code: 
  
  
  '''simple docstring'''
  
  
  
  from dataclasses import dataclass
  from typing import List, Optional, Tuple, Union
  
  import numpy as np
  import torch
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput, logging, randn_tensor
  from .scheduling_utils import SchedulerMixin
  
  
  A__	:      str				=						logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  
  
  
  @dataclass
  class      UpperCAmelCase_						(_UpperCAmelCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      torch.FloatTensor
  
  
  
  
  
  
  
  class      UpperCAmelCase_						(_UpperCAmelCase      ,	_UpperCAmelCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Union[str, Any]							  =    1
  
  
  
  
  
  
  
      @register_to_config
      def __init__(							self    ,  SCREAMING_SNAKE_CASE_ = 40    ,  SCREAMING_SNAKE_CASE_ = 0.0_0_2    ,  SCREAMING_SNAKE_CASE_ = 80.0    ,  SCREAMING_SNAKE_CASE_ = 0.5    ,  SCREAMING_SNAKE_CASE_ = 1.0    ,  SCREAMING_SNAKE_CASE_ = 7.0    ,  SCREAMING_SNAKE_CASE_ = True    ,  )       ->  List[Any]:
           # standard deviation of the initial noise distribution
           __lowerCamelCase     :			Tuple              =	sigma_max
  
           __lowerCamelCase     :			Optional[Any]              =	np.linspace(0    ,  1    ,  SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			int              =	self._convert_to_karras(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Dict              =	self.sigma_to_t(SCREAMING_SNAKE_CASE_			)
  
           # setable values
           __lowerCamelCase     :			Optional[Any]              =	None
           __lowerCamelCase     :			Optional[int]              =	torch.from_numpy(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Optional[int]              =	torch.from_numpy(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			List[Any]              =	False
           __lowerCamelCase     :			List[Any]              =	False
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_=None			)       ->  int:
           if schedule_timesteps is None:
                __lowerCamelCase     :			int              =	self.timesteps
  
           __lowerCamelCase     :			str              =	(schedule_timesteps == timestep).nonzero()
           return indices.item()
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  torch.FloatTensor:
           # Get sigma corresponding to timestep
           if isinstance(SCREAMING_SNAKE_CASE_    ,  torch.Tensor			):
                __lowerCamelCase     :			str              =	timestep.to(self.timesteps.device			)
           __lowerCamelCase     :			Union[str, Any]              =	self.index_for_timestep(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Tuple              =	self.sigmas[step_idx]
  
           __lowerCamelCase     :			Union[str, Any]              =	sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)
  
           __lowerCamelCase     :			Optional[int]              =	True
           return sample
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  List[str]:
           if not isinstance(SCREAMING_SNAKE_CASE_    ,  np.ndarray			):
                __lowerCamelCase     :			Any              =	np.array(SCREAMING_SNAKE_CASE_    ,  dtype=np.floataa			)
  
           __lowerCamelCase     :			Tuple              =	10_00 * 0.2_5 * np.log(sigmas + 1E-44			)
  
           return timesteps
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = None    ,  )       ->  List[str]:
           if num_inference_steps is None and timesteps is None:
                raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.'			)
  
           if num_inference_steps is not None and timesteps is not None:
                raise ValueError('Can only pass one of `num_inference_steps` or `timesteps`.'			)
  
           # Follow DDPMScheduler custom timesteps logic
           if timesteps is not None:
                for i in range(1    ,  len(SCREAMING_SNAKE_CASE_			)			):
                     if timesteps[i] >= timesteps[i - 1]:
                          raise ValueError('`timesteps` must be in descending order.'			)
  
                if timesteps[0] >= self.config.num_train_timesteps:
                     raise ValueError(
                         f'`timesteps` must start before `self.config.train_timesteps`:'
                         f' {self.config.num_train_timesteps}.'			)
  
