



"""simple docstring"""






from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


snake_case   					=				logging.get_logger(__name__)  # pylint: disable=invalid-name




def 			snake_case		(		lowerCAmelCase_	)						->				Any:
  if not path:
    return "pipe"

  for ext in PipelineDataFormat.SUPPORTED_FORMATS:
    if path.endswith(lowerCAmelCase_	):
      return ext

  raise Exception(
      f"""Unable to determine file format from file extension {path}. """
      f"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"""	)




def 			snake_case		(		lowerCAmelCase_	)						->				List[str]:
  _snake_case   	=      pipeline(
      task=args.task  ,						model=args.model if args.model else None  ,						config=args.config  ,						tokenizer=args.tokenizer  ,						device=args.device  ,						)
  _snake_case   	=      try_infer_format_from_ext(args.input	) if args.format == '''infer''' else args.format
  _snake_case   	=      PipelineDataFormat.from_str(
      format=lowerCAmelCase_  ,						output_path=args.output  ,						input_path=args.input  ,						column=args.column if args.column else nlp.default_input_names  ,						overwrite=args.overwrite  ,						)
  return RunCommand(lowerCAmelCase_  ,						lowerCAmelCase_	)







class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):



   def __init__(       self    :	Optional[Any]							,       __lowerCamelCase    :	Pipeline							,       __lowerCamelCase    :	PipelineDataFormat				):



     """simple docstring"""
     _snake_case   	=      nlp
     _snake_case   	=      reader



   @staticmethod
   def    __UpperCAmelCase    (       __lowerCamelCase    :	ArgumentParser				):



     """simple docstring"""
     _snake_case   	=      parser.add_parser('''run'''							,       help='''Run a pipeline through the CLI'''				)
     run_parser.add_argument('''--task'''							,       choices=get_supported_tasks()							,       help='''Task to run'''				)
     run_parser.add_argument('''--input'''							,       type=__lowerCamelCase							,       help='''Path to the file to use for inference'''				)
     run_parser.add_argument('''--output'''							,       type=__lowerCamelCase							,       help='''Path to the file that will be used post to write results.'''				)
     run_parser.add_argument('''--model'''							,       type=__lowerCamelCase							,       help='''Name or path to the model to instantiate.'''				)
     run_parser.add_argument('''--config'''							,       type=__lowerCamelCase							,       help='''Name or path to the model\'s config to instantiate.'''				)
     run_parser.add_argument(
         '''--tokenizer'''							,       type=__lowerCamelCase							,       help='''Name of the tokenizer to use. (default: same as the model name)'''				)
     run_parser.add_argument(
         '''--column'''							,       type=__lowerCamelCase							,       help='''Name of the column to use as input. (For multi columns input as QA use column1,columns2)'''							,       )
     run_parser.add_argument(
         '''--format'''							,       type=__lowerCamelCase							,       default='''infer'''							,       choices=PipelineDataFormat.SUPPORTED_FORMATS							,       help='''Input format to read from'''							,       )
     run_parser.add_argument(
         '''--device'''							,       type=__lowerCamelCase							,       default=-1							,       help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'''							,       )
     run_parser.add_argument('''--overwrite'''							,       action='''store_true'''							,       help='''Allow overwriting the output file.'''				)
     run_parser.set_defaults(func=__lowerCamelCase				)





   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self._nlp, []

     for entry in self._reader:
       _snake_case   	=      nlp(**__lowerCamelCase				) if self._reader.is_multi_columns else nlp(__lowerCamelCase				)
       if isinstance(__lowerCamelCase							,       __lowerCamelCase				):
         outputs.append(__lowerCamelCase				)
       else:
         outputs += output

        # Saving data
     if self._nlp.binary_output:
       _snake_case   	=      self._reader.save_binary(__lowerCamelCase				)
       logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""				)
     else:
       self._reader.save(__lowerCamelCase				)












def      _UpperCamelCase  (a__ :int = 1000							):






							"""simple docstring"""





							UpperCamelCase__					  =      2**power
							UpperCamelCase__					  =      0
							while n:
														UpperCamelCase__  ,  UpperCamelCase__					  =      r + n % 10, n // 10
							return r


if __name__ == "__main__":
					print(solution(int(str(input()).strip())))




