





from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
SCREAMING_SNAKE_CASE_		     = 1.054_571_817e-34  # unit of ℏ : J * s

SCREAMING_SNAKE_CASE_		     = 3e8  # unit of c : m * s^-1



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     dict[str, float]:


      '''simple docstring'''


      if (force, area, distance).count(0			) != 1:
            raise ValueError("""One and only one argument must be 0"""			)
      if force < 0:
            raise ValueError("""Magnitude of force can not be negative"""			)
      if distance < 0:
            raise ValueError("""Distance can not be negative"""			)
      if area < 0:
            raise ValueError("""Area can not be negative"""			)
      if force == 0:
            SCREAMING_SNAKE_CASE	=		(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
                2_40 * (distance) ** 4
            )
            return {"force": force}
      elif area == 0:
            SCREAMING_SNAKE_CASE	=		(2_40 * force * (distance) ** 4) / (
                REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
            )
            return {"area": area}
      elif distance == 0:
            SCREAMING_SNAKE_CASE	=		(
                (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (2_40 * force)
            ) ** (1 / 4)
            return {"distance": distance}
      raise ValueError("""One and only one argument must be 0"""			)


# Run doctest
if __name__ == "__main__":
      import doctest

      doctest.testmod()









# 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		:					Optional[Any]		,lowerCamelCase__		:					Optional[int]		,lowerCamelCase__		:					Tuple		,lowerCamelCase__		:					bool = True		,lowerCamelCase__		:					bool = False )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		scheduler
           SCREAMING_SNAKE_CASE	=		optimizers if isinstance(lowerCamelCase__		,(list, tuple) ) else [optimizers]
           SCREAMING_SNAKE_CASE	=		split_batches
           SCREAMING_SNAKE_CASE	=		step_with_optimizer
           SCREAMING_SNAKE_CASE	=		GradientState()






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




           '''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
                 SCREAMING_SNAKE_CASE	=		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        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	Optional[Any]:




           '''simple docstring'''


           return self.scheduler.get_last_lr()






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any] )  ->	Optional[Any]:




           '''simple docstring'''


           return self.scheduler.state_dict()






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int]		,lowerCamelCase__		:					str )  ->	int:




           '''simple docstring'''


           self.scheduler.load_state_dict(lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any] )  ->	Optional[Any]:




           '''simple docstring'''


           return self.scheduler.get_lr()






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict		,*lowerCamelCase__		:					int		,**lowerCamelCase__		:					Tuple )  ->	Optional[int]:




           '''simple docstring'''


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


