


from argparse import ArgumentParser

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


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






def  __lowerCAmelCase					(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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

      snake_case_ =   pipeline(
          task=args.task      ,      model=args.model if args.model else None      ,      config=args.config      ,      tokenizer=args.tokenizer      ,      device=args.device      ,      )
      snake_case_ =   try_infer_format_from_ext(args.input      ) if args.format == '''infer''' else args.format
      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 lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   nlp
       snake_case_ =   reader



 @staticmethod
 def 		UpperCamelCase__  (			_UpperCAmelCase      ):
       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=_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 		UpperCamelCase__  (			self      ):
       snake_case_ ,					snake_case_ =   self._nlp, []

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



def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE = 3      ,      SCREAMING_SNAKE_CASE = 7      ,      SCREAMING_SNAKE_CASE = 100_0000      )->						int:




      """simple docstring"""

      snake_case_ =   0
      snake_case_ =   1

      for current_denominator in range(1      ,      limit + 1      ):
            snake_case_ =   current_denominator * numerator // denominator
            if current_denominator % denominator == 0:
                  current_numerator -= 1
            if current_numerator * max_denominator > current_denominator * max_numerator:
                  snake_case_ =   current_numerator
                  snake_case_ =   current_denominator
      return max_numerator


if __name__ == "__main__":
   print(solution(numerator=3, denominator=7, limit=100_0000))