

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__    (       lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=ElectraConfig.from_json_file(lowerCAmelCase_      )
     print(F'Building PyTorch model from configuration: {config}'      )

     if discriminator_or_generator == "discriminator":
          SCREAMING_SNAKE_CASE										=ElectraForPreTraining(lowerCAmelCase_      )
     elif discriminator_or_generator == "generator":
          SCREAMING_SNAKE_CASE										=ElectraForMaskedLM(lowerCAmelCase_      )
     else:
          raise ValueError('The discriminator_or_generator argument should be either \'discriminator\' or \'generator\''      )

     # Load weights from tf checkpoint
     load_tf_weights_in_electra(
         lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_,   discriminator_or_generator=lowerCAmelCase_      )

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


if __name__ == "__main__":
 _lowerCamelCase								=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'."
     ),
 )
 _lowerCamelCase								=parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
 )




import decimal







def       snake_case__    (       ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=0
     SCREAMING_SNAKE_CASE										=decimal.Context(prec=105      )
     for i in range(2,   100      ):
          SCREAMING_SNAKE_CASE										=decimal.Decimal(lowerCAmelCase_      )
          SCREAMING_SNAKE_CASE										=number.sqrt(lowerCAmelCase_      )
          if len(str(lowerCAmelCase_      )      ) > 1:
               answer += int(str(lowerCAmelCase_      )[0]      )
               SCREAMING_SNAKE_CASE										=str(lowerCAmelCase_      )[2:101]
               answer += sum(int(lowerCAmelCase_      ) for x in sqrt_number_str      )
     return answer


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(f'{solution() = }')

