import gradio as gr
import PIL
from PIL import Image
import numpy as np
import os
import uuid
import torch
from torch import autocast
import cv2

'''
from io import BytesIO
import requests
import PIL
from PIL import Image
import numpy as np
import os
import uuid
import torch
from torch import autocast
import cv2
from matplotlib import pyplot as plt
from torchvision import transforms
from diffusers import DiffusionPipeline

import io
import logging
import multiprocessing
import random
import time
import imghdr
from pathlib import Path
from typing import Union
from loguru import logger

from lama_cleaner.model_manager import ModelManager
from lama_cleaner.schema import Config

try:
    torch._C._jit_override_can_fuse_on_cpu(False)
    torch._C._jit_override_can_fuse_on_gpu(False)
    torch._C._jit_set_texpr_fuser_enabled(False)
    torch._C._jit_set_nvfuser_enabled(False)
except:
    pass


from lama_cleaner.helper import (
    load_img,
    numpy_to_bytes,
    resize_max_size,
)

NUM_THREADS = str(multiprocessing.cpu_count())

# fix libomp problem on windows https://github.com/Sanster/lama-cleaner/issues/56
os.environ["KMP_DUPLICATE_LIB_OK"] = "True"

os.environ["OMP_NUM_THREADS"] = NUM_THREADS
os.environ["OPENBLAS_NUM_THREADS"] = NUM_THREADS
os.environ["MKL_NUM_THREADS"] = NUM_THREADS
os.environ["VECLIB_MAXIMUM_THREADS"] = NUM_THREADS
os.environ["NUMEXPR_NUM_THREADS"] = NUM_THREADS
if os.environ.get("CACHE_DIR"):
    os.environ["TORCH_HOME"] = os.environ["CACHE_DIR"]

os.environ["TORCH_HOME"] = './'

BUILD_DIR = os.environ.get("LAMA_CLEANER_BUILD_DIR", "app/build")

from share_btn import community_icon_html, loading_icon_html, share_js

HF_TOKEN_SD = os.environ.get('HF_TOKEN_SD')

device = "cuda" if torch.cuda.is_available() else "cpu"
print(f'device = {device}')

def get_image_ext(img_bytes):
    w = imghdr.what("", img_bytes)
    if w is None:
        w = "jpeg"
    return w
    
def diffuser_callback(i, t, latents):
    pass

def preprocess_image(image):
    w, h = image.size
    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
    image = image.resize((w, h), resample=PIL.Image.LANCZOS)
    image = np.array(image).astype(np.float32) / 255.0
    image = image[None].transpose(0, 3, 1, 2)
    image = torch.from_numpy(image)
    return 2.0 * image - 1.0

def preprocess_mask(mask):
    mask = mask.convert("L")
    w, h = mask.size
    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
    mask = mask.resize((w // 8, h // 8), resample=PIL.Image.NEAREST)
    mask = np.array(mask).astype(np.float32) / 255.0
    mask = np.tile(mask, (4, 1, 1))
    mask = mask[None].transpose(0, 1, 2, 3)  # what does this step do?
    mask = 1 - mask  # repaint white, keep black
    mask = torch.from_numpy(mask)
    return mask
    
def model_process(init_image, mask):
    global model
    
    # input = request.files
    # RGB
    # origin_image_bytes = input["image"].read()
    
    
    print(f'liuyz_2_here_')
    
    # image, alpha_channel = load_img(origin_image_bytes)
    # Origin image shape: (512, 512, 3)
    original_shape = init_image.shape
    interpolation = cv2.INTER_CUBIC
    
