






from __future__ import annotations




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						int:
       _UpperCAmelCase							=    len(__snake_case ) // 2

       # choose the middle 3 elements
       _UpperCAmelCase							=    lst[m - 1 : m + 2]

       # if middle element is peak
       if three[1] > three[0] and three[1] > three[2]:
              return three[1]

       # if increasing, recurse on right
       elif three[0] < three[2]:
              if len(lst[:m] ) == 2:
                     m -= 1
              return peak(lst[m:] )

       # decreasing
       else:
              if len(lst[:m] ) == 2:
                     m += 1
              return peak(lst[:m] )


if __name__ == "__main__":
  import doctest

  doctest.testmod()






'''simple docstring'''




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


lowercase__       : Dict         =       logging.getLogger(__name__)


@dataclass
@add_start_docstrings(TrainingArguments.__doc__				)
class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			field(default=a__   ,						metadata={'''help''': '''Whether to use SortishSampler or not.'''}				)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}				)
     lowerCAmelCase						     =			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.'''
             )
         }   ,						)
     lowerCAmelCase						     =			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.'''
             )
         }   ,						)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={
             '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
         }   ,						)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[Any]						=			super().to_dict()
          for k, v in d.items():
               if isinstance(_UpperCAmelCase		,      _UpperCAmelCase):
                    __A		:					List[Any]						=			v.to_dict()
          return d