                __lowerCamelCase     :			Dict              =	np.array(SCREAMING_SNAKE_CASE_    ,  dtype=np.intaa			)
                __lowerCamelCase     :			Union[str, Any]              =	True
           else:
                if num_inference_steps > self.config.num_train_timesteps:
                     raise ValueError(
                         f'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
                         f' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
                         f' maximal {self.config.num_train_timesteps} timesteps.'			)
  
                __lowerCamelCase     :			int              =	num_inference_steps
  
                __lowerCamelCase     :			int              =	self.config.num_train_timesteps // self.num_inference_steps
                __lowerCamelCase     :			Union[str, Any]              =	(np.arange(0    ,  SCREAMING_SNAKE_CASE_			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
                __lowerCamelCase     :			Union[str, Any]              =	False
  
           # Map timesteps to Karras sigmas directly for multistep sampling
           # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
           __lowerCamelCase     :			List[Any]              =	self.config.num_train_timesteps
           __lowerCamelCase     :			Optional[Any]              =	timesteps[::-1].copy()
           __lowerCamelCase     :			List[Any]              =	ramp / (num_train_timesteps - 1)
           __lowerCamelCase     :			str              =	self._convert_to_karras(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			List[str]              =	self.sigma_to_t(SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			Optional[int]              =	np.concatenate([sigmas, [self.sigma_min]]			).astype(np.floataa			)
           __lowerCamelCase     :			Tuple              =	torch.from_numpy(SCREAMING_SNAKE_CASE_			).to(device=SCREAMING_SNAKE_CASE_			)
  
           if str(SCREAMING_SNAKE_CASE_			).startswith('mps'			):
                # mps does not support float64
                __lowerCamelCase     :			Dict              =	torch.from_numpy(SCREAMING_SNAKE_CASE_			).to(SCREAMING_SNAKE_CASE_    ,  dtype=torch.floataa			)
           else:
                __lowerCamelCase     :			int              =	torch.from_numpy(SCREAMING_SNAKE_CASE_			).to(device=SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Any:
           __lowerCamelCase     :			float              =	self.config.sigma_min
           __lowerCamelCase     :			float              =	self.config.sigma_max
  
           __lowerCamelCase     :			Tuple              =	self.config.rho
           __lowerCamelCase     :			Dict              =	sigma_min ** (1 / rho)
           __lowerCamelCase     :			List[Any]              =	sigma_max ** (1 / rho)
           __lowerCamelCase     :			List[str]              =	(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
           return sigmas
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  str:
           __lowerCamelCase     :			List[Any]              =	self.config.sigma_data
  
           __lowerCamelCase     :			int              =	sigma_data**2 / (sigma**2 + sigma_data**2)
           __lowerCamelCase     :			int              =	sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
           return c_skip, c_out
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Union[str, Any]:
           __lowerCamelCase     :			List[Any]              =	self.config.sigma_min
           __lowerCamelCase     :			Union[str, Any]              =	self.config.sigma_data
  
           __lowerCamelCase     :			List[str]              =	sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
           __lowerCamelCase     :			List[str]              =	(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
           return c_skip, c_out
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = True    ,  )       ->  Union[CMStochasticIterativeSchedulerOutput, Tuple]:
  
           if (
               isinstance(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
               or isinstance(SCREAMING_SNAKE_CASE_    ,  torch.IntTensor			)
               or isinstance(SCREAMING_SNAKE_CASE_    ,  torch.LongTensor			)
           ):
                raise ValueError(
                    (
                        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
                        f' `{self.__class__}.step()` is not supported. Make sure to pass'
                        ' one of the `scheduler.timesteps` as a timestep.'
                    )    ,  )
  
           if not self.is_scale_input_called:
                logger.warning(
                    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
                    'See `StableDiffusionPipeline` for a usage example.'			)
  
           if isinstance(SCREAMING_SNAKE_CASE_    ,  torch.Tensor			):
                __lowerCamelCase     :			Dict              =	timestep.to(self.timesteps.device			)
  
           __lowerCamelCase     :			str              =	self.config.sigma_min
           __lowerCamelCase     :			Tuple              =	self.config.sigma_max
  
