


'''simple docstring'''



import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[Any]						,      _SCREAMING_SNAKE_CASE      :   Optional[int]						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					# Initialise PyTorch model
					__a			:       int					    =  AlbertConfig.from_json_file(_SCREAMING_SNAKE_CASE   )
					print(F"""Building PyTorch model from configuration: {config}"""   )
					__a			:       Optional[Any]					    =  AlbertForPreTraining(_SCREAMING_SNAKE_CASE   )

					# Load weights from tf checkpoint
					load_tf_weights_in_albert(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

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


if __name__ == "__main__":
	__lowercase	:						Tuple       =	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(
	    '--albert_config_file',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        'The config json file corresponding to the pre-trained ALBERT 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.'
	)
	__lowercase	:						List[str]       =	parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)







'''simple docstring'''



import requests
from bsa import BeautifulSoup






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str = "https://www.worldometers.info/coronavirus"   ):
					__a			:       List[Any]					    =  BeautifulSoup(requests.get(_SCREAMING_SNAKE_CASE   ).text						,      'html.parser'   )
					__a			:       Union[str, Any]					    =  soup.findAll('h1'   )
					__a			:       int					    =  soup.findAll('div'						,      {'class': 'maincounter-number'}   )
					keys += soup.findAll('span'						,      {'class': 'panel-title'}   )
					values += soup.findAll('div'						,      {'class': 'number-table-main'}   )
					return {key.text.strip(): value.text.strip() for key, value in zip(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )}


if __name__ == "__main__":
	print('\033[1m' + 'COVID-19 Status of the World' + '\033[0m\n')
	for key, value in world_covidaa_stats().items():
		print(f'''{key}\n{value}\n''')



