





"""simple docstring"""


import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel




def 					lowercase__    (					)					->		Dict:




      """simple docstring"""

      _UpperCamelCase      :	Tuple						=       argparse.ArgumentParser()
      parser.add_argument(
          "-m"		,"--pretrained_model_name_or_path"		,type=lowercase_		,default=lowercase_		,required=lowercase_		,help="Path to pretrained model or model identifier from huggingface.co/models."		,)
      parser.add_argument(
          "-c"		,"--caption"		,type=lowercase_		,default="robotic cat with wings"		,help="Text used to generate images."		,)
      parser.add_argument(
          "-n"		,"--images_num"		,type=lowercase_		,default=4		,help="How much images to generate."		,)
      parser.add_argument(
          "-s"		,"--seed"		,type=lowercase_		,default=42		,help="Seed for random process."		,)
      parser.add_argument(
          "-ci"		,"--cuda_id"		,type=lowercase_		,default=0		,help="cuda_id."		,)
      _UpperCamelCase      :	Union[str, Any]						=       parser.parse_args()
      return args




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_			)					->		Union[str, Any]:




      """simple docstring"""

      if not len(lowercase_			) == rows * cols:
            raise ValueError("The specified number of rows and columns are not correct."			)

      _UpperCamelCase,  _UpperCamelCase      :	int						=       imgs[0].size
      _UpperCamelCase      :	Optional[int]						=       Image.new("RGB"		,size=(cols * w, rows * h)			)
      _UpperCamelCase,  _UpperCamelCase      :	List[str]						=       grid.size

      for i, img in enumerate(lowercase_			):
            grid.paste(lowercase_		,box=(i % cols * w, i // cols * h)			)
      return grid




def 					lowercase__    (					lowercase_		,lowercase_="robotic cat with wings"		,lowercase_=7.5		,lowercase_=50		,lowercase_=1		,lowercase_=42		,)					->		Any:




      """simple docstring"""

      _UpperCamelCase      :	List[Any]						=       torch.Generator(pipeline.device			).manual_seed(lowercase_			)
      _UpperCamelCase      :	Optional[Any]						=       pipeline(
          lowercase_		,guidance_scale=lowercase_		,num_inference_steps=lowercase_		,generator=lowercase_		,num_images_per_prompt=lowercase_		,).images
      _UpperCamelCase      :	List[Any]						=       int(math.sqrt(lowercase_			)			)
      _UpperCamelCase      :	Any						=       image_grid(lowercase_		,rows=_rows		,cols=num_images_per_prompt // _rows			)
      return grid, images


lowerCamelCase__				  =   parse_args()
# Load models and create wrapper for stable diffusion
lowerCamelCase__				  =   CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
lowerCamelCase__				  =   CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
lowerCamelCase__				  =   AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
lowerCamelCase__				  =   UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")

lowerCamelCase__				  =   StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
lowerCamelCase__				  =   lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, "best_model.pt")):
  lowerCamelCase__				  =   load(args.pretrained_model_name_or_path, model=unet)
  unet.eval()
  setattr(pipeline, "unet", unet)
else:
  lowerCamelCase__				  =   unet.to(torch.device("cuda", args.cuda_id))
lowerCamelCase__				  =   pipeline.to(unet.device)
lowerCamelCase__  ,    lowerCamelCase__				  =   generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, "{}.png".format("_".join(args.caption.split()))))
lowerCamelCase__				  =   os.path.join(args.pretrained_model_name_or_path, "_".join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
  image.save(os.path.join(dirname, "{}.png".format(idx + 1)))







"""simple docstring"""


from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
  lowerCamelCase__				  =   input("Enter image url: ").strip()
  print(f"""Downloading image from {url} ...""")
  lowerCamelCase__				  =   BeautifulSoup(requests.get(url).content, "html.parser")
  # The image URL is in the content field of the first meta tag with property og:image
  lowerCamelCase__				  =   soup.find("meta", {"property": "og:image"})["content"]
  lowerCamelCase__				  =   requests.get(image_url).content
  lowerCamelCase__				  =   f"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
  with open(file_name, "wb") as fp:
    fp.write(image_data)
  print(f"""Done. Image saved to disk as {file_name}.""")
