



"""simple docstring"""







import argparse

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


logging.set_verbosity_info()

def 							UpperCamelCase_       (       lowerCAmelCase__ :							List[str]       ,  lowerCAmelCase__ :							Tuple       ,  lowerCAmelCase__ :							str       ,  lowerCAmelCase__ :							Any       ,  lowerCAmelCase__ :							Any		)		->  Tuple:

  """simple docstring"""




  lowerCAmelCase_  :							int								=					BertConfig.from_pretrained(
      'bert-large-cased'       ,  vocab_size=lowerCAmelCase__       ,  max_position_embeddings=512       ,  is_decoder=lowerCAmelCase__       ,  add_cross_attention=lowerCAmelCase__       ,  )
  lowerCAmelCase_  :							List[str]								=					bert_config.to_dict()
  del bert_config_dict["type_vocab_size"]
  lowerCAmelCase_  :							Tuple								=					BertGenerationConfig(**lowerCAmelCase__		)
  if is_encoder:
    lowerCAmelCase_  :							str								=					BertGenerationEncoder(lowerCAmelCase__		)
  else:
    lowerCAmelCase_  :							Union[str, Any]								=					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__":
     lowercase__						:							Optional[Any]        =      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""")
     lowercase__						:							int        =      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 .stack import Stack

def 							UpperCamelCase_       (       lowerCAmelCase__ :							str		)		->  bool:

  """simple docstring"""




  lowerCAmelCase_  :							Stack[str]								=					Stack()
  lowerCAmelCase_  :							List[str]								=					{'(': ')', '[': ']', '{': '}'}
  for bracket in parentheses:
    if bracket in bracket_pairs:
      stack.push(lowerCAmelCase__		)
    elif bracket in (")", "]", "}"):
      if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
        return False
  return stack.is_empty()


if __name__ == "__main__":
     from doctest import testmod

     testmod()

     lowercase__						:							List[str]        =      ["""((()))""", """((())""", """(()))"""]
     print("""Balanced parentheses demonstration:\n""")
     for example in examples:
          lowercase__						:							List[Any]        =      """""" if balanced_parentheses(example) else """not """
          print(f'{example} is {not_str}balanced')





