import json
from PIL import Image
from torch.utils.data import Dataset, DataLoader
import torch
from .model_vlm import MiniMindVLM
import os
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize, InterpolationMode
import cv2
import numpy as np

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


def video2image(video_path, num_frames=8, size=224):
    def preprocess(size, n_px):
        return Compose([
            Resize(size, interpolation=InterpolationMode.BICUBIC),            
            CenterCrop(size),
            lambda image: image.convert("RGB"),
            ToTensor(),
            Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
        ])(n_px)
    
    cap = cv2.VideoCapture(video_path)
    cap = cv2.VideoCapture(video_path, cv2.CAP_FFMPEG)
    frameCount = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    fps = int(cap.get(cv2.CAP_PROP_FPS))
    
    if fps < 1 or frameCount < 1:
        images = np.zeros([3, size, size], dtype=np.float32) 
        print("ERROR: problem reading video file: ", video_path)
    else:
        frames_idx = np.sort(np.random.choice(frameCount, min(num_frames, frameCount), replace=False))
        
        images = np.zeros([len(frames_idx), 3, size, size], dtype=np.float32)
        
        for i, idx in enumerate(frames_idx):
            cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
            ret, frame = cap.read()    
            if not ret: 
                continue
            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)             
            images[i,:,:,:] = preprocess(size, Image.fromarray(frame).convert("RGB"))
    
    cap.release()
    video_frames = torch.tensor(images)
    return video_frames

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

        super().__init__()
        self.samples = self.load_data(jsonl_path)
        self.images_path = images_path

        self.tokenizer = tokenizer
        self.max_length = max_length
        self.preprocess = preprocess
        self.image_token = image_special_token
        self.bos_id = tokenizer('<s>assistant\n', add_special_tokens=False).input_ids
        self.eos_id = tokenizer('</s>\n', add_special_tokens=False).input_ids

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

    def load_data(self, path):
        samples = []
        with open(path, 'r', encoding='utf-8') as f:
            for line_num, line in enumerate(f, 1):
                data = json.loads(line.strip())
                samples.append(data)
        return samples

    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_loss_mask(self, input_ids):
        loss_mask = [0] * 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 + 1, min(end + len(self.eos_id) + 1, self.max_length)):
                    loss_mask[j] = 1
                i = end + len(self.eos_id) if end < len(input_ids) else len(input_ids)
            else:
                i += 1
        return loss_mask

    def __getitem__(self, index: int):
        sample = self.samples[index]
        image_paths = sample['image']
        prompt = self._create_chat_prompt(sample['conversations'])
        input_ids = self.tokenizer(prompt).input_ids[:self.max_length]
        input_ids += [self.tokenizer.pad_token_id] * (self.max_length - len(input_ids))
        loss_mask = self._generate_loss_mask(input_ids)

        X = torch.tensor(input_ids[:-1], dtype=torch.long)
        Y = torch.tensor(input_ids[1:], dtype=torch.long)
        loss_mask = torch.tensor(loss_mask[1:], dtype=torch.long)

        image_tensors = []
        for image_name in image_paths.split(','):
            image_name = image_name.strip()
            image = Image.open(f'{self.images_path}/{image_name}')
            image_tensor = MiniMindVLM.image2tensor(image, self.preprocess)
            image_tensors.append(image_tensor)
        image_tensors = torch.stack(image_tensors, dim=0)

        return X, Y, loss_mask, image_tensors
    

class VideoLMDataset(Dataset):
    def __init__(self, jsonl_path, videos_path, tokenizer, max_length=512,
                 video_special_token='@' * 196):

        super().__init__()
        self.samples = self.load_data(jsonl_path)
        self.videos_path = videos_path

        self.tokenizer = tokenizer
        self.max_length = max_length
        self.video_token = video_special_token
        self.bos_id = tokenizer('<s>assistant\n', add_special_tokens=False).input_ids
        self.eos_id = tokenizer('</s>\n', add_special_tokens=False).input_ids

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

    def load_data(self, path):
        samples = []
        with open(path, 'r', encoding='utf-8') as f:
            for line_num, line in enumerate(f, 1):
                data = json.loads(line.strip())
                samples.append(data)
        return samples

    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('<video>', self.video_token)})
        return self.tokenizer.apply_chat_template(
            messages,
            tokenize=False,
            add_generation_prompt=False
        )

    def _generate_loss_mask(self, input_ids):
        loss_mask = [0] * 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 + 1, min(end + len(self.eos_id) + 1, self.max_length)):
                    loss_mask[j] = 1
                i = end + len(self.eos_id) if end < len(input_ids) else len(input_ids)
            else:
                i += 1
        return loss_mask

    def __getitem__(self, index: int):
        sample = self.samples[index]
        video_paths = sample['video']
        prompt = self._create_chat_prompt(sample['conversations'])
        input_ids = self.tokenizer(prompt).input_ids[:self.max_length]
        input_ids += [self.tokenizer.pad_token_id] * (self.max_length - len(input_ids))
        loss_mask = self._generate_loss_mask(input_ids)

        X = torch.tensor(input_ids[:-1], dtype=torch.long)
        Y = torch.tensor(input_ids[1:], dtype=torch.long)
        loss_mask = torch.tensor(loss_mask[1:], dtype=torch.long)

        video_tensors = []
        for video_name in video_paths.split(','):
            video_name = video_name.strip()
            video_path = f'{self.videos_path}/{video_name}'
            video_tensor = video2image(video_path)
            video_tensors.append(video_tensor)
        video_tensors = torch.stack(video_tensors, dim=0)

        return X, Y, loss_mask, video_tensors