import spaces  # type: ignore
import os
import uuid
from PIL import Image
import gradio as gr
import numpy as np
import random
import torch
from diffusers import FluxPipeline, DiffusionPipeline, FluxTransformer2DModel  # noqa: F401
from torchao.quantization import quantize_, int8_weight_only
from sd_embed.embedding_funcs import get_weighted_text_embeddings_flux1

dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"

transformer = FluxTransformer2DModel.from_pretrained(
    "black-forest-labs/FLUX.1-dev", subfolder="transformer", torch_dtype=torch.bfloat16
)
quantize_(transformer, int8_weight_only())

pipe = DiffusionPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev", transformer=transformer, torch_dtype=torch.bfloat16
)

# 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)

    prompt_embeds, pooled_prompt_embeds = get_weighted_text_embeddings_flux1(
        pipe=pipe, prompt=prompt
    )

    image = pipe(
        prompt_embeds=prompt_embeds,
        pooled_prompt_embeds=pooled_prompt_embeds,
        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 cat holding a sign that says flux.1 is great",
    "an old man holding a sign that says Increase Zero-GPU Limit",
]

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):
            with gr.Column():
                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, download, seed],
            cache_examples="lazy",
        )

    gr.on(
        triggers=[run_button.click],
        fn=lambda: gr.update(visible=False),
        outputs=download,
        api_name=False,
    )
    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],
    )

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