





import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       Optional[int]      					=(CMStochasticIterativeScheduler,)
  a_							:       Any      					=10







  def UpperCamelCase_ (      self      :					Union[str, Any]      ,				**UpperCamelCase      :					Any							):


    '''simple docstring'''





    _snake_case   :				Optional[Any]														=	{
        'num_train_timesteps': 2_01,
        'sigma_min': 0.0_02,
        'sigma_max': 80.0,
    }

    config.update(**UpperCamelCase							)
    return config







  def UpperCamelCase_ (      self      :					str							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	10

    _snake_case   :				Tuple														=	self.get_scheduler_config()
    _snake_case   :				Optional[Any]														=	self.scheduler_classes[0](**UpperCamelCase							)

    scheduler.set_timesteps(UpperCamelCase							)

    _snake_case   :				List[str]														=	scheduler.timesteps[0]
    _snake_case   :				Union[str, Any]														=	scheduler.timesteps[1]

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

    _snake_case   :				Tuple														=	scheduler.step(UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase							).prev_sample
    _snake_case   :				Union[str, Any]														=	scheduler.step(UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase							).prev_sample

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







  def UpperCamelCase_ (      self      :					List[str]							):


    '''simple docstring'''





    for timesteps in [10, 50, 1_00, 10_00]:
      self.check_over_configs(num_train_timesteps=UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Union[str, Any]							):


    '''simple docstring'''





    for clip_denoised in [True, False]:
      self.check_over_configs(clip_denoised=UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				Optional[int]														=	self.scheduler_classes[0]
    _snake_case   :				Dict														=	self.get_scheduler_config()
    _snake_case   :				List[Any]														=	scheduler_class(**UpperCamelCase							)

    _snake_case   :				Tuple														=	1
    scheduler.set_timesteps(UpperCamelCase							)
    _snake_case   :				Any														=	scheduler.timesteps

    _snake_case   :				Dict														=	torch.manual_seed(0							)

    _snake_case   :				Optional[int]														=	self.dummy_model()
    _snake_case   :				Any														=	self.dummy_sample_deter * scheduler.init_noise_sigma

    for i, t in enumerate(UpperCamelCase							):
      # 1. scale model input
      _snake_case   :				Optional[int]														=	scheduler.scale_model_input(UpperCamelCase      ,				UpperCamelCase							)

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

      # 3. predict previous sample x_t-1
      _snake_case   :				Any														=	scheduler.step(UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase      ,				generator=UpperCamelCase							).prev_sample

      _snake_case   :				Tuple														=	pred_prev_sample

    _snake_case   :				List[str]														=	torch.sum(torch.abs(UpperCamelCase							)							)
    _snake_case   :				Optional[Any]														=	torch.mean(torch.abs(UpperCamelCase							)							)

    assert abs(result_sum.item() - 1_92.76_14							) < 1e-2
    assert abs(result_mean.item() - 0.25_10							) < 1e-3







  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    _snake_case   :				str														=	self.scheduler_classes[0]
    _snake_case   :				str														=	self.get_scheduler_config()
    _snake_case   :				Tuple														=	scheduler_class(**UpperCamelCase							)

    _snake_case   :				Optional[int]														=	[1_06, 0]
    scheduler.set_timesteps(timesteps=UpperCamelCase							)
    _snake_case   :				Optional[Any]														=	scheduler.timesteps

    _snake_case   :				Any														=	torch.manual_seed(0							)

    _snake_case   :				Union[str, Any]														=	self.dummy_model()
    _snake_case   :				Dict														=	self.dummy_sample_deter * scheduler.init_noise_sigma

    for t in timesteps:
      # 1. scale model input
      _snake_case   :				int														=	scheduler.scale_model_input(UpperCamelCase      ,				UpperCamelCase							)

      # 2. predict noise residual
      _snake_case   :				Union[str, Any]														=	model(UpperCamelCase      ,				UpperCamelCase							)

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

      _snake_case   :				Union[str, Any]														=	pred_prev_sample

    _snake_case   :				Union[str, Any]														=	torch.sum(torch.abs(UpperCamelCase							)							)
    _snake_case   :				Tuple														=	torch.mean(torch.abs(UpperCamelCase							)							)

    assert abs(result_sum.item() - 3_47.63_57							) < 1e-2
    assert abs(result_mean.item() - 0.45_27							) < 1e-3







  def UpperCamelCase_ (      self      :					List[Any]							):


    '''simple docstring'''





