"""
Text-to-video generation module.
"""

from typing import List, Optional, Union

try:
    from PIL import Image
    import numpy as np
except ImportError:
    raise ImportError(
        "Text-to-video generation requires Pillow and NumPy. "
        "Please install them with: pip install pillow numpy"
    )


class TextToVideoGenerator:
    """
    Base class for text-to-video generation models.
    """
    
    def __init__(self, model_name: str = "default", debug_mode: bool = False):
        """
        Initialize the text-to-video generator.
        
        Args:
            model_name: Name of the model to use
            debug_mode: Whether to enable debug logging
        """
        self.model_name = model_name
        self.debug_mode = debug_mode
        self._model = None
        
        if self.debug_mode:
            print(f"TextToVideoGenerator initialized with model: {model_name}")
    
    def load_model(self):
        """
        Load the text-to-video model.
        
        Note:
            This is a placeholder method. In a real implementation,
            it would load the actual model.
        """
        if self.debug_mode:
            print(f"Would load model: {self.model_name}")
        
        # Placeholder for model loading
        self._model = "placeholder_model"
    
    def unload_model(self):
        """
        Unload the model to free up resources.
        """
        if self._model is not None:
            if self.debug_mode:
                print(f"Unloading model: {self.model_name}")
            
            self._model = None
    
    def generate(
        self,
        prompt: str,
        negative_prompt: str = "",
        width: int = 512,
        height: int = 512,
        num_frames: int = 24,
        fps: float = 8.0,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
    ) -> List["Image.Image"]:
        """
        Generate a video from a text prompt.
        
        Args:
            prompt: Text prompt for video generation
            negative_prompt: Text to discourage in the generation
            width: Width of the output frames
            height: Height of the output frames
            num_frames: Number of frames to generate
            fps: Frames per second for the output video
            num_inference_steps: Number of denoising steps
            guidance_scale: Scale for classifier-free guidance
            
        Returns:
            List of PIL Image frames
            
        Note:
            This is a placeholder method. In a real implementation,
            it would use the loaded model to generate a video.
        """
        if self._model is None:
            self.load_model()
        
        if self.debug_mode:
            print(f"Generating video with prompt: {prompt}")
            print(f"Parameters: {width}x{height}, {num_frames} frames at {fps} FPS")
            print(f"Steps: {num_inference_steps}, guidance: {guidance_scale}")
        
        # Create placeholder frames (gradient with changing color)
        frames = []
        
        for frame_idx in range(num_frames):
            # Create a new frame
            image = Image.new("RGB", (width, height))
            pixels = image.load()
            
            # Frame-specific color offset
            offset = frame_idx / num_frames
            
            # Generate a simple gradient as a placeholder
            for i in range(width):
                for j in range(height):
                    r = int(255 * ((i / width) + offset) % 1.0)
                    g = int(255 * ((j / height) + offset) % 1.0)
                    b = int(255 * (((i + j) / (width + height)) + offset) % 1.0)
                    pixels[i, j] = (r, g, b)
            
            frames.append(image)
        
        return frames
    
    def __del__(self):
        """Clean up resources when the object is deleted."""
        self.unload_model() 