





'''simple docstring'''


from argparse import ArgumentParser

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


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


def 	__a(SCREAMING_SNAKE_CASE_  :					str    ):



    '''simple docstring'''





    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 	__a(SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    _lowerCAmelCase				=      pipeline(
        task=args.task		,       model=args.model if args.model else None		,       config=args.config		,       tokenizer=args.tokenizer		,       device=args.device		,       )
    _lowerCAmelCase				=      try_infer_format_from_ext(args.input    ) if args.format == "infer" else args.format
    _lowerCAmelCase				=      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    lowerCAmelCase_		(		__magic_name__		):




 def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							str:
     _lowerCAmelCase				=      nlp
     _lowerCAmelCase				=      reader




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





 def 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self._nlp, []

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

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









'''simple docstring'''


def 	__a(SCREAMING_SNAKE_CASE_  :					str		,       SCREAMING_SNAKE_CASE_  :					bool = False    ):



    '''simple docstring'''





    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        _lowerCAmelCase				=      F'''Expected string as input, found {type(SCREAMING_SNAKE_CASE_    )}'''
        raise ValueError(SCREAMING_SNAKE_CASE_    )
    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        _lowerCAmelCase				=      F'''Expected boolean as use_pascal parameter, found {type(SCREAMING_SNAKE_CASE_    )}'''
        raise ValueError(SCREAMING_SNAKE_CASE_    )

    _lowerCAmelCase				=      input_str.split("_"    )

    _lowerCAmelCase				=      0 if use_pascal else 1

    _lowerCAmelCase				=      words[start_index:]

    _lowerCAmelCase				=      [word[0].upper() + word[1:] for word in words_to_capitalize]

    _lowerCAmelCase				=      "" if use_pascal else words[0]

    return "".join([initial_word, *capitalized_words]    )


if __name__ == "__main__":
       from doctest import testmod

       testmod()


