import gradio as gr
import numpy as np
import random
from typing import Optional

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

from diffusers import (
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    KDPM2DiscreteScheduler,
    KDPM2AncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    EulerAncestralDiscreteScheduler,
    HeunDiscreteScheduler,
    LMSDiscreteScheduler,
)

import torch

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

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


# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(
    model_id,
    prompt,
    negative_prompt,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
    scheduler: Optional[str] = None,
    progress=gr.Progress(track_tqdm=True),
):

    pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype)
    match scheduler:
        case None:
            pass
        case "DPMSolverMultistepScheduler":
            if DPMSolverMultistepScheduler in pipe.scheduler.compatibles:
                scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
                pipe.scheduler = scheduler
        case "DPMSolverSinglestepScheduler":
            if DPMSolverSinglestepScheduler in pipe.scheduler.compatibles:
                scheduler = DPMSolverSinglestepScheduler.from_config(pipe.scheduler.config)
                pipe.scheduler = scheduler
        case "KDPM2DiscreteScheduler":
            if KDPM2DiscreteScheduler in pipe.scheduler.compatibles:
                scheduler = KDPM2DiscreteScheduler.from_config(pipe.scheduler.config)
                pipe.scheduler = scheduler
        case "KDPM2AncestralDiscreteScheduler":
            if KDPM2AncestralDiscreteScheduler in pipe.scheduler.compatibles:
                scheduler = KDPM2AncestralDiscreteScheduler.from_config(pipe.scheduler.config)
                pipe.scheduler = scheduler
        case "EulerDiscreteScheduler":
            if EulerDiscreteScheduler in pipe.scheduler.compatibles:
                scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
                pipe.scheduler = scheduler
        case "EulerAncestralDiscreteScheduler":
            if EulerAncestralDiscreteScheduler in pipe.scheduler.compatibles:
                scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
                pipe.scheduler = scheduler
        case "HeunDiscreteScheduler":
            if HeunDiscreteScheduler in pipe.scheduler.compatibles:
                scheduler = HeunDiscreteScheduler.from_config(pipe.scheduler.config)
                pipe.scheduler = scheduler
        case "LMSDiscreteScheduler":
            if LMSDiscreteScheduler in pipe.scheduler.compatibles:
                scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
                pipe.scheduler = scheduler
    
    pipe = pipe.to(device)
    
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    generator = torch.Generator().manual_seed(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_id = gr.Dropdown(
                ["stabilityai/sdxl-turbo", "lightx2v/Qwen-Image-Lightning", "tencent/SRPO", "hakurei/waifu-diffusion"],
                label="Image-to-text model",
                visible=True,
            )

        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)

            scheduler = gr.Dropdown(
                [None, "DPMSolverMultistepScheduler", "DPMSolverSinglestepScheduler", 
    "KDPM2DiscreteScheduler", "KDPM2AncestralDiscreteScheduler",
    "EulerDiscreteScheduler", "EulerAncestralDiscreteScheduler",
    "HeunDiscreteScheduler", "LMSDiscreteScheduler",],
                label="Scheduler",
                visible=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_id,
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
            scheduler,
        ],
        outputs=[result, seed],
    )

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