it as a PDF file from his official website. You can also find some excerpts and samples of the book on Scribd and PDFSLIDE.NET. These websites allow you to preview some of the pages and chapters of the book before you buy it. You can also read some reviews and comments from other readers who have enjoyed the book.
-One of the benefits of reading A Work in Progress by Chick Corea as a PDF file is that you can easily access it on your computer, tablet, or smartphone. You can also print it out if you prefer to read it on paper. You can also highlight, bookmark, and annotate the PDF file as you read it. This way, you can make notes of the parts that interest you or that you want to practice later.
-A Work in Progress by Chick Corea is not only a book for jazz musicians or pianists. It is a book for anyone who loves music and wants to improve their musical skills and knowledge. It is a book that will inspire you, challenge you, and entertain you. It is a book that will make you appreciate Chick Corea's music even more.
81aa517590DualStyleGAN
-
-' is path of the saved model if present in ##
- ##the same path as this file, we recommend to put in the same directory ##
- ##########################################################################################
- ##########################################################################################
-
- face = detected_face(img)
- if face==0:
- face = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY))
- face = trnscm(face).unsqueeze(0)
-
-
- # YOUR CODE HERE, load the model
- expression_model = ExpressionCNN()
- model = torch.load(current_path + '/expression_model_gpu.t7', map_location=device)
- expression_model.load_state_dict(model['net_dict'])
-
- outputs = expression_model(face)
- _, predicted = torch.max(outputs.data, 1)
-
- return classes[predicted.item()]
diff --git a/spaces/supertori/files/stable-diffusion-webui/modules/images.py b/spaces/supertori/files/stable-diffusion-webui/modules/images.py
deleted file mode 100644
index 04b2727bd5c3cb6faeb6aa38c24d61f8f2f6ea4b..0000000000000000000000000000000000000000
--- a/spaces/supertori/files/stable-diffusion-webui/modules/images.py
+++ /dev/null
@@ -1,669 +0,0 @@
-import datetime
-import sys
-import traceback
-
-import pytz
-import io
-import math
-import os
-from collections import namedtuple
-import re
-
-import numpy as np
-import piexif
-import piexif.helper
-from PIL import Image, ImageFont, ImageDraw, PngImagePlugin
-from fonts.ttf import Roboto
-import string
-import json
-import hashlib
-
-from modules import sd_samplers, shared, script_callbacks, errors
-from modules.shared import opts, cmd_opts
-
-LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
-
-
-def image_grid(imgs, batch_size=1, rows=None):
- if rows is None:
- if opts.n_rows > 0:
- rows = opts.n_rows
- elif opts.n_rows == 0:
- rows = batch_size
- elif opts.grid_prevent_empty_spots:
- rows = math.floor(math.sqrt(len(imgs)))
- while len(imgs) % rows != 0:
- rows -= 1
- else:
- rows = math.sqrt(len(imgs))
- rows = round(rows)
- if rows > len(imgs):
- rows = len(imgs)
-
- cols = math.ceil(len(imgs) / rows)
-
- params = script_callbacks.ImageGridLoopParams(imgs, cols, rows)
- script_callbacks.image_grid_callback(params)
-
- w, h = imgs[0].size
- grid = Image.new('RGB', size=(params.cols * w, params.rows * h), color='black')
-
- for i, img in enumerate(params.imgs):
- grid.paste(img, box=(i % params.cols * w, i // params.cols * h))
-
- return grid
-
-
-Grid = namedtuple("Grid", ["tiles", "tile_w", "tile_h", "image_w", "image_h", "overlap"])
-
-
-def split_grid(image, tile_w=512, tile_h=512, overlap=64):
- w = image.width
- h = image.height
-
- non_overlap_width = tile_w - overlap
- non_overlap_height = tile_h - overlap
-
- cols = math.ceil((w - overlap) / non_overlap_width)
- rows = math.ceil((h - overlap) / non_overlap_height)
-
- dx = (w - tile_w) / (cols - 1) if cols > 1 else 0
- dy = (h - tile_h) / (rows - 1) if rows > 1 else 0
-
- grid = Grid([], tile_w, tile_h, w, h, overlap)
- for row in range(rows):
- row_images = []
-
- y = int(row * dy)
-
- if y + tile_h >= h:
- y = h - tile_h
-
- for col in range(cols):
- x = int(col * dx)
-
- if x + tile_w >= w:
- x = w - tile_w
-
- tile = image.crop((x, y, x + tile_w, y + tile_h))
-
- row_images.append([x, tile_w, tile])
-
- grid.tiles.append([y, tile_h, row_images])
-
- return grid
-
-
-def combine_grid(grid):
- def make_mask_image(r):
- r = r * 255 / grid.overlap
- r = r.astype(np.uint8)
- return Image.fromarray(r, 'L')
-
- mask_w = make_mask_image(np.arange(grid.overlap, dtype=np.float32).reshape((1, grid.overlap)).repeat(grid.tile_h, axis=0))
- mask_h = make_mask_image(np.arange(grid.overlap, dtype=np.float32).reshape((grid.overlap, 1)).repeat(grid.image_w, axis=1))
-
- combined_image = Image.new("RGB", (grid.image_w, grid.image_h))
- for y, h, row in grid.tiles:
- combined_row = Image.new("RGB", (grid.image_w, h))
- for x, w, tile in row:
- if x == 0:
- combined_row.paste(tile, (0, 0))
- continue
-
- combined_row.paste(tile.crop((0, 0, grid.overlap, h)), (x, 0), mask=mask_w)
- combined_row.paste(tile.crop((grid.overlap, 0, w, h)), (x + grid.overlap, 0))
-
- if y == 0:
- combined_image.paste(combined_row, (0, 0))
- continue
-
- combined_image.paste(combined_row.crop((0, 0, combined_row.width, grid.overlap)), (0, y), mask=mask_h)
- combined_image.paste(combined_row.crop((0, grid.overlap, combined_row.width, h)), (0, y + grid.overlap))
-
- return combined_image
-
-
-class GridAnnotation:
- def __init__(self, text='', is_active=True):
- self.text = text
- self.is_active = is_active
- self.size = None
-
-
-def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0):
- def wrap(drawing, text, font, line_length):
- lines = ['']
- for word in text.split():
- line = f'{lines[-1]} {word}'.strip()
- if drawing.textlength(line, font=font) <= line_length:
- lines[-1] = line
- else:
- lines.append(word)
- return lines
-
- def get_font(fontsize):
- try:
- return ImageFont.truetype(opts.font or Roboto, fontsize)
- except Exception:
- return ImageFont.truetype(Roboto, fontsize)
-
- def draw_texts(drawing, draw_x, draw_y, lines, initial_fnt, initial_fontsize):
- for i, line in enumerate(lines):
- fnt = initial_fnt
- fontsize = initial_fontsize
- while drawing.multiline_textsize(line.text, font=fnt)[0] > line.allowed_width and fontsize > 0:
- fontsize -= 1
- fnt = get_font(fontsize)
- drawing.multiline_text((draw_x, draw_y + line.size[1] / 2), line.text, font=fnt, fill=color_active if line.is_active else color_inactive, anchor="mm", align="center")
-
- if not line.is_active:
- drawing.line((draw_x - line.size[0] // 2, draw_y + line.size[1] // 2, draw_x + line.size[0] // 2, draw_y + line.size[1] // 2), fill=color_inactive, width=4)
-
- draw_y += line.size[1] + line_spacing
-
- fontsize = (width + height) // 25
- line_spacing = fontsize // 2
-
- fnt = get_font(fontsize)
-
- color_active = (0, 0, 0)
- color_inactive = (153, 153, 153)
-
- pad_left = 0 if sum([sum([len(line.text) for line in lines]) for lines in ver_texts]) == 0 else width * 3 // 4
-
- cols = im.width // width
- rows = im.height // height
-
- assert cols == len(hor_texts), f'bad number of horizontal texts: {len(hor_texts)}; must be {cols}'
- assert rows == len(ver_texts), f'bad number of vertical texts: {len(ver_texts)}; must be {rows}'
-
- calc_img = Image.new("RGB", (1, 1), "white")
- calc_d = ImageDraw.Draw(calc_img)
-
- for texts, allowed_width in zip(hor_texts + ver_texts, [width] * len(hor_texts) + [pad_left] * len(ver_texts)):
- items = [] + texts
- texts.clear()
-
- for line in items:
- wrapped = wrap(calc_d, line.text, fnt, allowed_width)
- texts += [GridAnnotation(x, line.is_active) for x in wrapped]
-
- for line in texts:
- bbox = calc_d.multiline_textbbox((0, 0), line.text, font=fnt)
- line.size = (bbox[2] - bbox[0], bbox[3] - bbox[1])
- line.allowed_width = allowed_width
-
- hor_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing for lines in hor_texts]
- ver_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing * len(lines) for lines in ver_texts]
-
- pad_top = 0 if sum(hor_text_heights) == 0 else max(hor_text_heights) + line_spacing * 2
-
- result = Image.new("RGB", (im.width + pad_left + margin * (cols-1), im.height + pad_top + margin * (rows-1)), "white")
-
- for row in range(rows):
- for col in range(cols):
- cell = im.crop((width * col, height * row, width * (col+1), height * (row+1)))
- result.paste(cell, (pad_left + (width + margin) * col, pad_top + (height + margin) * row))
-
- d = ImageDraw.Draw(result)
-
- for col in range(cols):
- x = pad_left + (width + margin) * col + width / 2
- y = pad_top / 2 - hor_text_heights[col] / 2
-
- draw_texts(d, x, y, hor_texts[col], fnt, fontsize)
-
- for row in range(rows):
- x = pad_left / 2
- y = pad_top + (height + margin) * row + height / 2 - ver_text_heights[row] / 2
-
- draw_texts(d, x, y, ver_texts[row], fnt, fontsize)
-
- return result
-
-
-def draw_prompt_matrix(im, width, height, all_prompts, margin=0):
- prompts = all_prompts[1:]
- boundary = math.ceil(len(prompts) / 2)
-
- prompts_horiz = prompts[:boundary]
- prompts_vert = prompts[boundary:]
-
- hor_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_horiz)] for pos in range(1 << len(prompts_horiz))]
- ver_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_vert)] for pos in range(1 << len(prompts_vert))]
-
- return draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin)
-
-
-def resize_image(resize_mode, im, width, height, upscaler_name=None):
- """
- Resizes an image with the specified resize_mode, width, and height.
-
- Args:
- resize_mode: The mode to use when resizing the image.
- 0: Resize the image to the specified width and height.
- 1: Resize the image to fill the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, cropping the excess.
- 2: Resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, filling empty with data from image.
- im: The image to resize.
- width: The width to resize the image to.
- height: The height to resize the image to.
- upscaler_name: The name of the upscaler to use. If not provided, defaults to opts.upscaler_for_img2img.
- """
-
- upscaler_name = upscaler_name or opts.upscaler_for_img2img
-
- def resize(im, w, h):
- if upscaler_name is None or upscaler_name == "None" or im.mode == 'L':
- return im.resize((w, h), resample=LANCZOS)
-
- scale = max(w / im.width, h / im.height)
-
- if scale > 1.0:
- upscalers = [x for x in shared.sd_upscalers if x.name == upscaler_name]
- assert len(upscalers) > 0, f"could not find upscaler named {upscaler_name}"
-
- upscaler = upscalers[0]
- im = upscaler.scaler.upscale(im, scale, upscaler.data_path)
-
- if im.width != w or im.height != h:
- im = im.resize((w, h), resample=LANCZOS)
-
- return im
-
- if resize_mode == 0:
- res = resize(im, width, height)
-
- elif resize_mode == 1:
- ratio = width / height
- src_ratio = im.width / im.height
-
- src_w = width if ratio > src_ratio else im.width * height // im.height
- src_h = height if ratio <= src_ratio else im.height * width // im.width
-
- resized = resize(im, src_w, src_h)
- res = Image.new("RGB", (width, height))
- res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
-
- else:
- ratio = width / height
- src_ratio = im.width / im.height
-
- src_w = width if ratio < src_ratio else im.width * height // im.height
- src_h = height if ratio >= src_ratio else im.height * width // im.width
-
- resized = resize(im, src_w, src_h)
- res = Image.new("RGB", (width, height))
- res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
-
- if ratio < src_ratio:
- fill_height = height // 2 - src_h // 2
- res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0))
- res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h))
- elif ratio > src_ratio:
- fill_width = width // 2 - src_w // 2
- res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0))
- res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0))
-
- return res
-
-
-invalid_filename_chars = '<>:"/\\|?*\n'
-invalid_filename_prefix = ' '
-invalid_filename_postfix = ' .'
