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'''simple docstring'''
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
UpperCamelCase__ : Optional[int] = "base_with_context"
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""] ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(
torch.FloatTensor(weights["""Embed_0"""]["""embedding"""] ) , requires_grad=SCREAMING_SNAKE_CASE_ )
for lyr_num, lyr in enumerate(model.encoders ):
_SCREAMING_SNAKE_CASE = weights[F"layers_{lyr_num}"]
_SCREAMING_SNAKE_CASE = nn.Parameter(
torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""] ) )
_SCREAMING_SNAKE_CASE = ly_weight["""attention"""]
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""] ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""] ) )
return model
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(
torch.FloatTensor(weights["""Embed_0"""]["""embedding"""] ) , requires_grad=SCREAMING_SNAKE_CASE_ )
for lyr_num, lyr in enumerate(model.encoders ):
_SCREAMING_SNAKE_CASE = weights[F"layers_{lyr_num}"]
_SCREAMING_SNAKE_CASE = ly_weight["""attention"""]
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(
torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""] ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""] ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""] ) )
return model
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(
torch.FloatTensor(weights["""Embed_0"""]["""embedding"""] ) , requires_grad=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = nn.Parameter(
torch.FloatTensor(weights["""continuous_inputs_projection"""]["""kernel"""].T ) )
for lyr_num, lyr in enumerate(model.decoders ):
_SCREAMING_SNAKE_CASE = weights[F"layers_{lyr_num}"]
_SCREAMING_SNAKE_CASE = nn.Parameter(
torch.FloatTensor(ly_weight["""pre_self_attention_layer_norm"""]["""scale"""] ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(
torch.FloatTensor(ly_weight["""FiLMLayer_0"""]["""DenseGeneral_0"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = ly_weight["""self_attention"""]
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = ly_weight["""MultiHeadDotProductAttention_0"""]
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(
torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""] ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""] ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(
torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""] ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T ) )
return model
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = checkpoints.load_tax_checkpoint(args.checkpoint_path )
_SCREAMING_SNAKE_CASE = jnp.tree_util.tree_map(onp.array , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = [
"""from __gin__ import dynamic_registration""",
"""from music_spectrogram_diffusion.models.diffusion import diffusion_utils""",
"""diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0""",
"""diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()""",
]
_SCREAMING_SNAKE_CASE = os.path.join(args.checkpoint_path , """..""" , """config.gin""" )
_SCREAMING_SNAKE_CASE = inference.parse_training_gin_file(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = inference.InferenceModel(args.checkpoint_path , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = DDPMScheduler(beta_schedule="""squaredcos_cap_v2""" , variance_type="""fixed_large""" )
_SCREAMING_SNAKE_CASE = SpectrogramNotesEncoder(
max_length=synth_model.sequence_length["""inputs"""] , vocab_size=synth_model.model.module.config.vocab_size , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj="""gated-gelu""" , )
_SCREAMING_SNAKE_CASE = SpectrogramContEncoder(
input_dims=synth_model.audio_codec.n_dims , targets_context_length=synth_model.sequence_length["""targets_context"""] , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj="""gated-gelu""" , )
_SCREAMING_SNAKE_CASE = TaFilmDecoder(
input_dims=synth_model.audio_codec.n_dims , targets_length=synth_model.sequence_length["""targets_context"""] , max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time , d_model=synth_model.model.module.config.emb_dim , num_layers=synth_model.model.module.config.num_decoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , dropout_rate=synth_model.model.module.config.dropout_rate , )
_SCREAMING_SNAKE_CASE = load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""] , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""] , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = load_decoder(ta_checkpoint["""target"""]["""decoder"""] , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder""" )
_SCREAMING_SNAKE_CASE = SpectrogramDiffusionPipeline(
notes_encoder=SCREAMING_SNAKE_CASE_ , continuous_encoder=SCREAMING_SNAKE_CASE_ , decoder=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ , melgan=SCREAMING_SNAKE_CASE_ , )
if args.save:
pipe.save_pretrained(args.output_path )
if __name__ == "__main__":
UpperCamelCase__ : List[Any] = argparse.ArgumentParser()
parser.add_argument("--output_path", default=None, type=str, required=True, help="Path to the converted model.")
parser.add_argument(
"--save", default=True, type=bool, required=False, help="Whether to save the converted model or not."
)
parser.add_argument(
"--checkpoint_path",
default=f"""{MODEL}/checkpoint_500000""",
type=str,
required=False,
help="Path to the original jax model checkpoint.",
)
UpperCamelCase__ : List[Any] = parser.parse_args()
main(args)
| 0 |
'''simple docstring'''
import sys
UpperCamelCase__ : int = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ = N ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = -sys.maxsize - 1
for i in range(len(SCREAMING_SNAKE_CASE_ ) - 12 ):
_SCREAMING_SNAKE_CASE = 1
for j in range(13 ):
product *= int(n[i + j] )
if product > largest_product:
_SCREAMING_SNAKE_CASE = product
return largest_product
if __name__ == "__main__":
print(f"""{solution() = }""")
| 0 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
)
from transformers.models.roformer.modeling_tf_roformer import (
TFRoFormerSelfAttention,
TFRoFormerSinusoidalPositionalEmbedding,
)
class _a :
"""simple docstring"""
def __init__( self , A__ , A__=13 , A__=7 , A__=True , A__=True , A__=True , A__=True , A__=99 , A__=32 , A__=2 , A__=4 , A__=37 , A__="gelu" , A__=0.1 , A__=0.1 , A__=5_12 , A__=16 , A__=2 , A__=0.02 , A__=3 , A__=4 , A__=None , ) -> int:
_SCREAMING_SNAKE_CASE = parent
_SCREAMING_SNAKE_CASE = 13
_SCREAMING_SNAKE_CASE = 7
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = 99
_SCREAMING_SNAKE_CASE = 32
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = 4
_SCREAMING_SNAKE_CASE = 37
_SCREAMING_SNAKE_CASE = """gelu"""
_SCREAMING_SNAKE_CASE = 0.1
_SCREAMING_SNAKE_CASE = 0.1
_SCREAMING_SNAKE_CASE = 5_12
_SCREAMING_SNAKE_CASE = 16
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = 0.02
_SCREAMING_SNAKE_CASE = 3
_SCREAMING_SNAKE_CASE = 4
_SCREAMING_SNAKE_CASE = None
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_SCREAMING_SNAKE_CASE = None
if self.use_input_mask:
_SCREAMING_SNAKE_CASE = random_attention_mask([self.batch_size, self.seq_length] )
_SCREAMING_SNAKE_CASE = None
if self.use_token_type_ids:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
if self.use_labels:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_choices )
_SCREAMING_SNAKE_CASE = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , return_dict=A__ , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = TFRoFormerModel(config=A__ )
_SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
_SCREAMING_SNAKE_CASE = [input_ids, input_mask]
_SCREAMING_SNAKE_CASE = model(A__ )
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> str:
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = TFRoFormerForCausalLM(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )["""logits"""]
self.parent.assertListEqual(
list(prediction_scores.numpy().shape ) , [self.batch_size, self.seq_length, self.vocab_size] )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Dict:
_SCREAMING_SNAKE_CASE = TFRoFormerForMaskedLM(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = TFRoFormerForSequenceClassification(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Any:
_SCREAMING_SNAKE_CASE = self.num_choices
_SCREAMING_SNAKE_CASE = TFRoFormerForMultipleChoice(config=A__ )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(A__ , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(A__ , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(A__ , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
"""token_type_ids""": multiple_choice_token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = TFRoFormerForTokenClassification(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Tuple:
_SCREAMING_SNAKE_CASE = TFRoFormerForQuestionAnswering(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
(
(
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) ,
) = config_and_inputs
_SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class _a (_lowerCamelCase , _lowerCamelCase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = (
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
SCREAMING_SNAKE_CASE = (
{
'feature-extraction': TFRoFormerModel,
'fill-mask': TFRoFormerForMaskedLM,
'question-answering': TFRoFormerForQuestionAnswering,
'text-classification': TFRoFormerForSequenceClassification,
'text-generation': TFRoFormerForCausalLM,
'token-classification': TFRoFormerForTokenClassification,
'zero-shot': TFRoFormerForSequenceClassification,
}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ ) -> str:
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = TFRoFormerModelTester(self )
_SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=A__ , hidden_size=37 )
def UpperCamelCase ( self ) -> Optional[Any]:
self.config_tester.run_common_tests()
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__ )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*A__ )
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*A__ )
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*A__ )
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A__ )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*A__ )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*A__ )
@slow
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base""" )
self.assertIsNotNone(A__ )
@require_tf
class _a (unittest.TestCase):
"""simple docstring"""
@slow
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
_SCREAMING_SNAKE_CASE = tf.constant([[0, 1, 2, 3, 4, 5]] )
_SCREAMING_SNAKE_CASE = model(A__ )[0]
# TODO Replace vocab size
_SCREAMING_SNAKE_CASE = 5_00_00
_SCREAMING_SNAKE_CASE = [1, 6, vocab_size]
self.assertEqual(output.shape , A__ )
print(output[:, :3, :3] )
