
'''simple docstring'''

from argparse import ArgumentParser

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


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






def 	_snake_case						(      A_       :    str   ):
  """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_       :    int   ):
  """simple docstring"""







  a_      :	int      =							pipeline(
      task=args.task     ,		model=args.model if args.model else None     ,		config=args.config     ,		tokenizer=args.tokenizer     ,		device=args.device     ,		)
  a_      :	Dict      =							try_infer_format_from_ext(args.input   ) if args.format == """infer""" else args.format
  a_      :	List[str]      =							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  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							nlp
       a_      :	Any      =							reader


     @staticmethod
     def 							_lowerCAmelCase  (      lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	List[Any]      =							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 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      , a_      :	Optional[Any]      =							self._nlp, []

       for entry in self._reader:
         a_      :	Optional[int]      =							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:
         a_      :	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_ )




'''simple docstring'''

from math import factorial

__snake_case:  List[Any]								=			{str(d): factorial(d) for d in range(10)}






def 	_snake_case						(      A_       :    int   ):
  """simple docstring"""







  return sum(DIGIT_FACTORIAL[d] for d in str(A_   )   )






def 	_snake_case						(      ):
  """simple docstring"""







  a_      :	Union[str, Any]      =							7 * factorial(9   ) + 1
  return sum(i for i in range(3     ,		A_   ) if sum_of_digit_factorial(A_   ) == i   )


if __name__ == "__main__":
    print(F"""{solution() = }""")


