



import argparse

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


logging.set_verbosity_info()


def 			snake_case_					( _lowerCAmelCase  :						Any       ,				_lowerCAmelCase  :						Dict       ,				_lowerCAmelCase  :						Any       ,				_lowerCAmelCase  :						int       ,				_lowerCAmelCase  :						Dict  )				->       List[str]:
							UpperCAmelCase			:	Dict										=		BertConfig.from_pretrained(
							    '''bert-large-cased'''       ,				vocab_size=_lowerCAmelCase       ,				max_position_embeddings=512       ,				is_decoder=_lowerCAmelCase       ,				add_cross_attention=_lowerCAmelCase       ,				)
							UpperCAmelCase			:	Union[str, Any]										=		bert_config.to_dict()
							del bert_config_dict["type_vocab_size"]
							UpperCAmelCase			:	Tuple										=		BertGenerationConfig(**_lowerCAmelCase  )
							if is_encoder:
														UpperCAmelCase			:	str										=		BertGenerationEncoder(_lowerCAmelCase  )
							else:
														UpperCAmelCase			:	str										=		BertGenerationDecoder(_lowerCAmelCase  )
							print(f"""Building PyTorch model from configuration: {config}"""  )

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

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


if __name__ == "__main__":
						UpperCamelCase__:       int							     =      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=50358, type=int, help="Vocab size of model")
						UpperCamelCase__:       Union[str, Any]							     =      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 			snake_case_					( _lowerCAmelCase  :						list[int]       ,				_lowerCAmelCase  :						int  )				->       list[int]:
							UpperCAmelCase			:	dict[int, int]										=		{}
							for index, val in enumerate(_lowerCAmelCase  ):
														UpperCAmelCase			:	Tuple										=		target - val
														if compl in chk_map:
																					return [chk_map[compl], index]
														UpperCAmelCase			:	Any										=		index
							return []


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(F"{two_sum([2, 7, 11, 15], 9) = }")