-re_nonletters = re.compile(r'[\s' + string.punctuation + ']+')
-re_pattern = re.compile(r"(.*?)(?:\[([^\[\]]+)\]|$)")
-re_pattern_arg = re.compile(r"(.*)<([^>]*)>$")
-max_filename_part_length = 128
-
-
-def sanitize_filename_part(text, replace_spaces=True):
- if text is None:
- return None
-
- if replace_spaces:
- text = text.replace(' ', '_')
-
- text = text.translate({ord(x): '_' for x in invalid_filename_chars})
- text = text.lstrip(invalid_filename_prefix)[:max_filename_part_length]
- text = text.rstrip(invalid_filename_postfix)
- return text
-
-
-class FilenameGenerator:
- replacements = {
- 'seed': lambda self: self.seed if self.seed is not None else '',
- 'steps': lambda self: self.p and self.p.steps,
- 'cfg': lambda self: self.p and self.p.cfg_scale,
- 'width': lambda self: self.image.width,
- 'height': lambda self: self.image.height,
- 'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),
- 'sampler': lambda self: self.p and sanitize_filename_part(self.p.sampler_name, replace_spaces=False),
- 'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),
- 'model_name': lambda self: sanitize_filename_part(shared.sd_model.sd_checkpoint_info.model_name, replace_spaces=False),
- 'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'),
- 'datetime': lambda self, *args: self.datetime(*args), # accepts formats: [datetime], [datetime], [datetime]
- 'job_timestamp': lambda self: getattr(self.p, "job_timestamp", shared.state.job_timestamp),
- 'prompt_hash': lambda self: hashlib.sha256(self.prompt.encode()).hexdigest()[0:8],
- 'prompt': lambda self: sanitize_filename_part(self.prompt),
- 'prompt_no_styles': lambda self: self.prompt_no_style(),
- 'prompt_spaces': lambda self: sanitize_filename_part(self.prompt, replace_spaces=False),
- 'prompt_words': lambda self: self.prompt_words(),
- }
- default_time_format = '%Y%m%d%H%M%S'
-
- def __init__(self, p, seed, prompt, image):
- self.p = p
- self.seed = seed
- self.prompt = prompt
- self.image = image
-
- def prompt_no_style(self):
- if self.p is None or self.prompt is None:
- return None
-
- prompt_no_style = self.prompt
- for style in shared.prompt_styles.get_style_prompts(self.p.styles):
- if len(style) > 0:
- for part in style.split("{prompt}"):
- prompt_no_style = prompt_no_style.replace(part, "").replace(", ,", ",").strip().strip(',')
-
- prompt_no_style = prompt_no_style.replace(style, "").strip().strip(',').strip()
-
- return sanitize_filename_part(prompt_no_style, replace_spaces=False)
-
- def prompt_words(self):
- words = [x for x in re_nonletters.split(self.prompt or "") if len(x) > 0]
- if len(words) == 0:
- words = ["empty"]
- return sanitize_filename_part(" ".join(words[0:opts.directories_max_prompt_words]), replace_spaces=False)
-
- def datetime(self, *args):
- time_datetime = datetime.datetime.now()
-
- time_format = args[0] if len(args) > 0 and args[0] != "" else self.default_time_format
- try:
- time_zone = pytz.timezone(args[1]) if len(args) > 1 else None
- except pytz.exceptions.UnknownTimeZoneError as _:
- time_zone = None
-
- time_zone_time = time_datetime.astimezone(time_zone)
- try:
- formatted_time = time_zone_time.strftime(time_format)
- except (ValueError, TypeError) as _:
- formatted_time = time_zone_time.strftime(self.default_time_format)
-
- return sanitize_filename_part(formatted_time, replace_spaces=False)
-
- def apply(self, x):
- res = ''
-
- for m in re_pattern.finditer(x):
- text, pattern = m.groups()
- res += text
-
- if pattern is None:
- continue
-
- pattern_args = []
- while True:
- m = re_pattern_arg.match(pattern)
- if m is None:
- break
-
- pattern, arg = m.groups()
- pattern_args.insert(0, arg)
-
- fun = self.replacements.get(pattern.lower())
- if fun is not None:
- try:
- replacement = fun(self, *pattern_args)
- except Exception:
- replacement = None
- print(f"Error adding [{pattern}] to filename", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
-
- if replacement is not None:
- res += str(replacement)
- continue
-
- res += f'[{pattern}]'
-
- return res
-
-
-def get_next_sequence_number(path, basename):
- """
- Determines and returns the next sequence number to use when saving an image in the specified directory.
-
- The sequence starts at 0.
- """
- result = -1
- if basename != '':
- basename = basename + "-"
-
- prefix_length = len(basename)
- for p in os.listdir(path):
- if p.startswith(basename):
- l = os.path.splitext(p[prefix_length:])[0].split('-') # splits the filename (removing the basename first if one is defined, so the sequence number is always the first element)
- try:
- result = max(int(l[0]), result)
- except ValueError:
- pass
-
- return result + 1
-
-
-def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None):
- """Save an image.
-
- Args:
- image (`PIL.Image`):
- The image to be saved.
- path (`str`):
- The directory to save the image. Note, the option `save_to_dirs` will make the image to be saved into a sub directory.
- basename (`str`):
- The base filename which will be applied to `filename pattern`.
- seed, prompt, short_filename,
- extension (`str`):
- Image file extension, default is `png`.
- pngsectionname (`str`):
- Specify the name of the section which `info` will be saved in.
- info (`str` or `PngImagePlugin.iTXt`):
- PNG info chunks.
- existing_info (`dict`):
- Additional PNG info. `existing_info == {pngsectionname: info, ...}`
- no_prompt:
- TODO I don't know its meaning.
- p (`StableDiffusionProcessing`)
- forced_filename (`str`):
- If specified, `basename` and filename pattern will be ignored.
- save_to_dirs (bool):
- If true, the image will be saved into a subdirectory of `path`.
-
- Returns: (fullfn, txt_fullfn)
- fullfn (`str`):
- The full path of the saved imaged.
- txt_fullfn (`str` or None):
- If a text file is saved for this image, this will be its full path. Otherwise None.
- """
- namegen = FilenameGenerator(p, seed, prompt, image)
-
- if save_to_dirs is None:
- save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)
-
- if save_to_dirs:
- dirname = namegen.apply(opts.directories_filename_pattern or "[prompt_words]").lstrip(' ').rstrip('\\ /')
- path = os.path.join(path, dirname)
-
- os.makedirs(path, exist_ok=True)
-
- if forced_filename is None:
- if short_filename or seed is None:
- file_decoration = ""
- elif opts.save_to_dirs:
- file_decoration = opts.samples_filename_pattern or "[seed]"
- else:
- file_decoration = opts.samples_filename_pattern or "[seed]-[prompt_spaces]"
-
- add_number = opts.save_images_add_number or file_decoration == ''
-
- if file_decoration != "" and add_number:
- file_decoration = "-" + file_decoration
-
- file_decoration = namegen.apply(file_decoration) + suffix
-
- if add_number:
- basecount = get_next_sequence_number(path, basename)
- fullfn = None
- for i in range(500):
- fn = f"{basecount + i:05}" if basename == '' else f"{basename}-{basecount + i:04}"
- fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}")
- if not os.path.exists(fullfn):
- break
- else:
- fullfn = os.path.join(path, f"{file_decoration}.{extension}")
- else:
- fullfn = os.path.join(path, f"{forced_filename}.{extension}")
-
- pnginfo = existing_info or {}
- if info is not None:
- pnginfo[pnginfo_section_name] = info
-
- params = script_callbacks.ImageSaveParams(image, p, fullfn, pnginfo)
- script_callbacks.before_image_saved_callback(params)
-
- image = params.image
- fullfn = params.filename
- info = params.pnginfo.get(pnginfo_section_name, None)
-
- def _atomically_save_image(image_to_save, filename_without_extension, extension):
- # save image with .tmp extension to avoid race condition when another process detects new image in the directory
- temp_file_path = filename_without_extension + ".tmp"
- image_format = Image.registered_extensions()[extension]
-
- if extension.lower() == '.png':
- pnginfo_data = PngImagePlugin.PngInfo()
- if opts.enable_pnginfo:
- for k, v in params.pnginfo.items():
- pnginfo_data.add_text(k, str(v))
-
- image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
-
- elif extension.lower() in (".jpg", ".jpeg", ".webp"):
- if image_to_save.mode == 'RGBA':
- image_to_save = image_to_save.convert("RGB")
- elif image_to_save.mode == 'I;16':
- image_to_save = image_to_save.point(lambda p: p * 0.0038910505836576).convert("RGB" if extension.lower() == ".webp" else "L")
-
- image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality, lossless=opts.webp_lossless)
-
- if opts.enable_pnginfo and info is not None:
- exif_bytes = piexif.dump({
- "Exif": {
- piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(info or "", encoding="unicode")
- },
- })
-
- piexif.insert(exif_bytes, temp_file_path)
- else:
- image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality)
-
- # atomically rename the file with correct extension
- os.replace(temp_file_path, filename_without_extension + extension)
-
- fullfn_without_extension, extension = os.path.splitext(params.filename)
- _atomically_save_image(image, fullfn_without_extension, extension)
-
- image.already_saved_as = fullfn
-
- oversize = image.width > opts.target_side_length or image.height > opts.target_side_length
- if opts.export_for_4chan and (oversize or os.stat(fullfn).st_size > opts.img_downscale_threshold * 1024 * 1024):
- ratio = image.width / image.height
-
- if oversize and ratio > 1:
- image = image.resize((round(opts.target_side_length), round(image.height * opts.target_side_length / image.width)), LANCZOS)
- elif oversize:
- image = image.resize((round(image.width * opts.target_side_length / image.height), round(opts.target_side_length)), LANCZOS)
-
- try:
- _atomically_save_image(image, fullfn_without_extension, ".jpg")
- except Exception as e:
- errors.display(e, "saving image as downscaled JPG")
-
- if opts.save_txt and info is not None:
- txt_fullfn = f"{fullfn_without_extension}.txt"
- with open(txt_fullfn, "w", encoding="utf8") as file:
- file.write(info + "\n")
- else:
- txt_fullfn = None
-
- script_callbacks.image_saved_callback(params)
-
- return fullfn, txt_fullfn
-
-
-def read_info_from_image(image):
- items = image.info or {}
-
- geninfo = items.pop('parameters', None)
-
- if "exif" in items:
- exif = piexif.load(items["exif"])
- exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'')
- try:
- exif_comment = piexif.helper.UserComment.load(exif_comment)
- except ValueError:
- exif_comment = exif_comment.decode('utf8', errors="ignore")
-
- if exif_comment:
- items['exif comment'] = exif_comment
- geninfo = exif_comment
-
- for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
- 'loop', 'background', 'timestamp', 'duration']:
- items.pop(field, None)
-
- if items.get("Software", None) == "NovelAI":
- try:
- json_info = json.loads(items["Comment"])
- sampler = sd_samplers.samplers_map.get(json_info["sampler"], "Euler a")
-
- geninfo = f"""{items["Description"]}
-Negative prompt: {json_info["uc"]}
-Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}, Seed: {json_info["seed"]}, Size: {image.width}x{image.height}, Clip skip: 2, ENSD: 31337"""
- except Exception:
- print("Error parsing NovelAI image generation parameters:", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
-
- return geninfo, items
-
-
-def image_data(data):
- try:
- image = Image.open(io.BytesIO(data))
- textinfo, _ = read_info_from_image(image)
- return textinfo, None
- except Exception:
- pass
-
- try:
- text = data.decode('utf8')
- assert len(text) < 10000
- return text, None
-
- except Exception:
- pass
-
- return '', None
-
-
-def flatten(img, bgcolor):
- """replaces transparency with bgcolor (example: "#ffffff"), returning an RGB mode image with no transparency"""
-
- if img.mode == "RGBA":
- background = Image.new('RGBA', img.size, bgcolor)
- background.paste(img, mask=img)
- img = background
-
- return img.convert('RGB')
diff --git a/spaces/supertori/files/stable-diffusion-webui/modules/sd_disable_initialization.py b/spaces/supertori/files/stable-diffusion-webui/modules/sd_disable_initialization.py
deleted file mode 100644
index 50e4c180fc74988ec697e4cef2773bd2a785bccf..0000000000000000000000000000000000000000
--- a/spaces/supertori/files/stable-diffusion-webui/modules/sd_disable_initialization.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import ldm.modules.encoders.modules
-import open_clip
-import torch
-import transformers.utils.hub
-
-
-class DisableInitialization:
- """
- When an object of this class enters a `with` block, it starts:
- - preventing torch's layer initialization functions from working
- - changes CLIP and OpenCLIP to not download model weights
- - changes CLIP to not make requests to check if there is a new version of a file you already have
-
- When it leaves the block, it reverts everything to how it was before.
