






from __future__ import annotations






def    UpperCamelCase__  (   UpperCAmelCase     )     ->		int | None:







							"""simple docstring"""





							for r in range(1    ,	UpperCAmelCase     ):
														_a  :    List[str]			=						[]
														for x in range(n - 1     ):
																					_a  :    List[Any]			=						pow(UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )
																					if val in li:
																												break
																					li.append(UpperCAmelCase     )
														else:
																					return r
							return None


if __name__ == "__main__":
						__lowerCamelCase						= int(input('Enter a prime number q: '))
						__lowerCamelCase						= find_primitive(q)
						if a is None:
												print(F'''Cannot find the primitive for the value: {a!r}''')
						else:
												__lowerCamelCase						= int(input('Enter private key of A: '))
												__lowerCamelCase						= pow(a, a_private, q)
												__lowerCamelCase						= int(input('Enter private key of B: '))
												__lowerCamelCase						= pow(a, b_private, q)

												__lowerCamelCase						= pow(b_public, a_private, q)
												__lowerCamelCase						= pow(a_public, b_private, q)

												print('The key value generated by A is: ', a_secret)
												print('The key value generated by B is: ', b_secret)







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    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )     ->		str:







							"""simple docstring"""





							_a  :    Union[str, Any]			=						ElectraConfig.from_json_file(UpperCAmelCase     )
							print(F'Building PyTorch model from configuration: {config}'     )

							if discriminator_or_generator == "discriminator":
														_a  :    int			=						ElectraForPreTraining(UpperCAmelCase     )
							elif discriminator_or_generator == "generator":
														_a  :    Any			=						ElectraForMaskedLM(UpperCAmelCase     )
							else:
														raise ValueError('''The discriminator_or_generator argument should be either \'discriminator\' or \'generator\''''     )

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

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


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
						)