


'''simple docstring'''



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      UpperCAmelCase__   (      UpperCAmelCase_       :							str				)	->						List[str]:
     if not path:
          return "pipe"

     for ext in PipelineDataFormat.SUPPORTED_FORMATS:
          if path.endswith(UpperCAmelCase_				):
               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      UpperCAmelCase__   (      UpperCAmelCase_       :							int				)	->						List[Any]:
     __lowerCamelCase  :      Tuple														=						pipeline(
         task=args.task ,       model=args.model if args.model else None ,       config=args.config ,       tokenizer=args.tokenizer ,       device=args.device ,       )
     __lowerCamelCase  :      Tuple														=						try_infer_format_from_ext(args.input				) if args.format == 'infer' else args.format
     __lowerCamelCase  :      List[Any]														=						PipelineDataFormat.from_str(
         format=UpperCAmelCase_ ,       output_path=args.output ,       input_path=args.input ,       column=args.column if args.column else nlp.default_input_names ,       overwrite=args.overwrite ,       )
     return RunCommand(UpperCAmelCase_ ,       UpperCAmelCase_				)





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	Tuple:
         __lowerCamelCase  :      Tuple														=						nlp
         __lowerCamelCase  :      Dict														=						reader







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







    def  lowercase_							(				self      )							->	int:
         __lowerCamelCase							,			__lowerCamelCase  :      Union[str, Any]														=						self._nlp, []

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

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











'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


__lowerCAmelCase		     =  logging.get_logger(__name__)

class       __magic_name__		(       _UpperCamelCase  ):







							def __init__(				self   :			Tuple      ,*_UpperCAmelCase   :			str      ,**_UpperCAmelCase   :			int						):
									warnings.warn(
									    'The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
									    ' use DeiTImageProcessor instead.'      ,_UpperCAmelCase      ,)
									super().__init__(*_UpperCAmelCase      ,**_UpperCAmelCase						)




