
'''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
     )



'''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()

