





"""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       __A (     a_  :Dict				,    a_  :str				,    a_  :Dict)						->						str:
				# Initialise PyTorch model
				__a	:  Union[str, Any]         =					BertConfig.from_json_file(a_)
				print(F"""Building PyTorch model from configuration: {config}""")
				__a	:  Any         =					BertForPreTraining(a_)

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

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


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






"""simple docstring"""






def       __A (     a_  :int = 1_00_00_00)						->						int:
				__a	:  Tuple         =					[i - 1 for i in range(limit + 1)]

				for i in range(2				,    limit + 1):
								if phi[i] == i - 1:
												for j in range(2 * i				,    limit + 1				,    a_):
																phi[j] -= phi[j] // i

				return sum(phi[2 : limit + 1])


if __name__ == "__main__":
		print(solution())