

"""simple docstring"""



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

import requests
from bsa import BeautifulSoup

SCREAMING_SNAKE_CASE_					:	Tuple							  =						{
    '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     (    UpperCAmelCase_  :					int = "dhaka"					,   UpperCAmelCase_  :					List[Any] = 5			):
					A__										=  min(SCREAMING_SNAKE_CASE_					,   50			)  # Prevent abuse!
					A__										=  {
					    'q': query,
					    'tbm': 'isch',
					    'hl': 'en',
					    'ijn': '0',
					}

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

					A__										=  json.dumps(SCREAMING_SNAKE_CASE_			)
					A__										=  json.loads(SCREAMING_SNAKE_CASE_			)

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

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

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


if __name__ == "__main__":
	try:
		SCREAMING_SNAKE_CASE_					:	Dict							  =						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"""



# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE_					:	List[str]							  =						{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_					:	List[Any]							  =						[
     'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MraForMaskedLM',
     'MraForMultipleChoice',
     'MraForQuestionAnswering',
     'MraForSequenceClassification',
     'MraForTokenClassification',
     'MraLayer',
     'MraModel',
     'MraPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mra import (
      MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
      MraForMaskedLM,
      MraForMultipleChoice,
      MraForQuestionAnswering,
      MraForSequenceClassification,
      MraForTokenClassification,
      MraLayer,
      MraModel,
      MraPreTrainedModel,
  )
else:
 import sys

 SCREAMING_SNAKE_CASE_					:	List[str]							  =						_LazyModule(__name__, globals()['__file__'], _import_structure)






