





"""simple docstring"""





from argparse import ArgumentParser

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


lowercase__				:List[Any]				   =       logging.get_logger(__name__)  # pylint: disable=invalid-name


def       lowerCamelCase_       (   UpperCAmelCase_  )						->List[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       lowerCamelCase_       (   UpperCAmelCase_  )						->List[str]:





  """simple docstring"""


  __UpperCAmelCase    :						str       =						pipeline(
      task=args.task  ,			model=args.model if args.model else None  ,			config=args.config  ,			tokenizer=args.tokenizer  ,			device=args.device  ,			)
  __UpperCAmelCase    :						str       =						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 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      def __init__(							self       :      Any						,				__lowercase       :      Pipeline						,				__lowercase       :      PipelineDataFormat ):


        '''simple docstring'''




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

      @staticmethod
      def       A_					(							__lowercase       :      ArgumentParser ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						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'''




        __UpperCAmelCase       ,     __UpperCAmelCase    :						List[str]       =						self._nlp, []

        for entry in self._reader:
          __UpperCAmelCase    :						List[Any]       =						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:
          __UpperCAmelCase    :						Optional[int]       =						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 )






"""simple docstring"""





def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->Tuple:





  """simple docstring"""


  return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_=0  )						->Dict:





  """simple docstring"""


  return sorted(UpperCAmelCase_  ,			key=lambda UpperCAmelCase_  : x[column]  )


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=float('''inf'''  )  )						->str:





  """simple docstring"""


  for i in range(points_counts - 1  ):
    for j in range(i + 1  ,			UpperCAmelCase_  ):
      __UpperCAmelCase    :						List[Any]       =						euclidean_distance_sqr(points[i]  ,			points[j]  )
      if current_dis < min_dis:
        __UpperCAmelCase    :						Tuple       =						current_dis
  return min_dis


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=float('''inf'''  )  )						->str:





  """simple docstring"""


  for i in range(min(6  ,			points_counts - 1  )  ,			UpperCAmelCase_  ):
    for j in range(max(0  ,			i - 6  )  ,			UpperCAmelCase_  ):
      __UpperCAmelCase    :						Dict       =						euclidean_distance_sqr(points[i]  ,			points[j]  )
      if current_dis < min_dis:
        __UpperCAmelCase    :						Tuple       =						current_dis
  return min_dis


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Any:





  """simple docstring"""


  if points_counts <= 3:
    return dis_between_closest_pair(UpperCAmelCase_  ,			UpperCAmelCase_  )

  # recursion
  __UpperCAmelCase    :						Any       =						points_counts // 2
  __UpperCAmelCase    :						Any       =						closest_pair_of_points_sqr(
      UpperCAmelCase_  ,			points_sorted_on_y[:mid]  ,			UpperCAmelCase_  )
  __UpperCAmelCase    :						Tuple       =						closest_pair_of_points_sqr(
      UpperCAmelCase_  ,			points_sorted_on_y[mid:]  ,			points_counts - mid  )
  __UpperCAmelCase    :						List[Any]       =						min(UpperCAmelCase_  ,			UpperCAmelCase_  )
  __UpperCAmelCase    :						int       =						[]
  for point in points_sorted_on_x:
    if abs(point[0] - points_sorted_on_x[mid][0]  ) < closest_pair_dis:
      cross_strip.append(UpperCAmelCase_  )

  __UpperCAmelCase    :						Union[str, Any]       =						dis_between_closest_in_strip(
      UpperCAmelCase_  ,			len(UpperCAmelCase_  )  ,			UpperCAmelCase_  )
  return min(UpperCAmelCase_  ,			UpperCAmelCase_  )


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->List[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						str       =						column_based_sort(UpperCAmelCase_  ,			column=0  )
  __UpperCAmelCase    :						Any       =						column_based_sort(UpperCAmelCase_  ,			column=1  )
  return (
      closest_pair_of_points_sqr(
          UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )
  ) ** 0.5


if __name__ == "__main__":
  lowercase__				:Optional[Any]				   =       [(2, 3), (1_2, 3_0), (4_0, 5_0), (5, 1), (1_2, 1_0), (3, 4)]
  print('Distance:', closest_pair_of_points(points, len(points)))