


'''simple docstring'''







def _UpperCamelCase			(	__A       )				->						list:


      '''simple docstring'''



      UpperCamelCase__      =							[True for i in range(n + 1       )]
      UpperCamelCase__      =							[i - 1 for i in range(n + 1       )]
      UpperCamelCase__      =							[]
      for i in range(2 ,     n + 1       ):
            if is_prime[i]:
                  primes.append(__A       )
            for j in range(0 ,     len(__A       )       ):
                  if i * primes[j] >= n:
                        break
                  UpperCamelCase__      =							False

                  if i % primes[j] == 0:
                        UpperCamelCase__      =							totients[i] * primes[j]
                        break

                  UpperCamelCase__      =							totients[i] * (primes[j] - 1)

      return totients


if __name__ == "__main__":
  import doctest

  doctest.testmod()









'''simple docstring'''







import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						str:


      '''simple docstring'''



      UpperCamelCase__      =							RoFormerConfig.from_json_file(__A       )
      print(F'''Building PyTorch model from configuration: {config}'''       )
      UpperCamelCase__      =							RoFormerForMaskedLM(__A       )

      # Load weights from tf checkpoint
      load_tf_weights_in_roformer(__A ,     __A ,     __A       )

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


if __name__ == "__main__":
  a__    :   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(
      '--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__    :   Dict								=			parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





