




from argparse import ArgumentParser

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


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

def   _lowerCAmelCase   (						A__	):
      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   _lowerCAmelCase   (						A__	):
      lowercase__     =  pipeline(
          task=args.task						,  model=args.model if args.model else None						,  config=args.config						,  tokenizer=args.tokenizer						,  device=args.device						,  )
      lowercase__     =  try_infer_format_from_ext(args.input	) if args.format == 'infer' else args.format
      lowercase__     =  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  :			Optional[Any]			,				lowerCAmelCase  :			Pipeline			,				lowerCAmelCase  :			PipelineDataFormat)	->				List[str]:




         """simple docstring"""

         lowercase__     =  nlp
         lowercase__     =  reader


   @staticmethod
   def 			UpperCAmelCase		(						lowerCAmelCase  :			ArgumentParser)	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  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  :			List[str])	->				Union[str, Any]:




         """simple docstring"""

         lowercase__,	lowercase__     =  self._nlp, []

         for entry in self._reader:
               lowercase__     =  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:
               lowercase__     =  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)





from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors

def   _lowerCAmelCase   (						A__	):
      lowercase__     =  prime_factors(A__	)
      if is_square_free(A__	):
            return -1 if len(A__	) % 2 else 1
      return 0


if __name__ == "__main__":
       import doctest

       doctest.testmod()
