import gradio as gr
import os
hf_token = os.environ.get("HF_TOKEN")
import spaces
from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
import torch
import time

class Dummy():
    pass

resolutions = ["1024 1024","1280 768","1344 768","768 1344","768 1280"] 

# Ng
default_negative_prompt= "Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers"

# Load pipeline
model_id = "briaai/BRIA-2.3"
scheduler = EulerAncestralDiscreteScheduler(
                beta_start=0.00085,
                beta_end=0.012,
                beta_schedule="scaled_linear",
                num_train_timesteps=1000,
                steps_offset=1
            )
pipe = StableDiffusionXLPipeline.from_pretrained(model_id, torch_dtype=torch.float16,scheduler=scheduler).to("cuda")
pipe.force_zeros_for_empty_prompt = False

# print("Optimizing BRIA-2.3 - this could take a while")
# t=time.time()
# pipe.unet = torch.compile(
#     pipe.unet, mode="reduce-overhead", fullgraph=True # 600 secs compilation
# )
# with torch.no_grad():
#     outputs = pipe(
#         prompt="an apple",
#         num_inference_steps=30,
#     )

#     # This will avoid future compilations on different shapes
#     unet_compiled = torch._dynamo.run(pipe.unet)
#     unet_compiled.config=pipe.unet.config
#     unet_compiled.add_embedding = Dummy()
#     unet_compiled.add_embedding.linear_1 = Dummy()
#     unet_compiled.add_embedding.linear_1.in_features = pipe.unet.add_embedding.linear_1.in_features
#     pipe.unet = unet_compiled

# print(f"Optimizing finished successfully after {time.time()-t} secs")

@spaces.GPU(enable_queue=True)
def infer(prompt,negative_prompt,seed,resolution):
    print(f"""
    —/n
    {prompt}
    """)
    
    # generator = torch.Generator("cuda").manual_seed(555)
    t=time.time()

    if seed=="-1":
        generator=None
    else:
        try:
            seed=int(seed)
            generator = torch.Generator("cuda").manual_seed(seed)
        except:
            generator=None

    w,h = resolution.split()
    w,h = int(w),int(h)
    image = pipe(prompt,num_inference_steps=30, negative_prompt=negative_prompt,generator=generator,width=w,height=h).images[0]
    print(f'gen time is {time.time()-t} secs')
    
    # Future
    # Add amound of steps
    # if nsfw:
    #     raise gr.Error("Generated image is NSFW")
    
    return image

css = """
#col-container{
    margin: 0 auto;
    max-width: 580px;
}
"""
with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown("## BRIA 2.3")
        gr.HTML('''
          <p style="margin-bottom: 10px; font-size: 94%">
            This is a demo for 
            <a href="https://huggingface.co/briaai/BRIA-2.3" target="_blank">BRIA 2.3 text-to-image </a>. 
            BRIA 2.3 improve the generation of humans and illustrations compared to BRIA 2.2 while still trained on licensed data, and so provide full legal liability coverage for copyright and privacy infringement.
          </p>
        ''')
        with gr.Group():
            with gr.Column():
                prompt_in = gr.Textbox(label="Prompt", value="A smiling man with wavy brown hair and a trimmed beard")
                resolution = gr.Dropdown(value=resolutions[0], show_label=True, label="Resolution", choices=resolutions)
                seed = gr.Textbox(label="Seed", value=-1)
                negative_prompt = gr.Textbox(label="Negative Prompt", value=default_negative_prompt)
                submit_btn = gr.Button("Generate")
        result = gr.Image(label="BRIA-2.3 Result")

        # gr.Examples(
        #     examples = [ 
        #         "Dragon, digital art, by Greg Rutkowski",
        #         "Armored knight holding sword",
        #         "A flat roof villa near a river with black walls and huge windows",
        #         "A calm and peaceful office",
        #         "Pirate guinea pig"
        #     ],
        #     fn = infer, 
        #     inputs = [
        #         prompt_in
        #     ],
        #     outputs = [
        #         result
        #     ]
        # )

    submit_btn.click(
        fn = infer,
        inputs = [
            prompt_in,
            negative_prompt,
            seed,
            resolution
        ],
        outputs = [
            result
        ]
    )

demo.queue().launch(show_api=False)