"""
Text-to-image generation module.
"""

from typing import Optional, Union

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


class TextToImageGenerator:
    """
    Base class for text-to-image generation models.
    """
    
    def __init__(self, model_name: str = "default", debug_mode: bool = False):
        """
        Initialize the text-to-image 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"TextToImageGenerator initialized with model: {model_name}")
    
    def load_model(self):
        """
        Load the text-to-image 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_inference_steps: int = 50,
        guidance_scale: float = 7.5,
    ) -> "Image.Image":
        """
        Generate an image from a text prompt.
        
        Args:
            prompt: Text prompt for image generation
            negative_prompt: Text to discourage in the generation
            width: Width of the output image
            height: Height of the output image
            num_inference_steps: Number of denoising steps
            guidance_scale: Scale for classifier-free guidance
            
        Returns:
            Generated PIL Image
            
        Note:
            This is a placeholder method. In a real implementation,
            it would use the loaded model to generate an image.
        """
        if self._model is None:
            self.load_model()
        
        if self.debug_mode:
            print(f"Generating image with prompt: {prompt}")
            print(f"Parameters: {width}x{height}, steps={num_inference_steps}, guidance={guidance_scale}")
        
        # Create a placeholder image (gradient)
        image = Image.new("RGB", (width, height))
        pixels = image.load()
        
        # Generate a simple gradient as a placeholder
        for i in range(width):
            for j in range(height):
                r = int(255 * i / width)
                g = int(255 * j / height)
                b = int(255 * (i + j) / (width + height))
                pixels[i, j] = (r, g, b)
        
        return image
    
    def __del__(self):
        """Clean up resources when the object is deleted."""
        self.unload_model() 