import torch
from diffusers import UniPCMultistepScheduler, AutoencoderKL, ControlNetModel
from diffusers.pipelines import StableDiffusionControlNetPipeline
import gradio as gr
import argparse
from garment_adapter.garment_diffusion import ClothAdapter
import numpy as np

from pipelines.OmsDiffusionControlNetPipeline import OmsDiffusionControlNetPipeline

parser = argparse.ArgumentParser(description='oms diffusion')
parser.add_argument('--model_path', type=str, required=True)
parser.add_argument('--enable_cloth_guidance', action="store_true")
parser.add_argument('--pipe_path', type=str, default="SG161222/Realistic_Vision_V4.0_noVAE")

args = parser.parse_args()

device = "cuda"

control_net_openpose = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16)
vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse").to(dtype=torch.float16)
if args.enable_cloth_guidance:
    pipe = OmsDiffusionControlNetPipeline.from_pretrained(args.pipe_path, vae=vae, controlnet=control_net_openpose, torch_dtype=torch.float16)
else:
    pipe = StableDiffusionControlNetPipeline.from_pretrained(args.pipe_path, vae=vae, controlnet=control_net_openpose, torch_dtype=torch.float16)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
full_net = ClothAdapter(pipe, args.model_path, device, args.enable_cloth_guidance)


def make_inpaint_condition(image, image_mask):
    image = np.array(image.convert("RGB")).astype(np.float32) / 255.0
    image_mask = np.array(image_mask.convert("L")).astype(np.float32) / 255.0
    assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
    image[image_mask > 0.5] = -1.0  # set as masked pixel
    image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)
    image = torch.from_numpy(image)
    return image


def process(cloth_image, cloth_mask_image, prompt, a_prompt, n_prompt, num_samples, width, height, sample_steps, scale, cloth_guidance_scale, seed, person_image, person_mask):
    inpaint_image = make_inpaint_condition(person_image, person_mask)
    images, cloth_mask_image = full_net.generate(cloth_image, cloth_mask_image, prompt, a_prompt, num_samples, n_prompt, seed, scale,cloth_guidance_scale, sample_steps, height, width, image=inpaint_image)
    return images, cloth_mask_image


block = gr.Blocks().queue()
with block:
    with gr.Row():
        gr.Markdown("##You can enlarge image resolution to get better face, but the cloth maybe lose control, we will release high-resolution checkpoint soon##")
    with gr.Row():
        with gr.Column():
            cloth_image = gr.Image(label="cloth Image", type="pil")
            cloth_mask_image = gr.Image(label="cloth mask Image, if not support, will be produced by inner segment algorithm", type="pil")
            prompt = gr.Textbox(label="Prompt", value='a photography of a model')
            run_button = gr.Button(value="Run")
            with gr.Accordion("Advanced options", open=False):
                num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
                height = gr.Slider(label="Height", minimum=256, maximum=1024, value=768, step=64)
                width = gr.Slider(label="Width", minimum=192, maximum=768, value=576, step=64)
                sample_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1)
                guidance_scale = gr.Slider(label="Guidance Scale", minimum=1, maximum=10., value=5. if args.enable_cloth_guidance else 2.5, step=0.1)
                cloth_guidance_scale = gr.Slider(label="Cloth guidance Scale", minimum=1, maximum=10., value=2.5, step=0.1, visible=args.enable_cloth_guidance)
                seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, value=1234)
                a_prompt = gr.Textbox(label="Added Prompt", value='best quality, high quality')
                n_prompt = gr.Textbox(label="Negative Prompt", value='bare, monochrome, lowres, bad anatomy, worst quality, low quality')
        with gr.Column():
            person_image = gr.Image(label="person Image", type="pil")
            person_mask = gr.Image(label="person mask", type="pil")
        with gr.Column():
            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery", min_width=384)
            cloth_seg_image = gr.Image(label="cloth mask", type="pil", width=192, height=256)

    ips = [cloth_image, cloth_mask_image, prompt, a_prompt, n_prompt, num_samples, width, height, sample_steps, guidance_scale, cloth_guidance_scale, seed, person_image, person_mask]
    run_button.click(fn=process, inputs=ips, outputs=[result_gallery, cloth_seg_image])

block.launch(server_name="0.0.0.0", server_port=7860)