
"""simple docstring"""






import argparse

import torch

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


logging.set_verbosity_info()



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__      )						->Union[str, Any]:
			# Initialise PyTorch model
			__lowercase       =       BertConfig.from_json_file(__magic_name__      )
			print(F'''Building PyTorch model from configuration: {config}'''      )
			__lowercase       =       BertForPreTraining(__magic_name__      )

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

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


if __name__ == "__main__":
					_lowercase							=				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.'''
					)
					_lowercase							=				parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)







"""simple docstring"""






def      lowerCAmelCase__			(		__magic_name__ = 1_0      )						->str:
			if not isinstance(__magic_name__			,   __magic_name__      ) or n < 0:
						raise ValueError("Invalid input"      )
			__lowercase       =       1_0**n
			__lowercase       =       2_8_4_3_3 * (pow(2			,   7_8_3_0_4_5_7			,   __magic_name__      )) + 1
			return str(number % modulus      )


if __name__ == "__main__":
					from doctest import testmod

					testmod()
					print(F"{solution(10) = }")