# TODO Replace values below with what was printed above.
_SCREAMING_SNAKE_CASE = tf.constant(
[
[
[-0.1205_3341, -1.026_4901, 0.2922_1946],
[-1.513_3783, 0.19_7433, 0.1519_0607],
[-5.013_5403, -3.90_0256, -0.8403_8764],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , A__ , atol=1E-4 )
@require_tf
class _a (unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 1E-4
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = tf.constant([[4, 10]] )
_SCREAMING_SNAKE_CASE = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 , embedding_dim=6 )
_SCREAMING_SNAKE_CASE = emba(input_ids.shape )
_SCREAMING_SNAKE_CASE = tf.constant(
[[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]] )
tf.debugging.assert_near(A__ , A__ , atol=self.tolerance )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = tf.constant(
[
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
[0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
] )
_SCREAMING_SNAKE_CASE = TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12 , embedding_dim=5_12 )
emba([2, 16, 5_12] )
_SCREAMING_SNAKE_CASE = emba.weight[:3, :5]
tf.debugging.assert_near(A__ , A__ , atol=self.tolerance )
@require_tf
class _a (unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 1E-4
def UpperCamelCase ( self ) -> int:
# 2,12,16,64
_SCREAMING_SNAKE_CASE = tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 1_00
_SCREAMING_SNAKE_CASE = -tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 1_00
_SCREAMING_SNAKE_CASE = TFRoFormerSinusoidalPositionalEmbedding(num_positions=32 , embedding_dim=64 )
_SCREAMING_SNAKE_CASE = embed_positions([2, 16, 7_68] )[None, None, :, :]
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
A__ , A__ , A__ )
_SCREAMING_SNAKE_CASE = tf.constant(
[
[0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
[-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
[-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
[-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
[0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
[3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
] )
_SCREAMING_SNAKE_CASE = tf.constant(
[
[0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
[0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
[1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
[2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
[-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
[-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
] )
tf.debugging.assert_near(query_layer[0, 0, :6, :8] , A__ , atol=self.tolerance )
tf.debugging.assert_near(key_layer[0, 0, :6, :8] , A__ , atol=self.tolerance )
| 0 |
'''simple docstring'''
UpperCamelCase__ : Dict = {
"a": "AAAAA",
"b": "AAAAB",
"c": "AAABA",
"d": "AAABB",
"e": "AABAA",
"f": "AABAB",
"g": "AABBA",
"h": "AABBB",
"i": "ABAAA",
"j": "BBBAA",
"k": "ABAAB",
"l": "ABABA",
"m": "ABABB",
"n": "ABBAA",
"o": "ABBAB",
"p": "ABBBA",
"q": "ABBBB",
"r": "BAAAA",
"s": "BAAAB",
"t": "BAABA",
"u": "BAABB",
"v": "BBBAB",
"w": "BABAA",
"x": "BABAB",
"y": "BABBA",
"z": "BABBB",
" ": " ",
}
UpperCamelCase__ : str = {value: key for key, value in encode_dict.items()}
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """"""
for letter in word.lower():
if letter.isalpha() or letter == " ":
encoded += encode_dict[letter]
else:
raise Exception("""encode() accepts only letters of the alphabet and spaces""" )
return encoded
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
if set(SCREAMING_SNAKE_CASE_ ) - {"A", "B", " "} != set():
raise Exception("""decode() accepts only 'A', 'B' and spaces""" )
_SCREAMING_SNAKE_CASE = """"""
for word in coded.split():
while len(SCREAMING_SNAKE_CASE_ ) != 0:
decoded += decode_dict[word[:5]]
_SCREAMING_SNAKE_CASE = word[5:]
decoded += " "
return decoded.strip()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 0 | 1 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = ['image_processor', 'tokenizer']
SCREAMING_SNAKE_CASE = 'CLIPImageProcessor'
SCREAMING_SNAKE_CASE = ('XLMRobertaTokenizer', 'XLMRobertaTokenizerFast')
def __init__( self , A__=None , A__=None , **A__ ) -> Tuple:
_SCREAMING_SNAKE_CASE = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , A__ , )
_SCREAMING_SNAKE_CASE = kwargs.pop("""feature_extractor""" )
_SCREAMING_SNAKE_CASE = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(A__ , A__ )
def __call__( self , A__=None , A__=None , A__=None , **A__ ) -> List[str]:
if text is None and images is None:
raise ValueError("""You have to specify either text or images. Both cannot be none.""" )
if text is not None:
_SCREAMING_SNAKE_CASE = self.tokenizer(A__ , return_tensors=A__ , **A__ )
if images is not None:
_SCREAMING_SNAKE_CASE = self.image_processor(A__ , return_tensors=A__ , **A__ )
if text is not None and images is not None:
_SCREAMING_SNAKE_CASE = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**A__ ) , tensor_type=A__ )
def UpperCamelCase ( self , *A__ , **A__ ) -> Optional[int]:
return self.tokenizer.batch_decode(*A__ , **A__ )
def UpperCamelCase ( self , *A__ , **A__ ) -> Optional[Any]:
return self.tokenizer.decode(*A__ , **A__ )
@property
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = self.tokenizer.model_input_names
_SCREAMING_SNAKE_CASE = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 0 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [
"""encoder.version""",
"""decoder.version""",
"""model.encoder.version""",
"""model.decoder.version""",
"""decoder.output_projection.weight""",
"""_float_tensor""",
"""encoder.embed_positions._float_tensor""",
"""decoder.embed_positions._float_tensor""",
]
for k in ignore_keys:
state_dict.pop(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = emb.weight.shape
_SCREAMING_SNAKE_CASE = nn.Linear(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , bias=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = emb.weight.data
return lin_layer
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = torch.load(SCREAMING_SNAKE_CASE_ , map_location="""cpu""" )
_SCREAMING_SNAKE_CASE = mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
_SCREAMING_SNAKE_CASE = mam_aaa["""model"""]
remove_ignore_keys_(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = state_dict["""encoder.embed_tokens.weight"""].shape[0]
_SCREAMING_SNAKE_CASE = MaMaaaConfig(
vocab_size=SCREAMING_SNAKE_CASE_ , max_position_embeddings=10_24 , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , encoder_layerdrop=args.encoder_layerdrop , decoder_layerdrop=args.decoder_layerdrop , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function="""relu""" , )
_SCREAMING_SNAKE_CASE = state_dict["""decoder.embed_tokens.weight"""]
_SCREAMING_SNAKE_CASE = MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE_ )
model.model.load_state_dict(SCREAMING_SNAKE_CASE_ , strict=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
UpperCamelCase__ : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
UpperCamelCase__ : List[str] = parser.parse_args()
UpperCamelCase__ : Any = convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
model.save_pretrained(args.pytorch_dump_folder_path)
| 0 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class _a :
"""simple docstring"""
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
def UpperCamelCase ( self ) -> "DownloadConfig":
return self.__class__(**{k: copy.deepcopy(A__ ) for k, v in self.__dict__.items()} )
| 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ : str = {
"configuration_canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig"],
"tokenization_canine": ["CanineTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : List[Any] = [
"CANINE_PRETRAINED_MODEL_ARCHIVE_LIST",
"CanineForMultipleChoice",
"CanineForQuestionAnswering",
"CanineForSequenceClassification",
"CanineForTokenClassification",
"CanineLayer",
"CanineModel",
"CaninePreTrainedModel",
"load_tf_weights_in_canine",
]
if TYPE_CHECKING:
from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
from .tokenization_canine import CanineTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_canine import (
CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
CanineLayer,
CanineModel,
CaninePreTrainedModel,
load_tf_weights_in_canine,
)
else:
import sys
UpperCamelCase__ : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 | 1 |
'''simple docstring'''
import os
# Precomputes a list of the 100 first triangular numbers
UpperCamelCase__ : List[str] = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def lowerCAmelCase_ ( ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = os.path.dirname(os.path.realpath(SCREAMING_SNAKE_CASE_ ) )
_SCREAMING_SNAKE_CASE = os.path.join(SCREAMING_SNAKE_CASE_ , """words.txt""" )
_SCREAMING_SNAKE_CASE = """"""
with open(SCREAMING_SNAKE_CASE_ ) as f:
_SCREAMING_SNAKE_CASE = f.readline()
_SCREAMING_SNAKE_CASE = [word.strip("""\"""" ) for word in words.strip("""\r\n""" ).split(""",""" )]
_SCREAMING_SNAKE_CASE = [
word
for word in [sum(ord(SCREAMING_SNAKE_CASE_ ) - 64 for x in word ) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
print(solution())
| 0 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = ['image_processor', 'tokenizer']
SCREAMING_SNAKE_CASE = 'ChineseCLIPImageProcessor'
SCREAMING_SNAKE_CASE = ('BertTokenizer', 'BertTokenizerFast')
def __init__( self , A__=None , A__=None , **A__ ) -> int:
_SCREAMING_SNAKE_CASE = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , A__ , )
_SCREAMING_SNAKE_CASE = kwargs.pop("""feature_extractor""" )
_SCREAMING_SNAKE_CASE = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(A__ , A__ )
_SCREAMING_SNAKE_CASE = self.image_processor
def __call__( self , A__=None , A__=None , A__=None , **A__ ) -> Optional[int]:
if text is None and images is None:
raise ValueError("""You have to specify either text or images. Both cannot be none.""" )
if text is not None:
_SCREAMING_SNAKE_CASE = self.tokenizer(A__ , return_tensors=A__ , **A__ )
if images is not None:
_SCREAMING_SNAKE_CASE = self.image_processor(A__ , return_tensors=A__ , **A__ )
if text is not None and images is not None:
_SCREAMING_SNAKE_CASE = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**A__ ) , tensor_type=A__ )
def UpperCamelCase ( self , *A__ , **A__ ) -> Dict:
return self.tokenizer.batch_decode(*A__ , **A__ )
def UpperCamelCase ( self , *A__ , **A__ ) -> Optional[Any]:
return self.tokenizer.decode(*A__ , **A__ )
@property
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.tokenizer.model_input_names
_SCREAMING_SNAKE_CASE = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def UpperCamelCase ( self ) -> Optional[int]:
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , A__ , )
return self.image_processor_class
| 0 | 1 |
'''simple docstring'''
import itertools
import math
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowerCAmelCase_ ( ) -> Tuple:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 2
while True:
if is_prime(SCREAMING_SNAKE_CASE_ ):
yield num
num += 1
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ = 1_00_01 ) -> int:
"""simple docstring"""
return next(itertools.islice(prime_generator() , nth - 1 , SCREAMING_SNAKE_CASE_ ) )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 0 |
'''simple docstring'''
from sklearn.metrics import matthews_corrcoef
import datasets
UpperCamelCase__ : List[str] = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction. The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"
UpperCamelCase__ : List[Any] = "\nArgs:\n predictions (list of int): Predicted labels, as returned by a model.\n references (list of int): Ground truth labels.\n sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n Example 1, a basic example with only predictions and references as inputs:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3])\n >>> print(round(results['matthews_correlation'], 2))\n 0.54\n\n Example 2, the same example as above, but also including sample weights:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 3, 1, 1, 1, 2])\n >>> print(round(results['matthews_correlation'], 2))\n 0.1\n\n Example 3, the same example as above, but with sample weights that cause a negative correlation:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 1, 0, 0, 0, 1])\n >>> print(round(results['matthews_correlation'], 2))\n -0.25\n"
UpperCamelCase__ : Any = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class _a (datasets.Metric):
"""simple docstring"""
def UpperCamelCase ( self ) -> Optional[Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
] , )
def UpperCamelCase ( self , A__ , A__ , A__=None ) -> List[str]:
return {
"matthews_correlation": float(matthews_corrcoef(A__ , A__ , sample_weight=A__ ) ),
}
| 0 | 1 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
UpperCamelCase__ : Optional[int] = True
except (ImportError, ModuleNotFoundError):
UpperCamelCase__ : Any = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
re.sub("""<n>""" , """""" , SCREAMING_SNAKE_CASE_ ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(SCREAMING_SNAKE_CASE_ ) )
