



'''simple docstring'''






import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class 							a__(      SCREAMING_SNAKE_CASE_			):
    lowercase__				=					(CMStochasticIterativeScheduler,)
    lowercase__				=					10







    def     lowercase_ (   self				:						Optional[int]		,    **__snake_case				:						Tuple			):
      a		:      str       =  {
          '''num_train_timesteps''': 2_01,
          '''sigma_min''': 0.002,
          '''sigma_max''': 80.0,
      }

      config.update(**UpperCamelCase__			)
      return config







    def     lowercase_ (   self				:						List[Any]			):
      a		:      Tuple       =  10

      a		:      Any       =  self.get_scheduler_config()
      a		:      Tuple       =  self.scheduler_classes[0](**UpperCamelCase__			)

      scheduler.set_timesteps(UpperCamelCase__			)

      a		:      Dict       =  scheduler.timesteps[0]
      a		:      Dict       =  scheduler.timesteps[1]

      a		:      List[str]       =  self.dummy_sample
      a		:      str       =  0.1 * sample

      a		:      Dict       =  scheduler.step(UpperCamelCase__		,    UpperCamelCase__		,    UpperCamelCase__			).prev_sample
      a		:      int       =  scheduler.step(UpperCamelCase__		,    UpperCamelCase__		,    UpperCamelCase__			).prev_sample

      self.assertEqual(output_a.shape		,    sample.shape			)
      self.assertEqual(output_a.shape		,    output_a.shape			)







    def     lowercase_ (   self				:						Union[str, Any]			):
      for timesteps in [10, 50, 1_00, 10_00]:
        self.check_over_configs(num_train_timesteps=UpperCamelCase__			)







    def     lowercase_ (   self				:						Union[str, Any]			):
      for clip_denoised in [True, False]:
        self.check_over_configs(clip_denoised=UpperCamelCase__			)







    def     lowercase_ (   self				:						List[Any]			):
      a		:      List[Any]       =  self.scheduler_classes[0]
      a		:      Dict       =  self.get_scheduler_config()
      a		:      List[Any]       =  scheduler_class(**UpperCamelCase__			)

      a		:      Optional[Any]       =  1
      scheduler.set_timesteps(UpperCamelCase__			)
      a		:      Optional[int]       =  scheduler.timesteps

      a		:      Tuple       =  torch.manual_seed(0			)

      a		:      Optional[int]       =  self.dummy_model()
      a		:      Dict       =  self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(UpperCamelCase__			):
        # 1. scale model input
        a		:      Tuple       =  scheduler.scale_model_input(UpperCamelCase__		,    UpperCamelCase__			)

        # 2. predict noise residual
        a		:      Dict       =  model(UpperCamelCase__		,    UpperCamelCase__			)

        # 3. predict previous sample x_t-1
        a		:      Optional[int]       =  scheduler.step(UpperCamelCase__		,    UpperCamelCase__		,    UpperCamelCase__		,    generator=UpperCamelCase__			).prev_sample

        a		:      Any       =  pred_prev_sample

      a		:      Tuple       =  torch.sum(torch.abs(UpperCamelCase__			)			)
      a		:      str       =  torch.mean(torch.abs(UpperCamelCase__			)			)

      assert abs(result_sum.item() - 1_92.76_14			) < 1e-2
      assert abs(result_mean.item() - 0.2510			) < 1e-3







    def     lowercase_ (   self				:						Optional[int]			):
      a		:      Optional[int]       =  self.scheduler_classes[0]
      a		:      Any       =  self.get_scheduler_config()
      a		:      str       =  scheduler_class(**UpperCamelCase__			)

      a		:      Any       =  [1_06, 0]
      scheduler.set_timesteps(timesteps=UpperCamelCase__			)
      a		:      int       =  scheduler.timesteps

      a		:      Optional[int]       =  torch.manual_seed(0			)

      a		:      int       =  self.dummy_model()
      a		:      str       =  self.dummy_sample_deter * scheduler.init_noise_sigma

      for t in timesteps:
        # 1. scale model input
        a		:      Any       =  scheduler.scale_model_input(UpperCamelCase__		,    UpperCamelCase__			)

