





from argparse import ArgumentParser

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


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



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     Union[str, Any]:


      '''simple docstring'''


      if not path:
            return "pipe"

      for ext in PipelineDataFormat.SUPPORTED_FORMATS:
            if path.endswith(_SCREAMING_SNAKE_CASE			):
                  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      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     Any:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		pipeline(
          task=args.task				,		model=args.model if args.model else None				,		config=args.config				,		tokenizer=args.tokenizer				,		device=args.device				,		)
      SCREAMING_SNAKE_CASE	=		try_infer_format_from_ext(args.input			) if args.format == """infer""" else args.format
      SCREAMING_SNAKE_CASE	=		PipelineDataFormat.from_str(
          format=_SCREAMING_SNAKE_CASE				,		output_path=args.output				,		input_path=args.input				,		column=args.column if args.column else nlp.default_input_names				,		overwrite=args.overwrite				,		)
      return RunCommand(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)



class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     def __init__(							self		:					int		,lowerCamelCase__		:					Pipeline		,lowerCamelCase__		:					PipelineDataFormat )  ->	Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		nlp
           SCREAMING_SNAKE_CASE	=		reader






     @staticmethod
     def        SCREAMING_SNAKE_CASE__						(							lowerCamelCase__		:					ArgumentParser )  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		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        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		self._nlp, []

           for entry in self._reader:
                 SCREAMING_SNAKE_CASE	=		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:
                 SCREAMING_SNAKE_CASE	=		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__ )









def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     float:


      '''simple docstring'''


      return price * (1 + tax_rate)


if __name__ == "__main__":
      print(F'''{price_plus_tax(1_0_0, 0.25) = }''')
      print(F'''{price_plus_tax(125.50, 0.05) = }''')


