import sys
import os
__package__ = "dataset"
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
import json
import torch
import io
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from model.model_vlm import MiniMindVLM
import pyarrow.parquet as pq

os.environ["TOKENIZERS_PARALLELISM"] = "false"


class VLMDataset(Dataset):
    def __init__(self, parquet_path, tokenizer, preprocess=None, max_length=512,
                 image_special_token='@' * 196):

        super().__init__()
        self.table = pq.read_table(parquet_path)
        self.tokenizer = tokenizer
        self.max_length = max_length
        self.preprocess = preprocess
        self.image_token = image_special_token
        self.bos_id = tokenizer(f'{tokenizer.bos_token}assistant\n', add_special_tokens=False).input_ids
        self.eos_id = tokenizer(f'{tokenizer.eos_token}\n', add_special_tokens=False).input_ids

    def __len__(self):
        return len(self.table)

    def create_chat_prompt(self, conversations):
        messages = []
        for i, turn in enumerate(conversations):
            role = 'user' if i % 2 == 0 else 'assistant'
            messages.append({"role": role, "content": turn['content'].replace('<image>', self.image_token)})
        return self.tokenizer.apply_chat_template(
            messages,
            tokenize=False,
            add_generation_prompt=False
        )

    def generate_labels(self, input_ids):
        labels = [-100] * len(input_ids)
        i = 0
        while i < len(input_ids):
            if input_ids[i:i + len(self.bos_id)] == self.bos_id:
                start = i + len(self.bos_id)
                end = start
                while end < len(input_ids):
                    if input_ids[end:end + len(self.eos_id)] == self.eos_id:
                        break
                    end += 1
                for j in range(start, min(end + len(self.eos_id), self.max_length)):
                    labels[j] = input_ids[j]
                i = end + len(self.eos_id) if end < len(input_ids) else len(input_ids)
            else:
                i += 1
        return labels

    def __getitem__(self, index: int):
        conversations = json.loads(self.table['conversations'][index].as_py())
        image_bytes = self.table['image_bytes'][index].as_py()
        if not isinstance(image_bytes, list): image_bytes = [image_bytes]
        
        prompt = self.create_chat_prompt(conversations)
        input_ids = self.tokenizer(prompt).input_ids[:self.max_length]
        input_ids += [self.tokenizer.pad_token_id] * (self.max_length - len(input_ids))
        labels = self.generate_labels(input_ids)

        image_tensor = torch.stack([MiniMindVLM.image2tensor(Image.open(io.BytesIO(img)), self.preprocess) for img in image_bytes])
        # # === 调试打印 ===
        # print(f"\n--- Sample {index} ---")
        # for i, (x, y) in enumerate(zip(input_ids[:-1], labels[1:])):
        #     print(f"{i:3d}: X={self.tokenizer.decode([x])!r:16s} ---> Y={self.tokenizer.decode([input_ids[i+1]])!r:16s} label={y}")
        # # ================

        return torch.tensor(input_ids, dtype=torch.long), torch.tensor(labels, dtype=torch.long), image_tensor

# 测试parquet数据读取和可视化
if __name__ == '__main__':
    import matplotlib.pyplot as plt; plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei']
    for path in ['pretrain_i2t.parquet', 'sft_i2t.parquet']:
        t = pq.read_table(path); fig, ax = plt.subplots(1, 5, figsize=(20, 4))
        for i in range(5):
            ax[i].imshow(Image.open(io.BytesIO(t['image_bytes'][i].as_py()))); ax[i].axis('off')
            ax[i].set_title(json.loads(t['conversations'][i].as_py())[1]['content'][:30], fontsize=8)
        out = path.replace('.parquet', '_preview.png'); plt.savefig(out); print(f'已保存{out}, 共{len(t)}条')