| 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
"""simple docstring"""
print(F"Vertex\tShortest Distance from vertex {src}" )
for i, d in enumerate(SCREAMING_SNAKE_CASE_ ):
print(F"{i}\t\t{d}" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
for j in range(SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = (graph[j][k] for k in ["""src""", """dst""", """weight"""])
if distance[u] != float("""inf""" ) and distance[u] + w < distance[v]:
return True
return False
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> list[float]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [float("""inf""" )] * vertex_count
_SCREAMING_SNAKE_CASE = 0.0
for _ in range(vertex_count - 1 ):
for j in range(SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = (graph[j][k] for k in ["""src""", """dst""", """weight"""])
if distance[u] != float("""inf""" ) and distance[u] + w < distance[v]:
_SCREAMING_SNAKE_CASE = distance[u] + w
_SCREAMING_SNAKE_CASE = check_negative_cycle(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if negative_cycle_exists:
raise Exception("""Negative cycle found""" )
return distance
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase__ : int = int(input("Enter number of vertices: ").strip())
UpperCamelCase__ : int = int(input("Enter number of edges: ").strip())
UpperCamelCase__ : list[dict[str, int]] = [{} for _ in range(E)]
for i in range(E):
print("Edge ", i + 1)
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : Dict = (
int(x)
for x in input("Enter source, destination, weight: ").strip().split(" ")
)
UpperCamelCase__ : Optional[Any] = {"src": src, "dst": dest, "weight": weight}
UpperCamelCase__ : Optional[Any] = int(input("\nEnter shortest path source:").strip())
UpperCamelCase__ : Any = bellman_ford(graph, V, E, source)
print_distance(shortest_distance, 0)
| 0 | 1 |
'''simple docstring'''
import numpy as np
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> np.ndarray:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> np.ndarray:
"""simple docstring"""
return vector * sigmoid(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
)
from transformers.models.roformer.modeling_tf_roformer import (
TFRoFormerSelfAttention,
TFRoFormerSinusoidalPositionalEmbedding,
)
class _a :
"""simple docstring"""
def __init__( self , A__ , A__=13 , A__=7 , A__=True , A__=True , A__=True , A__=True , A__=99 , A__=32 , A__=2 , A__=4 , A__=37 , A__="gelu" , A__=0.1 , A__=0.1 , A__=5_12 , A__=16 , A__=2 , A__=0.02 , A__=3 , A__=4 , A__=None , ) -> int:
_SCREAMING_SNAKE_CASE = parent
_SCREAMING_SNAKE_CASE = 13
_SCREAMING_SNAKE_CASE = 7
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = 99
_SCREAMING_SNAKE_CASE = 32
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = 4
_SCREAMING_SNAKE_CASE = 37
_SCREAMING_SNAKE_CASE = """gelu"""
_SCREAMING_SNAKE_CASE = 0.1
_SCREAMING_SNAKE_CASE = 0.1
_SCREAMING_SNAKE_CASE = 5_12
_SCREAMING_SNAKE_CASE = 16
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = 0.02
_SCREAMING_SNAKE_CASE = 3
_SCREAMING_SNAKE_CASE = 4
_SCREAMING_SNAKE_CASE = None
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_SCREAMING_SNAKE_CASE = None
if self.use_input_mask:
_SCREAMING_SNAKE_CASE = random_attention_mask([self.batch_size, self.seq_length] )
_SCREAMING_SNAKE_CASE = None
if self.use_token_type_ids:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
if self.use_labels:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_choices )
_SCREAMING_SNAKE_CASE = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , return_dict=A__ , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = TFRoFormerModel(config=A__ )
_SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
_SCREAMING_SNAKE_CASE = [input_ids, input_mask]
_SCREAMING_SNAKE_CASE = model(A__ )
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> str:
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = TFRoFormerForCausalLM(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )["""logits"""]
self.parent.assertListEqual(
list(prediction_scores.numpy().shape ) , [self.batch_size, self.seq_length, self.vocab_size] )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Dict:
_SCREAMING_SNAKE_CASE = TFRoFormerForMaskedLM(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = TFRoFormerForSequenceClassification(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Any:
_SCREAMING_SNAKE_CASE = self.num_choices
_SCREAMING_SNAKE_CASE = TFRoFormerForMultipleChoice(config=A__ )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(A__ , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(A__ , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(A__ , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
"""token_type_ids""": multiple_choice_token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = TFRoFormerForTokenClassification(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Tuple:
_SCREAMING_SNAKE_CASE = TFRoFormerForQuestionAnswering(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
(
(
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) ,
) = config_and_inputs
_SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class _a (_lowerCamelCase , _lowerCamelCase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = (
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
SCREAMING_SNAKE_CASE = (
{
'feature-extraction': TFRoFormerModel,
'fill-mask': TFRoFormerForMaskedLM,
'question-answering': TFRoFormerForQuestionAnswering,
'text-classification': TFRoFormerForSequenceClassification,
'text-generation': TFRoFormerForCausalLM,
'token-classification': TFRoFormerForTokenClassification,
'zero-shot': TFRoFormerForSequenceClassification,
}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ ) -> str:
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = TFRoFormerModelTester(self )
_SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=A__ , hidden_size=37 )
def UpperCamelCase ( self ) -> Optional[Any]:
self.config_tester.run_common_tests()
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__ )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*A__ )
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*A__ )
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*A__ )
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A__ )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*A__ )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*A__ )
@slow
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base""" )
self.assertIsNotNone(A__ )
@require_tf
class _a (unittest.TestCase):
"""simple docstring"""
@slow
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
_SCREAMING_SNAKE_CASE = tf.constant([[0, 1, 2, 3, 4, 5]] )
_SCREAMING_SNAKE_CASE = model(A__ )[0]
# TODO Replace vocab size
_SCREAMING_SNAKE_CASE = 5_00_00
_SCREAMING_SNAKE_CASE = [1, 6, vocab_size]
self.assertEqual(output.shape , A__ )
print(output[:, :3, :3] )
# TODO Replace values below with what was printed above.
_SCREAMING_SNAKE_CASE = tf.constant(
[
[
[-0.1205_3341, -1.026_4901, 0.2922_1946],
[-1.513_3783, 0.19_7433, 0.1519_0607],
[-5.013_5403, -3.90_0256, -0.8403_8764],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , A__ , atol=1E-4 )
@require_tf
class _a (unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 1E-4
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = tf.constant([[4, 10]] )
_SCREAMING_SNAKE_CASE = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 , embedding_dim=6 )
_SCREAMING_SNAKE_CASE = emba(input_ids.shape )
_SCREAMING_SNAKE_CASE = tf.constant(
[[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]] )
tf.debugging.assert_near(A__ , A__ , atol=self.tolerance )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = tf.constant(
[
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
[0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
] )
_SCREAMING_SNAKE_CASE = TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12 , embedding_dim=5_12 )
emba([2, 16, 5_12] )
_SCREAMING_SNAKE_CASE = emba.weight[:3, :5]
tf.debugging.assert_near(A__ , A__ , atol=self.tolerance )
@require_tf
class _a (unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 1E-4
def UpperCamelCase ( self ) -> int:
# 2,12,16,64
_SCREAMING_SNAKE_CASE = tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 1_00
_SCREAMING_SNAKE_CASE = -tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 1_00
_SCREAMING_SNAKE_CASE = TFRoFormerSinusoidalPositionalEmbedding(num_positions=32 , embedding_dim=64 )
_SCREAMING_SNAKE_CASE = embed_positions([2, 16, 7_68] )[None, None, :, :]
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
A__ , A__ , A__ )
_SCREAMING_SNAKE_CASE = tf.constant(
[
[0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
[-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
[-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
[-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
[0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
[3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
] )
_SCREAMING_SNAKE_CASE = tf.constant(
[
[0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
[0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
[1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
[2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
[-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
[-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
] )
tf.debugging.assert_near(query_layer[0, 0, :6, :8] , A__ , atol=self.tolerance )
tf.debugging.assert_near(key_layer[0, 0, :6, :8] , A__ , atol=self.tolerance )
| 0 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : Optional[Any] = logging.get_logger(__name__)
UpperCamelCase__ : List[str] = {
"studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json",
"studio-ousia/luke-large": "https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json",
}
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 'luke'
def __init__( self , A__=5_02_67 , A__=50_00_00 , A__=7_68 , A__=2_56 , A__=12 , A__=12 , A__=30_72 , A__="gelu" , A__=0.1 , A__=0.1 , A__=5_12 , A__=2 , A__=0.02 , A__=1E-12 , A__=True , A__=None , A__=1 , A__=0 , A__=2 , **A__ , ) -> int:
super().__init__(pad_token_id=A__ , bos_token_id=A__ , eos_token_id=A__ , **A__ )
_SCREAMING_SNAKE_CASE = vocab_size
_SCREAMING_SNAKE_CASE = entity_vocab_size
_SCREAMING_SNAKE_CASE = hidden_size
_SCREAMING_SNAKE_CASE = entity_emb_size
_SCREAMING_SNAKE_CASE = num_hidden_layers
_SCREAMING_SNAKE_CASE = num_attention_heads
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = intermediate_size
_SCREAMING_SNAKE_CASE = hidden_dropout_prob
_SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
_SCREAMING_SNAKE_CASE = max_position_embeddings
_SCREAMING_SNAKE_CASE = type_vocab_size
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = layer_norm_eps
_SCREAMING_SNAKE_CASE = use_entity_aware_attention
_SCREAMING_SNAKE_CASE = classifier_dropout
| 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
UpperCamelCase__ : int = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Tuple = ["HerbertTokenizerFast"]
if TYPE_CHECKING:
from .tokenization_herbert import HerbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_herbert_fast import HerbertTokenizerFast
else:
import sys
UpperCamelCase__ : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ : Optional[Any] = {"configuration_mmbt": ["MMBTConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Dict = ["MMBTForClassification", "MMBTModel", "ModalEmbeddings"]
if TYPE_CHECKING:
from .configuration_mmbt import MMBTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings
else:
import sys
UpperCamelCase__ : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 |
'''simple docstring'''
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = XCLIPTextConfig()
# derive patch size from model name
_SCREAMING_SNAKE_CASE = model_name.find("""patch""" )
_SCREAMING_SNAKE_CASE = int(model_name[start_idx + len("""patch""" ) : start_idx + len("""patch""" ) + 2] )
_SCREAMING_SNAKE_CASE = XCLIPVisionConfig(patch_size=SCREAMING_SNAKE_CASE_ , num_frames=SCREAMING_SNAKE_CASE_ )
if "large" in model_name:
_SCREAMING_SNAKE_CASE = 7_68
_SCREAMING_SNAKE_CASE = 30_72
_SCREAMING_SNAKE_CASE = 12
_SCREAMING_SNAKE_CASE = 10_24
_SCREAMING_SNAKE_CASE = 40_96
_SCREAMING_SNAKE_CASE = 16
_SCREAMING_SNAKE_CASE = 24
_SCREAMING_SNAKE_CASE = 7_68
_SCREAMING_SNAKE_CASE = 30_72
if model_name == "xclip-large-patch14-16-frames":
_SCREAMING_SNAKE_CASE = 3_36
_SCREAMING_SNAKE_CASE = XCLIPConfig.from_text_vision_configs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if "large" in model_name:
_SCREAMING_SNAKE_CASE = 7_68
return config
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
# text encoder
if name == "token_embedding.weight":
_SCREAMING_SNAKE_CASE = name.replace("""token_embedding.weight""" , """text_model.embeddings.token_embedding.weight""" )
if name == "positional_embedding":
_SCREAMING_SNAKE_CASE = name.replace("""positional_embedding""" , """text_model.embeddings.position_embedding.weight""" )