           __lowerCamelCase     :			List[Any]              =	self.index_for_timestep(SCREAMING_SNAKE_CASE_			)
  
           # sigma_next corresponds to next_t in original implementation
           __lowerCamelCase     :			Optional[int]              =	self.sigmas[step_index]
           if step_index + 1 < self.config.num_train_timesteps:
                __lowerCamelCase     :			Tuple              =	self.sigmas[step_index + 1]
           else:
                # Set sigma_next to sigma_min
                __lowerCamelCase     :			Optional[Any]              =	self.sigmas[-1]
  
           # Get scalings for boundary conditions
           __lowerCamelCase     :			str              =	self.get_scalings_for_boundary_condition(SCREAMING_SNAKE_CASE_			)
  
           # 1. Denoise model output using boundary conditions
           __lowerCamelCase     :			List[Any]              =	c_out * model_output + c_skip * sample
           if self.config.clip_denoised:
                __lowerCamelCase     :			Tuple              =	denoised.clamp(-1    ,  1			)
  
           # 2. Sample z ~ N(0, s_noise^2 * I)
           # Noise is not used for onestep sampling.
           if len(self.timesteps			) > 1:
                __lowerCamelCase     :			Optional[Any]              =	randn_tensor(
                    model_output.shape    ,  dtype=model_output.dtype    ,  device=model_output.device    ,  generator=SCREAMING_SNAKE_CASE_			)
           else:
                __lowerCamelCase     :			int              =	torch.zeros_like(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			List[str]              =	noise * self.config.s_noise
  
           __lowerCamelCase     :			Dict              =	sigma_next.clamp(min=SCREAMING_SNAKE_CASE_    ,  max=SCREAMING_SNAKE_CASE_			)
  
           # 3. Return noisy sample
           # tau = sigma_hat, eps = sigma_min
           __lowerCamelCase     :			Optional[int]              =	denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5
  
           if not return_dict:
                return (prev_sample,)
  
           return CMStochasticIterativeSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  )       ->  torch.FloatTensor:
           # Make sure sigmas and timesteps have the same device and dtype as original_samples
           __lowerCamelCase     :			int              =	self.sigmas.to(device=original_samples.device    ,  dtype=original_samples.dtype			)
           if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE_			):
                # mps does not support float64
                __lowerCamelCase     :			List[str]              =	self.timesteps.to(original_samples.device    ,  dtype=torch.floataa			)
                __lowerCamelCase     :			Optional[Any]              =	timesteps.to(original_samples.device    ,  dtype=torch.floataa			)
           else:
                __lowerCamelCase     :			Any              =	self.timesteps.to(original_samples.device			)
                __lowerCamelCase     :			str              =	timesteps.to(original_samples.device			)
  
           __lowerCamelCase     :			int              =	[(schedule_timesteps == t).nonzero().item() for t in timesteps]
  
           __lowerCamelCase     :			Any              =	sigmas[step_indices].flatten()
           while len(sigma.shape			) < len(original_samples.shape			):
                __lowerCamelCase     :			Any              =	sigma.unsqueeze(-1			)
  
           __lowerCamelCase     :			Dict              =	original_samples + noise * sigma
           return noisy_samples
  
  
  
  
  
  
  
      def __len__(							self			)       ->  Optional[Any]:
           return self.config.num_train_timesteps
style_context: 
  
  
  '''simple docstring'''
  
  
  
  import torch
  
  from diffusers import KDPMaAncestralDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  
  class      UpperCAmelCase_						(_UpperCAmelCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Optional[Any]							  =    (KDPMaAncestralDiscreteScheduler,)
      lowerCamelCase						:      Tuple							  =    1_0
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  **SCREAMING_SNAKE_CASE_			)       ->  Tuple:
           __lowerCamelCase     :			Union[str, Any]              =	{
               'num_train_timesteps': 11_00,
               'beta_start': 0.0_0_0_1,
               'beta_end': 0.0_2,
               'beta_schedule': 'linear',
           }
  
