


import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 	__a   (       A__    ):
		_a			:						List[str]    			=			42
		_a			:						Optional[int]    			=			None



def  lowerCAmelCase__				(    a__:	int    ,						a__:	List[Any]=0.999    ,						a__:	List[str]="cosine"    ,						)						->	List[str]:


							'''simple docstring'''





							if alpha_transform_type == "cosine":

														def alpha_bar_fn(a__:	int ):
																					return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(a__:	Dict ):
																					return math.exp(t * -12.0 )

							else:
														raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )

							_UpperCAmelCase					=    []
							for i in range(a__ ):
														_UpperCAmelCase					=    i / num_diffusion_timesteps
														_UpperCAmelCase					=    (i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(a__ ) / alpha_bar_fn(a__ )    ,						a__ ) )
							return torch.tensor(a__    ,						dtype=torch.floataa )







class 	__a   (       A__					,   A__    ):
		_a			:						Tuple    			=			[e.name for e in KarrasDiffusionSchedulers]
		_a			:						Dict    			=			1
		@register_to_config
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE = 1000    ,					_SCREAMING_SNAKE_CASE = 0.0001    ,					_SCREAMING_SNAKE_CASE = 0.02    ,					_SCREAMING_SNAKE_CASE = "linear"    ,					_SCREAMING_SNAKE_CASE = None    ,					_SCREAMING_SNAKE_CASE = False    ,					_SCREAMING_SNAKE_CASE = "epsilon"    ,					_SCREAMING_SNAKE_CASE = "linspace"    ,					_SCREAMING_SNAKE_CASE = 0    ,					)				-> List[Any]:




									"""simple docstring"""





									if trained_betas is not None:
																_UpperCAmelCase					=    torch.tensor(_lowerCAmelCase    ,					dtype=torch.floataa		)
									elif beta_schedule == "linear":
																_UpperCAmelCase					=    torch.linspace(_lowerCAmelCase    ,					_lowerCAmelCase    ,					_lowerCAmelCase    ,					dtype=torch.floataa		)
									elif beta_schedule == "scaled_linear":
																# this schedule is very specific to the latent diffusion model.
																_UpperCAmelCase					=    (
																    torch.linspace(beta_start**0.5    ,					beta_end**0.5    ,					_lowerCAmelCase    ,					dtype=torch.floataa		) ** 2
																)
									elif beta_schedule == "squaredcos_cap_v2":
																# Glide cosine schedule
																_UpperCAmelCase					=    betas_for_alpha_bar(_lowerCAmelCase		)
									else:
																raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''		)

									_UpperCAmelCase					=    1.0 - self.betas
									_UpperCAmelCase					=    torch.cumprod(self.alphas    ,					dim=0		)

									_UpperCAmelCase					=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
									_UpperCAmelCase					=    np.concatenate([sigmas[::-1], [0.0]]		).astype(np.floataa		)
									_UpperCAmelCase					=    torch.from_numpy(_lowerCAmelCase		)

									# setable values
									_UpperCAmelCase					=    None
									_UpperCAmelCase					=    use_karras_sigmas
									self.set_timesteps(_lowerCAmelCase    ,					_lowerCAmelCase		)
									_UpperCAmelCase					=    []
									_UpperCAmelCase					=    False
		@property
		def 			UpperCAmelCase__       (    self		)				-> int:




									"""simple docstring"""





									if self.config.timestep_spacing in ["linspace", "trailing"]:
																return self.sigmas.max()

									return (self.sigmas.max() ** 2 + 1) ** 0.5
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> int:




									"""simple docstring"""





									if isinstance(_lowerCAmelCase    ,					torch.Tensor		):
																_UpperCAmelCase					=    timestep.to(self.timesteps.device		)
									_UpperCAmelCase					=    (self.timesteps == timestep).nonzero().item()
									_UpperCAmelCase					=    self.sigmas[step_index]
									_UpperCAmelCase					=    sample / ((sigma**2 + 1) ** 0.5)
									_UpperCAmelCase					=    True
									return sample
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> Union[str, Any]:




									"""simple docstring"""





									def lms_derivative(_SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase					=    1.0
																for k in range(_lowerCAmelCase		):
																							if current_order == k:
																														continue
																							prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
																return prod

									_UpperCAmelCase					=    integrate.quad(_lowerCAmelCase    ,					self.sigmas[t]    ,					self.sigmas[t + 1]    ,					epsrel=1e-4		)[0]

									return integrated_coeff
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE = None		)				-> Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    num_inference_steps

									# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
									if self.config.timestep_spacing == "linspace":
																_UpperCAmelCase					=    np.linspace(0    ,					self.config.num_train_timesteps - 1    ,					_lowerCAmelCase    ,					dtype=_lowerCAmelCase		)[
																    ::-1
																].copy()
									elif self.config.timestep_spacing == "leading":
																_UpperCAmelCase					=    self.config.num_train_timesteps // self.num_inference_steps
																# creates integer timesteps by multiplying by ratio
																# casting to int to avoid issues when num_inference_step is power of 3
																_UpperCAmelCase					=    (np.arange(0    ,					_lowerCAmelCase		) * step_ratio).round()[::-1].copy().astype(_lowerCAmelCase		)
																timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
																_UpperCAmelCase					=    self.config.num_train_timesteps / self.num_inference_steps
																# creates integer timesteps by multiplying by ratio
																# casting to int to avoid issues when num_inference_step is power of 3
																_UpperCAmelCase					=    (np.arange(self.config.num_train_timesteps    ,					0    ,					-step_ratio		)).round().copy().astype(_lowerCAmelCase		)
																timesteps -= 1
									else:
																raise ValueError(
																    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''		)