if "ln_1" in name:
_SCREAMING_SNAKE_CASE = name.replace("""ln_1""" , """layer_norm1""" )
if "ln_2" in name:
_SCREAMING_SNAKE_CASE = name.replace("""ln_2""" , """layer_norm2""" )
if "c_fc" in name:
_SCREAMING_SNAKE_CASE = name.replace("""c_fc""" , """fc1""" )
if "c_proj" in name:
_SCREAMING_SNAKE_CASE = name.replace("""c_proj""" , """fc2""" )
if name.startswith("""transformer.resblocks""" ):
_SCREAMING_SNAKE_CASE = name.replace("""transformer.resblocks""" , """text_model.encoder.layers""" )
if "attn.out_proj" in name and "message" not in name:
_SCREAMING_SNAKE_CASE = name.replace("""attn.out_proj""" , """self_attn.out_proj""" )
if "ln_final" in name:
_SCREAMING_SNAKE_CASE = name.replace("""ln_final""" , """text_model.final_layer_norm""" )
# visual encoder
if name == "visual.class_embedding":
_SCREAMING_SNAKE_CASE = name.replace("""visual.class_embedding""" , """vision_model.embeddings.class_embedding""" )
if name == "visual.positional_embedding":
_SCREAMING_SNAKE_CASE = name.replace("""visual.positional_embedding""" , """vision_model.embeddings.position_embedding.weight""" )
if name.startswith("""visual.transformer.resblocks""" ):
_SCREAMING_SNAKE_CASE = name.replace("""visual.transformer.resblocks""" , """vision_model.encoder.layers""" )
if "visual.conv1" in name:
_SCREAMING_SNAKE_CASE = name.replace("""visual.conv1""" , """vision_model.embeddings.patch_embedding""" )
if "visual.ln_pre" in name:
_SCREAMING_SNAKE_CASE = name.replace("""visual.ln_pre""" , """vision_model.pre_layernorm""" )
if "visual.ln_post" in name:
_SCREAMING_SNAKE_CASE = name.replace("""visual.ln_post""" , """vision_model.post_layernorm""" )
if "visual.proj" in name:
_SCREAMING_SNAKE_CASE = name.replace("""visual.proj""" , """visual_projection.weight""" )
if "text_projection" in name:
_SCREAMING_SNAKE_CASE = name.replace("""text_projection""" , """text_projection.weight""" )
# things on top
if "prompts_visual_proj" in name:
_SCREAMING_SNAKE_CASE = name.replace("""prompts_visual_proj""" , """prompts_visual_projection""" )
if "prompts_visual_ln" in name:
_SCREAMING_SNAKE_CASE = name.replace("""prompts_visual_ln""" , """prompts_visual_layernorm""" )
# mit
if name == "mit.positional_embedding":
_SCREAMING_SNAKE_CASE = name.replace("""positional""" , """position""" )
if name.startswith("""mit.resblocks""" ):
_SCREAMING_SNAKE_CASE = name.replace("""mit.resblocks""" , """mit.encoder.layers""" )
# prompts generator
if name.startswith("""prompts_generator.norm""" ):
_SCREAMING_SNAKE_CASE = name.replace("""prompts_generator.norm""" , """prompts_generator.layernorm""" )
return name
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
_SCREAMING_SNAKE_CASE = orig_state_dict.pop(SCREAMING_SNAKE_CASE_ )
if "attn.in_proj" in key:
_SCREAMING_SNAKE_CASE = key.split(""".""" )
if key.startswith("""visual""" ):
_SCREAMING_SNAKE_CASE = key_split[3]
_SCREAMING_SNAKE_CASE = config.vision_config.hidden_size
if "message_attn" in key:
if "weight" in key:
_SCREAMING_SNAKE_CASE = val[
:dim, :
]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2, :
]
_SCREAMING_SNAKE_CASE = val[
-dim:, :
]
else:
_SCREAMING_SNAKE_CASE = val[
:dim
]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2
]
_SCREAMING_SNAKE_CASE = val[
-dim:
]
else:
if "weight" in key:
_SCREAMING_SNAKE_CASE = val[
:dim, :
]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2, :
]
_SCREAMING_SNAKE_CASE = val[
-dim:, :
]
else:
_SCREAMING_SNAKE_CASE = val[:dim]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2
]
_SCREAMING_SNAKE_CASE = val[-dim:]
elif key.startswith("""mit""" ):
_SCREAMING_SNAKE_CASE = key_split[2]
_SCREAMING_SNAKE_CASE = config.vision_config.mit_hidden_size
if "weight" in key:
_SCREAMING_SNAKE_CASE = val[:dim, :]
_SCREAMING_SNAKE_CASE = val[dim : dim * 2, :]
_SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
_SCREAMING_SNAKE_CASE = val[:dim]
_SCREAMING_SNAKE_CASE = val[dim : dim * 2]
_SCREAMING_SNAKE_CASE = val[-dim:]
else:
_SCREAMING_SNAKE_CASE = key_split[2]
_SCREAMING_SNAKE_CASE = config.text_config.hidden_size
if "weight" in key:
_SCREAMING_SNAKE_CASE = val[:dim, :]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2, :
]
_SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
_SCREAMING_SNAKE_CASE = val[:dim]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2
]
_SCREAMING_SNAKE_CASE = val[-dim:]
else:
_SCREAMING_SNAKE_CASE = rename_key(SCREAMING_SNAKE_CASE_ )
if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
_SCREAMING_SNAKE_CASE = val.T
_SCREAMING_SNAKE_CASE = val
return orig_state_dict
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
if num_frames == 8:
_SCREAMING_SNAKE_CASE = """eating_spaghetti_8_frames.npy"""
elif num_frames == 16:
_SCREAMING_SNAKE_CASE = """eating_spaghetti.npy"""
elif num_frames == 32:
_SCREAMING_SNAKE_CASE = """eating_spaghetti_32_frames.npy"""
_SCREAMING_SNAKE_CASE = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""" , filename=SCREAMING_SNAKE_CASE_ , repo_type="""dataset""" , )
_SCREAMING_SNAKE_CASE = np.load(SCREAMING_SNAKE_CASE_ )
return list(SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=False ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = {
# fully supervised kinetics-400 checkpoints
"""xclip-base-patch32""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth""",
"""xclip-base-patch32-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"""
),
"""xclip-base-patch16""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth""",
"""xclip-base-patch16-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"""
),
"""xclip-large-patch14""": """https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&export=download&confirm=t&uuid=b26caedc-88e2-473e-830a-9d158b653cdb""",
"""xclip-large-patch14-16-frames""": """https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&export=download&confirm=t&uuid=538fa810-e671-4050-b385-9a623f89804f""",
# fully supervised kinetics-600 checkpoints
"""xclip-base-patch16-kinetics-600""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"""
),
"""xclip-base-patch16-kinetics-600-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"""
),
"""xclip-large-patch14-kinetics-600""": """https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&export=download&confirm=t&uuid=141d4977-4a65-44ae-864f-4b0c19f838be""",
# few shot
"""xclip-base-patch16-hmdb-2-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"""
),
"""xclip-base-patch16-hmdb-4-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"""
),
"""xclip-base-patch16-hmdb-8-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"""
),
"""xclip-base-patch16-hmdb-16-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"""
),
"""xclip-base-patch16-ucf-2-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"""
),
"""xclip-base-patch16-ucf-4-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"""
),
"""xclip-base-patch16-ucf-8-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"""
),
"""xclip-base-patch16-ucf-16-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"""
),
# zero shot
"""xclip-base-patch16-zero-shot""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth""",
}
_SCREAMING_SNAKE_CASE = model_to_url[model_name]
_SCREAMING_SNAKE_CASE = 8
if "16-frames" in model_name:
_SCREAMING_SNAKE_CASE = 16
elif "shot" in model_name:
_SCREAMING_SNAKE_CASE = 32
_SCREAMING_SNAKE_CASE = get_xclip_config(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = XCLIPModel(SCREAMING_SNAKE_CASE_ )
model.eval()
if "drive" in checkpoint_url:
_SCREAMING_SNAKE_CASE = """pytorch_model.bin"""
gdown.cached_download(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , quiet=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = torch.load(SCREAMING_SNAKE_CASE_ , map_location="""cpu""" )["""model"""]
else:
_SCREAMING_SNAKE_CASE = torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_ )["""model"""]
_SCREAMING_SNAKE_CASE = convert_state_dict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = XCLIPModel(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = model.load_state_dict(SCREAMING_SNAKE_CASE_ , strict=SCREAMING_SNAKE_CASE_ )
assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
model.eval()
_SCREAMING_SNAKE_CASE = 3_36 if model_name == """xclip-large-patch14-16-frames""" else 2_24
_SCREAMING_SNAKE_CASE = VideoMAEImageProcessor(size=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32""" )
_SCREAMING_SNAKE_CASE = CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32""" )
_SCREAMING_SNAKE_CASE = XCLIPProcessor(image_processor=SCREAMING_SNAKE_CASE_ , tokenizer=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = prepare_video(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = processor(
text=["""playing sports""", """eating spaghetti""", """go shopping"""] , videos=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , padding=SCREAMING_SNAKE_CASE_ )
print("""Shape of pixel values:""" , inputs.pixel_values.shape )
with torch.no_grad():
_SCREAMING_SNAKE_CASE = model(**SCREAMING_SNAKE_CASE_ )
# Verify outputs
_SCREAMING_SNAKE_CASE = outputs.logits_per_video
_SCREAMING_SNAKE_CASE = logits_per_video.softmax(dim=1 )
print("""Probs:""" , SCREAMING_SNAKE_CASE_ )
# kinetics-400
if model_name == "xclip-base-patch32":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0019, 0.9951, 0.0030]] )
elif model_name == "xclip-base-patch32-16-frames":
_SCREAMING_SNAKE_CASE = torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]] )
elif model_name == "xclip-base-patch16":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0083, 0.9681, 0.0236]] )
elif model_name == "xclip-base-patch16-16-frames":
_SCREAMING_SNAKE_CASE = torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]] )
elif model_name == "xclip-large-patch14":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0062, 0.9864, 0.0075]] )
elif model_name == "xclip-large-patch14-16-frames":
_SCREAMING_SNAKE_CASE = torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]] )
# kinetics-600
elif model_name == "xclip-base-patch16-kinetics-600":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0555, 0.8914, 0.0531]] )
elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
_SCREAMING_SNAKE_CASE = torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]] )
elif model_name == "xclip-large-patch14-kinetics-600":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0036, 0.9920, 0.0045]] )
# few shot
elif model_name == "xclip-base-patch16-hmdb-2-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]] )
elif model_name == "xclip-base-patch16-hmdb-4-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]] )
elif model_name == "xclip-base-patch16-hmdb-8-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]] )
elif model_name == "xclip-base-patch16-hmdb-16-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]] )
elif model_name == "xclip-base-patch16-ucf-2-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]] )
elif model_name == "xclip-base-patch16-ucf-4-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]] )
elif model_name == "xclip-base-patch16-ucf-8-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0027, 0.9904, 0.0070]] )
elif model_name == "xclip-base-patch16-ucf-16-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]] )
# zero shot
elif model_name == "xclip-base-patch16-zero-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]] )
else:
raise ValueError(F"Model name {model_name} not supported" )
assert torch.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-3 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(F"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
if push_to_hub:
print("""Pushing model, processor and slow tokenizer files to the hub...""" )
model.push_to_hub(SCREAMING_SNAKE_CASE_ , organization="""nielsr""" )
processor.push_to_hub(SCREAMING_SNAKE_CASE_ , organization="""nielsr""" )
slow_tokenizer.push_to_hub(SCREAMING_SNAKE_CASE_ , organization="""nielsr""" )
if __name__ == "__main__":
UpperCamelCase__ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="xclip-base-patch32",
type=str,
help="Name of the model.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
UpperCamelCase__ : str = parser.parse_args()
convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 0 | 1 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
UpperCamelCase__ : int = "pt"
elif is_tf_available():
UpperCamelCase__ : Dict = "tf"
else:
UpperCamelCase__ : Optional[int] = "jax"
class _a (_lowerCamelCase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = PerceiverTokenizer
SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self ) -> List[Any]:
super().setUp()
_SCREAMING_SNAKE_CASE = PerceiverTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def UpperCamelCase ( self ) -> str:
return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver""" )
def UpperCamelCase ( self , **A__ ) -> PerceiverTokenizer:
return self.tokenizer_class.from_pretrained(self.tmpdirname , **A__ )
def UpperCamelCase ( self , A__ , A__=False , A__=20 , A__=5 ) -> Tuple[str, list]:
