import spaces # type: ignore
import os
import time  # noqa: F401
import uuid
from PIL import Image
import gradio as gr # type: ignore
import numpy as np
import random
import torch
from diffusers import FluxPipeline

dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    torch_dtype=dtype,
)
pipe.to(device)

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


@spaces.GPU(duration=90)
def infer(
    prompt: str,
    seed=42,
    randomize_seed=False,
    width=1024,
    height=1024,
    guidance_scale=5.0,
    num_inference_steps=28,
    progress=gr.Progress(track_tqdm=True),
):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    generator = torch.Generator().manual_seed(seed)
    image = pipe(
        prompt=prompt,
        width=width,
        height=height,
        num_inference_steps=num_inference_steps,
        generator=generator,
        guidance_scale=guidance_scale,
    ).images[0]

    assert isinstance(
        image, Image.Image
    ), "The output is not an instance of Image.Image"

    filepath = os.path.join("images", "{uuid}.png".format(uuid=str(uuid.uuid4().hex)))
    image.save(filepath)

    return (
        image,
        gr.DownloadButton(
            label="Download PNG", value=filepath, size="sm", visible=True
        ),
        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("""# FLUX.1 
12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)  
[[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
        """)

        with gr.Row(equal_height=False):
            prompt = gr.TextArea(
                label="Prompt",
                show_label=False,
                lines=3,
                placeholder="Enter your prompt",
                container=False,
            )

            run_button = gr.Button("Run", variant="primary", scale=0)

        result = gr.Image(
            format="webp",
            type="pil",
            label="Result",
            show_label=False,
            show_download_button=False,
            show_share_button=False,
        )
        download = gr.DownloadButton(size="sm", visible=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)

            with gr.Row():
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=832,
                )

                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1216,
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance Scale",
                    minimum=0,
                    maximum=15,
                    step=0.1,
                    value=3.5,
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=28,
                )

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

    run_button.click(fn=lambda: gr.update(visible=False),inputs=None, outputs=download)
    
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, download, seed],
    )

# def delete_packed_tensors(
#     startup_time: int = time.time_ns(),
#     zerogpu_offload_dir: str = "/home/user/.zerogpu/tensors",
# ) -> None:
#     entries = os.scandir(zerogpu_offload_dir)
#     for entry in entries:
#         print(f"Found entry {entry.path}")
#         if entry.stat().st_atime_ns < startup_time:
#             try:
#                 file_path = entry.path
#                 os.remove(file_path)
#                 print(f"File deleted successfully: {file_path}")
#             except FileNotFoundError:
#                 print(f"File not found: {file_path}")
#             except OSError as e:
#                 print(f"Error deleting file: {e}")


if __name__ == "__main__":
    # delete_packed_tensors()
    os.makedirs("images", exist_ok=True)
    demo.launch()