






import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
 print("""Googling.....""")
 __lowerCamelCase :      Tuple										=				"""https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
 __lowerCamelCase :      Optional[Any]										=				requests.get(url, headers={"""UserAgent""": UserAgent().random})
 # res.raise_for_status()
 with open("""project1a.html""", """wb""") as out_file:  # only for knowing the class
  for data in res.iter_content(1_0000):
   out_file.write(data)
 __lowerCamelCase :      Union[str, Any]										=				BeautifulSoup(res.text, """html.parser""")
 __lowerCamelCase :      int										=				list(soup.select(""".eZt8xd"""))[:5]

 print(len(links))
 for link in links:
  if link.text == "Maps":
   webbrowser.open(link.get("""href"""))
  else:
   webbrowser.open(f"https://google.com{link.get('href')}")











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    (			snake_case_ :							Tuple					,  snake_case_ :							str					,  snake_case_ :							Union[str, Any]       ):
	# Initialise PyTorch model
	snake_case__			:				List[str]       =  MobileBertConfig.from_json_file(snake_case_       )
	print(F'''Building PyTorch model from configuration: {config}'''       )
	snake_case__			:				Dict       =  MobileBertForPreTraining(snake_case_       )
	# Load weights from tf checkpoint
	snake_case__			:				Any       =  load_tf_weights_in_mobilebert(snake_case_					,  snake_case_					,  snake_case_       )
	# Save pytorch-model
	print(F'''Save PyTorch model to {pytorch_dump_path}'''       )
	torch.save(model.state_dict()					,  snake_case_       )


if __name__ == "__main__":
	__lowerCamelCase :      List[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."""
	)
	__lowerCamelCase :      int										=				parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



