




import argparse

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


logging.set_verbosity_info()




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )							->						Dict:

    """simple docstring"""


    __UpperCamelCase			=    BertConfig.from_pretrained(
        '''bert-large-cased'''       ,  vocab_size=A__       ,  max_position_embeddings=5_12       ,  is_decoder=A__       ,  add_cross_attention=A__       ,  )
    __UpperCamelCase			=    bert_config.to_dict()
    del bert_config_dict["type_vocab_size"]
    __UpperCamelCase			=    BertGenerationConfig(**A__   )
    if is_encoder:
        __UpperCamelCase			=    BertGenerationEncoder(A__   )
    else:
        __UpperCamelCase			=    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_0358, 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,
 )










from __future__ import annotations

import sys







def 	A (		A__		,		A__			):
     # base cases
     if abs(A__			) == 1:
          return a, 0
     elif abs(A__			) == 1:
          return 0, b

     UpperCAmelCase	,			UpperCAmelCase    :					Any			=      a, b
     UpperCAmelCase	,			UpperCAmelCase    :					List[Any]			=      1, 0
     UpperCAmelCase	,			UpperCAmelCase    :					Union[str, Any]			=      0, 1

     while remainder != 0:
          UpperCAmelCase    :					Union[str, Any]			=      old_remainder // remainder
          UpperCAmelCase	,			UpperCAmelCase    :					Dict			=      remainder, old_remainder - quotient * remainder
          UpperCAmelCase	,			UpperCAmelCase    :					int			=      coeff_a, old_coeff_a - quotient * coeff_a
          UpperCAmelCase	,			UpperCAmelCase    :					Optional[Any]			=      coeff_b, old_coeff_b - quotient * coeff_b

     # sign correction for negative numbers
     if a < 0:
          UpperCAmelCase    :					Union[str, Any]			=      -old_coeff_a
     if b < 0:
          UpperCAmelCase    :					int			=      -old_coeff_b

     return old_coeff_a, old_coeff_b







def 	A (		):
     if len(sys.argv			) < 3:
          print("2 integer arguments required"			)
          return 1
     UpperCAmelCase    :					Union[str, Any]			=      int(sys.argv[1]			)
     UpperCAmelCase    :					Any			=      int(sys.argv[2]			)
     print(extended_euclidean_algorithm(A__		,		A__			)			)
     return 0


if __name__ == "__main__":
      raise SystemExit(main())