-
- Use it like this:
- ```
- with DisableInitialization():
- do_things()
- ```
- """
-
- def __init__(self, disable_clip=True):
- self.replaced = []
- self.disable_clip = disable_clip
-
- def replace(self, obj, field, func):
- original = getattr(obj, field, None)
- if original is None:
- return None
-
- self.replaced.append((obj, field, original))
- setattr(obj, field, func)
-
- return original
-
- def __enter__(self):
- def do_nothing(*args, **kwargs):
- pass
-
- def create_model_and_transforms_without_pretrained(*args, pretrained=None, **kwargs):
- return self.create_model_and_transforms(*args, pretrained=None, **kwargs)
-
- def CLIPTextModel_from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs):
- res = self.CLIPTextModel_from_pretrained(None, *model_args, config=pretrained_model_name_or_path, state_dict={}, **kwargs)
- res.name_or_path = pretrained_model_name_or_path
- return res
-
- def transformers_modeling_utils_load_pretrained_model(*args, **kwargs):
- args = args[0:3] + ('/', ) + args[4:] # resolved_archive_file; must set it to something to prevent what seems to be a bug
- return self.transformers_modeling_utils_load_pretrained_model(*args, **kwargs)
-
- def transformers_utils_hub_get_file_from_cache(original, url, *args, **kwargs):
-
- # this file is always 404, prevent making request
- if url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json' or url == 'openai/clip-vit-large-patch14' and args[0] == 'added_tokens.json':
- return None
-
- try:
- res = original(url, *args, local_files_only=True, **kwargs)
- if res is None:
- res = original(url, *args, local_files_only=False, **kwargs)
- return res
- except Exception as e:
- return original(url, *args, local_files_only=False, **kwargs)
-
- def transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs):
- return transformers_utils_hub_get_file_from_cache(self.transformers_utils_hub_get_from_cache, url, *args, **kwargs)
-
- def transformers_tokenization_utils_base_cached_file(url, *args, local_files_only=False, **kwargs):
- return transformers_utils_hub_get_file_from_cache(self.transformers_tokenization_utils_base_cached_file, url, *args, **kwargs)
-
- def transformers_configuration_utils_cached_file(url, *args, local_files_only=False, **kwargs):
- return transformers_utils_hub_get_file_from_cache(self.transformers_configuration_utils_cached_file, url, *args, **kwargs)
-
- self.replace(torch.nn.init, 'kaiming_uniform_', do_nothing)
- self.replace(torch.nn.init, '_no_grad_normal_', do_nothing)
- self.replace(torch.nn.init, '_no_grad_uniform_', do_nothing)
-
- if self.disable_clip:
- self.create_model_and_transforms = self.replace(open_clip, 'create_model_and_transforms', create_model_and_transforms_without_pretrained)
- self.CLIPTextModel_from_pretrained = self.replace(ldm.modules.encoders.modules.CLIPTextModel, 'from_pretrained', CLIPTextModel_from_pretrained)
- self.transformers_modeling_utils_load_pretrained_model = self.replace(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', transformers_modeling_utils_load_pretrained_model)
- self.transformers_tokenization_utils_base_cached_file = self.replace(transformers.tokenization_utils_base, 'cached_file', transformers_tokenization_utils_base_cached_file)
- self.transformers_configuration_utils_cached_file = self.replace(transformers.configuration_utils, 'cached_file', transformers_configuration_utils_cached_file)
- self.transformers_utils_hub_get_from_cache = self.replace(transformers.utils.hub, 'get_from_cache', transformers_utils_hub_get_from_cache)
-
- def __exit__(self, exc_type, exc_val, exc_tb):
- for obj, field, original in self.replaced:
- setattr(obj, field, original)
-
- self.replaced.clear()
-
diff --git a/spaces/supertori/files/stable-diffusion-webui/modules/ui_components.py b/spaces/supertori/files/stable-diffusion-webui/modules/ui_components.py
deleted file mode 100644
index d239d3f70938942f625f5f49e9398fcde10016bf..0000000000000000000000000000000000000000
--- a/spaces/supertori/files/stable-diffusion-webui/modules/ui_components.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import gradio as gr
-
-
-class ToolButton(gr.Button, gr.components.FormComponent):
- """Small button with single emoji as text, fits inside gradio forms"""
-
- def __init__(self, **kwargs):
- super().__init__(variant="tool", **kwargs)
-
- def get_block_name(self):
- return "button"
-
-
-class ToolButtonTop(gr.Button, gr.components.FormComponent):
- """Small button with single emoji as text, with extra margin at top, fits inside gradio forms"""
-
- def __init__(self, **kwargs):
- super().__init__(variant="tool-top", **kwargs)
-
- def get_block_name(self):
- return "button"
-
-
-class FormRow(gr.Row, gr.components.FormComponent):
- """Same as gr.Row but fits inside gradio forms"""
-
- def get_block_name(self):
- return "row"
-
-
-class FormGroup(gr.Group, gr.components.FormComponent):
- """Same as gr.Row but fits inside gradio forms"""
-
- def get_block_name(self):
- return "group"
-
-
-class FormHTML(gr.HTML, gr.components.FormComponent):
- """Same as gr.HTML but fits inside gradio forms"""
-
- def get_block_name(self):
- return "html"
-
-
-class FormColorPicker(gr.ColorPicker, gr.components.FormComponent):
- """Same as gr.ColorPicker but fits inside gradio forms"""
-
- def get_block_name(self):
- return "colorpicker"
-
-
-class DropdownMulti(gr.Dropdown):
- """Same as gr.Dropdown but always multiselect"""
- def __init__(self, **kwargs):
- super().__init__(multiselect=True, **kwargs)
-
- def get_block_name(self):
- return "dropdown"
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Campaign Cartographer 3 Download Free [EXCLUSIVE].md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Campaign Cartographer 3 Download Free [EXCLUSIVE].md
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--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Campaign Cartographer 3 Download Free [EXCLUSIVE].md
+++ /dev/null
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-Campaign Cartographer 3 Download Free: A Guide for Map Makers
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-If you are a fan of fantasy games, role-playing games, or any other genre that requires creating maps for your own worlds, you might be interested in Campaign Cartographer 3. This is a powerful map making software that can help you design stunning maps in minutes, with a variety of styles, symbols, and types to choose from. In this article, we will show you how to get Campaign Cartographer 3 for free and start mapping today.
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-Campaign Cartographer 3 is a software developed by ProFantasy Software, a company that specializes in fantasy map-making software for gamers. It is the latest version of their flagship product, which has been used by thousands of gamers, authors, and game designers since 1993. Campaign Cartographer 3 allows you to create maps for any genre, scale, or purpose, from world maps to dungeon maps, from floor plans to city maps. You can also create furniture, terrain features, and other details to make your maps more realistic and immersive.
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What are the features of Campaign Cartographer 3?
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-It supports both vector and bitmap graphics, which means you can create high-quality maps that can be scaled and printed without losing resolution.
-It can export your maps as jpg, png, bmp, or other formats that can be used in other applications or shared online.
-It can import maps from other sources, such as Fractal Terrains 3, a software that generates realistic random worlds.
-It can integrate with other ProFantasy products, such as Symbol Set 6, which adds more symbols for isometric cities.
-It offers free monthly content for CC3+ users, such as new symbols created by Mike Schley, a master artist who has worked for Wizards of the Coast and Paizo.
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-If you want to try Campaign Cartographer 3 for free, you can download it from the official website of ProFantasy Software. You will need to register an account and provide some basic information. You will then receive a download link and a license key via email. You can install the software on your PC and use it for 14 days without any limitations. If you like it, you can buy it from the webstore and keep using it. If you don't like it, you can get a refund within 14 days, no questions asked.
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3cee63e6c2How to Download and Install Adobe Animate CC 2017 for Free
-Adobe Animate CC 2017 is a powerful software that allows you to create and code 2D and 3D animations for web projects. It is compatible with digital and mobile platforms and can be used to develop games, movies and content for mobile devices. In this article, we will show you how to download and install Adobe Animate CC 2017 for free on your 64-bit Windows PC.
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Step 1: Download the software
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-Step 2: Extract the files
-After downloading the software, you will need to extract the files from the compressed folder. You can use a software such as WinRAR or 7-Zip to do this. Right-click on the folder and select "Extract Here" or "Extract to Adobe_Animate_CC_2017_v16.0.1". You will get a folder with the same name containing the setup files and the crack files.
-Step 3: Install the software
-To install the software, you will need to run the setup file as an administrator. Double-click on "Set-up.exe" and follow the instructions on the screen. Choose your language, accept the license agreement and select your installation location. Wait for the installation to complete.
-Step 4: Apply the crack
-To activate the software, you will need to apply the crack files. Go to the folder where you extracted the files and open the "Crack" folder. Copy all the files inside and paste them into the installation directory of Adobe Animate CC 2017. This is usually located at C:\Program Files\Adobe\Adobe Animate CC 2017. Replace any existing files if prompted.
-Step 5: Enjoy your software
-You have successfully installed Adobe Animate CC 2017 for free on your PC. You can now launch the software from your desktop or start menu and enjoy creating and coding amazing animations for your web projects.
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Step 6: Learn some tips and tricks
-Now that you have installed Adobe Animate CC 2017, you might want to learn some tips and tricks to improve your skills and workflow. Here are some resources that can help you:
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-Animate tutorials | Learn how to use Animate - Adobe Help Center[^1^]: This is the official website of Adobe Animate, where you can find tutorials for beginners and experienced users. You can learn about document types, drawing tools, symbols, color management, gradients, animation techniques, audio, virtual camera, asset warp, HTML5 components, code templates and more.
-Time-saving tips for Adobe Animate CC - YouTube[^2^]: This is a video by Devon Kong, a professional animator and educator. He shows you some pro tips and tricks that can save you time and energy in Adobe Animate CC. You can learn about paste in place, edit multiple frames, find and replace and more.
-Adobe Animate Tutorials: Practical Tips, Tricks and Techniques - YouTube[^3^]: This is a video by Graph Desk, a channel dedicated to graphic design and animation. They show you some practical tips, tricks and techniques that can enhance your animations in Adobe Animate CC. You can learn about easing curves, motion paths, shape tweens, masking layers and more.
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-Step 7: Have fun and be creative
-Adobe Animate CC 2017 is a powerful and versatile software that can help you create amazing animations for your web projects. You can use it to express your creativity and imagination in different ways. You can also explore other features and tools that are not covered in this article. The only limit is your own imagination. Have fun and be creative with Adobe Animate CC 2017!
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diff --git a/spaces/terfces0erbo/CollegeProjectV2/Death Race 3 Torrent Ita EXCLUSIVE.md b/spaces/terfces0erbo/CollegeProjectV2/Death Race 3 Torrent Ita EXCLUSIVE.md
deleted file mode 100644
index 3008b5bc7f0d1ab0688c66f91be1b8e7d7b9932e..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Death Race 3 Torrent Ita EXCLUSIVE.md
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-Death Race 3 Torrent Ita Download File ››››› https://bytlly.com/2uGjBB
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Dolly Parton Discography 21 CD 19682009 FLAC.md b/spaces/terfces0erbo/CollegeProjectV2/Dolly Parton Discography 21 CD 19682009 FLAC.md
deleted file mode 100644
index 7352540f96afbd6f9a40b1392c1a08b72074fdf8..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Dolly Parton Discography 21 CD 19682009 FLAC.md
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-Dolly Parton Discography 21 CD 19682009 FLAC Download Zip 🆗 https://bytlly.com/2uGlsR
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Comfast 98000g Drivers Download Best Practices and Optimization Tips.md b/spaces/tialenAdioni/chat-gpt-api/logs/Comfast 98000g Drivers Download Best Practices and Optimization Tips.md
deleted file mode 100644
index a037d218efabf6e1c3768c47bd643d93bd65d164..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Comfast 98000g Drivers Download Best Practices and Optimization Tips.md
+++ /dev/null
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-
-Comfast 98000g Drivers Download: How to Install and Use the Wireless Adapter
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- If you are looking for a way to improve your wireless internet connection, you might want to consider getting a Comfast 98000g wireless adapter. This device can help you boost your WiFi signal, increase your speed, and reduce interference. But before you can use it, you need to download and install the Comfast 98000g drivers on your computer. In this article, we will show you how to do that in three easy ways. We will also show you how to use the Comfast 98000g wireless adapter once you have installed the drivers.
