


import random





def __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					List[str]:



       '''simple docstring'''





       UpperCAmelCase__										=     a[left_index]
       UpperCAmelCase__										=     left_index + 1
       for j in range(left_index + 1 ,						__A			):
              if a[j] < pivot:
                     UpperCAmelCase__						,			UpperCAmelCase__										=     a[i], a[j]
                     i += 1
       UpperCAmelCase__						,			UpperCAmelCase__										=     a[i - 1], a[left_index]
       return i - 1





def __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					str:



       '''simple docstring'''





       if left < right:
              UpperCAmelCase__										=     random.randint(__A ,						right - 1			)
              UpperCAmelCase__						,			UpperCAmelCase__										=     (
                  a[left],
                  a[pivot],
              )  # switches the pivot with the left most bound
              UpperCAmelCase__										=     partition(__A ,						__A ,						__A			)
              quick_sort_random(
                  __A ,						__A ,						__A			)  # recursive quicksort to the left of the pivot point
              quick_sort_random(
                  __A ,						pivot_index + 1 ,						__A			)  # recursive quicksort to the right of the pivot point





def __UpperCAmelCase       (		)						->					Union[str, Any]:



       '''simple docstring'''





       UpperCAmelCase__										=     input("Enter numbers separated by a comma:\n"			).strip()
       UpperCAmelCase__										=     [int(__A			) for item in user_input.split(","			)]

       quick_sort_random(__A ,						0 ,						len(__A			)			)

       print(__A			)


if __name__ == "__main__":
    main()









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

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A      = logging.getLogger(__name__)


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





