



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

import requests
from bsa import BeautifulSoup

SCREAMING_SNAKE_CASE_:str					=						{
    """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      __UpperCamelCase     (  _lowerCAmelCase = "dhaka"				,					_lowerCAmelCase = 5		)	->  int:



    """simple docstring"""






    A			:	Optional[int]										=       min(_lowerCAmelCase				,					50		)  # Prevent abuse!
    A			:	List[str]										=       {
        """q""": query,
        """tbm""": """isch""",
        """hl""": """en""",
        """ijn""": """0""",
    }

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

    A			:	int										=       json.dumps(_lowerCAmelCase		)
    A			:	Optional[Any]										=       json.loads(_lowerCAmelCase		)

    A			:	Tuple										=       re.findall(
        R"""\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\","""				,					_lowerCAmelCase				,					)
    if not matched_google_image_data:
        return 0

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

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


if __name__ == "__main__":
     try:
          SCREAMING_SNAKE_CASE_:Union[str, 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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_:Optional[int]					=						{
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Optional[int]					=						[
         """TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TapasForMaskedLM""",
         """TapasForQuestionAnswering""",
         """TapasForSequenceClassification""",
         """TapasModel""",
         """TapasPreTrainedModel""",
         """load_tf_weights_in_tapas""",
     ]
try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Optional[Any]					=						[
         """TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFTapasForMaskedLM""",
         """TFTapasForQuestionAnswering""",
         """TFTapasForSequenceClassification""",
         """TFTapasModel""",
         """TFTapasPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
     from .tokenization_tapas import TapasTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tapas import (
              TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
              TapasForMaskedLM,
              TapasForQuestionAnswering,
              TapasForSequenceClassification,
              TapasModel,
              TapasPreTrainedModel,
              load_tf_weights_in_tapas,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_tapas import (
              TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFTapasForMaskedLM,
              TFTapasForQuestionAnswering,
              TFTapasForSequenceClassification,
              TFTapasModel,
              TFTapasPreTrainedModel,
          )


else:
     import sys

     SCREAMING_SNAKE_CASE_:str					=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