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- The next thing you need to do is connect to a wireless network that is available in your area. You can do this by clicking on the WiFi icon on your taskbar (the lower right corner of your screen) and choosing a network from the list that appears. If you don't see any networks, make sure that your WiFi is turned on and that there are no physical obstructions between your device and the router.
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- A: You can find the user manual for the Comfast 98000g wireless adapter on the official website of Comfast at http://en.comfast.com.cn/index.php?m=content&c=index&a=lists&catid=81 . You can search for the model name of your wireless adapter (CF-934AC) and click on the Download button next to it. You will get a zip file containing the user manual in PDF format.
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- Q: How can I troubleshoot the Comfast 98000g wireless adapter?
- A: If you encounter any problems with the Comfast 98000g wireless adapter, such as not being able to connect to a WiFi network, having a weak or unstable signal, or experiencing slow or interrupted internet connection, you can try some of these troubleshooting tips:
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-Make sure that your computer meets the system requirements for using the Comfast 98000g wireless adapter.
-Make sure that you have installed the correct and updated drivers for your device.
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-Try changing the WiFi channel or frequency on your router or device to avoid congestion or interference from other devices.
-Try restarting your computer, your wireless adapter, and your router and see if that solves the problem.
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- Q: How can I uninstall the Comfast 98000g drivers?
- A: If you want to uninstall the Comfast 98000g drivers from your computer, you can follow these steps:
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-Go to Start > Control Panel > Programs and Features (or Add or Remove Programs).
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-
- Q: Is Comfast 98000g compatible with Mac OS or Linux?
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- 0a6ba089ebAthlean X Workout Pdf 133golkesl: A Comprehensive Review
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-If you are looking for a simple and effective way to align your satellite dish, you might want to try DishPointer Pro on your Android device. DishPointer Pro is an app that uses your phone's camera and GPS to show you where the satellites are in the sky, and how to adjust your dish for the best signal.
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7b8c122e87Hollow Man: A Sci-Fi Thriller Dubbed in Telugu
-Hollow Man is a 2000 American science fiction thriller film directed by Paul Verhoeven and starring Kevin Bacon, Elisabeth Shue and Josh Brolin. The film is loosely based on the novel The Invisible Man by H.G. Wells and follows a group of scientists who experiment with invisibility on themselves.
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The film was dubbed in Telugu and released in India as Hollow Man Dubbed From English . The Telugu version of the film is available in 1080p high definition quality on various online platforms. The film received mixed reviews from critics and audiences, but was praised for its visual effects and action sequences.
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Here are a few more paragraphs for the article:
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-The film was directed by Paul Verhoeven, who is known for his provocative and violent films such as RoboCop , Total Recall and Basic Instinct . Verhoeven wanted to explore the psychological effects of invisibility on a human being, and how it would unleash his darkest impulses. He also wanted to create a realistic and scientific approach to the concept of invisibility, using state-of-the-art computer-generated imagery (CGI) to achieve the stunning effects.
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-``` 7b8c122e87the course planning and creation features in courselab are intended for use in the classroom only. you will need to have a courselab account in order to download and update the software and to create courses. however, if you are downloading courselab with the intent of using it outside of the classroom, you may not need to have an account. you can download and install the software for free from our website.
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the trial version is being updated to v2.7. you will need to buy the full version of courselab if you want to create lms courses with all the features that courselab is capable of delivering. a student may use coursework for 6 months without purchasing the full version. after the end of the 6-month trial period, the full version can be purchased with a credit card.
-the advantage of the free trial version is that the student can get familiar with the interface, which is one of the most important factors when choosing a lms for your institution. the full version allows you to create courses in the following languages: english, french, german, and spanish. the full version has more modules that the free version, and you can create units of study, assignment templates, quizzes, tests, and more. in addition, there is a database of over 1 million questions that you can use as a learning and assessment resource, courselab was developed for use by instructors and learning technologists.
-the free version of courselab was developed to be used by instructors and learning technologists. it is fully functional but cannot be used for commercial purposes. a student can use courselab for six months without purchasing the full version. after the end of the 6-month trial period, the full version can be purchased with a credit card.
-
899543212b token (equiv. to [CLS])
- x = self.dropout(x)
- x = self.dense(x)
- x = ACT2FN[self.config.hidden_act](x)
- x = self.dropout(x)
- x = self.out_proj(x)
- return x
-
-
-@add_start_docstrings(
- """
- BigBird Model transformer with a sequence classification/regression head on top (a linear layer on top of the
- pooled output) e.g. for GLUE tasks.
- """,
- BIG_BIRD_START_DOCSTRING,
-)
-class BigBirdForSequenceClassification(BigBirdPreTrainedModel):
- def __init__(self, config):
- super().__init__(config)
- self.num_labels = config.num_labels
- self.config = config
- self.bert = BigBirdModel(config)
- self.classifier = BigBirdClassificationHead(config)
-
- # Initialize weights and apply final processing
- self.post_init()
-
- @add_start_docstrings_to_model_forward(BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
- @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
- def forward(
- self,
- input_ids: torch.LongTensor = None,
- attention_mask: Optional[torch.FloatTensor] = None,
- token_type_ids: Optional[torch.LongTensor] = None,
- position_ids: Optional[torch.LongTensor] = None,
- head_mask: Optional[torch.FloatTensor] = None,
- inputs_embeds: Optional[torch.FloatTensor] = None,
- labels: Optional[torch.LongTensor] = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- ) -> Union[SequenceClassifierOutput, Tuple[torch.FloatTensor]]:
- r"""
- labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
- Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
- config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
- `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-
- Returns:
-
- Example:
-
- ```python
- >>> import torch
- >>> from transformers import AutoTokenizer, BigBirdForSequenceClassification
- >>> from datasets import load_dataset
-
- >>> tokenizer = AutoTokenizer.from_pretrained("l-yohai/bigbird-roberta-base-mnli")
- >>> model = BigBirdForSequenceClassification.from_pretrained("l-yohai/bigbird-roberta-base-mnli")
- >>> squad_ds = load_dataset("squad_v2", split="train") # doctest: +IGNORE_RESULT
-
- >>> LONG_ARTICLE = squad_ds[81514]["context"]
- >>> inputs = tokenizer(LONG_ARTICLE, return_tensors="pt")
- >>> # long input article
- >>> list(inputs["input_ids"].shape)
- [1, 919]
-
- >>> with torch.no_grad():
- ... logits = model(**inputs).logits
- >>> predicted_class_id = logits.argmax().item()
- >>> model.config.id2label[predicted_class_id]
- 'LABEL_0'
- ```
-
- ```python
- >>> num_labels = len(model.config.id2label)
- >>> model = BigBirdForSequenceClassification.from_pretrained(
- ... "l-yohai/bigbird-roberta-base-mnli", num_labels=num_labels
- ... )
- >>> labels = torch.tensor(1)
- >>> loss = model(**inputs, labels=labels).loss
- >>> round(loss.item(), 2)
- 1.13
- ```
- """
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- outputs = self.bert(
- input_ids,
- attention_mask=attention_mask,
- token_type_ids=token_type_ids,
- position_ids=position_ids,
- head_mask=head_mask,
- inputs_embeds=inputs_embeds,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- sequence_output = outputs[0]
- logits = self.classifier(sequence_output)
-
- loss = None
- if labels is not None:
- if self.config.problem_type is None:
- if self.num_labels == 1:
- self.config.problem_type = "regression"
- elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
- self.config.problem_type = "single_label_classification"
- else:
- self.config.problem_type = "multi_label_classification"
-
- if self.config.problem_type == "regression":
- loss_fct = MSELoss()
- if self.num_labels == 1:
- loss = loss_fct(logits.squeeze(), labels.squeeze())
- else:
- loss = loss_fct(logits, labels)
- elif self.config.problem_type == "single_label_classification":
- loss_fct = CrossEntropyLoss()
- loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
- elif self.config.problem_type == "multi_label_classification":
- loss_fct = BCEWithLogitsLoss()
- loss = loss_fct(logits, labels)
-
- if not return_dict:
- output = (logits,) + outputs[2:]
- return ((loss,) + output) if loss is not None else output
-
- return SequenceClassifierOutput(
- loss=loss,
- logits=logits,
- hidden_states=outputs.hidden_states,
- attentions=outputs.attentions,
- )
-
-
-@add_start_docstrings(
- """
- BigBird Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
- softmax) e.g. for RocStories/SWAG tasks.
- """,
- BIG_BIRD_START_DOCSTRING,
-)
-class BigBirdForMultipleChoice(BigBirdPreTrainedModel):
- def __init__(self, config):
- super().__init__(config)
-
- self.bert = BigBirdModel(config)
- self.dropout = nn.Dropout(config.hidden_dropout_prob)
- self.classifier = nn.Linear(config.hidden_size, 1)
-
- # Initialize weights and apply final processing
- self.post_init()
-
- @add_start_docstrings_to_model_forward(
- BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
- )
- @add_code_sample_docstrings(
- checkpoint=_CHECKPOINT_FOR_DOC,
- output_type=MultipleChoiceModelOutput,
- config_class=_CONFIG_FOR_DOC,
- )
- def forward(
- self,
- input_ids: torch.LongTensor = None,
- attention_mask: Optional[torch.FloatTensor] = None,
- token_type_ids: Optional[torch.LongTensor] = None,
- position_ids: Optional[torch.LongTensor] = None,
- head_mask: Optional[torch.FloatTensor] = None,
- inputs_embeds: Optional[torch.FloatTensor] = None,
- labels: Optional[torch.LongTensor] = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- ) -> Union[MultipleChoiceModelOutput, Tuple[torch.FloatTensor]]:
- r"""
- labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
- Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
- num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
- `input_ids` above)
- """
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
- num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
-
- input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
- attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
- token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
- position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
- inputs_embeds = (
- inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
- if inputs_embeds is not None
- else None
- )
-
- outputs = self.bert(
- input_ids,
- attention_mask=attention_mask,
- token_type_ids=token_type_ids,
- position_ids=position_ids,
- head_mask=head_mask,
- inputs_embeds=inputs_embeds,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- pooled_output = outputs[1]
-
- pooled_output = self.dropout(pooled_output)
- logits = self.classifier(pooled_output)
- reshaped_logits = logits.view(-1, num_choices)
-
- loss = None
- if labels is not None:
- loss_fct = CrossEntropyLoss()
- loss = loss_fct(reshaped_logits, labels)
-
- if not return_dict:
- output = (reshaped_logits,) + outputs[2:]
- return ((loss,) + output) if loss is not None else output
-
- return MultipleChoiceModelOutput(
- loss=loss,
- logits=reshaped_logits,
- hidden_states=outputs.hidden_states,
- attentions=outputs.attentions,
- )
-
-
-@add_start_docstrings(
- """
- BigBird Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
- Named-Entity-Recognition (NER) tasks.
