"""simple docstring"""
import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest




class    __snake_case       (     __lowerCAmelCase				):
      a__        =		(CMStochasticIterativeScheduler,)
      a__        =		10



      def   lowerCamelCase_   (						self    ,			**lowercase)	->			Any:





        '''simple docstring'''





        a__:     Tuple										=	{
            'num_train_timesteps': 2_01,
            'sigma_min': 0.002,
            'sigma_max': 80.0,
        }

        config.update(**lowercase)
        return config



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     List[str]										=	10

        a__:     List[Any]										=	self.get_scheduler_config()
        a__:     Optional[int]										=	self.scheduler_classes[0](**lowercase)

        scheduler.set_timesteps(lowercase)

        a__:     Union[str, Any]										=	scheduler.timesteps[0]
        a__:     Dict										=	scheduler.timesteps[1]

        a__:     Union[str, Any]										=	self.dummy_sample
        a__:     Optional[int]										=	0.1 * sample

        a__:     Optional[int]										=	scheduler.step(lowercase    ,			lowercase    ,			lowercase).prev_sample
        a__:     int										=	scheduler.step(lowercase    ,			lowercase    ,			lowercase).prev_sample

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



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        a__:     Any										=	self.scheduler_classes[0]
        a__:     str										=	self.get_scheduler_config()
        a__:     str										=	scheduler_class(**lowercase)

        a__:     Optional[int]										=	1
        scheduler.set_timesteps(lowercase)
        a__:     List[str]										=	scheduler.timesteps

        a__:     List[str]										=	torch.manual_seed(0)

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

        for i, t in enumerate(lowercase):
          # 1. scale model input
          a__:     int										=	scheduler.scale_model_input(lowercase    ,			lowercase)

          # 2. predict noise residual
          a__:     List[str]										=	model(lowercase    ,			lowercase)

          # 3. predict previous sample x_t-1
          a__:     Tuple										=	scheduler.step(lowercase    ,			lowercase    ,			lowercase    ,			generator=lowercase).prev_sample

          a__:     Optional[Any]										=	pred_prev_sample

        a__:     Optional[int]										=	torch.sum(torch.abs(lowercase))
        a__:     List[Any]										=	torch.mean(torch.abs(lowercase))

        assert abs(result_sum.item() - 192.7614) < 1e-2
        assert abs(result_mean.item() - 0.2510) < 1e-3



      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        a__:     List[Any]										=	self.scheduler_classes[0]
        a__:     List[Any]										=	self.get_scheduler_config()
        a__:     List[Any]										=	scheduler_class(**lowercase)

        a__:     Dict										=	[1_06, 0]
        scheduler.set_timesteps(timesteps=lowercase)
        a__:     Union[str, Any]										=	scheduler.timesteps

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

        a__:     Tuple										=	self.dummy_model()
        a__:     str										=	self.dummy_sample_deter * scheduler.init_noise_sigma

        for t in timesteps:
          # 1. scale model input
          a__:     Optional[Any]										=	scheduler.scale_model_input(lowercase    ,			lowercase)

          # 2. predict noise residual
          a__:     List[str]										=	model(lowercase    ,			lowercase)

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

          a__:     Union[str, Any]										=	pred_prev_sample

        a__:     List[str]										=	torch.sum(torch.abs(lowercase))
        a__:     Union[str, Any]										=	torch.mean(torch.abs(lowercase))

        assert abs(result_sum.item() - 347.6357) < 1e-2
        assert abs(result_mean.item() - 0.4527) < 1e-3



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     int										=	self.scheduler_classes[0]
        a__:     Any										=	self.get_scheduler_config()
        a__:     Union[str, Any]										=	scheduler_class(**lowercase)

        a__:     Tuple										=	[39, 30, 12, 15, 0]

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



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        a__:     Tuple										=	self.scheduler_classes[0]
        a__:     Tuple										=	self.get_scheduler_config()
        a__:     Any										=	scheduler_class(**lowercase)

        a__:     Tuple										=	[39, 30, 12, 1, 0]
        a__:     int										=	len(lowercase)

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



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        a__:     str										=	self.scheduler_classes[0]
        a__:     Union[str, Any]										=	self.get_scheduler_config()
        a__:     int										=	scheduler_class(**lowercase)

        a__:     Optional[int]										=	[scheduler.config.num_train_timesteps]

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



"""simple docstring"""
def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->int:
  while a != 0:
    a__							,    a__:     List[str]										=	b % a, a
  return b







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->int:
  if gcd(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ) != 1:
    a__:     Dict										=	F'mod inverse of {a!r} and {m!r} does not exist'
    raise ValueError(_SCREAMING_SNAKE_CASE   )
  a__							,    a__							,    a__:     Union[str, Any]										=	1, 0, a
  a__							,    a__							,    a__:     Any										=	0, 1, m
  while va != 0:
    a__:     int										=	ua // va
    a__							,    a__							,    a__							,    a__							,    a__							,    a__:     Any										=	(ua - q * va), (ua - q * va), (ua - q * va), va, va, va
  return ua % m


