class VideoLatentDiffusion(nn.Module):
    """Latent diffusion model for video generation"""
    
    def __init__(self):
        super().__init__()
        # Video autoencoder
        self.encoder = VideoEncoder()  # Compress to latent space
        self.decoder = VideoDecoder()  # Reconstruct from latent
        
        # Diffusion model in latent space
        self.unet = VideoUNet3D()
        
        # Text encoder
        self.text_encoder = T5Encoder()
    
    def encode(self, video):
        """Encode video to latent space"""
        return self.encoder(video)
    
    def decode(self, latent):
        """Decode latent to video"""
        return self.decoder(latent)
    
    def forward(self, video, text, timestep):
        """Predict noise in latent space"""
        # Encode video
        z = self.encode(video)
        
        # Encode text
        text_emb = self.text_encoder(text)
        
        # Predict noise
        noise_pred = self.unet(z, timestep, text_emb)
        
        return noise_pred

class AutoregressiveVideoModel(nn.Module):
    """Generate video frame by frame"""
    
    def __init__(self):
        super().__init__()
        self.frame_encoder = FrameEncoder()
        self.temporal_model = TransformerDecoder()
        self.frame_decoder = FrameDecoder()
    
    def generate(self, first_frame, text, num_frames=16):
        """Generate video autoregressively"""
        frames = [first_frame]
        
        # Encode first frame
        z = self.frame_encoder(first_frame)
        context = [z]
        
        for _ in range(num_frames - 1):
            # Predict next frame latent
            z_next = self.temporal_model(
                context,
                text_conditioning=text
            )
            
            # Decode to frame
            frame = self.frame_decoder(z_next)
            frames.append(frame)
            
            # Update context
            context.append(z_next)
        
        return torch.stack(frames, dim=1)

class VideoDataset(torch.utils.data.Dataset):
    def __init__(self, video_paths, caption_file, num_frames=16):
        self.video_paths = video_paths
        self.captions = load_captions(caption_file)
        self.num_frames = num_frames
    
    def __getitem__(self, idx):
        # Load video
        video = load_video(self.video_paths[idx])
        
        # Sample frames
        if len(video) > self.num_frames:
            start = random.randint(0, len(video) - self.num_frames)
            video = video[start:start + self.num_frames]
        
        # Get caption
        caption = self.captions[idx]
        
        # Augmentation
        video = self.augment(video)
        
        return {
            "video": video,
            "caption": caption
        }
    
    def augment(self, video):
        # Random horizontal flip
        if random.random() > 0.5:
            video = torch.flip(video, dims=[-1])
        
        # Color jitter
        video = color_jitter(video)
        
        return video

def train_video_model(model, dataloader, epochs=100):
    optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)
    noise_scheduler = DDPMScheduler(num_train_timesteps=1000)
    
    for epoch in range(epochs):
        for batch in dataloader:
            video = batch["video"]
            caption = batch["caption"]
            
            # Encode video to latent
            latent = model.encode(video)
            
            # Sample noise and timestep
            noise = torch.randn_like(latent)
            timesteps = torch.randint(0, 1000, (video.shape[0],))
            
            # Add noise
            noisy_latent = noise_scheduler.add_noise(latent, noise, timesteps)
            
            # Predict noise
            noise_pred = model(noisy_latent, caption, timesteps)
            
            # Loss
            loss = F.mse_loss(noise_pred, noise)
            
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

@torch.no_grad()
def generate_video(model, prompt, num_frames=16, num_steps=50):
    """Generate video from text prompt"""
    
    # Encode prompt
    text_emb = model.text_encoder(prompt)
    
    # Initialize with noise
    latent_shape = (1, num_frames, 4, 64, 64)
    latent = torch.randn(latent_shape)
    
    # Denoising loop
    scheduler = DDIMScheduler(num_inference_steps=num_steps)
    
    for t in scheduler.timesteps:
        # Predict noise
        noise_pred = model.unet(latent, t, text_emb)
        
        # Denoise step
        latent = scheduler.step(noise_pred, t, latent).prev_sample
    
    # Decode to video
    video = model.decode(latent)
    
    return video

def human_evaluation_protocol():
    """Protocol for human evaluation of generated videos"""
    criteria = {
        "visual_quality": "Rate the visual quality (1-5)",
        "motion_realism": "How realistic is the motion? (1-5)",
        "text_alignment": "Does the video match the prompt? (1-5)",
        "temporal_coherence": "Is the video temporally consistent? (1-5)",
        "physical_plausibility": "Are physics realistic? (1-5)"
    }
    return criteria