
"""simple docstring"""

import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def 		UpperCAmelCase					(		a_,						a_,						a_,						a_,						a_			):





		'''simple docstring'''


		lowerCamelCase     :			List[str]		=    BertConfig.from_pretrained(
		    'bert-large-cased',						vocab_size=a_,						max_position_embeddings=512,						is_decoder=a_,						add_cross_attention=a_,						)
		lowerCamelCase     :			Any		=    bert_config.to_dict()
		del bert_config_dict["type_vocab_size"]
		lowerCamelCase     :			List[Any]		=    BertGenerationConfig(**a_			)
		if is_encoder:
				lowerCamelCase     :			Optional[int]		=    BertGenerationEncoder(a_			)
		else:
				lowerCamelCase     :			Optional[Any]		=    BertGenerationDecoder(a_			)
		print(F"""Building PyTorch model from configuration: {config}"""			)

		# Load weights from tf checkpoint
		load_tf_weights_in_bert_generation(
		    a_,						a_,						model_class='bert',						is_encoder_named_decoder=a_,						is_encoder=a_,						)

		# Save pytorch-model
		print(F"""Save PyTorch model and config to {pytorch_dump_path}"""			)
		model.save_pretrained(a_			)


if __name__ == "__main__":
					_A            = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					parser.add_argument(
					    '--is_encoder_named_decoder',
					    action='store_true',
					    help='If decoder has to be renamed to encoder in PyTorch model.',
					)
					parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
					parser.add_argument('--vocab_size', default=5_0_3_5_8, type=int, help='Vocab size of model')
					_A            = parser.parse_args()
					convert_tf_checkpoint_to_pytorch(
					    args.tf_hub_path,
					    args.pytorch_dump_path,
					    args.is_encoder_named_decoder,
					    args.vocab_size,
					    is_encoder=args.is_encoder,
					)








"""simple docstring"""

from __future__ import annotations
def 		UpperCAmelCase					(		a_			):





		'''simple docstring'''


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


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

										_A            = pow(b_public, a_private, q)
										_A            = 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)