           config.update(**SCREAMING_SNAKE_CASE_			)
           return config
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Union[str, Any]:
           for timesteps in [10, 50, 1_00, 10_00]:
                self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Dict:
           for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]    ,  [0.0_0_0_2, 0.0_0_2, 0.0_2]			):
                self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_    ,  beta_end=SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           for schedule in ["linear", "scaled_linear"]:
                self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  int:
           if torch_device == "mps":
                return
           __lowerCamelCase     :			Union[str, Any]              =	self.scheduler_classes[0]
           __lowerCamelCase     :			Optional[Any]              =	self.get_scheduler_config()
           __lowerCamelCase     :			Optional[int]              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)
  
           scheduler.set_timesteps(self.num_inference_steps			)
  
           __lowerCamelCase     :			int              =	torch.manual_seed(0			)
  
           __lowerCamelCase     :			int              =	self.dummy_model()
           __lowerCamelCase     :			List[Any]              =	self.dummy_sample_deter * scheduler.init_noise_sigma
           __lowerCamelCase     :			Any              =	sample.to(SCREAMING_SNAKE_CASE_			)
  
           for i, t in enumerate(scheduler.timesteps			):
                __lowerCamelCase     :			Dict              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			Dict              =	model(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			Union[str, Any]              =	scheduler.step(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			)
                __lowerCamelCase     :			Any              =	output.prev_sample
  
           __lowerCamelCase     :			Tuple              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
           __lowerCamelCase     :			Optional[int]              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)
  
           assert abs(result_sum.item() - 1_3_8_4_9.3_8_7_7			) < 1E-2
           assert abs(result_mean.item() - 1_8.0_3_3_1			) < 5E-3
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           if torch_device == "mps":
                return
           __lowerCamelCase     :			Union[str, Any]              =	self.scheduler_classes[0]
           __lowerCamelCase     :			Optional[Any]              =	self.get_scheduler_config(prediction_type='v_prediction'			)
           __lowerCamelCase     :			str              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)
  
           scheduler.set_timesteps(self.num_inference_steps			)
  
           __lowerCamelCase     :			int              =	self.dummy_model()
           __lowerCamelCase     :			int              =	self.dummy_sample_deter * scheduler.init_noise_sigma
           __lowerCamelCase     :			Dict              =	sample.to(SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			int              =	torch.manual_seed(0			)
  
           for i, t in enumerate(scheduler.timesteps			):
                __lowerCamelCase     :			Any              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			List[str]              =	model(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			str              =	scheduler.step(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			)
                __lowerCamelCase     :			Tuple              =	output.prev_sample
  
           __lowerCamelCase     :			int              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
           __lowerCamelCase     :			int              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)
  
           assert abs(result_sum.item() - 3_2_8.9_9_7_0			) < 1E-2
           assert abs(result_mean.item() - 0.4_2_8_4			) < 1E-3
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Dict:
           if torch_device == "mps":
                return
           __lowerCamelCase     :			Optional[int]              =	self.scheduler_classes[0]
           __lowerCamelCase     :			str              =	self.get_scheduler_config()
           __lowerCamelCase     :			Union[str, Any]              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)
  
           scheduler.set_timesteps(self.num_inference_steps    ,  device=SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			List[Any]              =	torch.manual_seed(0			)
  
           __lowerCamelCase     :			Optional[Any]              =	self.dummy_model()
           __lowerCamelCase     :			Dict              =	self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_			) * scheduler.init_noise_sigma
  
           for t in scheduler.timesteps:
                __lowerCamelCase     :			Any              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			Optional[Any]              =	model(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			Dict              =	scheduler.step(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			)
                __lowerCamelCase     :			Union[str, Any]              =	output.prev_sample
  
           __lowerCamelCase     :			int              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
           __lowerCamelCase     :			Dict              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)
  
           assert abs(result_sum.item() - 1_3_8_4_9.3_8_1_8			) < 1E-1
           assert abs(result_mean.item() - 1_8.0_3_3_1			) < 1E-3
  
  
  
  
  
  
  