"""
Utility functions for TTV-1B model
Data preprocessing, video I/O, and helper functions
"""

import torch
import numpy as np
from pathlib import Path
from typing import Optional, List, Tuple, Dict
import json


# ============================================================================
# Video Processing Utilities
# ============================================================================

def load_video_frames(
    video_path: str,
    num_frames: int = 16,
    target_size: Tuple[int, int] = (256, 256),
) -> torch.Tensor:
    """
    Load video and extract frames
    
    Args:
        video_path: Path to video file
        num_frames: Number of frames to extract
        target_size: Target resolution (H, W)
        
    Returns:
        Video tensor (C, T, H, W) normalized to [-1, 1]
    """
    try:
        # Try using torchvision
        from torchvision.io import read_video
        
        video, _, _ = read_video(video_path, pts_unit='sec')
        video = video.permute(3, 0, 1, 2)  # (T, H, W, C) -> (C, T, H, W)
        
        # Sample frames uniformly
        total_frames = video.shape[1]
        indices = torch.linspace(0, total_frames - 1, num_frames).long()
        video = video[:, indices]
        
        # Resize
        import torch.nn.functional as F
        video = F.interpolate(
            video.float(),
            size=(num_frames, *target_size),
            mode='trilinear',
            align_corners=False
        )
        
        # Normalize to [-1, 1]
        video = video / 127.5 - 1.0
        
        return video
        
    except ImportError:
        # Fallback to opencv
        import cv2
        
        cap = cv2.VideoCapture(video_path)
        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
        
        # Calculate frame indices to sample
        indices = np.linspace(0, total_frames - 1, num_frames).astype(int)
        
        frames = []
        for idx in indices:
            cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
            ret, frame = cap.read()
            if ret:
                # Resize and convert BGR to RGB
                frame = cv2.resize(frame, target_size)
                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
                frames.append(frame)
        
        cap.release()
        
        # Convert to tensor
        video = np.stack(frames, axis=0)  # (T, H, W, C)
        video = torch.from_numpy(video).permute(3, 0, 1, 2).float()  # (C, T, H, W)
        
        # Normalize to [-1, 1]
        video = video / 127.5 - 1.0
        
        return video


def save_video_frames(
    frames: torch.Tensor,
    output_path: str,
    fps: int = 8,
    codec: str = 'libx264',
):
    """
    Save video tensor to file
    
    Args:
        frames: Video tensor (C, T, H, W) or (T, H, W, C) in range [-1, 1] or [0, 1]
        output_path: Output file path
        fps: Frames per second
        codec: Video codec
    """
    # Ensure frames are in [0, 1] range
    if frames.min() < 0:
        frames = (frames + 1) / 2  # [-1, 1] -> [0, 1]
    
    frames = torch.clamp(frames, 0, 1)
    
    # Convert to (T, H, W, C) format
    if frames.shape[0] == 3:  # (C, T, H, W)
        frames = frames.permute(1, 2, 3, 0)
    
    # Scale to [0, 255]
    frames = (frames * 255).to(torch.uint8).cpu()
    
    try:
        from torchvision.io import write_video
        write_video(output_path, frames, fps=fps, video_codec=codec)
        print(f"Video saved to {output_path}")
        
    except ImportError:
        # Fallback to opencv
        import cv2
        
        height, width = frames.shape[1:3]
        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
        out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
        
        for frame in frames:
            frame_bgr = cv2.cvtColor(frame.numpy(), cv2.COLOR_RGB2BGR)
            out.write(frame_bgr)
        
        out.release()
        print(f"Video saved to {output_path}")


def create_video_grid(
    videos: List[torch.Tensor],
    grid_size: Optional[Tuple[int, int]] = None,
) -> torch.Tensor:
    """
    Create a grid of videos for comparison
    
    Args:
        videos: List of video tensors (C, T, H, W)
        grid_size: (rows, cols). If None, automatically determined
        
