import gradio as gr
import numpy as np
import random
# import spaces
import torch
from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL
from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
# from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images

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

taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-schnell", subfolder="vae", torch_dtype=dtype).to(device)
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype, vae=taef1).to(device)
torch.cuda.empty_cache()

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

# pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)

# @spaces.GPU(duration=75)
def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, 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)
    
    for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
            prompt=prompt,
            guidance_scale=guidance_scale,
            num_inference_steps=num_inference_steps,
            width=width,
            height=height,
            generator=generator,
            output_type="pil",
            good_vae=good_vae,
        ):
            yield img, 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 [dev]
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():
            
            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)
            
            with gr.Row():
                
                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,
                )
            
            with gr.Row():

                guidance_scale = gr.Slider(
                    label="Guidance Scale",
                    minimum=1,
                    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"
        )

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

demo.launch()
# import torch
# import gradio as gr
# from diffusers.pipelines.flux.pipeline_flux import FluxPipeline
# from diffusers.models.controlnet_flux import FluxControlNetModel
# from controlnet_aux import CannyDetector
#
# dtype = torch.bfloat16
# device = "cuda" if torch.cuda.is_available() else "cpu"
#
# base_model = "black-forest-labs/FLUX.1-schnell"
# controlnet_model = "YishaoAI/flux-dev-controlnet-canny-kid-clothes"
#
# controlnet = FluxControlNetModel.from_pretrained(controlnet_model, torch_dtype=dtype)
# pipe = FluxPipeline.from_pretrained(
#     base_model, controlnet=controlnet, torch_dtype=dtype
# ).to(device)
#
# pipe.enable_model_cpu_offload()
# # pipe.to("cuda")
#
# canny = CannyDetector()
#
#
# def inpaint(
#     image,
#     mask,
#     prompt,
#     strength,
#     num_inference_steps,
#     guidance_scale,
#     controlnet_conditioning_scale,
# ):
#     canny_image = canny(image)
#
#     image_res = pipe(
#         prompt,
#         image=image,
#         control_image=canny_image,
#         controlnet_conditioning_scale=controlnet_conditioning_scale,
#         mask_image=mask,
#         strength=strength,
#         num_inference_steps=num_inference_steps,
#         guidance_scale=guidance_scale,
#     ).images[0]
#
#     return image_res
#
#
# iface = gr.Interface(
#     fn=inpaint,
#     inputs=[
#         gr.Image(type="pil", label="Input Image"),
#         gr.Image(type="pil", label="Mask Image"),
#         gr.Textbox(label="Prompt"),
#         gr.Slider(0, 1, value=0.95, label="Strength"),
#         gr.Slider(1, 100, value=50, step=1, label="Number of Inference Steps"),
#         gr.Slider(0, 20, value=5, label="Guidance Scale"),
#         gr.Slider(0, 1, value=0.5, label="ControlNet Conditioning Scale"),
#     ],
#     outputs=gr.Image(type="pil", label="Output Image"),
#     title="Flux Inpaint AI Model",
#     description="Upload an image and a mask, then provide a prompt to generate an inpainted image.",
# )
#
# iface.launch()