





"""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 					_lowerCAmelCase   :
       """simple docstring"""




       def __init__(    self    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase = True    , __UpperCAmelCase = False      ):



         '''simple docstring'''

         lowerCAmelCase__			:Union[str, Any]											=							scheduler
         lowerCAmelCase__			:Optional[Any]											=							optimizers if isinstance(__UpperCAmelCase    , (list, tuple)      ) else [optimizers]
         lowerCAmelCase__			:Union[str, Any]											=							split_batches
         lowerCAmelCase__			:str											=							step_with_optimizer
         lowerCAmelCase__			:Tuple											=							GradientState()






       def      snake_case						(    self    , *__UpperCAmelCase    , **__UpperCAmelCase      ):



         '''simple docstring'''

         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__			:Optional[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      snake_case						(    self      ):



         '''simple docstring'''

         return self.scheduler.get_last_lr()






       def      snake_case						(    self      ):



         '''simple docstring'''

         return self.scheduler.state_dict()






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         self.scheduler.load_state_dict(__UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         return self.scheduler.get_lr()






       def      snake_case						(    self    , *__UpperCAmelCase    , **__UpperCAmelCase      ):



         '''simple docstring'''

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







"""simple docstring"""





from __future__ import annotations

__A											=		1.6_021e-19  # units = C
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   )					->tuple[str, float]:





  """simple docstring"""


  if (conductivity, electron_conc, mobility).count(0			) != 1:
    raise ValueError('You cannot supply more or less than 2 values'			)
  elif conductivity < 0:
    raise ValueError('Conductivity cannot be negative'			)
  elif electron_conc < 0:
    raise ValueError('Electron concentration cannot be negative'			)
  elif mobility < 0:
    raise ValueError('mobility cannot be negative'			)
  elif conductivity == 0:
    return (
        "conductivity",
        mobility * electron_conc * ELECTRON_CHARGE,
    )
  elif electron_conc == 0:
    return (
        "electron_conc",
        conductivity / (mobility * ELECTRON_CHARGE),
    )
  else:
    return (
        "mobility",
        conductivity / (electron_conc * ELECTRON_CHARGE),
    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