        # 2. predict noise residual
        a		:      Optional[Any]       =  model(UpperCamelCase__		,    UpperCamelCase__			)

        # 3. predict previous sample x_t-1
        a		:      Union[str, Any]       =  scheduler.step(UpperCamelCase__		,    UpperCamelCase__		,    UpperCamelCase__		,    generator=UpperCamelCase__			).prev_sample

        a		:      Tuple       =  pred_prev_sample

      a		:      List[Any]       =  torch.sum(torch.abs(UpperCamelCase__			)			)
      a		:      str       =  torch.mean(torch.abs(UpperCamelCase__			)			)

      assert abs(result_sum.item() - 3_47.63_57			) < 1e-2
      assert abs(result_mean.item() - 0.4527			) < 1e-3







    def     lowercase_ (   self				:						Tuple			):
      a		:      Optional[int]       =  self.scheduler_classes[0]
      a		:      str       =  self.get_scheduler_config()
      a		:      int       =  scheduler_class(**UpperCamelCase__			)

      a		:      Optional[Any]       =  [39, 30, 12, 15, 0]

      with self.assertRaises(UpperCamelCase__		,    msg='`timesteps` must be in descending order.'			):
        scheduler.set_timesteps(timesteps=UpperCamelCase__			)







    def     lowercase_ (   self				:						Dict			):
      a		:      List[Any]       =  self.scheduler_classes[0]
      a		:      Tuple       =  self.get_scheduler_config()
      a		:      Optional[int]       =  scheduler_class(**UpperCamelCase__			)

      a		:      Optional[int]       =  [39, 30, 12, 1, 0]
      a		:      Tuple       =  len(UpperCamelCase__			)

      with self.assertRaises(UpperCamelCase__		,    msg='Can only pass one of `num_inference_steps` or `timesteps`.'			):
        scheduler.set_timesteps(num_inference_steps=UpperCamelCase__		,    timesteps=UpperCamelCase__			)







    def     lowercase_ (   self				:						Tuple			):
      a		:      List[str]       =  self.scheduler_classes[0]
      a		:      int       =  self.get_scheduler_config()
      a		:      Union[str, Any]       =  scheduler_class(**UpperCamelCase__			)

      a		:      str       =  [scheduler.config.num_train_timesteps]

      with self.assertRaises(
          UpperCamelCase__		,    msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'		,    ):
        scheduler.set_timesteps(timesteps=UpperCamelCase__			)










'''simple docstring'''






from math import factorial, pi
def    lowerCamelCase__ (		_A				,    _A = 30       ):

  if not isinstance(_A				,    (int, float)       ):
    raise ValueError('maclaurin_sin() requires either an int or float for theta'       )

  if not isinstance(_A				,    _A       ) or accuracy <= 0:
    raise ValueError('maclaurin_sin() requires a positive int for accuracy'       )

  a		:      List[Any]       =  float(_A       )
  a		:      Optional[int]       =  theta // (2 * pi)
  theta -= 2 * div * pi
  return sum(
      (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1       ) for r in range(_A       )       )
def    lowerCamelCase__ (		_A				,    _A = 30       ):

  if not isinstance(_A				,    (int, float)       ):
    raise ValueError('maclaurin_cos() requires either an int or float for theta'       )

  if not isinstance(_A				,    _A       ) or accuracy <= 0:
    raise ValueError('maclaurin_cos() requires a positive int for accuracy'       )

  a		:      List[str]       =  float(_A       )
  a		:      List[str]       =  theta // (2 * pi)
  theta -= 2 * div * pi
  return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r       ) for r in range(_A       )       )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     print(maclaurin_sin(1_0))
     print(maclaurin_sin(-1_0))
     print(maclaurin_sin(1_0, 1_5))
     print(maclaurin_sin(-1_0, 1_5))

     print(maclaurin_cos(5))
     print(maclaurin_cos(-5))
     print(maclaurin_cos(1_0, 1_5))
     print(maclaurin_cos(-1_0, 1_5))