# XXX The default common tokenizer tests assume that every ID is decodable on its own.
# This assumption is invalid for Perceiver because single bytes might not be
# valid utf-8 (byte 128 for instance).
# Here we're overriding the smallest possible method to provide
# a clean sequence without making the same assumption.
_SCREAMING_SNAKE_CASE = []
for i in range(len(A__ ) ):
try:
_SCREAMING_SNAKE_CASE = tokenizer.decode([i] , clean_up_tokenization_spaces=A__ )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
_SCREAMING_SNAKE_CASE = list(filter(lambda A__ : re.match(R"""^[ a-zA-Z]+$""" , t[1] ) , A__ ) )
_SCREAMING_SNAKE_CASE = list(filter(lambda A__ : [t[0]] == tokenizer.encode(t[1] , add_special_tokens=A__ ) , A__ ) )
if max_length is not None and len(A__ ) > max_length:
_SCREAMING_SNAKE_CASE = toks[:max_length]
if min_length is not None and len(A__ ) < min_length and len(A__ ) > 0:
while len(A__ ) < min_length:
_SCREAMING_SNAKE_CASE = toks + toks
# toks_str = [t[1] for t in toks]
_SCREAMING_SNAKE_CASE = [t[0] for t in toks]
# Ensure consistency
_SCREAMING_SNAKE_CASE = tokenizer.decode(A__ , clean_up_tokenization_spaces=A__ )
if " " not in output_txt and len(A__ ) > 1:
_SCREAMING_SNAKE_CASE = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=A__ )
+ """ """
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=A__ )
)
if with_prefix_space:
_SCREAMING_SNAKE_CASE = """ """ + output_txt
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ , add_special_tokens=A__ )
return output_txt, output_ids
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = self.perceiver_tokenizer
_SCREAMING_SNAKE_CASE = """Unicode €."""
_SCREAMING_SNAKE_CASE = tokenizer(A__ )
_SCREAMING_SNAKE_CASE = [4, 91, 1_16, 1_11, 1_05, 1_17, 1_06, 1_07, 38, 2_32, 1_36, 1_78, 52, 5]
self.assertEqual(encoded["""input_ids"""] , A__ )
# decoding
_SCREAMING_SNAKE_CASE = tokenizer.decode(A__ )
self.assertEqual(A__ , """[CLS]Unicode €.[SEP]""" )
_SCREAMING_SNAKE_CASE = tokenizer("""e è é ê ë""" )
_SCREAMING_SNAKE_CASE = [4, 1_07, 38, 2_01, 1_74, 38, 2_01, 1_75, 38, 2_01, 1_76, 38, 2_01, 1_77, 5]
self.assertEqual(encoded["""input_ids"""] , A__ )
# decoding
_SCREAMING_SNAKE_CASE = tokenizer.decode(A__ )
self.assertEqual(A__ , """[CLS]e è é ê ë[SEP]""" )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë""" ) ) , """[CLS]e è é ê ë[SEP]""" )
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = self.perceiver_tokenizer
_SCREAMING_SNAKE_CASE = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
# fmt: off
_SCREAMING_SNAKE_CASE = [4, 71, 38, 1_14, 1_17, 1_16, 1_09, 38, 1_18, 1_03, 1_20, 1_03, 1_09, 1_20, 1_03, 1_18, 1_10, 38, 1_08, 1_17, 1_20, 38, 1_21, 1_23, 1_15, 1_15, 1_03, 1_20, 1_11, 1_28, 1_03, 1_22, 1_11, 1_17, 1_16, 52, 5, 0]
# fmt: on
_SCREAMING_SNAKE_CASE = tokenizer(A__ , padding=A__ , return_tensors=A__ )
self.assertIsInstance(A__ , A__ )
if FRAMEWORK != "jax":
_SCREAMING_SNAKE_CASE = list(batch.input_ids.numpy()[0] )
else:
_SCREAMING_SNAKE_CASE = list(batch.input_ids.tolist()[0] )
self.assertListEqual(A__ , A__ )
self.assertEqual((2, 38) , batch.input_ids.shape )
self.assertEqual((2, 38) , batch.attention_mask.shape )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = self.perceiver_tokenizer
_SCREAMING_SNAKE_CASE = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
_SCREAMING_SNAKE_CASE = tokenizer(A__ , padding=A__ , return_tensors=A__ )
# check if input_ids are returned and no decoder_input_ids
self.assertIn("""input_ids""" , A__ )
self.assertIn("""attention_mask""" , A__ )
self.assertNotIn("""decoder_input_ids""" , A__ )
self.assertNotIn("""decoder_attention_mask""" , A__ )
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = self.perceiver_tokenizer
_SCREAMING_SNAKE_CASE = [
"""Summary of the text.""",
"""Another summary.""",
]
_SCREAMING_SNAKE_CASE = tokenizer(
text_target=A__ , max_length=32 , padding="""max_length""" , truncation=A__ , return_tensors=A__ )
self.assertEqual(32 , targets["""input_ids"""].shape[1] )
def UpperCamelCase ( self ) -> Dict:
# safety check on max_len default value so we are sure the test works
_SCREAMING_SNAKE_CASE = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"{tokenizer.__class__.__name__}" ):
self.assertNotEqual(tokenizer.model_max_length , 42 )
# Now let's start the test
_SCREAMING_SNAKE_CASE = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"{tokenizer.__class__.__name__}" ):
# Isolate this from the other tests because we save additional tokens/etc
_SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
_SCREAMING_SNAKE_CASE = """ He is very happy, UNwant\u00E9d,running"""
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ , add_special_tokens=A__ )
tokenizer.save_pretrained(A__ )
_SCREAMING_SNAKE_CASE = tokenizer.__class__.from_pretrained(A__ )
_SCREAMING_SNAKE_CASE = after_tokenizer.encode(A__ , add_special_tokens=A__ )
self.assertListEqual(A__ , A__ )
shutil.rmtree(A__ )
_SCREAMING_SNAKE_CASE = self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(F"{tokenizer.__class__.__name__}" ):
# Isolate this from the other tests because we save additional tokens/etc
_SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
_SCREAMING_SNAKE_CASE = """ He is very happy, UNwant\u00E9d,running"""
tokenizer.add_tokens(["""bim""", """bambam"""] )
_SCREAMING_SNAKE_CASE = tokenizer.additional_special_tokens
additional_special_tokens.append("""new_additional_special_token""" )
tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens} )
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ , add_special_tokens=A__ )
tokenizer.save_pretrained(A__ )
_SCREAMING_SNAKE_CASE = tokenizer.__class__.from_pretrained(A__ )
_SCREAMING_SNAKE_CASE = after_tokenizer.encode(A__ , add_special_tokens=A__ )
self.assertListEqual(A__ , A__ )
self.assertIn("""new_additional_special_token""" , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 42 )
_SCREAMING_SNAKE_CASE = tokenizer.__class__.from_pretrained(A__ , model_max_length=43 )
self.assertEqual(tokenizer.model_max_length , 43 )
shutil.rmtree(A__ )
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(A__ )
with open(os.path.join(A__ , """special_tokens_map.json""" ) , encoding="""utf-8""" ) as json_file:
_SCREAMING_SNAKE_CASE = json.load(A__ )
with open(os.path.join(A__ , """tokenizer_config.json""" ) , encoding="""utf-8""" ) as json_file:
_SCREAMING_SNAKE_CASE = json.load(A__ )
_SCREAMING_SNAKE_CASE = [F"<extra_id_{i}>" for i in range(1_25 )]
_SCREAMING_SNAKE_CASE = added_tokens_extra_ids + [
"""an_additional_special_token"""
]
_SCREAMING_SNAKE_CASE = added_tokens_extra_ids + [
"""an_additional_special_token"""
]
with open(os.path.join(A__ , """special_tokens_map.json""" ) , """w""" , encoding="""utf-8""" ) as outfile:
json.dump(A__ , A__ )
with open(os.path.join(A__ , """tokenizer_config.json""" ) , """w""" , encoding="""utf-8""" ) as outfile:
json.dump(A__ , A__ )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
_SCREAMING_SNAKE_CASE = tokenizer_class.from_pretrained(
A__ , )
self.assertIn(
"""an_additional_special_token""" , tokenizer_without_change_in_init.additional_special_tokens )
self.assertEqual(
["""an_additional_special_token"""] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""] ) ) , )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
_SCREAMING_SNAKE_CASE = added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token""" , lstrip=A__ )]
_SCREAMING_SNAKE_CASE = tokenizer_class.from_pretrained(
A__ , additional_special_tokens=A__ , )
self.assertIn("""a_new_additional_special_token""" , tokenizer.additional_special_tokens )
self.assertEqual(
["""a_new_additional_special_token"""] , tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""] ) ) , )
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = self.perceiver_tokenizer
self.assertEqual(tokenizer.decode([1_78] ) , """�""" )
def UpperCamelCase ( self ) -> Tuple:
pass
def UpperCamelCase ( self ) -> Dict:
pass
def UpperCamelCase ( self ) -> Dict:
pass
def UpperCamelCase ( self ) -> Tuple:
pass
def UpperCamelCase ( self ) -> Union[str, Any]:
# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
# strings and special added tokens as tokens
_SCREAMING_SNAKE_CASE = self.get_tokenizers(fast=A__ , do_lower_case=A__ )
for tokenizer in tokenizers:
with self.subTest(F"{tokenizer.__class__.__name__}" ):
_SCREAMING_SNAKE_CASE = ["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
_SCREAMING_SNAKE_CASE = tokenizer.convert_tokens_to_string(A__ )
self.assertIsInstance(A__ , A__ )
| 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class _a (_lowerCamelCase):
"""simple docstring"""
def __init__( self , A__ , A__ ) -> Any:
_SCREAMING_SNAKE_CASE = params
_SCREAMING_SNAKE_CASE = np.array(A__ )
_SCREAMING_SNAKE_CASE = np.array([len(A__ ) for t in data] )
self.check()
self.remove_long_sequences()
self.remove_empty_sequences()
self.remove_unknown_sequences()
self.check()
self.print_statistics()
def __getitem__( self , A__ ) -> Dict:
return (self.token_ids[index], self.lengths[index])
def __len__( self ) -> Tuple:
