






'''simple docstring'''

def 							snake_case					(     a_							: int	)       ->				int:





      """simple docstring"""

      UpperCamelCase_				:   int					    =							abs(a_	)
      UpperCamelCase_				:   Tuple					    =							0
      while n > 0:
            res += n % 10
            n //= 10
      return res



def 							snake_case					(     a_							: int	)       ->				int:





      """simple docstring"""

      UpperCamelCase_				:   Optional[int]					    =							abs(a_	)
      return n if n < 10 else n % 10 + sum_of_digits(n // 10	)



def 							snake_case					(     a_							: int	)       ->				int:





      """simple docstring"""

      return sum(int(a_	) for c in str(abs(a_	)	)	)



def 							snake_case					(     )       ->				None:





      """simple docstring"""

      from collections.abc import Callable
      from timeit import timeit

      def benchmark_a_function(a_							: Callable    ,   a_							: int	) -> None:
            UpperCamelCase_				:   Any					    =							f"{func.__name__}({value})"
            UpperCamelCase_				:   str					    =							timeit(f"__main__.{call}"    ,   setup="""import __main__"""	)
            print(f"{call:56} = {func(a_	)} -- {timing:.4f} seconds"	)

      for value in (262_144, 1_125_899_906_842_624, 1_267_650_600_228_229_401_496_703_205_376):
            for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
                  benchmark_a_function(a_    ,   a_	)
            print()


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      benchmark()








'''simple docstring'''

from argparse import ArgumentParser

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


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



def 							snake_case					(     a_							: str	)       ->				int:





      """simple docstring"""

      if not path:
            return "pipe"

      for ext in PipelineDataFormat.SUPPORTED_FORMATS:
            if path.endswith(a_	):
                  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					(     a_							: List[Any]	)       ->				Tuple:





      """simple docstring"""

      UpperCamelCase_				:   Any					    =							pipeline(
          task=args.task    ,   model=args.model if args.model else None    ,   config=args.config    ,   tokenizer=args.tokenizer    ,   device=args.device    ,   )
      UpperCamelCase_				:   List[Any]					    =							try_infer_format_from_ext(args.input	) if args.format == """infer""" else args.format
      UpperCamelCase_				:   Tuple					    =							PipelineDataFormat.from_str(
          format=a_    ,   output_path=args.output    ,   input_path=args.input    ,   column=args.column if args.column else nlp.default_input_names    ,   overwrite=args.overwrite    ,   )
      return RunCommand(a_    ,   a_	)




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Union[str, Any]					    =							nlp
       UpperCamelCase_				:   Optional[Any]					    =							reader
 @staticmethod
 def   _UpperCAmelCase       (							__lowerCAmelCase ):
       UpperCamelCase_				:   List[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=__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 ):
       UpperCamelCase_    ,			UpperCamelCase_				:   str					    =							self._nlp, []

       for entry in self._reader:
             UpperCamelCase_				:   List[Any]					    =							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:
             UpperCamelCase_				:   int					    =							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 )