    
    # form = request.form
    
    size_limit = 1080 # : Union[int, str] = form.get("sizeLimit", "1080")
    if size_limit == "Original":
        size_limit = max(image.shape)
    else:
        size_limit = int(size_limit)

    config = Config(
        ldm_steps=25,
        ldm_sampler='plms',
        zits_wireframe=True,
        hd_strategy='Original',
        hd_strategy_crop_margin=196,
        hd_strategy_crop_trigger_size=1280,
        hd_strategy_resize_limit=2048,
        prompt='',
        use_croper=False,
        croper_x=0,
        croper_y=0,
        croper_height=512,
        croper_width=512,
        sd_mask_blur=5,
        sd_strength=0.75,
        sd_steps=50,
        sd_guidance_scale=7.5,
        sd_sampler='ddim',
        sd_seed=42,
        cv2_flag='INPAINT_NS',
        cv2_radius=5,
    )

    if config.sd_seed == -1:
        config.sd_seed = random.randint(1, 999999999)

    # logger.info(f"Origin image shape: {original_shape}")
    print(f"Origin image shape: {original_shape}")
    image = resize_max_size(image, size_limit=size_limit, interpolation=interpolation)
    # logger.info(f"Resized image shape: {image.shape}")
    print(f"Resized image shape: {image.shape}")

    mask, _ = load_img(input["mask"].read(), gray=True)
    mask = resize_max_size(mask, size_limit=size_limit, interpolation=interpolation)

    start = time.time()
    res_np_img = model(image, mask, config)
    logger.info(f"process time: {(time.time() - start) * 1000}ms")

    torch.cuda.empty_cache()

    if alpha_channel is not None:
        if alpha_channel.shape[:2] != res_np_img.shape[:2]:
            alpha_channel = cv2.resize(
                alpha_channel, dsize=(res_np_img.shape[1], res_np_img.shape[0])
            )
        res_np_img = np.concatenate(
            (res_np_img, alpha_channel[:, :, np.newaxis]), axis=-1
        )

    ext = get_image_ext(origin_image_bytes)
    return ext
    
model = ModelManager(
        name='lama',
        device=device,
        # hf_access_token=HF_TOKEN_SD,
        # sd_disable_nsfw=False,
        # sd_cpu_textencoder=True,
        # sd_run_local=True,
        # callback=diffuser_callback,
    )
'''    

'''
pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", dtype=torch.float16, revision="fp16", use_auth_token=auth_token).to(device)

transform = transforms.Compose([
      transforms.ToTensor(),
      transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
      transforms.Resize((512, 512)),
])
'''

def read_content(file_path: str) -> str:
    """read the content of target file
    """
    with open(file_path, 'r', encoding='utf-8') as f:
        content = f.read()

    return content

def predict(dict):
    print(f'liuyz_0_', dict)
    init_image = dict["image"] # .convert("RGB") #.resize((512, 512))
    target_size = (init_image.shape[0], init_image.shape[1])
    print(f'liuyz_1_', init_image, target_size)
    print(f'liuyz_2_', init_image.convert("RGB"))
    print(f'liuyz_3_', init_image.convert("RGB").resize((512, 512)))
    mask = dict["mask"] # .convert("RGB") #.resize((512, 512))
    # output = model_process(init_image, mask)
    output = None # output.images[0]
    # output = pipe(prompt = prompt, image=init_image, mask_image=mask,guidance_scale=7.5)
    
    return output #, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)

print(f'liuyz_400_here_')

css = '''
.container {max-width: 1150px;margin: auto;padding-top: 1.5rem}
#image_upload{min-height:400px}
#image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 400px}
#mask_radio .gr-form{background:transparent; border: none}
#word_mask{margin-top: .75em !important}
#word_mask textarea:disabled{opacity: 0.3}
.footer {margin-bottom: 45px;margin-top: 35px;text-align: center;border-bottom: 1px solid #e5e5e5}
.footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white}
.dark .footer {border-color: #303030}
.dark .footer>p {background: #0b0f19}
.acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%}
#image_upload .touch-none{display: flex}
@keyframes spin {
    from {
        transform: rotate(0deg);
    }
    to {
        transform: rotate(360deg);
    }
}
#share-btn-container {
    display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
}
#share-btn {
    all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;
}
#share-btn * {
    all: unset;
}
#share-btn-container div:nth-child(-n+2){
    width: auto !important;
    min-height: 0px !important;
}
#share-btn-container .wrap {
    display: none !important;
}
'''

