






'''simple docstring'''







import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       Union[str, Any]:
							# Initialise PyTorch model
							lowerCamelCase__					:	Optional[Any]         =       RemBertConfig.from_json_file(UpperCamelCase      )
							print("""Building PyTorch model from configuration: {}""".format(str(UpperCamelCase      )      )      )
							lowerCamelCase__					:	Optional[Any]         =       RemBertModel(UpperCamelCase      )

							# Load weights from tf checkpoint
							load_tf_weights_in_rembert(UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )

							# Save pytorch-model
							print("""Save PyTorch model to {}""".format(UpperCamelCase      )      )
							torch.save(model.state_dict()				,			UpperCamelCase      )


if __name__ == "__main__":
			_A	:			Union[str, 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(
			    '''--rembert_config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained RemBERT 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.'''
			)
			_A	:			Union[str, Any]         =parser.parse_args()
			convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)




def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							if edge <= 0 or not isinstance(UpperCamelCase__   ,							UpperCamelCase__      ):
														raise ValueError('Length must be a positive.'      )
							return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							if edge <= 0 or not isinstance(UpperCamelCase__   ,							UpperCamelCase__      ):
														raise ValueError('Length must be a positive.'      )
							return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




