import numpy as np
import torch
import torchvision.transforms as T
from decord import VideoReader, cpu
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
from transformers import AutoModel, AutoTokenizer
from longva.model.builder import load_pretrained_model
from longva.mm_utils import tokenizer_image_token
from longva.constants import IMAGE_TOKEN_INDEX


def load_images(images_path):
    images = []
    for image_path in images_path:
        # 打开图像并转换为 NumPy 数组
        image = Image.open(image_path).convert("RGB")  # 确保图像是 RGB 格式
        image_array = np.array(image)
        images.append(image_array)
    
    # 将所有图像组合成一个 NumPy 数组
    images_array = np.stack(images, axis=0)
    return images_array


class LongVA(object):
    def __init__(self, gpu=1, model_path="lmms-lab/LongVA-7B"):
        self.model_path = model_path
        self.device = torch.device(f"cuda:{gpu}" if torch.cuda.is_available() else "cpu")
        self.device_map = {"": f"cuda:{gpu}"}
        self.gen_kwargs = {"do_sample": True, "temperature": 0.5, "top_p": None, "num_beams": 1, "use_cache": True, "max_new_tokens": 1024}
        self.tokenizer, self.model, self.image_processor, _ = load_pretrained_model(
            model_path, None, 
            "llava_qwen", 
            device_map=self.device_map
        )
    
    def inference(self, images_path, qa):
        prompt = f"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<image>\nThis question is about the main topic discussed in the video. Question: {qa['question']} Choices: A) {qa['choice_a']} B) {qa['choice_b']} C) {qa['choice_c']} D) {qa['choice_d']}. Respond with a single capital letter (A, B, C, or D) only. No explanation. No punctuation. Just the letter.<|im_end|>\n<|im_start|>assistant\n"
        input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.model.device)
        #print(f"input_ids: {input_ids}")
        images = load_images(images_path)
        images_tensor = self.image_processor.preprocess(images, return_tensors="pt")["pixel_values"].to(self.model.device, dtype=torch.float16)
        with torch.inference_mode():
            output_ids = self.model.generate(input_ids, images=[images_tensor],  modalities=["video"], **self.gen_kwargs)
        result = self.tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
        return result