- """,
- BIG_BIRD_START_DOCSTRING,
-)
-class BigBirdForTokenClassification(BigBirdPreTrainedModel):
- def __init__(self, config):
- super().__init__(config)
- self.num_labels = config.num_labels
-
- self.bert = BigBirdModel(config)
- classifier_dropout = (
- config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
- )
- self.dropout = nn.Dropout(classifier_dropout)
- self.classifier = nn.Linear(config.hidden_size, config.num_labels)
-
- # Initialize weights and apply final processing
- self.post_init()
-
- @add_start_docstrings_to_model_forward(BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
- @add_code_sample_docstrings(
- checkpoint=_CHECKPOINT_FOR_DOC,
- output_type=TokenClassifierOutput,
- config_class=_CONFIG_FOR_DOC,
- )
- def forward(
- self,
- input_ids: torch.LongTensor = None,
- attention_mask: Optional[torch.FloatTensor] = None,
- token_type_ids: Optional[torch.LongTensor] = None,
- position_ids: Optional[torch.LongTensor] = None,
- head_mask: Optional[torch.FloatTensor] = None,
- inputs_embeds: Optional[torch.FloatTensor] = None,
- labels: Optional[torch.LongTensor] = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- ) -> Union[TokenClassifierOutput, Tuple[torch.FloatTensor]]:
- r"""
- labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
- Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
- """
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- outputs = self.bert(
- input_ids,
- attention_mask=attention_mask,
- token_type_ids=token_type_ids,
- position_ids=position_ids,
- head_mask=head_mask,
- inputs_embeds=inputs_embeds,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- sequence_output = outputs[0]
-
- sequence_output = self.dropout(sequence_output)
- logits = self.classifier(sequence_output)
-
- loss = None
- if labels is not None:
- loss_fct = CrossEntropyLoss()
- loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-
- if not return_dict:
- output = (logits,) + outputs[2:]
- return ((loss,) + output) if loss is not None else output
-
- return TokenClassifierOutput(
- loss=loss,
- logits=logits,
- hidden_states=outputs.hidden_states,
- attentions=outputs.attentions,
- )
-
-
-class BigBirdForQuestionAnsweringHead(nn.Module):
- """Head for question answering tasks."""
-
- def __init__(self, config):
- super().__init__()
- self.dropout = nn.Dropout(config.hidden_dropout_prob)
- self.intermediate = BigBirdIntermediate(config)
- self.output = BigBirdOutput(config)
- self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
-
- def forward(self, encoder_output):
- hidden_states = self.dropout(encoder_output)
- hidden_states = self.intermediate(hidden_states)
- hidden_states = self.output(hidden_states, encoder_output)
- hidden_states = self.qa_outputs(hidden_states)
- return hidden_states
-
-
-@add_start_docstrings(
- """
- BigBird Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
- layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
- """,
- BIG_BIRD_START_DOCSTRING,
-)
-class BigBirdForQuestionAnswering(BigBirdPreTrainedModel):
- def __init__(self, config, add_pooling_layer=False):
- super().__init__(config)
-
- config.num_labels = 2
- self.num_labels = config.num_labels
- self.sep_token_id = config.sep_token_id
-
- self.bert = BigBirdModel(config, add_pooling_layer=add_pooling_layer)
- self.qa_classifier = BigBirdForQuestionAnsweringHead(config)
-
- # Initialize weights and apply final processing
- self.post_init()
-
- @add_start_docstrings_to_model_forward(BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
- @replace_return_docstrings(output_type=BigBirdForQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
- def forward(
- self,
- input_ids: Optional[torch.LongTensor] = None,
- attention_mask: Optional[torch.FloatTensor] = None,
- question_lengths: Optional[torch.Tensor] = None,
- token_type_ids: Optional[torch.LongTensor] = None,
- position_ids: Optional[torch.LongTensor] = None,
- head_mask: Optional[torch.FloatTensor] = None,
- inputs_embeds: Optional[torch.FloatTensor] = None,
- start_positions: Optional[torch.LongTensor] = None,
- end_positions: Optional[torch.LongTensor] = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- ) -> Union[BigBirdForQuestionAnsweringModelOutput, Tuple[torch.FloatTensor]]:
- r"""
- start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
- Labels for position (index) of the start of the labelled span for computing the token classification loss.
- Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
- are not taken into account for computing the loss.
- end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
- Labels for position (index) of the end of the labelled span for computing the token classification loss.
- Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
- are not taken into account for computing the loss.
-
- Returns:
-
- Example:
-
- ```python
- >>> import torch
- >>> from transformers import AutoTokenizer, BigBirdForQuestionAnswering
- >>> from datasets import load_dataset
-
- >>> tokenizer = AutoTokenizer.from_pretrained("google/bigbird-roberta-base")
- >>> model = BigBirdForQuestionAnswering.from_pretrained("google/bigbird-roberta-base")
- >>> squad_ds = load_dataset("squad_v2", split="train") # doctest: +IGNORE_RESULT
-
- >>> # select random article and question
- >>> LONG_ARTICLE = squad_ds[81514]["context"]
- >>> QUESTION = squad_ds[81514]["question"]
- >>> QUESTION
- 'During daytime how high can the temperatures reach?'
-
- >>> inputs = tokenizer(QUESTION, LONG_ARTICLE, return_tensors="pt")
- >>> # long article and question input
- >>> list(inputs["input_ids"].shape)
- [1, 929]
-
- >>> with torch.no_grad():
- ... outputs = model(**inputs)
-
- >>> answer_start_index = outputs.start_logits.argmax()
- >>> answer_end_index = outputs.end_logits.argmax()
- >>> predict_answer_token_ids = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
- >>> predict_answer_token = tokenizer.decode(predict_answer_token_ids)
- ```
-
- ```python
- >>> target_start_index, target_end_index = torch.tensor([130]), torch.tensor([132])
- >>> outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
- >>> loss = outputs.loss
- ```
- """
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
- seqlen = input_ids.size(1) if input_ids is not None else inputs_embeds.size(1)
-
- if question_lengths is None and input_ids is not None:
- # assuming input_ids format: context
- question_lengths = torch.argmax(input_ids.eq(self.sep_token_id).int(), dim=-1) + 1
- question_lengths.unsqueeze_(1)
-
- logits_mask = None
- if question_lengths is not None:
- # setting lengths logits to `-inf`
- logits_mask = self.prepare_question_mask(question_lengths, seqlen)
- if token_type_ids is None:
- token_type_ids = torch.ones(logits_mask.size(), dtype=int, device=logits_mask.device) - logits_mask
- logits_mask = logits_mask
- logits_mask[:, 0] = False
- logits_mask.unsqueeze_(2)
-
- outputs = self.bert(
- input_ids,
- attention_mask=attention_mask,
- token_type_ids=token_type_ids,
- position_ids=position_ids,
- head_mask=head_mask,
- inputs_embeds=inputs_embeds,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- sequence_output = outputs[0]
- logits = self.qa_classifier(sequence_output)
-
- if logits_mask is not None:
- # removing question tokens from the competition
- logits = logits - logits_mask * 1e6
-
- start_logits, end_logits = logits.split(1, dim=-1)
- start_logits = start_logits.squeeze(-1).contiguous()
- end_logits = end_logits.squeeze(-1).contiguous()
-
- total_loss = None
- if start_positions is not None and end_positions is not None:
- # If we are on multi-GPU, split add a dimension
- if len(start_positions.size()) > 1:
- start_positions = start_positions.squeeze(-1)
- if len(end_positions.size()) > 1:
- end_positions = end_positions.squeeze(-1)
- # sometimes the start/end positions are outside our model inputs, we ignore these terms
- ignored_index = start_logits.size(1)
- start_positions = start_positions.clamp(0, ignored_index)
- end_positions = end_positions.clamp(0, ignored_index)
-
- loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
- start_loss = loss_fct(start_logits, start_positions)
- end_loss = loss_fct(end_logits, end_positions)
- total_loss = (start_loss + end_loss) / 2
-
- if not return_dict:
- output = (start_logits, end_logits) + outputs[2:]
- return ((total_loss,) + output) if total_loss is not None else output
-
- return BigBirdForQuestionAnsweringModelOutput(
- loss=total_loss,
- start_logits=start_logits,
- end_logits=end_logits,
- pooler_output=outputs.pooler_output,
- hidden_states=outputs.hidden_states,
- attentions=outputs.attentions,
- )
-
- @staticmethod
- def prepare_question_mask(q_lengths: torch.Tensor, maxlen: int):
- # q_lengths -> (bz, 1)
- mask = torch.arange(0, maxlen).to(q_lengths.device)
- mask.unsqueeze_(0) # -> (1, maxlen)
- mask = torch.where(mask < q_lengths, 1, 0)
- return mask
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/layoutlmv2/tokenization_layoutlmv2_fast.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/layoutlmv2/tokenization_layoutlmv2_fast.py
deleted file mode 100644
index bed4e133aa3c5ceec5b2277390ecfb41e56b4e1c..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/layoutlmv2/tokenization_layoutlmv2_fast.py
+++ /dev/null
@@ -1,817 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Fast tokenization class for LayoutLMv2. It overwrites 2 methods of the slow tokenizer class, namely _batch_encode_plus
-and _encode_plus, in which the Rust tokenizer is used.
-"""
-
-import json
-from typing import Dict, List, Optional, Tuple, Union
-
-from tokenizers import normalizers
-
-from ...tokenization_utils_base import (
- BatchEncoding,
- EncodedInput,
- PaddingStrategy,
- PreTokenizedInput,
- TensorType,
- TextInput,
- TextInputPair,
- TruncationStrategy,
-)
-from ...tokenization_utils_fast import PreTrainedTokenizerFast
-from ...utils import add_end_docstrings, logging
-from .tokenization_layoutlmv2 import (
- LAYOUTLMV2_ENCODE_KWARGS_DOCSTRING,
- LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING,
- LayoutLMv2Tokenizer,
-)
-
-
-logger = logging.get_logger(__name__)
-
-VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
-
-PRETRAINED_VOCAB_FILES_MAP = {
- "vocab_file": {
- "microsoft/layoutlmv2-base-uncased": (
- "https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/vocab.txt"
- ),
- },
- "tokenizer_file": {
- "microsoft/layoutlmv2-base-uncased": (
- "https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/tokenizer.json"
- ),
- },
-}
-
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
- "microsoft/layoutlmv2-base-uncased": 512,
-}
-
-PRETRAINED_INIT_CONFIGURATION = {
- "microsoft/layoutlmv2-base-uncased": {"do_lower_case": True},
-}
-
-
-class LayoutLMv2TokenizerFast(PreTrainedTokenizerFast):
- r"""
- Construct a "fast" LayoutLMv2 tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
-
- This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
- refer to this superclass for more information regarding those methods.
-
- Args:
- vocab_file (`str`):
- File containing the vocabulary.
- do_lower_case (`bool`, *optional*, defaults to `True`):
- Whether or not to lowercase the input when tokenizing.
- unk_token (`str`, *optional*, defaults to `"[UNK]"`):
- The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
- token instead.
- sep_token (`str`, *optional*, defaults to `"[SEP]"`):
- The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
- sequence classification or for a text and a question for question answering. It is also used as the last
- token of a sequence built with special tokens.
- pad_token (`str`, *optional*, defaults to `"[PAD]"`):
- The token used for padding, for example when batching sequences of different lengths.
- cls_token (`str`, *optional*, defaults to `"[CLS]"`):
- The classifier token which is used when doing sequence classification (classification of the whole sequence
- instead of per-token classification). It is the first token of the sequence when built with special tokens.
- mask_token (`str`, *optional*, defaults to `"[MASK]"`):
- The token used for masking values. This is the token used when training this model with masked language
- modeling. This is the token which the model will try to predict.
- cls_token_box (`List[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
- The bounding box to use for the special [CLS] token.
- sep_token_box (`List[int]`, *optional*, defaults to `[1000, 1000, 1000, 1000]`):
- The bounding box to use for the special [SEP] token.
- pad_token_box (`List[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
- The bounding box to use for the special [PAD] token.
- pad_token_label (`int`, *optional*, defaults to -100):
- The label to use for padding tokens. Defaults to -100, which is the `ignore_index` of PyTorch's
- CrossEntropyLoss.
- only_label_first_subword (`bool`, *optional*, defaults to `True`):
- Whether or not to only label the first subword, in case word labels are provided.
- tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
- Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see [this
- issue](https://github.com/huggingface/transformers/issues/328)).
- strip_accents (`bool`, *optional*):
- Whether or not to strip all accents. If this option is not specified, then it will be determined by the
- value for `lowercase` (as in the original LayoutLMv2).
