#!/usr/bin/env python
from __future__ import annotations

import os
import random
import time

import gradio as gr
import numpy as np
import PIL.Image

from huggingface_hub import snapshot_download
from diffusers import DiffusionPipeline

from optimum.intel.openvino.modeling_diffusion import OVModelVaeDecoder, OVBaseModel, OVStableDiffusionPipeline

import os
from tqdm import tqdm
import gradio_user_history as gr_user_history

from concurrent.futures import ThreadPoolExecutor
import uuid

DESCRIPTION = '''# Latent Consistency Model OpenVino CPU
Based on [Latency Consistency Model OpenVINO CPU](https://huggingface.co/deinferno/Latent_Consistency_Model_OpenVino_CPU) HF space 

Converted from [SoteMix](https://huggingface.co/Disty0/SoteMix) to [LCM_SoteMix](https://huggingface.co/Disty0/LCM_SoteMix)

[Project page](https://latent-consistency-models.github.io)

<p>Running on CPU 🥶.</p>
'''

MAX_SEED = np.iinfo(np.int32).max
CACHE_EXAMPLES = os.getenv("CACHE_EXAMPLES") == "1"

model_id = "Disty0/LCM_SoteMix"
batch_size = -1
width = int(os.getenv("IMAGE_WIDTH", "512"))
height = int(os.getenv("IMAGE_HEIGHT", "512"))
num_images = int(os.getenv("NUM_IMAGES", "1"))

class CustomOVModelVaeDecoder(OVModelVaeDecoder):
    def __init__(
        self, model: openvino.runtime.Model, parent_model: OVBaseModel, ov_config: Optional[Dict[str, str]] = None, model_dir: str = None,
    ):
        super(OVModelVaeDecoder, self).__init__(model, parent_model, ov_config, "vae_decoder", model_dir)

pipe = OVStableDiffusionPipeline.from_pretrained(model_id, compile = False, ov_config = {"CACHE_DIR":""})

# Inject TAESD

taesd_dir = snapshot_download(repo_id="deinferno/taesd-openvino")
pipe.vae_decoder = CustomOVModelVaeDecoder(model = OVBaseModel.load_model(f"{taesd_dir}/vae_decoder/openvino_model.xml"), parent_model = pipe, model_dir = taesd_dir)

pipe.reshape(batch_size=batch_size, height=height, width=width, num_images_per_prompt=num_images)
pipe.compile()

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):
    unique_name = str(uuid.uuid4()) + '.png'
    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 = []
    with ThreadPoolExecutor() as executor:
        paths = list(executor.map(save_image, image_array, [profile]*len(image_array), [metadata]*len(image_array)))
    return paths

def generate(
    prompt: str,
    seed: int = 0,
    guidance_scale: float = 1.0,
    num_inference_steps: int = 4,
    randomize_seed: bool = False,
    progress = gr.Progress(track_tqdm=True),
    profile: gr.OAuthProfile | None = None,
) -> PIL.Image.Image:
    global batch_size
    global width
    global height
    global num_images

    seed = randomize_seed_fn(seed, randomize_seed)
    np.random.seed(seed)
    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,
        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 = [
    "(masterpiece, best quality, highres), anime art style, pixiv, 1girl, solo",
    "(masterpiece, best quality, highres), anime art style, pixiv, 1girl, solo, dark red hair, blue eyes, long hair, straight hair, medium breasts, mature female, sweater",
    "(masterpiece, best quality, highres), anime art style, pixiv, 1girl, solo, pov, scenery, flowers, shrine, dark red hair, blue eyes, long hair, straight hair, medium breasts, mature female, sweater",
    "(masterpiece, best quality, highres), anime art style, pixiv, 1girl, solo, abstract, abstract background, (bloom, swirling lights, light particles), floating, romanticized, blush, emotional, cat ears, fire, galaxy, dark red hair, blue eyes, long hair, straight hair, medium breasts, mature female, sweater",
]


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", grid=[2]
        )
    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():
            guidance_scale = gr.Slider(
                label="Guidance scale for base",
                minimum=1,
                maximum=8,
                step=0.1,
                value=1.0,
            )
            num_inference_steps = gr.Slider(
                label="Number of inference steps for base",
                minimum=1,
                maximum=8,
                step=1,
                value=4,
            )

    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,
            guidance_scale,
            num_inference_steps,
            randomize_seed
        ],
        outputs=[result, seed],
        api_name="run",
    )

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