
from __future__ import annotations


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   Optional[int]					=				list(range(len(UpperCamelCase__		)		)		)
      UpperCamelCase__    :   Optional[int]					=				[v / w for v, w in zip(UpperCamelCase__ ,		UpperCamelCase__		)]
      index.sort(key=lambda UpperCamelCase__		: ratio[i] ,		reverse=UpperCamelCase__		)

      UpperCamelCase__    :   float					=				0
      UpperCamelCase__    :   list[float]					=				[0] * len(UpperCamelCase__		)
      for i in index:
            if weight[i] <= capacity:
                  UpperCamelCase__    :   int					=				1
                  max_value += value[i]
                  capacity -= weight[i]
            else:
                  UpperCamelCase__    :   Any					=				capacity / weight[i]
                  max_value += value[i] * capacity / weight[i]
                  break

      return max_value, fractions


if __name__ == "__main__":
    import doctest

    doctest.testmod()




from argparse import ArgumentParser

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


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


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__    :   Tuple					=				pipeline(
          task=args.task ,		model=args.model if args.model else None ,		config=args.config ,		tokenizer=args.tokenizer ,		device=args.device ,		)
      UpperCamelCase__    :   Any					=				try_infer_format_from_ext(args.input		) if args.format == '''infer''' else args.format
      UpperCamelCase__    :   Tuple					=				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 (      UpperCamelCase_ ):





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				nlp
           UpperCamelCase__    :   Dict					=				reader






     @staticmethod
     def    __SCREAMING_SNAKE_CASE			(							__SCREAMING_SNAKE_CASE  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   Optional[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=__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    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







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

           for entry in self._reader:
                 UpperCamelCase__    :   List[str]					=				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:
                 UpperCamelCase__    :   Union[str, Any]					=				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  )