return len(self.lengths )
def UpperCamelCase ( self ) -> Dict:
assert len(self.token_ids ) == len(self.lengths )
assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.params.max_model_input_size
_SCREAMING_SNAKE_CASE = self.lengths > max_len
logger.info(F"Splitting {sum(A__ )} too long sequences." )
def divide_chunks(A__ , A__ ):
return [l[i : i + n] for i in range(0 , len(A__ ) , A__ )]
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = []
if self.params.mlm:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
else:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""bos_token"""], self.params.special_tok_ids["""eos_token"""]
for seq_, len_ in zip(self.token_ids , self.lengths ):
assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
if len_ <= max_len:
new_tok_ids.append(seq_ )
new_lengths.append(len_ )
else:
_SCREAMING_SNAKE_CASE = []
for sub_s in divide_chunks(seq_ , max_len - 2 ):
if sub_s[0] != cls_id:
_SCREAMING_SNAKE_CASE = np.insert(A__ , 0 , A__ )
if sub_s[-1] != sep_id:
_SCREAMING_SNAKE_CASE = np.insert(A__ , len(A__ ) , A__ )
assert len(A__ ) <= max_len
assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
sub_seqs.append(A__ )
new_tok_ids.extend(A__ )
new_lengths.extend([len(A__ ) for l in sub_seqs] )
_SCREAMING_SNAKE_CASE = np.array(A__ )
_SCREAMING_SNAKE_CASE = np.array(A__ )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = len(self )
_SCREAMING_SNAKE_CASE = self.lengths > 11
_SCREAMING_SNAKE_CASE = self.token_ids[indices]
_SCREAMING_SNAKE_CASE = self.lengths[indices]
_SCREAMING_SNAKE_CASE = len(self )
logger.info(F"Remove {init_size - new_size} too short (<=11 tokens) sequences." )
def UpperCamelCase ( self ) -> int:
if "unk_token" not in self.params.special_tok_ids:
return
else:
_SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""unk_token"""]
_SCREAMING_SNAKE_CASE = len(self )
_SCREAMING_SNAKE_CASE = np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
_SCREAMING_SNAKE_CASE = (unk_occs / self.lengths) < 0.5
_SCREAMING_SNAKE_CASE = self.token_ids[indices]
_SCREAMING_SNAKE_CASE = self.lengths[indices]
_SCREAMING_SNAKE_CASE = len(self )
logger.info(F"Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)." )
def UpperCamelCase ( self ) -> Optional[Any]:
if not self.params.is_master:
return
logger.info(F"{len(self )} sequences" )
# data_len = sum(self.lengths)
# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
# unk_idx = self.params.special_tok_ids['unk_token']
# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
def UpperCamelCase ( self , A__ ) -> Any:
_SCREAMING_SNAKE_CASE = [t[0] for t in batch]
_SCREAMING_SNAKE_CASE = [t[1] for t in batch]
assert len(A__ ) == len(A__ )
# Max for paddings
_SCREAMING_SNAKE_CASE = max(A__ )
# Pad token ids
if self.params.mlm:
_SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""pad_token"""]
else:
_SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""unk_token"""]
_SCREAMING_SNAKE_CASE = [list(t.astype(A__ ) ) + [pad_idx] * (max_seq_len_ - len(A__ )) for t in token_ids]
assert len(tk_ ) == len(A__ )
assert all(len(A__ ) == max_seq_len_ for t in tk_ )
_SCREAMING_SNAKE_CASE = torch.tensor(tk_ ) # (bs, max_seq_len_)
_SCREAMING_SNAKE_CASE = torch.tensor(A__ ) # (bs)
return tk_t, lg_t
| 0 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ : List[Any] = logging.get_logger(__name__)
UpperCamelCase__ : Any = "▁"
UpperCamelCase__ : Any = {"vocab_file": "spiece.model"}
UpperCamelCase__ : int = {
"vocab_file": {
"google/reformer-crime-and-punishment": (
"https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
)
}
}
UpperCamelCase__ : Optional[int] = {
"google/reformer-crime-and-punishment": 524_288,
}
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE = ['input_ids', 'attention_mask']
def __init__( self , A__ , A__="</s>" , A__="<unk>" , A__=[] , A__ = None , **A__ , ) -> None:
_SCREAMING_SNAKE_CASE = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=A__ , unk_token=A__ , additional_special_tokens=A__ , sp_model_kwargs=self.sp_model_kwargs , **A__ , )
_SCREAMING_SNAKE_CASE = vocab_file
_SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(A__ )
@property
def UpperCamelCase ( self ) -> Any:
return self.sp_model.get_piece_size()
def UpperCamelCase ( self ) -> Dict[str, int]:
_SCREAMING_SNAKE_CASE = {self.convert_ids_to_tokens(A__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> int:
_SCREAMING_SNAKE_CASE = self.__dict__.copy()
_SCREAMING_SNAKE_CASE = None
return state
def __setstate__( self , A__ ) -> str:
_SCREAMING_SNAKE_CASE = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCamelCase ( self , A__ ) -> List[str]:
return self.sp_model.encode(A__ , out_type=A__ )
def UpperCamelCase ( self , A__ ) -> Union[str, Any]:
return self.sp_model.piece_to_id(A__ )
def UpperCamelCase ( self , A__ ) -> List[Any]:
if index < self.sp_model.get_piece_size():
_SCREAMING_SNAKE_CASE = self.sp_model.IdToPiece(A__ )
return token
def UpperCamelCase ( self , A__ ) -> str:
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(A__ ) + token
_SCREAMING_SNAKE_CASE = []
else:
current_sub_tokens.append(A__ )
out_string += self.sp_model.decode(A__ )
return out_string.strip()
def UpperCamelCase ( self , A__ , A__ = None ) -> Tuple[str]:
if not os.path.isdir(A__ ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
_SCREAMING_SNAKE_CASE = os.path.join(
A__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , A__ )
elif not os.path.isfile(self.vocab_file ):
with open(A__ , """wb""" ) as fi:
_SCREAMING_SNAKE_CASE = self.sp_model.serialized_model_proto()
fi.write(A__ )
return (out_vocab_file,)
| 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ : List[Any] = logging.get_logger(__name__)
UpperCamelCase__ : Any = "▁"
UpperCamelCase__ : Any = {"vocab_file": "spiece.model"}
UpperCamelCase__ : int = {
"vocab_file": {
"google/reformer-crime-and-punishment": (
"https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
)
}
}
UpperCamelCase__ : Optional[int] = {
"google/reformer-crime-and-punishment": 524_288,
}
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE = ['input_ids', 'attention_mask']
def __init__( self , A__ , A__="</s>" , A__="<unk>" , A__=[] , A__ = None , **A__ , ) -> None:
_SCREAMING_SNAKE_CASE = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=A__ , unk_token=A__ , additional_special_tokens=A__ , sp_model_kwargs=self.sp_model_kwargs , **A__ , )
_SCREAMING_SNAKE_CASE = vocab_file
_SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(A__ )
@property
def UpperCamelCase ( self ) -> Any:
return self.sp_model.get_piece_size()
def UpperCamelCase ( self ) -> Dict[str, int]:
_SCREAMING_SNAKE_CASE = {self.convert_ids_to_tokens(A__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> int:
_SCREAMING_SNAKE_CASE = self.__dict__.copy()
_SCREAMING_SNAKE_CASE = None
return state
def __setstate__( self , A__ ) -> str:
_SCREAMING_SNAKE_CASE = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCamelCase ( self , A__ ) -> List[str]:
return self.sp_model.encode(A__ , out_type=A__ )
def UpperCamelCase ( self , A__ ) -> Union[str, Any]:
return self.sp_model.piece_to_id(A__ )
def UpperCamelCase ( self , A__ ) -> List[Any]:
if index < self.sp_model.get_piece_size():
_SCREAMING_SNAKE_CASE = self.sp_model.IdToPiece(A__ )
return token
def UpperCamelCase ( self , A__ ) -> str:
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(A__ ) + token
_SCREAMING_SNAKE_CASE = []
else:
current_sub_tokens.append(A__ )
out_string += self.sp_model.decode(A__ )
return out_string.strip()
def UpperCamelCase ( self , A__ , A__ = None ) -> Tuple[str]:
if not os.path.isdir(A__ ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
_SCREAMING_SNAKE_CASE = os.path.join(
A__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , A__ )
elif not os.path.isfile(self.vocab_file ):
with open(A__ , """wb""" ) as fi:
_SCREAMING_SNAKE_CASE = self.sp_model.serialized_model_proto()
fi.write(A__ )
return (out_vocab_file,)
| 0 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
UpperCamelCase__ : List[Any] = logging.get_logger(__name__)
class _a (_lowerCamelCase):
"""simple docstring"""
def __init__( self , *A__ , **A__ ) -> None:
warnings.warn(
"""The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use DPTImageProcessor instead.""" , A__ , )
super().__init__(*A__ , **A__ )
| 0 |
'''simple docstring'''
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class _a (_lowerCamelCase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = MobileBertTokenizer
SCREAMING_SNAKE_CASE = MobileBertTokenizerFast
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = filter_non_english
SCREAMING_SNAKE_CASE = 'google/mobilebert-uncased'
def UpperCamelCase ( self ) -> Any:
super().setUp()
_SCREAMING_SNAKE_CASE = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
_SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
_SCREAMING_SNAKE_CASE = [
(tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2]) # else the 'google/' prefix is stripped
for tokenizer_def in self.tokenizers_list
]
def UpperCamelCase ( self , A__ ) -> List[str]:
_SCREAMING_SNAKE_CASE = """UNwant\u00E9d,running"""
_SCREAMING_SNAKE_CASE = """unwanted, running"""
return input_text, output_text
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = self.tokenizer_class(self.vocab_file )
_SCREAMING_SNAKE_CASE = tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(A__ , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(A__ ) , [9, 6, 7, 12, 10, 11] )
def UpperCamelCase ( self ) -> Optional[int]:
if not self.test_rust_tokenizer:
return
_SCREAMING_SNAKE_CASE = self.get_tokenizer()
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE = """UNwant\u00E9d,running"""
_SCREAMING_SNAKE_CASE = tokenizer.tokenize(A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(A__ , add_special_tokens=A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(A__ )
self.assertListEqual(A__ , A__ )
# With lower casing
_SCREAMING_SNAKE_CASE = self.get_tokenizer(do_lower_case=A__ )
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer(do_lower_case=A__ )
_SCREAMING_SNAKE_CASE = """UNwant\u00E9d,running"""
_SCREAMING_SNAKE_CASE = tokenizer.tokenize(A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(A__ , add_special_tokens=A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(A__ )
self.assertListEqual(A__ , A__ )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz""" ) , ["""ah""", """\u535A""", """\u63A8""", """zz"""] )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""hello""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , strip_accents=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hällo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""h\u00E9llo"""] )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , strip_accents=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , strip_accents=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , strip_accents=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , never_split=["""[UNK]"""] )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? [UNK]""" ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""] )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]
_SCREAMING_SNAKE_CASE = {}
for i, token in enumerate(A__ ):
_SCREAMING_SNAKE_CASE = i
_SCREAMING_SNAKE_CASE = WordpieceTokenizer(vocab=A__ , unk_token="""[UNK]""" )
self.assertListEqual(tokenizer.tokenize("""""" ) , [] )
self.assertListEqual(tokenizer.tokenize("""unwanted running""" ) , ["""un""", """##want""", """##ed""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.tokenize("""unwantedX running""" ) , ["""[UNK]""", """runn""", """##ing"""] )
def UpperCamelCase ( self ) -> str:
self.assertTrue(_is_whitespace(""" """ ) )
self.assertTrue(_is_whitespace("""\t""" ) )
self.assertTrue(_is_whitespace("""\r""" ) )
self.assertTrue(_is_whitespace("""\n""" ) )
self.assertTrue(_is_whitespace("""\u00A0""" ) )
self.assertFalse(_is_whitespace("""A""" ) )
self.assertFalse(_is_whitespace("""-""" ) )
def UpperCamelCase ( self ) -> Union[str, Any]:
self.assertTrue(_is_control("""\u0005""" ) )
self.assertFalse(_is_control("""A""" ) )
self.assertFalse(_is_control(""" """ ) )
self.assertFalse(_is_control("""\t""" ) )
self.assertFalse(_is_control("""\r""" ) )
def UpperCamelCase ( self ) -> Dict:
self.assertTrue(_is_punctuation("""-""" ) )
self.assertTrue(_is_punctuation("""$""" ) )
self.assertTrue(_is_punctuation("""`""" ) )
self.assertTrue(_is_punctuation(""".""" ) )
self.assertFalse(_is_punctuation("""A""" ) )
self.assertFalse(_is_punctuation(""" """ ) )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = self.get_tokenizer()
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(A__ ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] )
self.assertListEqual(
[rust_tokenizer.tokenize(A__ ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] )
@slow
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained("""google/mobilebert-uncased""" )
_SCREAMING_SNAKE_CASE = tokenizer.encode("""sequence builders""" , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer.encode("""multi-sequence build""" , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer.build_inputs_with_special_tokens(A__ )
_SCREAMING_SNAKE_CASE = tokenizer.build_inputs_with_special_tokens(A__ , A__ )
assert encoded_sentence == [1_01] + text + [1_02]
assert encoded_pair == [1_01] + text + [1_02] + text_a + [1_02]
def UpperCamelCase ( self ) -> List[str]:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
_SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = F"A, naïve {tokenizer_r.mask_token} AllenNLP sentence."
_SCREAMING_SNAKE_CASE = tokenizer_r.encode_plus(
A__ , return_attention_mask=A__ , return_token_type_ids=A__ , return_offsets_mapping=A__ , add_special_tokens=A__ , )
_SCREAMING_SNAKE_CASE = tokenizer_r.do_lower_case if hasattr(A__ , """do_lower_case""" ) else False
_SCREAMING_SNAKE_CASE = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), """A"""),
((1, 2), ""","""),
((3, 5), """na"""),
((5, 6), """##ï"""),
((6, 8), """##ve"""),
((9, 15), tokenizer_r.mask_token),
((16, 21), """Allen"""),
((21, 23), """##NL"""),
((23, 24), """##P"""),
((25, 33), """sentence"""),
((33, 34), """."""),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), """a"""),
((1, 2), ""","""),
((3, 8), """naive"""),
((9, 15), tokenizer_r.mask_token),
((16, 21), """allen"""),
((21, 23), """##nl"""),
((23, 24), """##p"""),
((25, 33), """sentence"""),
((33, 34), """."""),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""] ) )
self.assertEqual([e[0] for e in expected_results] , tokens["""offset_mapping"""] )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = ["""的""", """人""", """有"""]
_SCREAMING_SNAKE_CASE = """""".join(A__ )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = tokenizer_p.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer_r.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer_r.convert_ids_to_tokens(A__ )
_SCREAMING_SNAKE_CASE = tokenizer_p.convert_ids_to_tokens(A__ )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(A__ , A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = tokenizer_r.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer_p.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer_r.convert_ids_to_tokens(A__ )
_SCREAMING_SNAKE_CASE = tokenizer_p.convert_ids_to_tokens(A__ )
# it is expected that only the first Chinese character is not preceded by "##".
_SCREAMING_SNAKE_CASE = [
F"##{token}" if idx != 0 else token for idx, token in enumerate(A__ )
]
self.assertListEqual(A__ , A__ )
self.assertListEqual(A__ , A__ )
| 0 | 1 |
'''simple docstring'''
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
UpperCamelCase__ : Optional[Any] = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
" (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"
}
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ = "dhaka" , SCREAMING_SNAKE_CASE_ = 5 ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = min(SCREAMING_SNAKE_CASE_ , 50 ) # Prevent abuse!
_SCREAMING_SNAKE_CASE = {
"""q""": query,
"""tbm""": """isch""",
"""hl""": """en""",
"""ijn""": """0""",
}
_SCREAMING_SNAKE_CASE = requests.get("""https://www.google.com/search""" , params=SCREAMING_SNAKE_CASE_ , headers=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = BeautifulSoup(html.text , """html.parser""" )
_SCREAMING_SNAKE_CASE = """""".join(
re.findall(r"""AF_initDataCallback\(([^<]+)\);""" , str(soup.select("""script""" ) ) ) )
_SCREAMING_SNAKE_CASE = json.dumps(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = json.loads(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = re.findall(
r"""\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",""" , SCREAMING_SNAKE_CASE_ , )
if not matched_google_image_data:
return 0
_SCREAMING_SNAKE_CASE = re.sub(
r"""\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]""" , """""" , str(SCREAMING_SNAKE_CASE_ ) , )
_SCREAMING_SNAKE_CASE = re.findall(
r"""(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]""" , SCREAMING_SNAKE_CASE_ , )
for index, fixed_full_res_image in enumerate(SCREAMING_SNAKE_CASE_ ):
if index >= max_images:
return index
_SCREAMING_SNAKE_CASE = bytes(SCREAMING_SNAKE_CASE_ , """ascii""" ).decode(
"""unicode-escape""" )
_SCREAMING_SNAKE_CASE = bytes(SCREAMING_SNAKE_CASE_ , """ascii""" ).decode(
"""unicode-escape""" )
_SCREAMING_SNAKE_CASE = urllib.request.build_opener()
_SCREAMING_SNAKE_CASE = [
(
"""User-Agent""",
"""Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"""
""" (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582""",
)
]
urllib.request.install_opener(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = F"query_{query.replace(' ' , '_' )}"
if not os.path.exists(SCREAMING_SNAKE_CASE_ ):
os.makedirs(SCREAMING_SNAKE_CASE_ )
urllib.request.urlretrieve( # noqa: S310
SCREAMING_SNAKE_CASE_ , F"{path_name}/original_size_img_{index}.jpg" )
return index
if __name__ == "__main__":
try:
UpperCamelCase__ : List[str] = download_images_from_google_query(sys.argv[1])
print(f"""{image_count} images were downloaded to disk.""")
except IndexError:
print("Please provide a search term.")
