


'''simple docstring'''

def __snake_case					(      UpperCAmelCase_						:						int		):




 if len(_UpperCamelCase		) < 2:
  return collection

 def circle_sort_util(UpperCAmelCase_						:						str					,				UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						Optional[Any]		) -> bool:
  lowerCamelCase_      =  False

  if low == high:
   return swapped

  lowerCamelCase_      =  low
  lowerCamelCase_      =  high

  while left < right:
   if collection[left] > collection[right]:
    lowerCamelCase_ ,lowerCamelCase_      =  (
        collection[right],
        collection[left],
    )
    lowerCamelCase_      =  True

   left += 1
   right -= 1

  if left == right and collection[left] > collection[right + 1]:
   lowerCamelCase_ ,lowerCamelCase_      =  (
       collection[right + 1],
       collection[left],
   )

   lowerCamelCase_      =  True

  lowerCamelCase_      =  low + int((high - low) / 2		)
  lowerCamelCase_      =  circle_sort_util(_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase		)
  lowerCamelCase_      =  circle_sort_util(_UpperCamelCase					,				mid + 1					,				_UpperCamelCase		)

  return swapped or left_swap or right_swap

 lowerCamelCase_      =  True

 while is_not_sorted is True:
  lowerCamelCase_      =  circle_sort_util(_UpperCamelCase					,				0					,				len(_UpperCamelCase		) - 1		)

 return collection


if __name__ == "__main__":
     a_  :				str						= input("""Enter numbers separated by a comma:\n""").strip()
     a_  :				str						= [int(item) for item in user_input.split(""",""")]
     print(circle_sort(unsorted))







"""simple docstring"""


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

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A     :   Dict			    =			logging.getLogger(__name__)
@dataclass
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







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