- """
-
- vocab_files_names = VOCAB_FILES_NAMES
- pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
- pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
- max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
- slow_tokenizer_class = LayoutLMv2Tokenizer
-
- def __init__(
- self,
- vocab_file=None,
- tokenizer_file=None,
- do_lower_case=True,
- unk_token="[UNK]",
- sep_token="[SEP]",
- pad_token="[PAD]",
- cls_token="[CLS]",
- mask_token="[MASK]",
- cls_token_box=[0, 0, 0, 0],
- sep_token_box=[1000, 1000, 1000, 1000],
- pad_token_box=[0, 0, 0, 0],
- pad_token_label=-100,
- only_label_first_subword=True,
- tokenize_chinese_chars=True,
- strip_accents=None,
- **kwargs,
- ):
- super().__init__(
- vocab_file,
- tokenizer_file=tokenizer_file,
- do_lower_case=do_lower_case,
- unk_token=unk_token,
- sep_token=sep_token,
- pad_token=pad_token,
- cls_token=cls_token,
- mask_token=mask_token,
- cls_token_box=cls_token_box,
- sep_token_box=sep_token_box,
- pad_token_box=pad_token_box,
- pad_token_label=pad_token_label,
- only_label_first_subword=only_label_first_subword,
- tokenize_chinese_chars=tokenize_chinese_chars,
- strip_accents=strip_accents,
- **kwargs,
- )
-
- pre_tok_state = json.loads(self.backend_tokenizer.normalizer.__getstate__())
- if (
- pre_tok_state.get("lowercase", do_lower_case) != do_lower_case
- or pre_tok_state.get("strip_accents", strip_accents) != strip_accents
- ):
- pre_tok_class = getattr(normalizers, pre_tok_state.pop("type"))
- pre_tok_state["lowercase"] = do_lower_case
- pre_tok_state["strip_accents"] = strip_accents
- self.backend_tokenizer.normalizer = pre_tok_class(**pre_tok_state)
-
- self.do_lower_case = do_lower_case
-
- # additional properties
- self.cls_token_box = cls_token_box
- self.sep_token_box = sep_token_box
- self.pad_token_box = pad_token_box
- self.pad_token_label = pad_token_label
- self.only_label_first_subword = only_label_first_subword
-
- @add_end_docstrings(LAYOUTLMV2_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
- def __call__(
- self,
- text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
- text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
- boxes: Union[List[List[int]], List[List[List[int]]]] = None,
- word_labels: Optional[Union[List[int], List[List[int]]]] = None,
- add_special_tokens: bool = True,
- padding: Union[bool, str, PaddingStrategy] = False,
- truncation: Union[bool, str, TruncationStrategy] = None,
- max_length: Optional[int] = None,
- stride: int = 0,
- pad_to_multiple_of: Optional[int] = None,
- return_tensors: Optional[Union[str, TensorType]] = None,
- return_token_type_ids: Optional[bool] = None,
- return_attention_mask: Optional[bool] = None,
- return_overflowing_tokens: bool = False,
- return_special_tokens_mask: bool = False,
- return_offsets_mapping: bool = False,
- return_length: bool = False,
- verbose: bool = True,
- **kwargs,
- ) -> BatchEncoding:
- """
- Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
- sequences with word-level normalized bounding boxes and optional labels.
-
- Args:
- text (`str`, `List[str]`, `List[List[str]]`):
- The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings
- (words of a single example or questions of a batch of examples) or a list of list of strings (batch of
- words).
- text_pair (`List[str]`, `List[List[str]]`):
- The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
- (pretokenized string).
- boxes (`List[List[int]]`, `List[List[List[int]]]`):
- Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale.
- word_labels (`List[int]`, `List[List[int]]`, *optional*):
- Word-level integer labels (for token classification tasks such as FUNSD, CORD).
- """
-
- # Input type checking for clearer error
- def _is_valid_text_input(t):
- if isinstance(t, str):
- # Strings are fine
- return True
- elif isinstance(t, (list, tuple)):
- # List are fine as long as they are...
- if len(t) == 0:
- # ... empty
- return True
- elif isinstance(t[0], str):
- # ... list of strings
- return True
- elif isinstance(t[0], (list, tuple)):
- # ... list with an empty list or with a list of strings
- return len(t[0]) == 0 or isinstance(t[0][0], str)
- else:
- return False
- else:
- return False
-
- if text_pair is not None:
- # in case text + text_pair are provided, text = questions, text_pair = words
- if not _is_valid_text_input(text):
- raise ValueError("text input must of type `str` (single example) or `List[str]` (batch of examples). ")
- if not isinstance(text_pair, (list, tuple)):
- raise ValueError(
- "Words must be of type `List[str]` (single pretokenized example), "
- "or `List[List[str]]` (batch of pretokenized examples)."
- )
- else:
- # in case only text is provided => must be words
- if not isinstance(text, (list, tuple)):
- raise ValueError(
- "Words must be of type `List[str]` (single pretokenized example), "
- "or `List[List[str]]` (batch of pretokenized examples)."
- )
-
- if text_pair is not None:
- is_batched = isinstance(text, (list, tuple))
- else:
- is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))
-
- words = text if text_pair is None else text_pair
- if boxes is None:
- raise ValueError("You must provide corresponding bounding boxes")
- if is_batched:
- if len(words) != len(boxes):
- raise ValueError("You must provide words and boxes for an equal amount of examples")
- for words_example, boxes_example in zip(words, boxes):
- if len(words_example) != len(boxes_example):
- raise ValueError("You must provide as many words as there are bounding boxes")
- else:
- if len(words) != len(boxes):
- raise ValueError("You must provide as many words as there are bounding boxes")
-
- if is_batched:
- if text_pair is not None and len(text) != len(text_pair):
- raise ValueError(
- f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:"
- f" {len(text_pair)}."
- )
- batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
- is_pair = bool(text_pair is not None)
- return self.batch_encode_plus(
- batch_text_or_text_pairs=batch_text_or_text_pairs,
- is_pair=is_pair,
- boxes=boxes,
- word_labels=word_labels,
- add_special_tokens=add_special_tokens,
- padding=padding,
- truncation=truncation,
- max_length=max_length,
- stride=stride,
- pad_to_multiple_of=pad_to_multiple_of,
- return_tensors=return_tensors,
- return_token_type_ids=return_token_type_ids,
- return_attention_mask=return_attention_mask,
- return_overflowing_tokens=return_overflowing_tokens,
- return_special_tokens_mask=return_special_tokens_mask,
- return_offsets_mapping=return_offsets_mapping,
- return_length=return_length,
- verbose=verbose,
- **kwargs,
- )
- else:
- return self.encode_plus(
- text=text,
- text_pair=text_pair,
- boxes=boxes,
- word_labels=word_labels,
- add_special_tokens=add_special_tokens,
- padding=padding,
- truncation=truncation,
- max_length=max_length,
- stride=stride,
- pad_to_multiple_of=pad_to_multiple_of,
- return_tensors=return_tensors,
- return_token_type_ids=return_token_type_ids,
- return_attention_mask=return_attention_mask,
- return_overflowing_tokens=return_overflowing_tokens,
- return_special_tokens_mask=return_special_tokens_mask,
- return_offsets_mapping=return_offsets_mapping,
- return_length=return_length,
- verbose=verbose,
- **kwargs,
- )
-
- @add_end_docstrings(LAYOUTLMV2_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
- def batch_encode_plus(
- self,
- batch_text_or_text_pairs: Union[
- List[TextInput],
- List[TextInputPair],
- List[PreTokenizedInput],
- ],
- is_pair: bool = None,
- boxes: Optional[List[List[List[int]]]] = None,
- word_labels: Optional[Union[List[int], List[List[int]]]] = None,
- add_special_tokens: bool = True,
- padding: Union[bool, str, PaddingStrategy] = False,
- truncation: Union[bool, str, TruncationStrategy] = None,
- max_length: Optional[int] = None,
- stride: int = 0,
- pad_to_multiple_of: Optional[int] = None,
- return_tensors: Optional[Union[str, TensorType]] = None,
- return_token_type_ids: Optional[bool] = None,
- return_attention_mask: Optional[bool] = None,
- return_overflowing_tokens: bool = False,
- return_special_tokens_mask: bool = False,
- return_offsets_mapping: bool = False,
- return_length: bool = False,
- verbose: bool = True,
- **kwargs,
- ) -> BatchEncoding:
- # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
- padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
- padding=padding,
- truncation=truncation,
- max_length=max_length,
- pad_to_multiple_of=pad_to_multiple_of,
- verbose=verbose,
- **kwargs,
- )
-
- return self._batch_encode_plus(
- batch_text_or_text_pairs=batch_text_or_text_pairs,
- is_pair=is_pair,
- boxes=boxes,
- word_labels=word_labels,
- add_special_tokens=add_special_tokens,
- padding_strategy=padding_strategy,
- truncation_strategy=truncation_strategy,
- max_length=max_length,
- stride=stride,
- pad_to_multiple_of=pad_to_multiple_of,
- return_tensors=return_tensors,
- return_token_type_ids=return_token_type_ids,
- return_attention_mask=return_attention_mask,
- return_overflowing_tokens=return_overflowing_tokens,
- return_special_tokens_mask=return_special_tokens_mask,
- return_offsets_mapping=return_offsets_mapping,
- return_length=return_length,
- verbose=verbose,
- **kwargs,
- )
-
- def tokenize(self, text: str, pair: Optional[str] = None, add_special_tokens: bool = False, **kwargs) -> List[str]:
- batched_input = [(text, pair)] if pair else [text]
- encodings = self._tokenizer.encode_batch(
- batched_input, add_special_tokens=add_special_tokens, is_pretokenized=False, **kwargs
- )
-
- return encodings[0].tokens
-
- @add_end_docstrings(LAYOUTLMV2_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
- def encode_plus(
- self,
- text: Union[TextInput, PreTokenizedInput],
- text_pair: Optional[PreTokenizedInput] = None,
- boxes: Optional[List[List[int]]] = None,
- word_labels: Optional[List[int]] = None,
- add_special_tokens: bool = True,
- padding: Union[bool, str, PaddingStrategy] = False,
- truncation: Union[bool, str, TruncationStrategy] = None,
- max_length: Optional[int] = None,
- stride: int = 0,
- pad_to_multiple_of: Optional[int] = None,
- return_tensors: Optional[Union[str, TensorType]] = None,
- return_token_type_ids: Optional[bool] = None,
- return_attention_mask: Optional[bool] = None,
- return_overflowing_tokens: bool = False,
- return_special_tokens_mask: bool = False,
- return_offsets_mapping: bool = False,
- return_length: bool = False,
- verbose: bool = True,
- **kwargs,
- ) -> BatchEncoding:
- """
- Tokenize and prepare for the model a sequence or a pair of sequences. .. warning:: This method is deprecated,
- `__call__` should be used instead.
-
- Args:
- text (`str`, `List[str]`, `List[List[str]]`):
- The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
- text_pair (`List[str]` or `List[int]`, *optional*):
- Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a
- list of list of strings (words of a batch of examples).
