






import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:List[str]   =   BigBirdConfig.from_json_file(snake_case			)
				print(f'''Building PyTorch model from configuration: {config}'''			)

				if is_trivia_qa:
								__magic_name__		:List[Any]   =   BigBirdForQuestionAnswering(snake_case			)
				else:
								__magic_name__		:List[Any]   =   BigBirdForPreTraining(snake_case			)

				# Load weights from tf checkpoint
				load_tf_weights_in_big_bird(snake_case,	snake_case,	is_trivia_qa=snake_case			)

				# Save pytorch-model
				print(f'''Save PyTorch model to {pytorch_dump_path}'''			)
				model.save_pretrained(snake_case			)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :      List[str]				=		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(
					    """--big_bird_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."""
					)
					parser.add_argument(
					    """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
					)
					SCREAMING_SNAKE_CASE__   :      Dict				=		parser.parse_args()
					convert_tf_checkpoint_to_pytorch(
					    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
					)













"""simple docstring"""



from collections import namedtuple

import requests
from lxml import html  # type: ignore

a_					=     namedtuple("""covid_data""", """cases deaths recovered""")







def 						__lowercase	(  snake_case_    :    str = "https://www.worldometers.info/coronavirus/"		)  ->covid_data:



    '''simple docstring'''







    __A       :		List[str]							=	'''//div[@class = "maincounter-number"]/span/text()'''
    return covid_data(*html.fromstring(requests.get(snake_case_		).content		).xpath(snake_case_		)		)


a_					=     """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()))




