


'''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      ,      __lowercase      ,      __lowercase      ,      __lowercase = True      ,      __lowercase = False						):

  """simple docstring"""







  __A  :		Optional[int]       =			scheduler
  __A  :		Dict       =			optimizers if isinstance(__lowercase      ,      (list, tuple)						) else [optimizers]
  __A  :		int       =			split_batches
  __A  :		int       =			step_with_optimizer
  __A  :		str       =			GradientState()


 def  snake_case__   (      self      ,      *__lowercase      ,      **__lowercase						):

  """simple docstring"""







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


 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      ,      __lowercase						):

  """simple docstring"""







  self.scheduler.load_state_dict(__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  return self.scheduler.get_lr()







 def  snake_case__   (      self      ,      *__lowercase      ,      **__lowercase						):

  """simple docstring"""







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










'''simple docstring'''
from collections.abc import Sequence


def 			_lowercase       (				UpperCamelCase__       :					Sequence[float],				UpperCamelCase__       :					float			):
 return sum(c * (x**i) for i, c in enumerate(UpperCamelCase__			)			)


def 			_lowercase       (				UpperCamelCase__       :					Sequence[float],				UpperCamelCase__       :					float			):
 __A  :		Optional[Any]       =			0.0
 for coeff in reversed(UpperCamelCase__			):
  __A  :		List[str]       =			result * x + coeff
 return result


if __name__ == "__main__":
 UpperCAmelCase_ :     Any								=      (0.0, 0.0, 5.0, 9.3, 7.0)
 UpperCAmelCase_ :     Any								=      10.0
 print(evaluate_poly(poly, x))
 print(horner(poly, x))






