





import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> List[Any]:



       """simple docstring"""

       A__   				=					BertConfig.from_json_file(_lowerCamelCase		)
       print(f"""Building PyTorch model from configuration: {config}"""		)
       A__   				=					BertForPreTraining(_lowerCamelCase		)

       # Load weights from tf checkpoint
       load_tf_weights_in_bert(_lowerCamelCase				,       _lowerCamelCase				,       _lowerCamelCase		)

       # Save pytorch-model
       print(f"""Save PyTorch model to {pytorch_dump_path}"""		)
       torch.save(model.state_dict()				,       _lowerCamelCase		)


if __name__ == "__main__":
  _lowerCamelCase			:	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(
      """--bert_config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained BERT 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			:	Tuple							 =     parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)








from collections import namedtuple

import requests
from lxml import html  # type: ignore

_lowerCamelCase			:	Optional[int]							 =     namedtuple("""covid_data""", """cases deaths recovered""")







def SCREAMING_SNAKE_CASE       ( lowercase_ = "https://www.worldometers.info/coronavirus/"		)     -> covid_data:



       """simple docstring"""

       A__   				=					'''//div[@class = "maincounter-number"]/span/text()'''
       return covid_data(*html.fromstring(requests.get(lowercase_		).content		).xpath(lowercase_		)		)


_lowerCamelCase			:	Optional[int]							 =     """Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""
print(fmt.format(*covid_stats()))


