import gradio as gr
import numpy as np
import random

# import spaces #[uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline
import torch
from typing import Optional

# кэш для пайплайнов (чтобы не перезагружать модель при каждом запросе)
PIPE_CACHE: dict[str, DiffusionPipeline] = {}
DEFAULT_MODEL = "CompVis/stable-diffusion-v1-4"
MODEL_OPTIONS = [
    "CompVis/stable-diffusion-v1-4",
    "stabilityai/stable-diffusion-2-1",
    "stabilityai/sdxl-turbo",
]


device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use

if torch.cuda.is_available():
    torch_dtype = torch.float16
else:
    torch_dtype = torch.float32

def get_pipe(model_id: str):
    if model_id in PIPE_CACHE:
        return PIPE_CACHE[model_id]
    # загружаем и кэшируем
    p = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype)
    p = p.to(device)
    PIPE_CACHE[model_id] = p
    return p


# pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
# pipe = pipe.to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024


# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(
    model_id: Optional[str] = DEFAULT_MODEL,
    prompt: str = "",
    negative_prompt: str = "",
    seed: int = 42,
    randomize_seed: bool = False,
    width: int = 512,
    height: int = 512,
    guidance_scale: float = 7.0,
    num_inference_steps: int = 20,
    scheduler_name: Optional[str] = None,
    progress=gr.Progress(track_tqdm=True),
):
    # получаем/загружаем нужный pipe
    pipe = get_pipe(model_id)

    # при желании можно подменить scheduler по имени (опционально)
    if scheduler_name:
        # примерная схема: словарь name->класс scheduler
        # при необходимости добавить другие scheduler'ы — импортируйте их сверху и добавьте сюда
        try:
            from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler, PNDMScheduler
            sched_map = {
                "DDIM": DDIMScheduler,
                "EulerAncestral": EulerAncestralDiscreteScheduler,
                "PNDM": PNDMScheduler,
            }
            if scheduler_name in sched_map:
                pipe.scheduler = sched_map[scheduler_name].from_config(pipe.scheduler.config)
        except Exception:
            # если что-то пошло не так — просто используем дефолтный scheduler
            pass

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    generator = torch.Generator().manual_seed(int(seed))

    image = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        width=width,
        height=height,
        generator=generator,
    ).images[0]

    return image, seed



examples = [
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "An astronaut riding a green horse",
    "A delicious ceviche cheesecake slice",
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 640px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(" # Text-to-Image Gradio Template")

        # Model selector (выпадающий список)
        model_select = gr.Dropdown(
            label="Model",
            choices=MODEL_OPTIONS,
            value=DEFAULT_MODEL,
            interactive=True,
        )
    
        # опциональный селектор scheduler
        scheduler_select = gr.Dropdown(
            label="Scheduler (optional)",
            choices=["", "DDIM", "EulerAncestral", "PNDM"],
            value="",
        )

        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, variant="primary")

        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                visible=True,
            )

            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=42,
            )

            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)

            with gr.Row():
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,  # Replace with defaults that work for your model
                )

                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,  # Replace with defaults that work for your model
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=7.0,  # Replace with defaults that work for your model
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=20,  # Replace with defaults that work for your model
                )

        gr.Examples(examples=examples, inputs=[prompt])
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            model_select,
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
            scheduler_select
        ],
        outputs=[result, seed],
    )

if __name__ == "__main__":
    demo.launch()