



"""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 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)						->				Tuple:
  # Initialise PyTorch model
  _snake_case   	=      RemBertConfig.from_json_file(lowerCAmelCase_	)
  print('''Building PyTorch model from configuration: {}'''.format(str(lowerCAmelCase_	)	)	)
  _snake_case   	=      RemBertModel(lowerCAmelCase_	)

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

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


if __name__ == "__main__":
     snake_case   					=				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.'''
     )
     snake_case   					=				parser.parse_args()
     convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)










from __future__ import annotations





def 		a__      (		_UpperCamelCase      :			int | float | str			,_UpperCamelCase      :			int | float | str				):
   if nth_term == "":
      return [""]
   __lowerCamelCase    				=     int(_UpperCamelCase				)
   __lowerCamelCase    				=     int(_UpperCamelCase				)
   __lowerCamelCase    				=     []
   for temp in range(int(_UpperCamelCase				)				):
      series.append(F"""1 / {pow(temp + 1			,int(_UpperCamelCase				)				)}""" if series else '''1'''				)
   return series


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  a_								=       int(input("""Enter the last number (nth term) of the P-Series"""))
  a_								=       int(input("""Enter the power for  P-Series"""))
  print("""Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p""")
  print(p_series(nth_term, power))






