





"""simple docstring"""





def  __A   (_SCREAMING_SNAKE_CASE			)					->list:





  """simple docstring"""


  if len(_SCREAMING_SNAKE_CASE			) <= 1:
    return [tuple(_SCREAMING_SNAKE_CASE			)]

  lowerCAmelCase__			:Optional[Any]											=							[]

  def generate(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			):
    lowerCAmelCase__			:Optional[Any]											=							[0] * n
    res.append(tuple(_SCREAMING_SNAKE_CASE			)			)

    lowerCAmelCase__			:Union[str, Any]											=							0
    while i < n:
      if c[i] < i:
        if i % 2 == 0:
          lowerCAmelCase__							,				lowerCAmelCase__			:str											=							arr[i], arr[0]
        else:
          lowerCAmelCase__							,				lowerCAmelCase__			:Dict											=							arr[i], arr[c[i]]
        res.append(tuple(_SCREAMING_SNAKE_CASE			)			)
        c[i] += 1
        lowerCAmelCase__			:Any											=							0
      else:
        lowerCAmelCase__			:List[Any]											=							0
        i += 1

  generate(len(_SCREAMING_SNAKE_CASE			)     ,   _SCREAMING_SNAKE_CASE			)
  return res


if __name__ == "__main__":
  __A											=		input("""Enter numbers separated by a comma:\n""").strip()
  __A											=		[int(item) for item in user_input.split(""",""")]
  print(heaps(arr))







"""simple docstring"""





import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Dict:





  """simple docstring"""


  lowerCAmelCase__			:str											=							BertConfig.from_json_file(_SCREAMING_SNAKE_CASE			)
  print(F"Building PyTorch model from configuration: {config}"			)
  lowerCAmelCase__			:int											=							BertForPreTraining(_SCREAMING_SNAKE_CASE			)

  # Load weights from tf checkpoint
  load_tf_weights_in_bert(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)

  # Save pytorch-model
  print(F"Save PyTorch model to {pytorch_dump_path}"			)
  torch.save(model.state_dict()     ,   _SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
  __A											=		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--bert_config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained BERT model. \n"""
          """This specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  __A											=		parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
