





import argparse

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


logging.set_verbosity_info()





def     lowerCamelCase__  (  UpperCamelCase__      :							Any							,     UpperCamelCase__      :							Tuple							,     UpperCamelCase__      :							Union[str, Any]							,     UpperCamelCase__      :							Optional[int]							,     UpperCamelCase__      :							int    )      ->			Optional[Any]:



					'''simple docstring'''




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

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

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


if __name__ == "__main__":
					UpperCAmelCase_   				=  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_   				=  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"""





_lowercase						   =     [0.4_85, 0.4_56, 0.4_06]
_lowercase						   =     [0.2_29, 0.2_24, 0.2_25]
_lowercase						   =     [0.5, 0.5, 0.5]
_lowercase						   =     [0.5, 0.5, 0.5]
_lowercase						   =     [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
_lowercase						   =     [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]


