




'''simple docstring'''







def        lowerCamelCase__				(      __lowercase     ,   __lowercase				):

     def get_matched_characters(__lowercase     ,   __lowercase				) -> str:
          snake_case			:      List[str]        =			[]
          snake_case			:      Optional[Any]        =			min(len(_stra				)     ,   len(_stra				)				) // 2
          for i, l in enumerate(_stra				):
               snake_case			:      List[Any]        =			int(max(0     ,   i - limit				)				)
               snake_case			:      Optional[int]        =			int(min(i + limit + 1     ,   len(_stra				)				)				)
               if l in _stra[left:right]:
                    matched.append(__lowercase				)
                    snake_case			:      Union[str, Any]        =			F'''{_stra[0:_stra.index(__lowercase				)]} {_stra[_stra.index(__lowercase				) + 1:]}'''

          return "".join(__lowercase				)

     # matching characters
     snake_case			:      Dict        =			get_matched_characters(__lowercase     ,   __lowercase				)
     snake_case			:      Optional[int]        =			get_matched_characters(__lowercase     ,   __lowercase				)
     snake_case			:      Optional[Any]        =			len(__lowercase				)

     # transposition
     snake_case			:      Optional[int]        =			(
         len([(ca, ca) for ca, ca in zip(__lowercase     ,   __lowercase				) if ca != ca]				) // 2
     )

     if not match_count:
          snake_case			:      Optional[int]        =			0.0
     else:
          snake_case			:      List[str]        =			(
              1
              / 3
              * (
                  match_count / len(__lowercase				)
                  + match_count / len(__lowercase				)
                  + (match_count - transpositions) / match_count
              )
          )

     # common prefix up to 4 characters
     snake_case			:      Tuple        =			0
     for ca, ca in zip(stra[:4]     ,   stra[:4]				):
          if ca == ca:
               prefix_len += 1
          else:
               break

     return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(jaro_winkler("""hello""", """world"""))









'''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 :





     '''simple docstring'''



     def __init__(				self							,__a							,__a							,__a = True							,__a = False							)      ->				Dict:
          snake_case			:      List[Any]        =			scheduler
          snake_case			:      List[Any]        =			optimizers if isinstance(__a							,(list, tuple)							) else [optimizers]
          snake_case			:      List[str]        =			split_batches
          snake_case			:      List[str]        =			step_with_optimizer
          snake_case			:      int        =			GradientState()







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







     def 			snake_case_      (				self							)      ->				Dict:
          return self.scheduler.get_last_lr()







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







     def 			snake_case_      (				self							,__a							)      ->				Any:
          self.scheduler.load_state_dict(__a							)







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







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