    _snake_case   :				Any														=	self.scheduler_classes[0]
    _snake_case   :				Optional[Any]														=	self.get_scheduler_config()
    _snake_case   :				Optional[int]														=	scheduler_class(**UpperCamelCase							)

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

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







  def UpperCamelCase_ (      self      :					Tuple							):


    '''simple docstring'''





    _snake_case   :				Optional[int]														=	self.scheduler_classes[0]
    _snake_case   :				List[Any]														=	self.get_scheduler_config()
    _snake_case   :				int														=	scheduler_class(**UpperCamelCase							)

    _snake_case   :				Dict														=	[39, 30, 12, 1, 0]
    _snake_case   :				Union[str, Any]														=	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 UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    _snake_case   :				Dict														=	self.scheduler_classes[0]
    _snake_case   :				str														=	self.get_scheduler_config()
    _snake_case   :				int														=	scheduler_class(**UpperCamelCase							)

    _snake_case   :				Dict														=	[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							)











from random import randint, random


def     lowerCamelCase_	(      lowerCAmelCase:	int    ,							lowerCAmelCase:	int    ,							lowerCAmelCase:	int    ,							lowerCAmelCase:	bool = False    ,							lowerCAmelCase:	bool = False    ,							lowerCAmelCase:	int = 5    ,							)->		list:
  _snake_case   :				Dict														=	[[-1] * number_of_cells]  # Create a highway without any car
  _snake_case   :				List[str]														=	0
  _snake_case   :				List[str]														=	max(lowerCAmelCase    ,							0				)
  while i < number_of_cells:
    _snake_case   :				Optional[Any]														=	(
        randint(0    ,							lowerCAmelCase				) if random_speed else initial_speed
    )  # Place the cars
    i += (
        randint(1    ,							max_speed * 2				) if random_frequency else frequency
    )  # Arbitrary number, may need tuning
  return highway


def     lowerCamelCase_	(      lowerCAmelCase:	list    ,							lowerCAmelCase:	int				)->		int:
  _snake_case   :				Dict														=	0
  _snake_case   :				Optional[Any]														=	highway_now[car_index + 1 :]
  for cell in range(len(lowerCAmelCase				)				):  # May need a better name for this
    if cells[cell] != -1:  # If the cell is not empty then
      return distance  # we have the distance we wanted
    distance += 1
  # Here if the car is near the end of the highway
  return distance + get_distance(lowerCAmelCase    ,							-1				)


def     lowerCamelCase_	(      lowerCAmelCase:	list    ,							lowerCAmelCase:	float    ,							lowerCAmelCase:	int				)->		list:
  _snake_case   :				List[Any]														=	len(lowerCAmelCase				)
  # Beforce calculations, the highway is empty
  _snake_case   :				List[Any]														=	[-1] * number_of_cells

  for car_index in range(lowerCAmelCase				):
    if highway_now[car_index] != -1:
      # Add 1 to the current speed of the car and cap the speed
      _snake_case   :				int														=	min(highway_now[car_index] + 1    ,							lowerCAmelCase				)
      # Number of empty cell before the next car
      _snake_case   :				Tuple														=	get_distance(lowerCAmelCase    ,							lowerCAmelCase				) - 1
      # We can't have the car causing an accident
      _snake_case   :				Union[str, Any]														=	min(next_highway[car_index]    ,							lowerCAmelCase				)
      if random() < probability:
        # Randomly, a driver will slow down
        _snake_case   :				int														=	max(next_highway[car_index] - 1    ,							0				)
  return next_highway


def     lowerCamelCase_	(      lowerCAmelCase:	list    ,							lowerCAmelCase:	int    ,							lowerCAmelCase:	float    ,							lowerCAmelCase:	int				)->		list:
  _snake_case   :				Dict														=	len(highway[0]				)

  for i in range(lowerCAmelCase				):
    _snake_case   :				Any														=	update(highway[i]    ,							lowerCAmelCase    ,							lowerCAmelCase				)
    _snake_case   :				Tuple														=	[-1] * number_of_cells

    for car_index in range(lowerCAmelCase				):
      _snake_case   :				Union[str, Any]														=	next_speeds_calculated[car_index]
      if speed != -1:
        # Change the position based on the speed (with % to create the loop)
        _snake_case   :				Union[str, Any]														=	(car_index + speed) % number_of_cells
        # Commit the change of position
        _snake_case   :				Tuple														=	speed
    highway.append(lowerCAmelCase				)

  return highway


if __name__ == "__main__":
      import doctest

      doctest.testmod()




