from __future__ import annotations


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):

      '''simple docstring'''





      if len(__UpperCamelCase				) == 0:
            raise ValueError("find_max() arg is an empty sequence"				)
      if (
          left >= len(__UpperCamelCase				)
          or left < -len(__UpperCamelCase				)
          or right >= len(__UpperCamelCase				)
          or right < -len(__UpperCamelCase				)
      ):
            raise IndexError("list index out of range"				)
      if left == right:
            return nums[left]
      SCREAMING_SNAKE_CASE_          =  (left + right) >> 1  # the middle
      SCREAMING_SNAKE_CASE_          =  find_max(__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				)  # find max in range[left, mid]
      SCREAMING_SNAKE_CASE_          =  find_max(__UpperCamelCase     ,      mid + 1     ,      __UpperCamelCase				)  # find max in range[mid + 1, right]

      return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)



import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A     :  str					     =       logging.getLogger(__name__)


@dataclass
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						field(
      default=0.0  ,      metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}					)
  lowerCamelCase__     =						field(default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Whether to SortishSamler or not.'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}					)
  lowerCamelCase__     =						field(default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''whether to use adafactor'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}					)
  lowerCamelCase__     =						field(default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Dropout probability. Goes into model.config.'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}					)
  lowerCamelCase__     =						field(
      default='''linear'''  ,      metadata={'''help''': f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()					)}"}  ,      )


