import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__lowerCAmelCase					: str        =logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__			)
class 				_A		( a__			):
      snake_case__    :     int													=			field(default=a__						,					metadata={'help': 'Whether to use SortishSampler or not.'}			)
      snake_case__    :     List[str]													=			field(
          default=a__						,					metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}			)
      snake_case__    :     List[str]													=			field(
          default=a__						,					metadata={
              'help': (
                  'The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '
                  'to the `max_length` value of the model configuration.'
              )
          }						,					)
      snake_case__    :     Dict													=			field(
          default=a__						,					metadata={
              'help': (
                  'The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '
                  'to the `num_beams` value of the model configuration.'
              )
          }						,					)
      snake_case__    :     Any													=			field(
          default=a__						,					metadata={
              'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
          }						,					)

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			super().to_dict()
            for k, v in d.items():
                  if isinstance(_A	, _A		):
                        lowercase														=			v.to_dict()
            return d




"""simple docstring"""






from __future__ import annotations


def     UpperCAmelCase__ (       lowerCAmelCase__  :list[float]      ,	lowerCAmelCase__  :list[float]	)				->					float:
      '''simple docstring'''


      lowercase														=			sorted(numsa + numsa	)
      lowercase							,							lowercase														=			divmod(len(lowerCAmelCase__	)      ,	2	)
      if mod == 1:
            return all_numbers[div]
      else:
            return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       __lowerCAmelCase					: List[Any]        =[float(x) for x in input("""Enter the elements of first array: """).split()]
       __lowerCAmelCase					: List[Any]        =[float(x) for x in input("""Enter the elements of second array: """).split()]
       print(F"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")



