






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


import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Optional[Any]			,a__    :   Union[str, Any]			,a__    :   Optional[int]       )					->				List[Any]:
	# Initialise PyTorch model
	__A :			Dict		    =			MobileBertConfig.from_json_file(a__       )
	print(f"""Building PyTorch model from configuration: {config}"""       )
	__A :			Tuple		    =			MobileBertForPreTraining(a__       )
	# Load weights from tf checkpoint
	__A :			Dict		    =			load_tf_weights_in_mobilebert(a__			,a__			,a__       )
	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
	torch.save(model.state_dict()			,a__       )


if __name__ == "__main__":
			UpperCAmelCase_							:					Any		 =   argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--mobilebert_config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained MobileBERT model. \n'''
			        '''This specifies the model architecture.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			UpperCAmelCase_							:					Tuple		 =   parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)