    Returns:
        Grid video tensor (C, T, H_grid, W_grid)
    """
    n_videos = len(videos)
    
    if grid_size is None:
        cols = int(np.ceil(np.sqrt(n_videos)))
        rows = int(np.ceil(n_videos / cols))
    else:
        rows, cols = grid_size
    
    C, T, H, W = videos[0].shape
    
    # Pad with blank videos if needed
    while len(videos) < rows * cols:
        videos.append(torch.zeros_like(videos[0]))
    
    # Arrange in grid
    grid_rows = []
    for i in range(rows):
        row_videos = videos[i * cols:(i + 1) * cols]
        row = torch.cat(row_videos, dim=-1)  # Concatenate along width
        grid_rows.append(row)
    
    grid = torch.cat(grid_rows, dim=-2)  # Concatenate along height
    
    return grid


# ============================================================================
# Text Processing Utilities
# ============================================================================

class SimpleTokenizer:
    """Simple character-level tokenizer (replace with proper tokenizer in production)"""
    
    def __init__(self, vocab_size: int = 50257):
        self.vocab_size = vocab_size
    
    def encode(self, text: str, max_length: int = 256) -> torch.Tensor:
        """Encode text to token IDs"""
        # Simple character-level encoding
        tokens = [ord(c) % self.vocab_size for c in text[:max_length]]
        
        # Pad to max length
        tokens = tokens + [0] * (max_length - len(tokens))
        
        return torch.tensor(tokens, dtype=torch.long)
    
    def decode(self, tokens: torch.Tensor) -> str:
        """Decode token IDs to text"""
        chars = [chr(t.item()) for t in tokens if t.item() != 0]
        return ''.join(chars)
    
    def batch_encode(self, texts: List[str], max_length: int = 256) -> torch.Tensor:
        """Encode batch of texts"""
        return torch.stack([self.encode(text, max_length) for text in texts])


# ============================================================================
# Dataset Utilities
# ============================================================================

def create_dataset_split(
    annotation_file: str,
    train_ratio: float = 0.9,
    seed: int = 42,
) -> Tuple[Dict, Dict]:
    """
    Split dataset into train and validation sets
    
    Args:
        annotation_file: Path to annotations JSON
        train_ratio: Ratio of training data
        seed: Random seed
        
    Returns:
        train_annotations, val_annotations
    """
    with open(annotation_file, 'r') as f:
        annotations = json.load(f)
    
    # Shuffle keys
    keys = list(annotations.keys())
    np.random.seed(seed)
    np.random.shuffle(keys)
    
    # Split
    split_idx = int(len(keys) * train_ratio)
    train_keys = keys[:split_idx]
    val_keys = keys[split_idx:]
    
    train_annotations = {k: annotations[k] for k in train_keys}
    val_annotations = {k: annotations[k] for k in val_keys}
    
    return train_annotations, val_annotations


def validate_dataset(video_dir: str, annotation_file: str) -> Dict[str, any]:
    """
    Validate dataset integrity
    
    Returns:
        Dictionary with validation results
    """
    video_dir = Path(video_dir)
    
    with open(annotation_file, 'r') as f:
        annotations = json.load(f)
    
    results = {
        'total_videos': len(annotations),
        'missing_videos': [],
        'invalid_captions': [],
        'warnings': [],
    }
    
    for video_id, data in annotations.items():
        # Check video file exists
        video_path = video_dir / f"{video_id}.mp4"
        if not video_path.exists():
            results['missing_videos'].append(video_id)
        
        # Check caption
        if 'caption' not in data or not data['caption'].strip():
            results['invalid_captions'].append(video_id)
        
