






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

import requests
from bsa import BeautifulSoup

__a:     Union[str, 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       _SCREAMING_SNAKE_CASE (							__snake_case = "dhaka"   ,      __snake_case = 5 )				->						int:
       _UpperCAmelCase							=    min(__snake_case   ,      5_0 )  # Prevent abuse!
       _UpperCAmelCase							=    {
           """q""": query,
           """tbm""": """isch""",
           """hl""": """en""",
           """ijn""": """0""",
       }

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

       _UpperCAmelCase							=    json.dumps(__snake_case )
       _UpperCAmelCase							=    json.loads(__snake_case )

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

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

       _UpperCAmelCase							=    re.findall(
           r"""(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]"""   ,      __snake_case   ,      )
       for index, fixed_full_res_image in enumerate(__snake_case ):
              if index >= max_images:
                     return index
              _UpperCAmelCase							=    bytes(__snake_case   ,      """ascii""" ).decode(
                  """unicode-escape""" )
              _UpperCAmelCase							=    bytes(__snake_case   ,      """ascii""" ).decode(
                  """unicode-escape""" )
              _UpperCAmelCase							=    urllib.request.build_opener()
              _UpperCAmelCase							=    [
                  (
                      """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(__snake_case )
              _UpperCAmelCase							=    f"""query_{query.replace(" "   ,      "_" )}"""
              if not os.path.exists(__snake_case ):
                     os.makedirs(__snake_case )
              urllib.request.urlretrieve(  # noqa: S310
                  __snake_case   ,      f"""{path_name}/original_size_img_{index}.jpg""" )
       return index


if __name__ == "__main__":
  try:
    __a:     Optional[int]   					=					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






'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase            =       logging.get_logger(__name__)

__UpperCAmelCase            =       {
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''fnet'''


					def __init__(						self				:			Any      ,						lowerCamelCase_				:			List[str]=3_20_00      ,						lowerCamelCase_				:			List[Any]=7_68      ,						lowerCamelCase_				:			Union[str, Any]=12      ,						lowerCamelCase_				:			Optional[Any]=30_72      ,						lowerCamelCase_				:			Optional[Any]="gelu_new"      ,						lowerCamelCase_				:			Any=0.1      ,						lowerCamelCase_				:			Union[str, Any]=5_12      ,						lowerCamelCase_				:			Optional[int]=4      ,						lowerCamelCase_				:			Optional[int]=0.02      ,						lowerCamelCase_				:			str=1e-12      ,						lowerCamelCase_				:			Any=False      ,						lowerCamelCase_				:			str=5_12      ,						lowerCamelCase_				:			str=3      ,						lowerCamelCase_				:			int=1      ,						lowerCamelCase_				:			Optional[Any]=2      ,						**lowerCamelCase_				:			Optional[int]      ,						):

							'''simple docstring'''



							super().__init__(pad_token_id=lowerCamelCase_      ,						bos_token_id=lowerCamelCase_      ,						eos_token_id=lowerCamelCase_      ,						**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Dict		=			vocab_size
							SCREAMING_SNAKE_CASE :				Dict		=			max_position_embeddings
							SCREAMING_SNAKE_CASE :				str		=			hidden_size
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			num_hidden_layers
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			intermediate_size
							SCREAMING_SNAKE_CASE :				int		=			hidden_act
							SCREAMING_SNAKE_CASE :				Dict		=			hidden_dropout_prob
							SCREAMING_SNAKE_CASE :				Tuple		=			initializer_range
							SCREAMING_SNAKE_CASE :				List[Any]		=			type_vocab_size
							SCREAMING_SNAKE_CASE :				Any		=			layer_norm_eps
							SCREAMING_SNAKE_CASE :				int		=			use_tpu_fourier_optimizations
							SCREAMING_SNAKE_CASE :				List[Any]		=			tpu_short_seq_length



