import gradio as gr
import numpy as np
import random
import torch
from PIL import Image
from diffusers import DiffusionPipeline, StableDiffusionImg2ImgPipeline
from transformers import CLIPTextModel, CLIPTokenizer

# Configuración del dispositivo
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dtype = torch.bfloat16 if device.type == "cuda" else torch.float32

# Cargar los pipelines para las diferentes tareas
pipe_diffusion = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype).to(device)
pipe_img2img = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5").to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048

# Configurar tokenizer y modelo CLIP para truncar correctamente los prompts
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
text_model = CLIPTextModel.from_pretrained("openai/clip-vit-base-patch32")

def truncate_prompt(prompt, max_length=77):
    inputs = tokenizer(prompt, return_tensors="pt")
    input_ids = inputs["input_ids"][0][:max_length]
    truncated_prompt = tokenizer.decode(input_ids, skip_special_tokens=True)
    return truncated_prompt

def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, init_image=None, img2img_strength=0.75, progress=gr.Progress(track_tqdm=True)):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator(device=device).manual_seed(seed)

    # Truncar el prompt si es demasiado largo
    prompt = truncate_prompt(prompt)
    
    # Convertir init_image a formato PIL si no lo está
    if init_image and not isinstance(init_image, Image.Image):
        init_image = Image.fromarray(np.array(init_image))

    # Liberar memoria CUDA antes de la generación
    torch.cuda.empty_cache()
    
    try:
        if init_image:
            init_image = init_image.convert("RGB")
            generated_image = pipe_img2img(
                prompt=prompt, 
                init_image=init_image,
                strength=img2img_strength,
                num_inference_steps=num_inference_steps,
                generator=generator
            ).images[0]
        else:
            generated_image = pipe_diffusion(
                prompt=prompt, 
                width=width,
                height=height,
                num_inference_steps=num_inference_steps, 
                generator=generator,
                guidance_scale=0.0
            ).images[0]
    except RuntimeError as e:
        if "CUDACachingAllocator.cpp" in str(e):
            print("CUDA Memory Error: ", e)
            torch.cuda.empty_cache()
            return None, seed
        else:
            raise e
    
    # Mostrar la imagen generada
    return generated_image, seed

examples = [
    "a tiny astronaut hatching from an egg on the moon",
    "a cat holding a sign that says hello world",
    "an anime illustration of a wiener schnitzel",
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 520px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""# FLUX.1 [schnell] + Stable Diffusion img2img
Combinación de generación de imágenes y transformación de imágenes con img2img.
        """)
        
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )
            run_button = gr.Button("Run", scale=0)
        
        result = gr.Image(label="Result", show_label=False)
        
        with gr.Accordion("Advanced Settings", open=False):
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )
            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
            width = gr.Slider(
                label="Width",
                minimum=256,
                maximum=MAX_IMAGE_SIZE,
                step=32,
                value=1024,
            )
            height = gr.Slider(
                label="Height",
                minimum=256,
                maximum=MAX_IMAGE_SIZE,
                step=32,
                value=1024,
            )
            num_inference_steps = gr.Slider(
                label="Number of inference steps",
                minimum=1,
                maximum=50,
                step=1,
                value=4,
            )
            init_image = gr.Image(type="pil", label="Imagen Inicial (opcional)")
            img2img_strength = gr.Slider(
                label="Img2Img Strength",
                minimum=0.0,
                maximum=1.0,
                step=0.05,
                value=0.75,
            )

        gr.Examples(
            examples=examples,
            fn=infer,
            inputs=[prompt],
            outputs=[result, seed],
            cache_examples="lazy"
        )

        gr.on(
            triggers=[run_button.click, prompt.submit],
            fn=infer,
            inputs=[prompt, seed, randomize_seed, width, height, num_inference_steps, init_image, img2img_strength],
            outputs=[result, seed]
        )

demo.launch()