
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')





class 			__lowerCamelCase	:




    """simple docstring"""

    def __init__(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					bool = True       ,     SCREAMING_SNAKE_CASE__					:					bool = False       )				->	Optional[Any]:
         lowerCAmelCase__          =			scheduler
         lowerCAmelCase__          =			optimizers if isinstance(UpperCamelCase_       ,     (list, tuple)       ) else [optimizers]
         lowerCAmelCase__          =			split_batches
         lowerCAmelCase__          =			step_with_optimizer
         lowerCAmelCase__          =			GradientState()


    def        a				(	self					:					Union[str, Any]       ,     *SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     **SCREAMING_SNAKE_CASE__					:					Optional[Any]       )				->	Dict:







         if not self.step_with_optimizer:
              # No link between scheduler and optimizer -> just step
              self.scheduler.step(*UpperCamelCase_       ,     **UpperCamelCase_       )
              return

         # Otherwise, first make sure the optimizer was stepped.
         if not self.gradient_state.sync_gradients:
              if self.gradient_state.adjust_scheduler:
                   self.scheduler._step_count += 1
              return

         for opt in self.optimizers:
              if opt.step_was_skipped:
                   return
         if self.split_batches:
              # Split batches -> the training dataloader batch size is not changed so one step per training step
              self.scheduler.step(*UpperCamelCase_       ,     **UpperCamelCase_       )
         else:
              # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
              # num_processes steps per training step
              lowerCAmelCase__          =			AcceleratorState().num_processes
              for _ in range(UpperCamelCase_       ):
                   # Special case when using OneCycle and `drop_last` was not used
                   if hasattr(self.scheduler       ,     "total_steps"       ):
                        if self.scheduler._step_count <= self.scheduler.total_steps:
                             self.scheduler.step(*UpperCamelCase_       ,     **UpperCamelCase_       )
                   else:
                        self.scheduler.step(*UpperCamelCase_       ,     **UpperCamelCase_       )


    def        a				(	self					:					Any       )				->	List[str]:
         return self.scheduler.get_last_lr()


    def        a				(	self					:					Tuple       )				->	List[str]:
         return self.scheduler.state_dict()


    def        a				(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					List[str]       )				->	List[Any]:
         self.scheduler.load_state_dict(UpperCamelCase_       )


    def        a				(	self					:					Any       )				->	Optional[Any]:
         return self.scheduler.get_lr()


    def        a				(	self					:					Any       ,     *SCREAMING_SNAKE_CASE__					:					List[str]       ,     **SCREAMING_SNAKE_CASE__					:					Any       )				->	Any:
         return self.scheduler.print_lr(*UpperCamelCase_       ,     **UpperCamelCase_       )






import random



def 							_A       (						lowerCAmelCase_					: list       ,    lowerCAmelCase_					: List[str]      ):
     """simple docstring"""

     lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			[], [], []
     for element in data:
          if element < pivot:
               less.append(lowerCAmelCase_      )
          elif element > pivot:
               greater.append(lowerCAmelCase_      )
          else:
               equal.append(lowerCAmelCase_      )
     return less, equal, greater






def 							_A       (						lowerCAmelCase_					: list       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     if index >= len(lowerCAmelCase_      ) or index < 0:
          return None

     lowerCAmelCase__          =			items[random.randint(0       ,    len(lowerCAmelCase_      ) - 1      )]
     lowerCAmelCase__          =			0
     lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			_partition(lowerCAmelCase_       ,    lowerCAmelCase_      )
     lowerCAmelCase__          =			len(lowerCAmelCase_      )
     lowerCAmelCase__          =			len(lowerCAmelCase_      )

     # index is the pivot
     if m <= index < m + count:
          return pivot
     # must be in smaller
     elif m > index:
          return quick_select(lowerCAmelCase_       ,    lowerCAmelCase_      )
     # must be in larger
     else:
          return quick_select(lowerCAmelCase_       ,    index - (m + count)      )
