from argparse import ArgumentParser

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


__lowerCamelCase      :			Union[str, Any]							       =		logging.get_logger(__name__)  # pylint: disable=invalid-name



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	str     )							-> Optional[Any]:







    """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      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Tuple     )							-> List[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=__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 __snake_case    (						lowerCamelCase_		):




   def __init__(   self      :    int	,     _lowercase      :    Pipeline	,     _lowercase      :    PipelineDataFormat				):



       """simple docstring"""






       SCREAMING_SNAKE_CASE__      							=				nlp
       SCREAMING_SNAKE_CASE__      							=				reader




   @staticmethod
   def      __a						(   _lowercase      :    ArgumentParser				):



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




   def      __a						(   self      :    Optional[int]				):



       """simple docstring"""






       SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self._nlp, []

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

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

from __future__ import annotations

__lowerCamelCase      :			Dict							       =		[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__lowerCamelCase      :			Union[str, Any]							       =		[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	list[float]     )							-> list[float]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[]
  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     )

  for i in range(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				-1
    for j in range(i + 1			,					__UpperCamelCase     ):
      if arr[i] < arr[j]:
        SCREAMING_SNAKE_CASE__      							=				arr[j]
        break
    result.append(__UpperCamelCase     )
  return result



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	list[float]     )							-> list[float]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[]
  for i, outer in enumerate(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				-1
    for inner in arr[i + 1 :]:
      if outer < inner:
        SCREAMING_SNAKE_CASE__      							=				inner
        break
    result.append(__UpperCamelCase     )
  return result



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	list[float]     )							-> list[float]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				[]
  SCREAMING_SNAKE_CASE__      							=				[-1] * arr_size

  for index in reversed(range(__UpperCamelCase     )     ):
    if stack:
      while stack[-1] <= arr[index]:
        stack.pop()
        if not stack:
          break
    if stack:
      SCREAMING_SNAKE_CASE__      							=				stack[-1]
    stack.append(arr[index]     )
  return result


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

   testmod()
   print(next_greatest_element_slow(arr))
   print(next_greatest_element_fast(arr))
   print(next_greatest_element(arr))

   __lowerCamelCase      :			List[Any]							       =		(
       '''from __main__ import arr, next_greatest_element_slow, '''
       '''next_greatest_element_fast, next_greatest_element'''
   )
   print(
       '''next_greatest_element_slow():''',
       timeit('''next_greatest_element_slow(arr)''', setup=setup),
   )
   print(
       '''next_greatest_element_fast():''',
       timeit('''next_greatest_element_fast(arr)''', setup=setup),
   )
   print(
       '''     next_greatest_element():''',
       timeit('''next_greatest_element(arr)''', setup=setup),
   )
