
"""simple docstring"""




def   __magic_name__	(      __snake_case				:       Any  ) ->				str:
       lowercase		,    lowercase    :				Tuple       		=							[], []
       while len(__snake_case  ) > 1:
              lowercase		,    lowercase    :				List[Any]       		=							min(__snake_case  ), max(__snake_case  )
              start.append(__snake_case  )
              end.append(__snake_case  )
              collection.remove(__snake_case  )
              collection.remove(__snake_case  )
       end.reverse()
       return start + collection + end


if __name__ == "__main__":
   _A			:       str   		=       input("""Enter numbers separated by a comma:\n""").strip()
   _A			:       Tuple   		=       [int(item) for item in user_input.split(""",""")]
   print(*merge_sort(unsorted), sep=""",""")




"""simple docstring"""




import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()


def   __magic_name__	(      __snake_case				:       Optional[Any]     , __snake_case				:       Dict     , __snake_case				:       Any  ) ->				Any:
       # Initialise PyTorch model
       lowercase    :				Union[str, Any]       		=							RemBertConfig.from_json_file(__snake_case  )
       print("Building PyTorch model from configuration: {}".format(str(__snake_case  )  )  )
       lowercase    :				str       		=							RemBertModel(__snake_case  )

       # Load weights from tf checkpoint
       load_tf_weights_in_rembert(__snake_case     , __snake_case     , __snake_case  )

       # Save pytorch-model
       print("Save PyTorch model to {}".format(__snake_case  )  )
       torch.save(model.state_dict()     , __snake_case  )


if __name__ == "__main__":
   _A			:       str   		=       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(
       """--rembert_config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained RemBERT 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			:       List[str]   		=       parser.parse_args()
   convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)


