

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

import requests
from bsa import BeautifulSoup

a_     :    Dict     		=   {
    "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 							__lowerCAmelCase     (							_UpperCamelCase		:	str = "dhaka"		,				_UpperCamelCase		:	int = 5						)		->				int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      min(_UpperCamelCase		,				50						)  # Prevent abuse!
 SCREAMING_SNAKE_CASE		    =      {
     'q': query,
     'tbm': 'isch',
     'hl': 'en',
     'ijn': '0',
 }

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

 SCREAMING_SNAKE_CASE		    =      json.dumps(_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      json.loads(_UpperCamelCase						)

 SCREAMING_SNAKE_CASE		    =      re.findall(
     R'\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",'		,				_UpperCamelCase		,				)
 if not matched_google_image_data:
  return 0

 SCREAMING_SNAKE_CASE		    =      re.sub(
     R'\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]'		,				''		,				str(_UpperCamelCase						)		,				)

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


if __name__ == "__main__":
      try:
            a_     :    str     		=   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 warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class   UpperCamelCase			(  SCREAMING_SNAKE_CASE ):
    __UpperCamelCase    =["image_processor", "tokenizer"]
    __UpperCamelCase    ="CLIPImageProcessor"
    __UpperCamelCase    =("CLIPTokenizer", "CLIPTokenizerFast")

    def __init__(      self			:   int				,     snake_case__			:   List[Any]=None				,     snake_case__			:   Any=None				,     **snake_case__			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      None
     if "feature_extractor" in kwargs:
      warnings.warn(
          'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
          ' instead.'				,     snake_case__				,     )
      SCREAMING_SNAKE_CASE		    =      kwargs.pop('feature_extractor'     )

     SCREAMING_SNAKE_CASE		    =      image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
      raise ValueError('You need to specify an `image_processor`.'     )
     if tokenizer is None:
      raise ValueError('You need to specify a `tokenizer`.'     )

     super().__init__(snake_case__				,     snake_case__     )

    def __call__(      self			:   List[Any]				,     snake_case__			:   Tuple=None				,     snake_case__			:   Optional[int]=None				,     snake_case__			:   List[str]=None				,     **snake_case__			:   Dict     ):

     """simple docstring"""






     if text is None and images is None:
      raise ValueError('You have to specify either text or images. Both cannot be none.'     )

     if text is not None:
      SCREAMING_SNAKE_CASE		    =      self.tokenizer(snake_case__				,     return_tensors=snake_case__				,     **snake_case__     )

     if images is not None:
      SCREAMING_SNAKE_CASE		    =      self.image_processor(snake_case__				,     return_tensors=snake_case__				,     **snake_case__     )

     if text is not None and images is not None:
      SCREAMING_SNAKE_CASE		    =      image_features.pixel_values
      return encoding
     elif text is not None:
      return encoding
     else:
      return BatchEncoding(data=dict(**snake_case__     )				,     tensor_type=snake_case__     )

    def UpperCamelCase      (      self			:   Union[str, Any]				,     *snake_case__			:   Optional[Any]				,     **snake_case__			:   Optional[int]     ):

     """simple docstring"""






     return self.tokenizer.batch_decode(*snake_case__				,     **snake_case__     )

    def UpperCamelCase      (      self			:   Dict				,     *snake_case__			:   Optional[Any]				,     **snake_case__			:   Dict     ):

     """simple docstring"""






     return self.tokenizer.decode(*snake_case__				,     **snake_case__     )

    @property
    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.tokenizer.model_input_names
     SCREAMING_SNAKE_CASE		    =      self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )

    @property
    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     warnings.warn(
         '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'				,     snake_case__				,     )
     return self.image_processor_class




    @property
    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     warnings.warn(
         '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'				,     snake_case__				,     )
     return self.image_processor

