


import math







class 				lowerCamelCase__       :





     def __init__(self		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase	)					->       Optional[Any]:
      _lowercase											=x
      _lowercase											=y
      _lowercase											=z





     def __repr__(self	)					->       str:
      return f"Point({self.x}, {self.y}, {self.z})"



def     UpperCAmelCase_ (							__snake_case					,						__snake_case						)					->      float:







 """simple docstring"""


 return math.sqrt(abs((b.x - a.x) ** 2 + (b.y - a.y) ** 2 + (b.z - a.z) ** 2						)						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()





import argparse

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


logging.set_verbosity_info()
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)					->      Tuple:







 """simple docstring"""


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

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

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


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=5_0358, 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,
  )

