


'''simple docstring'''

from argparse import ArgumentParser

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


lowerCAmelCase				:Optional[Any]        =      logging.get_logger(__name__)  # pylint: disable=invalid-name





def lowerCamelCase      (      lowerCAmelCase    :     str		):





	"""simple docstring"""







	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 lowerCamelCase      (      lowerCAmelCase    :     Union[str, Any]		):





	"""simple docstring"""







	__magic_name__	:   List[str]					     =					pipeline(
	    task=args.task		,		model=args.model if args.model else None		,		config=args.config		,		tokenizer=args.tokenizer		,		device=args.device		,		)
	__magic_name__	:   Any					     =					try_infer_format_from_ext(args.input		) if args.format == 'infer' else args.format
	__magic_name__	:   Union[str, Any]					     =					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 _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		def __init__( self		:						Union[str, Any]  ,					_A		:						Pipeline  ,					_A		:						PipelineDataFormat      )  ->			Dict:
			__magic_name__	:   List[Any]					     =					nlp
			__magic_name__	:   Union[str, Any]					     =					reader







		@staticmethod
		def        __lowerCAmelCase		( _A		:						ArgumentParser      )  ->			List[Any]:
			__magic_name__	:   str					     =					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=_A  ,					help='Path to the file to use for inference'      )
			run_parser.add_argument('--output'  ,					type=_A  ,					help='Path to the file that will be used post to write results.'      )
			run_parser.add_argument('--model'  ,					type=_A  ,					help='Name or path to the model to instantiate.'      )
			run_parser.add_argument('--config'  ,					type=_A  ,					help='Name or path to the model\'s config to instantiate.'      )
			run_parser.add_argument(
			    '--tokenizer'  ,					type=_A  ,					help='Name of the tokenizer to use. (default: same as the model name)'      )
			run_parser.add_argument(
			    '--column'  ,					type=_A  ,					help='Name of the column to use as input. (For multi columns input as QA use column1,columns2)'  ,					)
			run_parser.add_argument(
			    '--format'  ,					type=_A  ,					default='infer'  ,					choices=PipelineDataFormat.SUPPORTED_FORMATS  ,					help='Input format to read from'  ,					)
			run_parser.add_argument(
			    '--device'  ,					type=_A  ,					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=_A      )







		def        __lowerCAmelCase		( self		:						Optional[Any]      )  ->			List[Any]:
			__magic_name__     ,    __magic_name__	:   Optional[int]					     =					self._nlp, []

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

        # Saving data
			if self._nlp.binary_output:
				__magic_name__	:   Union[str, Any]					     =					self._reader.save_binary(_A      )
				logger.warning(F'Current pipeline requires output to be in binary format, saving at {binary_path}'      )
			else:
				self._reader.save(_A      )



'''simple docstring'''

def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	__magic_name__	:   Optional[int]					     =					1
	for i in range(1		,		num + 1		):
		fact *= i
	return fact





def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	__magic_name__	:   List[str]					     =					0
	while number > 0:
		__magic_name__	:   str					     =					number % 10
		sum_of_digits += last_digit
		__magic_name__	:   Optional[int]					     =					number // 10  # Removing the last_digit from the given number
	return sum_of_digits





def lowerCamelCase      (      lowerCAmelCase    :     int = 100		):





	"""simple docstring"""







	__magic_name__	:   int					     =					factorial(lowerCAmelCase		)
	__magic_name__	:   Any					     =					split_and_add(lowerCAmelCase		)
	return result


if __name__ == "__main__":
							print(solution(int(input('''Enter the Number: ''').strip())))