





'''simple docstring'''



import json
import os
import re
import sys
import urllib.request

import requests
from bsa import BeautifulSoup

__magic_name__ :    Any			 =      {
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
    ' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582'
}




def 					snake_case_   (		SCREAMING_SNAKE_CASE__ = "dhaka"       ,						SCREAMING_SNAKE_CASE__ = 5							):







  '''simple docstring'''



  _snake_case  		=					min(__a       ,						50							)  # Prevent abuse!
  _snake_case  		=					{
      "q": query,
      "tbm": "isch",
      "hl": "en",
      "ijn": "0",
  }

  _snake_case  		=					requests.get("https://www.google.com/search"       ,						params=__a       ,						headers=__a							)
  _snake_case  		=					BeautifulSoup(html.text       ,						"html.parser"							)
  _snake_case  		=					"".join(
      re.findall(r"AF_initDataCallback\(([^<]+)\);"       ,						str(soup.select("script"							)							)							)							)

  _snake_case  		=					json.dumps(__a							)
  _snake_case  		=					json.loads(__a							)

  _snake_case  		=					re.findall(
      r"\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\","       ,						__a       ,						)
  if not matched_google_image_data:
    return 0

  _snake_case  		=					re.sub(
      r"\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]"       ,						""       ,						str(__a							)       ,						)

  _snake_case  		=					re.findall(
      r"(?:\'|,),\[\"(https:|http.*?)\",\d+,\d+\]"       ,						__a       ,						)
  for index, fixed_full_res_image in enumerate(__a							):
    if index >= max_images:
      return index
    _snake_case  		=					bytes(__a       ,						"ascii"							).decode(
        "unicode-escape"							)
    _snake_case  		=					bytes(__a       ,						"ascii"							).decode(
        "unicode-escape"							)
    _snake_case  		=					urllib.request.build_opener()
    _snake_case  		=					[
        (
            "User-Agent",
            "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
            " (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582",
        )
    ]
    urllib.request.install_opener(__a							)
    _snake_case  		=					f'''query_{query.replace(" "       ,						"_"							)}'''
    if not os.path.exists(__a							):
      os.makedirs(__a							)
    urllib.request.urlretrieve(  # noqa: S310
        __a       ,						f'''{path_name}/original_size_img_{index}.jpg'''							)
  return index


if __name__ == "__main__":
 try:
  __magic_name__ :    Optional[Any]			 =      download_images_from_google_query(sys.argv[1])
  print(F'{image_count} images were downloaded to disk.')
 except IndexError:
  print("""Please provide a search term.""")
  raise






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
	_lowerCAmelCase:						int   				=       argparse.ArgumentParser()

	parser.add_argument(
	    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
	)
	parser.add_argument(
	    '--original_config_file',
	    type=str,
	    required=True,
	    help='The YAML config file corresponding to the original architecture.',
	)
	parser.add_argument(
	    '--num_in_channels',
	    default=None,
	    type=int,
	    help='The number of input channels. If `None` number of input channels will be automatically inferred.',
	)
	parser.add_argument(
	    '--image_size',
	    default=512,
	    type=int,
	    help=(
	        'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
	        ' Base. Use 768 for Stable Diffusion v2.'
	    ),
	)
	parser.add_argument(
	    '--extract_ema',
	    action='store_true',
	    help=(
	        'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
	        ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
	        ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
	    ),
	)
	parser.add_argument(
	    '--upcast_attention',
	    action='store_true',
	    help=(
	        'Whether the attention computation should always be upcasted. This is necessary when running stable'
	        ' diffusion 2.1.'
	    ),
	)
	parser.add_argument(
	    '--from_safetensors',
	    action='store_true',
	    help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
	)
	parser.add_argument(
	    '--to_safetensors',
	    action='store_true',
	    help='Whether to store pipeline in safetensors format or not.',
	)
	parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
	parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')






	def   _lowercase(					__a				:						Optional[Any]     ):
								if string == "True":
															return True
								elif string == "False":
															return False
								else:
															raise ValueError(f"""could not parse string as bool {string}"""     )

	parser.add_argument(
	    '--use_linear_projection', help='Override for use linear projection', required=False, type=parse_bool
	)

	parser.add_argument('--cross_attention_dim', help='Override for cross attention_dim', required=False, type=int)

	_lowerCAmelCase:						str   				=       parser.parse_args()

	_lowerCAmelCase:						Tuple   				=       download_controlnet_from_original_ckpt(
	    checkpoint_path=args.checkpoint_path,
	    original_config_file=args.original_config_file,
	    image_size=args.image_size,
	    extract_ema=args.extract_ema,
	    num_in_channels=args.num_in_channels,
	    upcast_attention=args.upcast_attention,
	    from_safetensors=args.from_safetensors,
	    device=args.device,
	    use_linear_projection=args.use_linear_projection,
	    cross_attention_dim=args.cross_attention_dim,
	)







	controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