'''
sketchpad = Sketchpad()
imageupload = ImageUplaod()
interface = gr.Interface(fn=predict, inputs="image", outputs="image", sketchpad, imageupload)

interface.launch(share=True)
'''

'''
# gr.Interface(fn=predict, inputs="image", outputs="image").launch(share=True)

image = gr.Image(source='upload', tool='sketch', type="pil", label="Upload")# .style(height=400)
image_blocks = gr.Interface(
             fn=predict, 
             inputs=image,
             outputs=image,
             # examples=[["cheetah.jpg"]],
             )
             
image_blocks.launch(inline=True)

import gradio as gr
'''

def greet(dict, name, is_morning, temperature):
    image = dict['image']
    target_size = (image.shape[0], image.shape[1])
    print(f'liuyz_1_', target_size)
    salutation = "Good morning" if is_morning else "Good evening"
    greeting = f"{salutation} {name}. It is {temperature} degrees today"
    celsius = (temperature - 32) * 5 / 9
    return image, greeting, round(celsius, 2)

image = gr.Image(source='upload', tool='sketch', label="上传")# .style(height=400)

demo = gr.Interface(
    fn=greet,
    inputs=[image, "text", "checkbox", gr.Slider(0, 100)],
    outputs=['image', "text", "number"],
)
demo.launch()



'''
image_blocks = gr.Blocks(css=css)
with image_blocks as demo:
    gr.HTML(read_content("header.html"))
    with gr.Group():
        with gr.Box():
            with gr.Row():
                with gr.Column():
                    image = gr.Image(source='upload', tool='sketch', elem_id="image_upload", type="pil", label="Upload").style(height=400)
                    with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True):
                        prompt = gr.Textbox(placeholder = 'Your prompt (what you want in place of what is erased)', show_label=False, elem_id="input-text")
                        btn = gr.Button("Done!").style(
                            margin=True,
                            rounded=(True, True, True, True),
                            full_width=True,
                        )                
                with gr.Column():
                    image_out = gr.Image(label="Output", elem_id="output-img").style(height=400)
                    with gr.Group(elem_id="share-btn-container"):
                        community_icon = gr.HTML(community_icon_html, visible=False)
                        loading_icon = gr.HTML(loading_icon_html, visible=False)
                        share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
                

            btn.click(fn=predict, inputs=[image, prompt], outputs=[image_out, community_icon, loading_icon, share_button])
            #btn.click(fn=predict, inputs=[image], outputs=[image]) #, community_icon, loading_icon, share_button])
            share_button.click(None, [], [], _js=share_js)
            
            gr.HTML(
                """
                    <div class="footer">
                        <p>Model by <a href="https://huggingface.co/runwayml" style="text-decoration: underline;" target="_blank">RunwayML</a> - Gradio Demo by 🤗 Hugging Face
                        </p>
                    </div>
                    <div class="acknowledgments">
                        <p><h4>LICENSE</h4>
        The model is licensed with a <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" style="text-decoration: underline;" target="_blank">CreativeML Open RAIL-M</a> license. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in this license. The license forbids you from sharing any content that violates any laws, produce any harm to a person, disseminate any personal information that would be meant for harm, spread misinformation and target vulnerable groups. For the full list of restrictions please <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" target="_blank" style="text-decoration: underline;" target="_blank">read the license</a></p>
                        <p><h4>Biases and content acknowledgment</h4>
        Despite how impressive being able to turn text into image is, beware to the fact that this model may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography and violence. The model was trained on the <a href="https://laion.ai/blog/laion-5b/" style="text-decoration: underline;" target="_blank">LAION-5B dataset</a>, which scraped non-curated image-text-pairs from the internet (the exception being the removal of illegal content) and is meant for research purposes. You can read more in the <a href="https://huggingface.co/CompVis/stable-diffusion-v1-4" style="text-decoration: underline;" target="_blank">model card</a></p>
                    </div>
                """
            )
            
image_blocks.launch(share=True) 
'''