import torch
import gradio as gr
from functools import partial

from diffusers_patch import OMSPipeline


def create_sdxl_lcm_lora_pipe(sd_pipe_name_or_path, oms_name_or_path, lora_name_or_path):
    from diffusers import StableDiffusionXLPipeline, LCMScheduler
    sd_pipe = StableDiffusionXLPipeline.from_pretrained(sd_pipe_name_or_path, torch_dtype=torch.float16, variant="fp16", add_watermarker=False).to('cuda')
    print('successfully load pipe')
    sd_scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
    sd_pipe.load_lora_weights(lora_name_or_path, variant="fp16")

    pipe = OMSPipeline.from_pretrained(oms_name_or_path, sd_pipeline = sd_pipe, torch_dtype=torch.float16, variant="fp16", trust_remote_code=True, sd_scheduler=sd_scheduler)
    pipe.to('cuda')
    
    return pipe
        

class GradioDemo:
    def __init__(
        self, 
        sd_pipe_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0", 
        oms_name_or_path = 'h1t/oms_b_openclip_xl', 
        lora_name_or_path = 'latent-consistency/lcm-lora-sdxl'
    ):
        self.pipe = create_sdxl_lcm_lora_pipe(sd_pipe_name_or_path, oms_name_or_path, lora_name_or_path)
        
    def _inference(
            self,
            prompt = None,
            oms_prompt = None,
            oms_guidance_scale = 1.0,
            num_inference_steps = 4,
            sd_pipe_guidance_scale = 1.0,
            seed = 1024,
        ):
        pipe_kwargs = dict(
            prompt = prompt,
            num_inference_steps = num_inference_steps,
            guidance_scale = sd_pipe_guidance_scale,
        )

        generator = torch.Generator(device=self.pipe.device).manual_seed(seed)
        pipe_kwargs.update(oms_flag=False)
        print(f'raw kwargs: {pipe_kwargs}')
        image_raw = self.pipe(
            **pipe_kwargs,
            generator=generator
        )['images'][0]
        
        generator = torch.Generator(device=self.pipe.device).manual_seed(seed)
        pipe_kwargs.update(oms_flag=True, oms_prompt=oms_prompt, oms_guidance_scale=1.0)
        print(f'w/ oms wo/ cfg kwargs: {pipe_kwargs}')
        image_oms = self.pipe(
            **pipe_kwargs,
            generator=generator
        )['images'][0]
        
        oms_guidance_flag = oms_guidance_scale != 1.0
        if oms_guidance_flag:
            generator = torch.Generator(device=self.pipe.device).manual_seed(seed)
            pipe_kwargs.update(oms_guidance_scale=oms_guidance_scale)
            print(f'w/ oms +cfg kwargs: {pipe_kwargs}')
            image_oms_cfg = self.pipe(
                **pipe_kwargs,
                generator=generator
            )['images'][0]
        else:
            image_oms_cfg = None
        
        return image_raw, image_oms, image_oms_cfg, gr.update(visible=oms_guidance_flag)
    
    def mainloop(self):
        with gr.Blocks() as demo:
            gr.Markdown("# One More Step Demo")
            with gr.Row():
                with gr.Column():
                    prompt = gr.Textbox(label="Prompt", value="a cat")
                    oms_prompt = gr.Textbox(label="OMS Prompt", value="orange car")
                    oms_guidance_scale = gr.Slider(label="OMS Guidance Scale", minimum=1.0, maximum=5.0, value=1.5, step=0.1)
                    run_button = gr.Button(value="Generate images")
                    with gr.Accordion("Advanced options", open=False):
                        num_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=4, step=1)
                        sd_guidance_scale = gr.Slider(label="SD Pipe Guidance Scale", minimum=0.1, maximum=30.0, value=1.0, step=0.1)
                        seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=False, value=1024)
                with gr.Column():
                    output_raw = gr.Image(label="SDXL w/ LCM-LoRA w/o OMS ")
                    output_oms = gr.Image(label="w/ OMS w/o OMS CFG")
                    with gr.Column(visible=False) as oms_cfg_wd:
                        output_oms_cfg = gr.Image(label=f"w/ OMS w/ OMS CFG")

            ips = [prompt, oms_prompt, oms_guidance_scale, num_steps, sd_guidance_scale, seed]
            run_button.click(fn=self._inference, inputs=ips, outputs=[output_raw, output_oms, output_oms_cfg, oms_cfg_wd])

            demo.queue(max_size=20)
            demo.launch()


if __name__ == "__main__":
    gradio_demo = GradioDemo()
    gradio_demo.mainloop()