code: 
  '''simple docstring'''
  
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  def   snake_case_       (							_a     :							Tuple ,					_a     :							Optional[Any] ,					_a     :							List[Any] ,					_a     :							Optional[Any]			):
        # Initialise PyTorch model
        snake_case_						       =							ElectraConfig.from_json_file(_a			)
        print(F'''Building PyTorch model from configuration: {config}'''			)
  
        if discriminator_or_generator == "discriminator":
              snake_case_						       =							ElectraForPreTraining(_a			)
        elif discriminator_or_generator == "generator":
              snake_case_						       =							ElectraForMaskedLM(_a			)
        else:
              raise ValueError("The discriminator_or_generator argument should be either 'discriminator' or 'generator'"			)
  
        # Load weights from tf checkpoint
        load_tf_weights_in_electra(
            _a ,					_a ,					_a ,					discriminator_or_generator=_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(
           "--config_file",
           default=None,
           type=str,
           required=True,
           help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
       )
       parser.add_argument(
           "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
       )
       parser.add_argument(
           "--discriminator_or_generator",
           default=None,
           type=str,
           required=True,
           help=(
               "Whether to export the generator or the discriminator. Should be a string, either 'discriminator' or "
               "'generator'."
           ),
       )
       A           =   parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
       )
  
  
style_context: 
  '''simple docstring'''
  
  
  
  
  
  from __future__ import annotations
  
  import random
  
  
  
  class  lowerCAmelCase__   :
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    _UpperCamelCase						= 6
  
    def __init__(self  : List[Any]    )   ->       Optional[Any]:
          snake_case_						       =							list(range(1	, Dice.NUM_SIDES + 1    )    )
  
  
    def 		_lowerCamelCase	(self  : Optional[int]    )   ->       int:
          return random.choice(self.sides    )
  
  def   snake_case_       (							_a     :							int ,					_a     :							int = 2			):
        snake_case_						       =							[Dice() for i in range(_a			)]
        snake_case_						       =							[0] * (len(_a			) * Dice.NUM_SIDES + 1)
        for _ in range(_a			):
              count_of_sum[sum(dice.roll() for dice in dices			)] += 1
        snake_case_						       =							[round((count * 100) / num_throws ,					2			) for count in count_of_sum]
        return probability[num_dice:]  # remove probability of sums that never appear
  
  
  if __name__ == "__main__":
       import doctest
  
       doctest.testmod()
  
  