



# 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					:					int			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					Optional[int]			,						__lowerCAmelCase					:					bool = True			,						__lowerCAmelCase					:					bool = False      )   ->	Union[str, Any]:
    _A      =       scheduler
    _A      =       optimizers if isinstance(__lowerCAmelCase			,						(list, tuple)      ) else [optimizers]
    _A      =       split_batches
    _A      =       step_with_optimizer
    _A      =       GradientState()

 def     snake_case_      (    self					:					List[str]			,						*__lowerCAmelCase					:					List[str]			,						**__lowerCAmelCase					:					List[Any]      )   ->	Optional[int]:
    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
       _A      =       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     snake_case_      (    self					:					int      )   ->	List[Any]:
    return self.scheduler.get_last_lr()

 def     snake_case_      (    self					:					str      )   ->	Optional[int]:
    return self.scheduler.state_dict()

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					Optional[int]      )   ->	Optional[Any]:
    self.scheduler.load_state_dict(__lowerCAmelCase      )

 def     snake_case_      (    self					:					str      )   ->	int:
    return self.scheduler.get_lr()

 def     snake_case_      (    self					:					Optional[Any]			,						*__lowerCAmelCase					:					List[Any]			,						**__lowerCAmelCase					:					List[Any]      )   ->	Tuple:
    return self.scheduler.print_lr(*__lowerCAmelCase			,						**__lowerCAmelCase      )









def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     while a != 0:
          UpperCAmelCase_,       UpperCAmelCase_     :				Optional[int]  =     b % a, a
     return b







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


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