from __future__ import annotations

import os
import random
import time

import gradio as gr
import numpy as np
import PIL.Image
import torch
try:
    import intel_extension_for_pytorch as ipex
except:
    pass


from diffusers import DiffusionPipeline
import torch

import os
import torch
from tqdm import tqdm

from concurrent.futures import ThreadPoolExecutor
import uuid

DESCRIPTION = '''# Latent Consistency Model
Distilled from [Dreamshaper v7](https://huggingface.co/Lykon/dreamshaper-7) fine-tune of [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) with only 4,000 training iterations (~32 A100 GPU Hours). [Project page](https://latent-consistency-models.github.io)
'''
if torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CUDA 😀</p>"
elif hasattr(torch, 'xpu') and torch.xpu.is_available():
    DESCRIPTION += "\n<p>Running on XPU 🤓</p>"
else:
    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"

MAX_SEED = np.iinfo(np.int32).max
CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1"
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "768"))
USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"



"""
   Operation System Options:
      If you are using MacOS, please set the following (device="mps") ;
      If you are using Linux & Windows with Nvidia GPU, please set the device="cuda";
      If you are using Linux & Windows with Intel Arc GPU, please set the device="xpu";
"""
# device = "mps"    # MacOS
#device = "xpu"    # Intel Arc GPU
device = "cuda"   # Linux & Windows


"""
   DTYPE Options:
      To reduce GPU memory you can set "DTYPE=torch.float16",
      but image quality might be compromised
"""
DTYPE = torch.float16  # torch.float16 works as well, but pictures seem to be a bit worse


#pipe = DiffusionPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7")
pipe = DiffusionPipeline.from_pretrained("D:/git-work/LCM_Dreamshaper_v7")


#pipe = DiffusionPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7", custom_pipeline="latent_consistency_txt2img", custom_revision="main")
pipe.to(torch_device=device, torch_dtype=DTYPE)


def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    return seed

def save_image(img, profile: gr.OAuthProfile | None, metadata: dict, root_path='./'):
    unique_name = str(uuid.uuid4()) + '.png'
    unique_name = os.path.join(root_path, unique_name)
    img.save(unique_name)
    # gr_user_history.save_image(label=metadata["prompt"], image=img, profile=profile, metadata=metadata)
    return unique_name

def save_images(image_array, profile: gr.OAuthProfile | None, metadata: dict):
    paths = []
    root_path = './images/'
    os.makedirs(root_path, exist_ok=True)
    with ThreadPoolExecutor() as executor:
        paths = list(executor.map(save_image, image_array, [profile]*len(image_array), [metadata]*len(image_array), [root_path]*len(image_array)))
    return paths

def generate(
    prompt: str,
    seed: int = 0,
    width: int = 512,
    height: int = 512,
    guidance_scale: float = 8.0,
    num_inference_steps: int = 4,
    num_images: int = 4,
    randomize_seed: bool = False,
    param_dtype='torch.float16',
    progress = gr.Progress(track_tqdm=True),
    profile: gr.OAuthProfile | None = None,
) -> PIL.Image.Image:
    seed = randomize_seed_fn(seed, randomize_seed)
    torch.manual_seed(seed)
    pipe.to(torch_device=device, torch_dtype=torch.float16 if param_dtype == 'torch.float16' else torch.float32)
    start_time = time.time()
    result = pipe(
        prompt=prompt,
        width=width,
        height=height,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        num_images_per_prompt=num_images,
        lcm_origin_steps=50,
        output_type="pil",
    ).images
    paths = save_images(result, profile, metadata={"prompt": prompt, "seed": seed, "width": width, "height": height, "guidance_scale": guidance_scale, "num_inference_steps": num_inference_steps})
    print(time.time() - start_time)
    return paths, seed

examples = [
    "portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography",
    "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k",
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "A photo of beautiful mountain with realistic sunset and blue lake, highly detailed, masterpiece",
]

with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)
    gr.DuplicateButton(
        value="Duplicate Space for private use",
        elem_id="duplicate-button",
        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
    )
    with gr.Group():
        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.Gallery(
            label="Generated images", show_label=False, elem_id="gallery", 
        )
    with gr.Accordion("Advanced options", open=False):
        seed = gr.Slider(
            label="Seed",
            minimum=0,
            maximum=MAX_SEED,
            step=1,
            value=0,
            randomize=True
        )
        randomize_seed = gr.Checkbox(label="Randomize seed across runs", value=True)
        with gr.Row():
            width = gr.Slider(
                label="Width",
                #minimum=256,
                minimum=128,
                maximum=MAX_IMAGE_SIZE,
                step=32,
                value=512,
            )
            height = gr.Slider(
                label="Height",
                minimum=256,
                maximum=MAX_IMAGE_SIZE,
                step=32,
                value=512,
            )
        with gr.Row():
            guidance_scale = gr.Slider(
                label="Guidance scale for base",
                minimum=2,
                maximum=14,
                step=0.1,
                value=8.0,
            )
            num_inference_steps = gr.Slider(
                label="Number of inference steps for base",
                minimum=1,
                maximum=8,
                step=1,
                value=4,
            )
        with gr.Row():
            num_images = gr.Slider(
                label="Number of images",
                minimum=1,
                maximum=8,
                step=1,
                value=1,#生成图片的数量
                visible=True,
            )
            dtype_choices = ['torch.float16','torch.float32']
            param_dtype = gr.Radio(dtype_choices,label='torch.dtype',  
                                      value=dtype_choices[0],
                                      interactive=True,
                                      info='To save GPU memory, use torch.float16. For better quality, use torch.float32.')

    # with gr.Accordion("Past generations", open=False):
    #     gr_user_history.render()
    
    gr.Examples(
        examples=examples,
        inputs=prompt,
        outputs=result,
        fn=generate,
        cache_examples=CACHE_EXAMPLES,
    )

    gr.on(
        triggers=[
            prompt.submit,
            run_button.click,
        ],
        fn=generate,
        inputs=[
            prompt,
            seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
            num_images,
            randomize_seed,
            param_dtype
        ],
        outputs=[result, seed],
        api_name="run",
    )

if __name__ == "__main__":
    demo.queue(api_open=False)
    # demo.queue(max_size=20).launch()
    demo.launch(share=True)
    #demo.launch()