raise
| 0 |
'''simple docstring'''
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
UpperCamelCase__ : Tuple = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class _a (_lowerCamelCase):
"""simple docstring"""
def __init__( self , *A__ , A__=None , A__=None , A__=None , **A__ ) -> Optional[int]:
super().__init__(*A__ , **A__ )
_SCREAMING_SNAKE_CASE = eval_examples
_SCREAMING_SNAKE_CASE = post_process_function
_SCREAMING_SNAKE_CASE = quant_trainer_args
_SCREAMING_SNAKE_CASE = 1_28 # default number of calibration samples
def UpperCamelCase ( self , A__=None ) -> Union[str, Any]:
if calib_dataset is None and self.calib_dataset is None:
raise ValueError("""Trainer: calibration requires an calib_dataset.""" )
_SCREAMING_SNAKE_CASE = calib_dataset if calib_dataset is not None else self.calib_dataset
_SCREAMING_SNAKE_CASE = self._remove_unused_columns(A__ , description="""Calibration""" )
return DataLoader(
A__ , batch_size=self.args.eval_batch_size , collate_fn=self.data_collator , drop_last=self.args.dataloader_drop_last , num_workers=self.args.dataloader_num_workers , pin_memory=self.args.dataloader_pin_memory , shuffle=A__ , )
def UpperCamelCase ( self , A__=None ) -> str:
_SCREAMING_SNAKE_CASE = self.train_dataset if calib_dataset is None else calib_dataset
_SCREAMING_SNAKE_CASE = self.get_calib_dataloader(A__ )
_SCREAMING_SNAKE_CASE = self.model
quant_trainer.configure_model(A__ , self.quant_trainer_args , calib=A__ )
model.eval()
quant_trainer.enable_calibration(A__ )
logger.info("""***** Running calibration *****""" )
logger.info(F" Num examples = {self.calib_num}" )
logger.info(F" Batch size = {calib_dataloader.batch_size}" )
for step, inputs in enumerate(A__ ):
# Prediction step
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.prediction_step(A__ , A__ , prediction_loss_only=A__ )
if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
break
quant_trainer.finish_calibration(A__ , self.quant_trainer_args )
_SCREAMING_SNAKE_CASE = model
def UpperCamelCase ( self , A__=None , A__=None , A__=None , A__ = "eval" ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.eval_dataset if eval_dataset is None else eval_dataset
_SCREAMING_SNAKE_CASE = self.get_eval_dataloader(A__ )
_SCREAMING_SNAKE_CASE = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
_SCREAMING_SNAKE_CASE = self.compute_metrics
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_SCREAMING_SNAKE_CASE = eval_loop(
A__ , description="""Evaluation""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=A__ , )
finally:
_SCREAMING_SNAKE_CASE = compute_metrics
if self.post_process_function is not None and self.compute_metrics is not None:
_SCREAMING_SNAKE_CASE = self.post_process_function(A__ , A__ , output.predictions )
_SCREAMING_SNAKE_CASE = self.compute_metrics(A__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"{metric_key_prefix}_" ):
_SCREAMING_SNAKE_CASE = metrics.pop(A__ )
self.log(A__ )
else:
_SCREAMING_SNAKE_CASE = {}
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
_SCREAMING_SNAKE_CASE = self.callback_handler.on_evaluate(self.args , self.state , self.control , A__ )
return metrics
def UpperCamelCase ( self , A__ , A__ , A__=None , A__ = "test" ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.get_test_dataloader(A__ )
# Temporarily disable metric computation, we will do it in the loop here.
_SCREAMING_SNAKE_CASE = self.compute_metrics
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_SCREAMING_SNAKE_CASE = eval_loop(
A__ , description="""Prediction""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=A__ , )
finally:
_SCREAMING_SNAKE_CASE = compute_metrics
if self.post_process_function is None or self.compute_metrics is None:
return output
_SCREAMING_SNAKE_CASE = self.post_process_function(A__ , A__ , output.predictions , """predict""" )
_SCREAMING_SNAKE_CASE = self.compute_metrics(A__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"{metric_key_prefix}_" ):
_SCREAMING_SNAKE_CASE = metrics.pop(A__ )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=A__ )
def UpperCamelCase ( self , A__="./" ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.eval_dataset
_SCREAMING_SNAKE_CASE = self.get_eval_dataloader(A__ )
_SCREAMING_SNAKE_CASE = next(iter(A__ ) )
# saving device - to make it consistent
_SCREAMING_SNAKE_CASE = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
# convert to tuple
_SCREAMING_SNAKE_CASE = tuple(v.to(A__ ) for k, v in batch.items() )
logger.info("""Converting model to be onnx compatible""" )
from pytorch_quantization.nn import TensorQuantizer
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = self.model.to(A__ )
model.eval()
model.float()
_SCREAMING_SNAKE_CASE = model.module if hasattr(A__ , """module""" ) else model
quant_trainer.configure_model(A__ , self.quant_trainer_args )
_SCREAMING_SNAKE_CASE = os.path.join(A__ , """model.onnx""" )
logger.info(F"exporting model to {output_model_file}" )
_SCREAMING_SNAKE_CASE = {0: """batch_size""", 1: """seq_len"""}
torch.onnx.export(
A__ , A__ , A__ , export_params=A__ , opset_version=13 , do_constant_folding=A__ , input_names=["""input_ids""", """attention_mask""", """token_type_ids"""] , output_names=["""output_start_logits""", """output_end_logits"""] , dynamic_axes={
"""input_ids""": axes,
"""attention_mask""": axes,
"""token_type_ids""": axes,
"""output_start_logits""": axes,
"""output_end_logits""": axes,
} , verbose=A__ , )
logger.info("""onnx export finished""" )
| 0 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : Tuple = logging.get_logger(__name__)
UpperCamelCase__ : List[str] = {
"RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json",
"RWKV/rwkv-4-430m-pile": "https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json",
"RWKV/rwkv-4-1b5-pile": "https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json",
"RWKV/rwkv-4-3b-pile": "https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json",
"RWKV/rwkv-4-7b-pile": "https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json",
"RWKV/rwkv-4-14b-pile": "https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json",
"RWKV/rwkv-raven-1b5": "https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json",
"RWKV/rwkv-raven-3b": "https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json",
"RWKV/rwkv-raven-7b": "https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json",
"RWKV/rwkv-raven-14b": "https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json",
}
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 'rwkv'
SCREAMING_SNAKE_CASE = {'max_position_embeddings': 'context_length'}
def __init__( self , A__=5_02_77 , A__=10_24 , A__=40_96 , A__=32 , A__=None , A__=None , A__=1E-5 , A__=0 , A__=0 , A__=6 , A__=False , A__=True , **A__ , ) -> Any:
_SCREAMING_SNAKE_CASE = vocab_size
_SCREAMING_SNAKE_CASE = context_length
_SCREAMING_SNAKE_CASE = hidden_size
_SCREAMING_SNAKE_CASE = num_hidden_layers
_SCREAMING_SNAKE_CASE = attention_hidden_size if attention_hidden_size is not None else hidden_size
_SCREAMING_SNAKE_CASE = intermediate_size if intermediate_size is not None else 4 * hidden_size
_SCREAMING_SNAKE_CASE = layer_norm_epsilon
_SCREAMING_SNAKE_CASE = rescale_every
_SCREAMING_SNAKE_CASE = use_cache
_SCREAMING_SNAKE_CASE = bos_token_id
_SCREAMING_SNAKE_CASE = eos_token_id
super().__init__(
tie_word_embeddings=A__ , bos_token_id=A__ , eos_token_id=A__ , **A__ )
| 0 |
'''simple docstring'''
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
return "".join([hex(SCREAMING_SNAKE_CASE_ )[2:].zfill(2 ).upper() for byte in list(SCREAMING_SNAKE_CASE_ )] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> bytes:
"""simple docstring"""
# Check data validity, following RFC3548
# https://www.ietf.org/rfc/rfc3548.txt
if (len(SCREAMING_SNAKE_CASE_ ) % 2) != 0:
raise ValueError(
"""Base16 encoded data is invalid:
Data does not have an even number of hex digits.""" )
# Check the character set - the standard base16 alphabet
# is uppercase according to RFC3548 section 6
if not set(SCREAMING_SNAKE_CASE_ ) <= set("""0123456789ABCDEF""" ):
raise ValueError(
"""Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters.""" )
# For every two hexadecimal digits (= a byte), turn it into an integer.
# Then, string the result together into bytes, and return it.
return bytes(int(data[i] + data[i + 1] , 16 ) for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) , 2 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 | 1 |
'''simple docstring'''
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class _a :
"""simple docstring"""
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> bool:
"""simple docstring"""
# Validation
def is_valid_tree(SCREAMING_SNAKE_CASE_ ) -> bool:
if node is None:
return True
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return False
try:
float(node.data )
except (TypeError, ValueError):
return False
return is_valid_tree(node.left ) and is_valid_tree(node.right )
if not is_valid_tree(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Each node should be type of TreeNode and data should be float.""" )
def is_binary_search_tree_recursive_check(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left , SCREAMING_SNAKE_CASE_ , node.data )
and is_binary_search_tree_recursive_check(
node.right , node.data , SCREAMING_SNAKE_CASE_ )
)
return is_binary_search_tree_recursive_check(SCREAMING_SNAKE_CASE_ , -float("""inf""" ) , float("""inf""" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def lowerCAmelCase_ ( ) -> List[Any]:
"""simple docstring"""
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
requests.request("""GET""" , """https://huggingface.co""" )
with pytest.raises(requests.exceptions.ConnectTimeout ):
requests.request("""GET""" , """https://huggingface.co""" , timeout=1.0 )
@pytest.mark.integration
def lowerCAmelCase_ ( ) -> int:
"""simple docstring"""
with offline(OfflineSimulationMode.CONNECTION_FAILS ):
with pytest.raises(requests.exceptions.ConnectionError ):
requests.request("""GET""" , """https://huggingface.co""" )
def lowerCAmelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
http_head("""https://huggingface.co""" )
| 0 | 1 |
End of preview. Expand
in Data Studio
Dataset Card for "python_codestyles-single-1k"
This dataset contains negative and positive examples with python code of compliance with a code style. A positive
example represents compliance with the code style (label is 1). Each example is composed of two components, the first
component consists of a code that either conforms to the code style or violates it and the second component
corresponding to an example code that already conforms to a code style. In total, the dataset contains 1.000 completely
different code styles. The code styles differ in exactly one codestyle rule, which is called a single codestyle
dataset variant. The dataset consists of a training and test group, with none of the code styles overlapping between
groups. In addition, both groups contain completely different underlying codes.
The examples contain source code from the following repositories:
| repository | tag or commit |
|---|---|
| TheAlgorithms/Python | f614ed72170011d2d439f7901e1c8daa7deac8c4 |
| huggingface/transformers | v4.31.0 |
| huggingface/datasets | 2.13.1 |
| huggingface/diffusers | v0.18.2 |
| huggingface/accelerate | v0.21.0 |
You can find the corresponding code styles of the examples in the file additional_data.json.
The code styles in the file are split by training and test group and the index corresponds to the class for the
columns code_codestyle and style_context_codestyle in the dataset.
There are 364.381 samples in total and 182.181 positive and 182.200 negative samples.
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