- """
-
- # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
- padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
- padding=padding,
- truncation=truncation,
- max_length=max_length,
- pad_to_multiple_of=pad_to_multiple_of,
- verbose=verbose,
- **kwargs,
- )
-
- return self._encode_plus(
- text=text,
- boxes=boxes,
- text_pair=text_pair,
- word_labels=word_labels,
- add_special_tokens=add_special_tokens,
- padding_strategy=padding_strategy,
- truncation_strategy=truncation_strategy,
- max_length=max_length,
- stride=stride,
- pad_to_multiple_of=pad_to_multiple_of,
- return_tensors=return_tensors,
- return_token_type_ids=return_token_type_ids,
- return_attention_mask=return_attention_mask,
- return_overflowing_tokens=return_overflowing_tokens,
- return_special_tokens_mask=return_special_tokens_mask,
- return_offsets_mapping=return_offsets_mapping,
- return_length=return_length,
- verbose=verbose,
- **kwargs,
- )
-
- def _batch_encode_plus(
- self,
- batch_text_or_text_pairs: Union[
- List[TextInput],
- List[TextInputPair],
- List[PreTokenizedInput],
- ],
- is_pair: bool = None,
- boxes: Optional[List[List[List[int]]]] = None,
- word_labels: Optional[List[List[int]]] = None,
- add_special_tokens: bool = True,
- padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
- truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
- max_length: Optional[int] = None,
- stride: int = 0,
- pad_to_multiple_of: Optional[int] = None,
- return_tensors: Optional[str] = None,
- return_token_type_ids: Optional[bool] = None,
- return_attention_mask: Optional[bool] = None,
- return_overflowing_tokens: bool = False,
- return_special_tokens_mask: bool = False,
- return_offsets_mapping: bool = False,
- return_length: bool = False,
- verbose: bool = True,
- ) -> BatchEncoding:
- if not isinstance(batch_text_or_text_pairs, list):
- raise TypeError(f"batch_text_or_text_pairs has to be a list (got {type(batch_text_or_text_pairs)})")
-
- # Set the truncation and padding strategy and restore the initial configuration
- self.set_truncation_and_padding(
- padding_strategy=padding_strategy,
- truncation_strategy=truncation_strategy,
- max_length=max_length,
- stride=stride,
- pad_to_multiple_of=pad_to_multiple_of,
- )
-
- if is_pair:
- batch_text_or_text_pairs = [(text.split(), text_pair) for text, text_pair in batch_text_or_text_pairs]
-
- encodings = self._tokenizer.encode_batch(
- batch_text_or_text_pairs,
- add_special_tokens=add_special_tokens,
- is_pretokenized=True, # we set this to True as LayoutLMv2 always expects pretokenized inputs
- )
-
- # Convert encoding to dict
- # `Tokens` has type: Tuple[
- # List[Dict[str, List[List[int]]]] or List[Dict[str, 2D-Tensor]],
- # List[EncodingFast]
- # ]
- # with nested dimensions corresponding to batch, overflows, sequence length
- tokens_and_encodings = [
- self._convert_encoding(
- encoding=encoding,
- return_token_type_ids=return_token_type_ids,
- return_attention_mask=return_attention_mask,
- return_overflowing_tokens=return_overflowing_tokens,
- return_special_tokens_mask=return_special_tokens_mask,
- return_offsets_mapping=True
- if word_labels is not None
- else return_offsets_mapping, # we use offsets to create the labels
- return_length=return_length,
- verbose=verbose,
- )
- for encoding in encodings
- ]
-
- # Convert the output to have dict[list] from list[dict] and remove the additional overflows dimension
- # From (variable) shape (batch, overflows, sequence length) to ~ (batch * overflows, sequence length)
- # (we say ~ because the number of overflow varies with the example in the batch)
- #
- # To match each overflowing sample with the original sample in the batch
- # we add an overflow_to_sample_mapping array (see below)
- sanitized_tokens = {}
- for key in tokens_and_encodings[0][0].keys():
- stack = [e for item, _ in tokens_and_encodings for e in item[key]]
- sanitized_tokens[key] = stack
- sanitized_encodings = [e for _, item in tokens_and_encodings for e in item]
-
- # If returning overflowing tokens, we need to return a mapping
- # from the batch idx to the original sample
- if return_overflowing_tokens:
- overflow_to_sample_mapping = []
- for i, (toks, _) in enumerate(tokens_and_encodings):
- overflow_to_sample_mapping += [i] * len(toks["input_ids"])
- sanitized_tokens["overflow_to_sample_mapping"] = overflow_to_sample_mapping
-
- for input_ids in sanitized_tokens["input_ids"]:
- self._eventual_warn_about_too_long_sequence(input_ids, max_length, verbose)
-
- # create the token boxes
- token_boxes = []
- for batch_index in range(len(sanitized_tokens["input_ids"])):
- if return_overflowing_tokens:
- original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index]
- else:
- original_index = batch_index
- token_boxes_example = []
- for id, sequence_id, word_id in zip(
- sanitized_tokens["input_ids"][batch_index],
- sanitized_encodings[batch_index].sequence_ids,
- sanitized_encodings[batch_index].word_ids,
- ):
- if word_id is not None:
- if is_pair and sequence_id == 0:
- token_boxes_example.append(self.pad_token_box)
- else:
- token_boxes_example.append(boxes[original_index][word_id])
- else:
- if id == self.cls_token_id:
- token_boxes_example.append(self.cls_token_box)
- elif id == self.sep_token_id:
- token_boxes_example.append(self.sep_token_box)
- elif id == self.pad_token_id:
- token_boxes_example.append(self.pad_token_box)
- else:
- raise ValueError("Id not recognized")
- token_boxes.append(token_boxes_example)
-
- sanitized_tokens["bbox"] = token_boxes
-
- # optionally, create the labels
- if word_labels is not None:
- labels = []
- for batch_index in range(len(sanitized_tokens["input_ids"])):
- if return_overflowing_tokens:
- original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index]
- else:
- original_index = batch_index
- labels_example = []
- for id, offset, word_id in zip(
- sanitized_tokens["input_ids"][batch_index],
- sanitized_tokens["offset_mapping"][batch_index],
- sanitized_encodings[batch_index].word_ids,
- ):
- if word_id is not None:
- if self.only_label_first_subword:
- if offset[0] == 0:
- # Use the real label id for the first token of the word, and padding ids for the remaining tokens
- labels_example.append(word_labels[original_index][word_id])
- else:
- labels_example.append(self.pad_token_label)
- else:
- labels_example.append(word_labels[original_index][word_id])
- else:
- labels_example.append(self.pad_token_label)
- labels.append(labels_example)
-
- sanitized_tokens["labels"] = labels
- # finally, remove offsets if the user didn't want them
- if not return_offsets_mapping:
- del sanitized_tokens["offset_mapping"]
-
- return BatchEncoding(sanitized_tokens, sanitized_encodings, tensor_type=return_tensors)
-
- def _encode_plus(
- self,
- text: Union[TextInput, PreTokenizedInput],
- text_pair: Optional[PreTokenizedInput] = None,
- boxes: Optional[List[List[int]]] = None,
- word_labels: Optional[List[int]] = None,
- add_special_tokens: bool = True,
- padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
- truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
- max_length: Optional[int] = None,
- stride: int = 0,
- pad_to_multiple_of: Optional[int] = None,
- return_tensors: Optional[bool] = None,
- return_token_type_ids: Optional[bool] = None,
- return_attention_mask: Optional[bool] = None,
- return_overflowing_tokens: bool = False,
- return_special_tokens_mask: bool = False,
- return_offsets_mapping: bool = False,
- return_length: bool = False,
- verbose: bool = True,
- **kwargs,
- ) -> BatchEncoding:
- # make it a batched input
- # 2 options:
- # 1) only text, in case text must be a list of str
- # 2) text + text_pair, in which case text = str and text_pair a list of str
- batched_input = [(text, text_pair)] if text_pair else [text]
- batched_boxes = [boxes]
- batched_word_labels = [word_labels] if word_labels is not None else None
- batched_output = self._batch_encode_plus(
- batched_input,
- is_pair=bool(text_pair is not None),
- boxes=batched_boxes,
- word_labels=batched_word_labels,
- add_special_tokens=add_special_tokens,
- padding_strategy=padding_strategy,
- truncation_strategy=truncation_strategy,
- max_length=max_length,
- stride=stride,
- pad_to_multiple_of=pad_to_multiple_of,
- return_tensors=return_tensors,
- return_token_type_ids=return_token_type_ids,
- return_attention_mask=return_attention_mask,
- return_overflowing_tokens=return_overflowing_tokens,
- return_special_tokens_mask=return_special_tokens_mask,
- return_offsets_mapping=return_offsets_mapping,
- return_length=return_length,
- verbose=verbose,
- **kwargs,
- )
-
- # Return tensor is None, then we can remove the leading batch axis
- # Overflowing tokens are returned as a batch of output so we keep them in this case
- if return_tensors is None and not return_overflowing_tokens:
- batched_output = BatchEncoding(
- {
- key: value[0] if len(value) > 0 and isinstance(value[0], list) else value
- for key, value in batched_output.items()
- },
- batched_output.encodings,
- )
-
- self._eventual_warn_about_too_long_sequence(batched_output["input_ids"], max_length, verbose)
-
- return batched_output
-
- def _pad(
- self,
- encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
- max_length: Optional[int] = None,
- padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
- pad_to_multiple_of: Optional[int] = None,
- return_attention_mask: Optional[bool] = None,
- ) -> dict:
- """
- Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
-
- Args:
- encoded_inputs:
- Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
- max_length: maximum length of the returned list and optionally padding length (see below).
- Will truncate by taking into account the special tokens.
- padding_strategy: PaddingStrategy to use for padding.
-
- - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
- - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
- - PaddingStrategy.DO_NOT_PAD: Do not pad
- The tokenizer padding sides are defined in self.padding_side:
-
- - 'left': pads on the left of the sequences
- - 'right': pads on the right of the sequences
- pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
- This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
- `>= 7.5` (Volta).
- return_attention_mask:
- (optional) Set to False to avoid returning attention mask (default: set to model specifics)
- """
- # Load from model defaults
- if return_attention_mask is None:
- return_attention_mask = "attention_mask" in self.model_input_names
-
- required_input = encoded_inputs[self.model_input_names[0]]
-
- if padding_strategy == PaddingStrategy.LONGEST:
- max_length = len(required_input)
-
- if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
- max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
-
- needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
-
- # Initialize attention mask if not present.
- if return_attention_mask and "attention_mask" not in encoded_inputs:
- encoded_inputs["attention_mask"] = [1] * len(required_input)
-
- if needs_to_be_padded:
- difference = max_length - len(required_input)
- if self.padding_side == "right":
- if return_attention_mask:
- encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference
- if "token_type_ids" in encoded_inputs:
- encoded_inputs["token_type_ids"] = (
- encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
- )
- if "bbox" in encoded_inputs:
- encoded_inputs["bbox"] = encoded_inputs["bbox"] + [self.pad_token_box] * difference
- if "labels" in encoded_inputs:
- encoded_inputs["labels"] = encoded_inputs["labels"] + [self.pad_token_label] * difference
- if "special_tokens_mask" in encoded_inputs:
- encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
- encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference
- elif self.padding_side == "left":
- if return_attention_mask:
- encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
- if "token_type_ids" in encoded_inputs:
- encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
- "token_type_ids"
- ]
- if "bbox" in encoded_inputs:
- encoded_inputs["bbox"] = [self.pad_token_box] * difference + encoded_inputs["bbox"]
- if "labels" in encoded_inputs:
- encoded_inputs["labels"] = [self.pad_token_label] * difference + encoded_inputs["labels"]
- if "special_tokens_mask" in encoded_inputs:
- encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
- encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
- else:
- raise ValueError("Invalid padding strategy:" + str(self.padding_side))
-
- return encoded_inputs
-
- def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
- """
- Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
- adding special tokens. A BERT sequence has the following format:
-
- - single sequence: `[CLS] X [SEP]`
- - pair of sequences: `[CLS] A [SEP] B [SEP]`
-
- Args:
- token_ids_0 (`List[int]`):
- List of IDs to which the special tokens will be added.
- token_ids_1 (`List[int]`, *optional*):
- Optional second list of IDs for sequence pairs.
-
- Returns:
- `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
- """
- output = [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
-
- if token_ids_1:
- output += token_ids_1 + [self.sep_token_id]
-
- return output
-
- def create_token_type_ids_from_sequences(
- self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
- ) -> List[int]:
- """
- Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence
- pair mask has the following format: :: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second
- sequence | If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
-
- Args:
- token_ids_0 (`List[int]`):
- List of IDs.
- token_ids_1 (`List[int]`, *optional*):
- Optional second list of IDs for sequence pairs.
-
- Returns:
- `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
- """
- sep = [self.sep_token_id]
- cls = [self.cls_token_id]
- if token_ids_1 is None:
- return len(cls + token_ids_0 + sep) * [0]
- return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
-
- def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
- files = self._tokenizer.model.save(save_directory, name=filename_prefix)
- return tuple(files)
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/dev/run_inference_tests.sh b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/dev/run_inference_tests.sh
deleted file mode 100644
index bc9dcc56f06f79fc5efa42c04ffdc07c2787e3ac..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/dev/run_inference_tests.sh
+++ /dev/null
@@ -1,44 +0,0 @@
-#!/bin/bash -e
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-BIN="python tools/train_net.py"
-OUTPUT="inference_test_output"
-NUM_GPUS=2
-
-CFG_LIST=( "${@:1}" )
-
-if [ ${#CFG_LIST[@]} -eq 0 ]; then
- CFG_LIST=( ./configs/quick_schedules/*inference_acc_test.yaml )
-fi
-
-echo "========================================================================"
-echo "Configs to run:"
-echo "${CFG_LIST[@]}"
-echo "========================================================================"
-
-
-for cfg in "${CFG_LIST[@]}"; do
- echo "========================================================================"
- echo "Running $cfg ..."
- echo "========================================================================"
- $BIN \
- --eval-only \
- --num-gpus $NUM_GPUS \
- --config-file "$cfg" \
- OUTPUT_DIR $OUTPUT
- rm -rf $OUTPUT
-done
-
-
-echo "========================================================================"
-echo "Running demo.py ..."
