


# 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      UpperCamelCase					:


   '''simple docstring'''







   def __init__(     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_ = True   ,							UpperCamelCase_ = False		):
        lowercase_       :int     =       scheduler
        lowercase_       :Optional[Any]     =       optimizers if isinstance(UpperCamelCase_   ,							(list, tuple)		) else [optimizers]
        lowercase_       :int     =       split_batches
        lowercase_       :Tuple     =       step_with_optimizer
        lowercase_       :Dict     =       GradientState()
   def 		UpperCamelCase  (     self   ,							*UpperCamelCase_   ,							**UpperCamelCase_		):
        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
             lowercase_       :int     =       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 		UpperCamelCase  (     self		):
        return self.scheduler.get_last_lr()
   def 		UpperCamelCase  (     self		):
        return self.scheduler.state_dict()
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        self.scheduler.load_state_dict(UpperCamelCase_		)
   def 		UpperCamelCase  (     self		):
        return self.scheduler.get_lr()




   def 		UpperCamelCase  (     self   ,							*UpperCamelCase_   ,							**UpperCamelCase_		):
        return self.scheduler.print_lr(*UpperCamelCase_   ,							**UpperCamelCase_		)





def     UpperCamelCase    (      _a       ,		_a		)					->							int:
     '''simple docstring'''





     while a != 0:
          lowercase_    ,      lowercase_       :Union[str, Any]     =       b % a, a
     return b




def     UpperCamelCase    (      _a       ,		_a		)					->							int:
     '''simple docstring'''





     if gcd(_a       ,		_a		) != 1:
          lowercase_       :Union[str, Any]     =       f"mod inverse of {a!r} and {m!r} does not exist"
          raise ValueError(_a		)
     lowercase_    ,      lowercase_    ,      lowercase_       :Any     =       1, 0, a
     lowercase_    ,      lowercase_    ,      lowercase_       :List[str]     =       0, 1, m
     while va != 0:
          lowercase_       :Tuple     =       ua // va
          lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_       :Optional[int]     =       (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
     return ua % m