        # Check caption length
        if len(data.get('caption', '')) > 256:
            results['warnings'].append(f"{video_id}: Caption too long")
    
    results['valid'] = (
        len(results['missing_videos']) == 0 and
        len(results['invalid_captions']) == 0
    )
    
    return results


# ============================================================================
# Model Utilities
# ============================================================================

def count_model_parameters(model: torch.nn.Module) -> Dict[str, int]:
    """Count model parameters"""
    total_params = sum(p.numel() for p in model.parameters())
    trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
    
    return {
        'total': total_params,
        'trainable': trainable_params,
        'non_trainable': total_params - trainable_params,
    }


def load_checkpoint_safe(
    model: torch.nn.Module,
    checkpoint_path: str,
    strict: bool = True,
) -> Dict[str, any]:
    """
    Safely load checkpoint with error handling
    
    Returns:
        Dictionary with loading results
    """
    try:
        checkpoint = torch.load(checkpoint_path, map_location='cpu')
        
        # Load model state
        if 'model_state_dict' in checkpoint:
            model.load_state_dict(checkpoint['model_state_dict'], strict=strict)
        else:
            model.load_state_dict(checkpoint, strict=strict)
        
        return {
            'success': True,
            'step': checkpoint.get('global_step', -1),
            'epoch': checkpoint.get('epoch', -1),
        }
        
    except Exception as e:
        return {
            'success': False,
            'error': str(e),
        }


# ============================================================================
# Visualization Utilities
# ============================================================================

def create_comparison_video(
    original: torch.Tensor,
    generated: torch.Tensor,
    prompt: str,
    output_path: str,
):
    """
    Create side-by-side comparison video
    
    Args:
        original: Original video (C, T, H, W)
        generated: Generated video (C, T, H, W)
        prompt: Text prompt
        output_path: Where to save
    """
    # Concatenate videos horizontally
    combined = torch.cat([original, generated], dim=-1)
    
    save_video_frames(combined, output_path)
    print(f"Comparison video saved to {output_path}")
    print(f"Prompt: {prompt}")


# ============================================================================
# Logging Utilities
# ============================================================================

class TrainingLogger:
    """Simple training logger"""
    
    def __init__(self, log_dir: str):
        self.log_dir = Path(log_dir)
        self.log_dir.mkdir(parents=True, exist_ok=True)
        self.log_file = self.log_dir / 'training.log'
        
        self.metrics = {
            'step': [],
            'loss': [],
            'lr': [],
        }
    
    def log(self, step: int, loss: float, lr: float):
        """Log training metrics"""
        self.metrics['step'].append(step)
        self.metrics['loss'].append(loss)
        self.metrics['lr'].append(lr)
        
        # Write to file
        with open(self.log_file, 'a') as f:
            f.write(f"{step},{loss},{lr}\n")
    
    def save_metrics(self):
        """Save metrics to JSON"""
        output_file = self.log_dir / 'metrics.json'
        with open(output_file, 'w') as f:
            json.dump(self.metrics, f, indent=2)


# ============================================================================
# Testing Utilities
# ============================================================================

def test_video_pipeline():
    """Test video loading and saving pipeline"""
    print("Testing video pipeline...")
    
    # Create dummy video
    video = torch.randn(3, 16, 256, 256)
    video = (video - video.min()) / (video.max() - video.min())
    
    # Save
    output_path = "test_video.mp4"
    save_video_frames(video, output_path)
    
    # Load
    loaded = load_video_frames(output_path, num_frames=16)
    
    print(f"Original shape: {video.shape}")
    print(f"Loaded shape: {loaded.shape}")
    print("✓ Video pipeline test passed")


def test_tokenizer():
    """Test tokenizer"""
    print("Testing tokenizer...")
    
    tokenizer = SimpleTokenizer()
    
    text = "A beautiful sunset over the ocean"
    tokens = tokenizer.encode(text, max_length=128)
    decoded = tokenizer.decode(tokens)
    
    print(f"Original: {text}")
    print(f"Tokens shape: {tokens.shape}")
    print(f"Decoded: {decoded[:len(text)]}")
    print("✓ Tokenizer test passed")


if __name__ == "__main__":
    print("Running utility tests...\n")
    test_tokenizer()
    print("\n" + "="*60 + "\n")
    print("Note: Video pipeline test requires torchvision or opencv")
    print("Run after installing dependencies")