-echo "========================================================================"
-DEMO_BIN="python demo/demo.py"
-COCO_DIR=datasets/coco/val2014
-mkdir -pv $OUTPUT
-
-set -v
-
-$DEMO_BIN --config-file ./configs/quick_schedules/panoptic_fpn_R_50_inference_acc_test.yaml \
- --input $COCO_DIR/COCO_val2014_0000001933* --output $OUTPUT
-rm -rf $OUTPUT
diff --git a/spaces/yuan2023/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/controlnet/controlnet_hed.py b/spaces/yuan2023/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/controlnet/controlnet_hed.py
deleted file mode 100644
index d85742c8cad946a4d25ac4a4493abcd9b88979c9..0000000000000000000000000000000000000000
--- a/spaces/yuan2023/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/controlnet/controlnet_hed.py
+++ /dev/null
@@ -1,170 +0,0 @@
-import gradio as gr
-import torch
-from controlnet_aux import HEDdetector
-from diffusers import (
- ControlNetModel,
- StableDiffusionControlNetPipeline,
- UniPCMultistepScheduler,
-)
-from PIL import Image
-
-stable_model_list = [
- "runwayml/stable-diffusion-v1-5",
- "stabilityai/stable-diffusion-2-1",
-]
-
-controlnet_hed_model_list = [
- "lllyasviel/sd-controlnet-hed",
- "thibaud/controlnet-sd21-hed-diffusers",
-]
-
-stable_prompt_list = ["a photo of a man.", "a photo of a girl."]
-
-stable_negative_prompt_list = ["bad, ugly", "deformed"]
-
-data_list = [
- "data/test.png",
-]
-
-
-def controlnet_hed(image_path: str, controlnet_hed_model_path: str):
- hed = HEDdetector.from_pretrained("lllyasviel/ControlNet")
-
- image = Image.open(image_path)
- image = hed(image)
-
- controlnet = ControlNetModel.from_pretrained(
- controlnet_hed_model_path, torch_dtype=torch.float16
- )
- return controlnet, image
-
-
-def stable_diffusion_controlnet_hed(
- image_path: str,
- stable_model_path: str,
- controlnet_hed_model_path: str,
- prompt: str,
- negative_prompt: str,
- guidance_scale: int,
- num_inference_step: int,
-):
-
- controlnet, image = controlnet_hed(
- image_path=image_path,
- controlnet_hed_model_path=controlnet_hed_model_path,
- )
-
- pipe = StableDiffusionControlNetPipeline.from_pretrained(
- pretrained_model_name_or_path=stable_model_path,
- controlnet=controlnet,
- safety_checker=None,
- torch_dtype=torch.float16,
- )
-
- pipe.to("cuda")
- pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
- pipe.enable_xformers_memory_efficient_attention()
-
- output = pipe(
- prompt=prompt,
- image=image,
- negative_prompt=negative_prompt,
- num_inference_steps=num_inference_step,
- guidance_scale=guidance_scale,
- ).images
-
- return output[0]
-
-
-def stable_diffusion_controlnet_hed_app():
- with gr.Blocks():
- with gr.Row():
- with gr.Column():
- controlnet_hed_image_file = gr.Image(
- type="filepath", label="Image"
- )
-
- controlnet_hed_stable_model_id = gr.Dropdown(
- choices=stable_model_list,
- value=stable_model_list[0],
- label="Stable Model Id",
- )
-
- controlnet_hed_model_id = gr.Dropdown(
- choices=controlnet_hed_model_list,
- value=controlnet_hed_model_list[1],
- label="ControlNet Model Id",
- )
-
- controlnet_hed_prompt = gr.Textbox(
- lines=1, value=stable_prompt_list[0], label="Prompt"
- )
-
- controlnet_hed_negative_prompt = gr.Textbox(
- lines=1,
- value=stable_negative_prompt_list[0],
- label="Negative Prompt",
- )
-
- with gr.Accordion("Advanced Options", open=False):
- controlnet_hed_guidance_scale = gr.Slider(
- minimum=0.1,
- maximum=15,
- step=0.1,
- value=7.5,
- label="Guidance Scale",
- )
-
- controlnet_hed_num_inference_step = gr.Slider(
- minimum=1,
- maximum=100,
- step=1,
- value=50,
- label="Num Inference Step",
- )
-
- controlnet_hed_predict = gr.Button(value="Generator")
-
- with gr.Column():
- output_image = gr.Image(label="Output")
-
- gr.Examples(
- fn=stable_diffusion_controlnet_hed,
- examples=[
- [
- data_list[0],
- stable_model_list[0],
- controlnet_hed_model_list[0],
- stable_prompt_list[0],
- stable_negative_prompt_list[0],
- 7.5,
- 50,
- ]
- ],
- inputs=[
- controlnet_hed_image_file,
- controlnet_hed_stable_model_id,
- controlnet_hed_model_id,
- controlnet_hed_prompt,
- controlnet_hed_negative_prompt,
- controlnet_hed_guidance_scale,
- controlnet_hed_num_inference_step,
- ],
- outputs=[output_image],
- cache_examples=False,
- label="ControlNet HED Example",
- )
-
- controlnet_hed_predict.click(
- fn=stable_diffusion_controlnet_hed,
- inputs=[
- controlnet_hed_image_file,
- controlnet_hed_stable_model_id,
- controlnet_hed_model_id,
- controlnet_hed_prompt,
- controlnet_hed_negative_prompt,
- controlnet_hed_guidance_scale,
- controlnet_hed_num_inference_step,
- ],
- outputs=[output_image],
- )
diff --git a/spaces/zachriek/chatgpt-clone/app.py b/spaces/zachriek/chatgpt-clone/app.py
deleted file mode 100644
index 231f6e952ab746c61d870f1b99aac87386214081..0000000000000000000000000000000000000000
--- a/spaces/zachriek/chatgpt-clone/app.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import os
-import openai
-import gradio as gr
-from dotenv import load_dotenv
-
-load_dotenv()
-
-openai.api_key = os.getenv("API_KEY")
-
-start_sequence = "\nAI: "
-restart_sequence = "\nHuman: "
-
-prompt = "The following is a conversation with an AI assistant. The assistant is helpful, creative, clever, and very friendly.\n\nHuman: Hello, who are you?\nAI: I am an AI created by OpenAI. How can I help you today?\nHuman: "
-
-
-def openai_create(prompt):
- response = openai.Completion.create(
- model="text-davinci-003",
- prompt=prompt,
- temperature=0.9,
- max_tokens=150,
- top_p=1,
- frequency_penalty=0,
- presence_penalty=0,
- stop=[" Human:", " AI:"]
- )
- return response.choices[0].text
-
-
-def chatgpt_clone(input, history):
- history = history or []
- s = list(sum(history, ()))
- s.append(input)
- inp = ' '.join(s)
- output = openai_create(inp)
- history.append((input, output))
- return history, history
-
-
-block = gr.Blocks()
-
-
-with block:
- gr.Markdown("""Build Your Own ChatGPT with OpenAI API & Gradio vits-models\n"
- "## Please do not generate content that could infringe upon the rights or cause harm to individuals or organizations.\n"
- "## 请不要生成会对个人以及组织造成侵害的内容\n"
- "\n\n"
- "[](https://colab.research.google.com/drive/10QOk9NPgoKZUXkIhhuVaZ7SYra1MPMKH?usp=share_link)\n\n"
- "[](https://huggingface.co/spaces/sayashi/vits-models?duplicate=true)\n\n"
- "[](https://github.com/SayaSS/vits-finetuning)"
- )
-
- with gr.Tabs():
- for category in categories:
- with gr.TabItem(category):
- with gr.TabItem("EN"):
- for (sid, name_en, name_zh, title, cover, example, language, net_g_ms, tts_fn, to_symbol_fn) in models:
- if title.split("-")[0] != category:
- continue
- with gr.TabItem(name_en):
- with gr.Row():
- gr.Markdown(
- ''
- f'
{title} '
- f'
' if cover else ""
- '
'
- f'
{title} '
- f'
' if cover else ""
- '
- Click to Go
-
-
- '''
- )
- app.queue(concurrency_count=1, api_open=args.api).launch(share=args.share)
diff --git a/spaces/zxc314/vits-uma-genshin-honkai/Docker/Dockerfile b/spaces/zxc314/vits-uma-genshin-honkai/Docker/Dockerfile
deleted file mode 100644
index 4d39cdf02a2ec151686cc1d61234bf723068fed8..0000000000000000000000000000000000000000
--- a/spaces/zxc314/vits-uma-genshin-honkai/Docker/Dockerfile
+++ /dev/null
@@ -1,12 +0,0 @@
-FROM python:3.9-bullseye
-VOLUME ["/app"]
-WORKDIR /app
-# Set apt to Chinese mirror
-RUN sed -i 's/deb.debian.org/mirrors.ustc.edu.cn/g' /etc/apt/sources.list
-RUN apt-get update && apt-get -y install cmake git
-RUN git clone https://huggingface.co/spaces/ikechan8370/vits-uma-genshin-honkai
-WORKDIR /app/vits-uma-genshin-honkai
-RUN sed -i "s/\.launch()/\.launch(server_name=\"0.0.0.0\")/" /app/vits-uma-genshin-honkai/app.py
-ADD vits.sh /app/vits.sh
-EXPOSE 7860
-ENTRYPOINT [ "/app/vits.sh" ]
\ No newline at end of file
diff --git a/spaces/zzz666/ChuanhuChatGPT/assets/Kelpy-Codos.js b/spaces/zzz666/ChuanhuChatGPT/assets/Kelpy-Codos.js
deleted file mode 100644
index cfbaeedb4f371dfb5fe157db545b364046fca3e1..0000000000000000000000000000000000000000
--- a/spaces/zzz666/ChuanhuChatGPT/assets/Kelpy-Codos.js
+++ /dev/null
@@ -1,76 +0,0 @@
-// ==UserScript==
-// @name Kelpy Codos
-// @namespace https://github.com/Keldos-Li/Kelpy-Codos
-// @version 1.0.5
-// @author Keldos; https://keldos.me/
-// @description Add copy button to PRE tags before CODE tag, for Chuanhu ChatGPT especially.
-// Based on Chuanhu ChatGPT version: ac04408 (2023-3-22)
-// @license GPL-3.0
-// @grant none
-// ==/UserScript==
-
-(function () {
- 'use strict';
-
- function addCopyButton(pre) {
- var code = pre.querySelector('code');
- if (!code) {
- return; // 如果没有找到 元素，则不添加按钮
- }
- var firstChild = code.firstChild;
- if (!firstChild) {
- return; // 如果 元素没有子节点，则不添加按钮
- }
- var button = document.createElement('button');
- button.textContent = '\uD83D\uDCCE'; // 使用 📎 符号作为“复制”按钮的文本
- button.style.position = 'relative';
- button.style.float = 'right';
- button.style.fontSize = '1em'; // 可选：调整按钮大小
- button.style.background = 'none'; // 可选：去掉背景颜色
- button.style.border = 'none'; // 可选：去掉边框
- button.style.cursor = 'pointer'; // 可选：显示指针样式
- button.addEventListener('click', function () {
- var range = document.createRange();
- range.selectNodeContents(code);
- range.setStartBefore(firstChild); // 将范围设置为第一个子节点之前
- var selection = window.getSelection();
- selection.removeAllRanges();
- selection.addRange(range);
-
- try {
- var success = document.execCommand('copy');
- if (success) {
- button.textContent = '\u2714';
- setTimeout(function () {
- button.textContent = '\uD83D\uDCCE'; // 恢复按钮为“复制”
- }, 2000);
- } else {
- button.textContent = '\u2716';
- }
- } catch (e) {
- console.error(e);
- button.textContent = '\u2716';
- }
-
- selection.removeAllRanges();
- });
- code.insertBefore(button, firstChild); // 将按钮插入到第一个子元素之前
- }
-
- function handleNewElements(mutationsList, observer) {
- for (var mutation of mutationsList) {
- if (mutation.type === 'childList') {
- for (var node of mutation.addedNodes) {
- if (node.nodeName === 'PRE') {
- addCopyButton(node);
- }
- }
- }
- }
- }
-
- var observer = new MutationObserver(handleNewElements);
- observer.observe(document.documentElement, { childList: true, subtree: true });
-
- document.querySelectorAll('pre').forEach(addCopyButton);
-})();
-
-