


import numpy as np






def 						a__			(				A__				):
			return (2 / (1 + np.exp(-2 * vector				))) - 1


if __name__ == "__main__":
				import doctest

				doctest.testmod()







"""simple docstring"""






# 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 				a__					:



     def __init__(							self :List[str]      ,  _lowerCamelCase :Any      ,  _lowerCamelCase :Optional[Any]      ,  _lowerCamelCase :bool = True      ,  _lowerCamelCase :bool = False   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	=scheduler
      UpperCamelCase_     :					Any   	=optimizers if isinstance(_lowerCamelCase      ,  (list, tuple)   ) else [optimizers]
      UpperCamelCase_     :					Dict   	=split_batches
      UpperCamelCase_     :					str   	=step_with_optimizer
      UpperCamelCase_     :					Dict   	=GradientState()



     def  lowerCamelCase_						(							self :Optional[int]      ,  *_lowerCamelCase :str      ,  **_lowerCamelCase :Union[str, Any]   ):







      '''simple docstring'''



      if not self.step_with_optimizer:
       # No link between scheduler and optimizer -> just step
       self.scheduler.step(*_lowerCamelCase      ,  **_lowerCamelCase   )
       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(*_lowerCamelCase      ,  **_lowerCamelCase   )
      else:
       # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
       # num_processes steps per training step
       UpperCamelCase_     :					Optional[Any]   	=AcceleratorState().num_processes
       for _ in range(_lowerCamelCase   ):
        # 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(*_lowerCamelCase      ,  **_lowerCamelCase   )
        else:
         self.scheduler.step(*_lowerCamelCase      ,  **_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Dict   ):







      '''simple docstring'''



      return self.scheduler.get_last_lr()



     def  lowerCamelCase_						(							self :Tuple   ):







      '''simple docstring'''



      return self.scheduler.state_dict()



     def  lowerCamelCase_						(							self :Any      ,  _lowerCamelCase :Any   ):







      '''simple docstring'''



      self.scheduler.load_state_dict(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Any   ):







      '''simple docstring'''



      return self.scheduler.get_lr()




     def  lowerCamelCase_						(							self :Optional[int]      ,  *_lowerCamelCase :Union[str, Any]      ,  **_lowerCamelCase :Optional[int]   ):







      '''simple docstring'''



      return self.scheduler.print_lr(*_lowerCamelCase      ,  **_lowerCamelCase   )
