

'''simple docstring'''






import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')






class 			lowerCAmelCase__     :




     """simple docstring"""





     def __init__(		self						:	Dict      ,						A__						:	Optional[int]      ,						A__						:	Any      ,						A__						:	int = True      ,						A__						:	str = False	)       ->			List[Any]:
          '''simple docstring'''




          a__   :      Any     =						scheduler
          a__   :      List[str]     =						optimizers if isinstance(SCREAMING_SNAKE_CASE__      ,						(list, tuple)	) else [optimizers]
          a__   :      int     =						split_batches
          a__   :      Union[str, Any]     =						step_with_optimizer
          a__   :      List[str]     =						GradientState()







     def 			__lowerCAmelCase	(		self						:	int      ,						*A__						:	Union[str, Any]      ,						**A__						:	Any	)       ->			int:
          '''simple docstring'''




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







     def 			__lowerCAmelCase	(		self						:	str	)       ->			str:
          '''simple docstring'''




          return self.scheduler.get_last_lr()







     def 			__lowerCAmelCase	(		self						:	Union[str, Any]	)       ->			Union[str, Any]:
          '''simple docstring'''




          return self.scheduler.state_dict()







     def 			__lowerCAmelCase	(		self						:	int      ,						A__						:	Tuple	)       ->			int:
          '''simple docstring'''




          self.scheduler.load_state_dict(SCREAMING_SNAKE_CASE__	)







     def 			__lowerCAmelCase	(		self						:	Optional[Any]	)       ->			Dict:
          '''simple docstring'''




          return self.scheduler.get_lr()







     def 			__lowerCAmelCase	(		self						:	Dict      ,						*A__						:	Tuple      ,						**A__						:	Tuple	)       ->			Dict:
          '''simple docstring'''




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




"""simple docstring"""






from collections.abc import Callable

import numpy as np





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)					->  np.ndarray:
			_UpperCamelCase					:str					     =	int(np.ceil((x_end - xa) / step_size		)		)
			_UpperCamelCase					:Union[str, Any]					     =	np.zeros((n + 1,)		)
			_UpperCamelCase					:List[str]					     =	ya
			_UpperCamelCase					:Any					     =	xa

			for k in range(snake_case__		):
						_UpperCamelCase					:Union[str, Any]					     =	y[k] + step_size * ode_func(snake_case__       ,					y[k]		)
						x += step_size

			return y


if __name__ == "__main__":
					import doctest

					doctest.testmod()

