


# 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 ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase = True ,						__lowerCamelCase = False)			->				Dict:
            _A      :       Dict				    = scheduler
            _A      :       Union[str, Any]				    = optimizers if isinstance(__lowerCamelCase ,						(list, tuple)) else [optimizers]
            _A      :       int				    = split_batches
            _A      :       int				    = step_with_optimizer
            _A      :       Any				    = GradientState()


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				str:
            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
                   _A      :       List[Any]				    = 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  _lowerCamelCase (						self)			->				Union[str, Any]:
            return self.scheduler.get_last_lr()


     def  _lowerCamelCase (						self)			->				Optional[Any]:
            return self.scheduler.state_dict()


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Union[str, Any]:
            self.scheduler.load_state_dict(__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Tuple:
            return self.scheduler.get_lr()


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				str:
            return self.scheduler.print_lr(*__lowerCamelCase ,						**__lowerCamelCase)




"""simple docstring"""







from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray


def      lowercase			(				A_  ,   A_  ,   A_ = False       )->      list[float]:
  '''simple docstring'''







  if radian_mode:
    return [magnitude * cos(A_       ), magnitude * sin(A_       )]
  return [magnitude * cos(radians(A_       )       ), magnitude * sin(radians(A_       )       )]


def      lowercase			(				A_  ,   A_  ,   A_ = 10**-1       )->      bool:
  '''simple docstring'''







  a						:		NDArray[floataa]      							=			cross(A_  ,   A_       )
  a						:		float      							=			sum(A_       )
  return abs(A_       ) < eps


if __name__ == "__main__":
 # Test to check if it works
 __lowercase    					=      array(
     [
         polar_force(7_18.4, 180 - 30),
         polar_force(8_79.54, 45),
         polar_force(100, -90),
     ]
 )

 __lowercase    					=      array([[0, 0], [0, 0], [0, 0]])

 assert in_static_equilibrium(forces, location)

 # Problem 1 in image_data/2D_problems.jpg
 __lowercase    					=      array(
     [
         polar_force(30 * 9.81, 15),
         polar_force(215, 180 - 45),
         polar_force(264, 90 - 30),
     ]
 )

 __lowercase    					=      array([[0, 0], [0, 0], [0, 0]])

 assert in_static_equilibrium(forces, location)

 # Problem in image_data/2D_problems_1.jpg
 __lowercase    					=      array([[0, -2000], [0, -1200], [0, 15600], [0, -12400]])

 __lowercase    					=      array([[0, 0], [6, 0], [10, 0], [12, 0]])

 assert in_static_equilibrium(forces, location)

 import doctest

 doctest.testmod()






