code: 
  
  
  '''simple docstring'''
  
  def _SCREAMING_SNAKE_CASE   (A ) -> list:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				len(snake_case__ )
         for cycle_start in range(0						,				array_len - 1 ):
                lowercase__       =				array[cycle_start]
  
                lowercase__       =				cycle_start
                for i in range(cycle_start + 1						,				snake_case__ ):
                       if array[i] < item:
                              pos += 1
  
                if pos == cycle_start:
                       continue
  
                while item == array[pos]:
                       pos += 1
  
                lowercase__       =				item, array[pos]
                while pos != cycle_start:
                       lowercase__       =				cycle_start
                       for i in range(cycle_start + 1						,				snake_case__ ):
                              if array[i] < item:
                                     pos += 1
  
                       while item == array[pos]:
                              pos += 1
  
                       lowercase__       =				item, array[pos]
  
         return array
  
  
  if __name__ == "__main__":
   assert cycle_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
   assert cycle_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  import torch
  
  from accelerate import Accelerator, DistributedType
  class 						__lowerCAmelCase		:
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def __enter__(self					:					List[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if self.enabled:
                      lowercase__       =				self.model.no_sync()
                      self.model_sync_obj.__enter__()
  
               return self
  
  
  
        def __exit__(self					:					List[str]     ,		UpperCamelCase					:					List[Any]     ,		UpperCamelCase					:					Optional[int]     ,		UpperCamelCase					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if self.enabled:
                      # Average all models on exit
                      self._sync_and_avg_model_params()
                      self.model_sync_obj.__exit__(UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase				)
  
  
  
        def __init__(self					:					Dict     ,		UpperCamelCase					:					Accelerator     ,		UpperCamelCase					:					torch.nn.Module     ,		UpperCamelCase					:					int     ,		UpperCamelCase					:					bool = True				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if accelerator.distributed_type not in [
                   DistributedType.NO,
                   DistributedType.MULTI_CPU,
                   DistributedType.MULTI_GPU,
               ]:
                      raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''				)
               lowercase__       =				enabled and accelerator.distributed_type != DistributedType.NO
               lowercase__       =				0
               if self.enabled:
                      lowercase__       =				accelerator
                      lowercase__       =				model
                      lowercase__       =				local_sgd_steps
  
  
  
        def    UpperCamelCase__	(self					:					List[str]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               self.num_steps += 1
               if not self.enabled:
                      return
  
               if self.num_steps % self.local_sgd_steps == 0:
                      self._sync_and_avg_model_params()
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					Any				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               self.accelerator.wait_for_everyone()
               with self.accelerator.autocast():
                      for param in self.model.parameters():
                             lowercase__       =				self.accelerator.reduce(param.data     ,		reduction='''mean'''				)
  
  
  
  
  