


'''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    lowerCAmelCase__ (       lowerCamelCase  :    int						,lowerCamelCase  :    List[str]						,lowerCamelCase  :    Optional[int]						,lowerCamelCase  :    Any     ):
       # Initialise PyTorch model
       _A  :			Optional[Any]											=      ElectraConfig.from_json_file(lowerCamelCase     )
       print(F'Building PyTorch model from configuration: {config}'     )

       if discriminator_or_generator == "discriminator":
              _A  :			Tuple											=      ElectraForPreTraining(lowerCamelCase     )
       elif discriminator_or_generator == "generator":
              _A  :			List[Any]											=      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__":
   A   :  Any       		=				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   :  Union[str, Any]       		=				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'''







import math






def    lowerCAmelCase__ (       lowerCamelCase  :    int     ):

       if 1 < number < 4:
              # 2 and 3 are primes
              return True
       elif number < 2 or number % 2 == 0 or number % 3 == 0:
              # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
              return False

       # All primes number are in format of 6k +/- 1
       for i in range(5						,int(math.sqrt(lowerCamelCase     ) + 1     )						,6     ):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True






def    lowerCAmelCase__ (       lowerCamelCase  :    int = 1000						,lowerCamelCase  :    int = 1000     ):
       _A  :			int											=      [0, 0, 0]  # length, a, b
       for a in range((a_limit * -1) + 1						,lowerCamelCase     ):
              for b in range(2						,lowerCamelCase     ):
                     if is_prime(lowerCamelCase     ):
                            _A  :			Dict											=      0
                            _A  :			Tuple											=      0
                            while is_prime((n**2) + (a * n) + b     ):
                                   count += 1
                                   n += 1
                            if count > longest[0]:
                                   _A  :			List[str]											=      [count, a, b]
       _A  :			str											=      longest[1] * longest[2]
       return ans


if __name__ == "__main__":
   print(solution(1000, 1000))