									_UpperCAmelCase					=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
									_UpperCAmelCase					=    np.log(_lowerCAmelCase		)
									_UpperCAmelCase					=    np.interp(_lowerCAmelCase    ,					np.arange(0    ,					len(_lowerCAmelCase		)		)    ,					_lowerCAmelCase		)

									if self.use_karras_sigmas:
																_UpperCAmelCase					=    self._convert_to_karras(in_sigmas=_lowerCAmelCase		)
																_UpperCAmelCase					=    np.array([self._sigma_to_t(_lowerCAmelCase    ,					_lowerCAmelCase		) for sigma in sigmas]		)

									_UpperCAmelCase					=    np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)

									_UpperCAmelCase					=    torch.from_numpy(_lowerCAmelCase		).to(device=_lowerCAmelCase		)
									if str(_lowerCAmelCase		).startswith('mps'		):
																# mps does not support float64
																_UpperCAmelCase					=    torch.from_numpy(_lowerCAmelCase		).to(_lowerCAmelCase    ,					dtype=torch.floataa		)
									else:
																_UpperCAmelCase					=    torch.from_numpy(_lowerCAmelCase		).to(device=_lowerCAmelCase		)

									_UpperCAmelCase					=    []
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> Dict:




									"""simple docstring"""





									_UpperCAmelCase					=    np.log(_lowerCAmelCase		)

									# get distribution
									_UpperCAmelCase					=    log_sigma - log_sigmas[:, np.newaxis]

									# get sigmas range
									_UpperCAmelCase					=    np.cumsum((dists >= 0)    ,					axis=0		).argmax(axis=0		).clip(max=log_sigmas.shape[0] - 2		)
									_UpperCAmelCase					=    low_idx + 1

									_UpperCAmelCase					=    log_sigmas[low_idx]
									_UpperCAmelCase					=    log_sigmas[high_idx]

									# interpolate sigmas
									_UpperCAmelCase					=    (low - log_sigma) / (low - high)
									_UpperCAmelCase					=    np.clip(_lowerCAmelCase    ,					0    ,					1		)

									# transform interpolation to time range
									_UpperCAmelCase					=    (1 - w) * low_idx + w * high_idx
									_UpperCAmelCase					=    t.reshape(sigma.shape		)
									return t
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE		)				-> Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    in_sigmas[-1].item()
									_UpperCAmelCase					=    in_sigmas[0].item()

									_UpperCAmelCase					=    7.0  # 7.0 is the value used in the paper
									_UpperCAmelCase					=    np.linspace(0    ,					1    ,					self.num_inference_steps		)
									_UpperCAmelCase					=    sigma_min ** (1 / rho)
									_UpperCAmelCase					=    sigma_max ** (1 / rho)
									_UpperCAmelCase					=    (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE = 4    ,					_SCREAMING_SNAKE_CASE = True    ,					)				-> Optional[int]:




									"""simple docstring"""





									if not self.is_scale_input_called:
																warnings.warn(
																    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
																    'See `StableDiffusionPipeline` for a usage example.'		)

									if isinstance(_lowerCAmelCase    ,					torch.Tensor		):
																_UpperCAmelCase					=    timestep.to(self.timesteps.device		)
									_UpperCAmelCase					=    (self.timesteps == timestep).nonzero().item()
									_UpperCAmelCase					=    self.sigmas[step_index]

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
																_UpperCAmelCase					=    sample - sigma * model_output
									elif self.config.prediction_type == "v_prediction":
																# * c_out + input * c_skip
																_UpperCAmelCase					=    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
									elif self.config.prediction_type == "sample":
																_UpperCAmelCase					=    model_output
									else:
																raise ValueError(
																    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''		)

									# 2. Convert to an ODE derivative
									_UpperCAmelCase					=    (sample - pred_original_sample) / sigma
									self.derivatives.append(_lowerCAmelCase		)
									if len(self.derivatives		) > order:
																self.derivatives.pop(0		)

									# 3. Compute linear multistep coefficients
									_UpperCAmelCase					=    min(step_index + 1    ,					_lowerCAmelCase		)
									_UpperCAmelCase					=    [self.get_lms_coefficient(_lowerCAmelCase    ,					_lowerCAmelCase    ,					_lowerCAmelCase		) for curr_order in range(_lowerCAmelCase		)]

									# 4. Compute previous sample based on the derivatives path
									_UpperCAmelCase					=    sample + sum(
									    coeff * derivative for coeff, derivative in zip(_lowerCAmelCase    ,					reversed(self.derivatives		)		)		)

									if not return_dict:
																return (prev_sample,)

									return LMSDiscreteSchedulerOutput(prev_sample=_lowerCAmelCase    ,					pred_original_sample=_lowerCAmelCase		)
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					)				-> Dict:




									"""simple docstring"""





									_UpperCAmelCase					=    self.sigmas.to(device=original_samples.device    ,					dtype=original_samples.dtype		)
									if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCAmelCase		):
																# mps does not support float64
																_UpperCAmelCase					=    self.timesteps.to(original_samples.device    ,					dtype=torch.floataa		)
																_UpperCAmelCase					=    timesteps.to(original_samples.device    ,					dtype=torch.floataa		)
									else:
																_UpperCAmelCase					=    self.timesteps.to(original_samples.device		)
																_UpperCAmelCase					=    timesteps.to(original_samples.device		)

									_UpperCAmelCase					=    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

									_UpperCAmelCase					=    sigmas[step_indices].flatten()
									while len(sigma.shape		) < len(original_samples.shape		):
																_UpperCAmelCase					=    sigma.unsqueeze(-1		)

									_UpperCAmelCase					=    original_samples + noise * sigma
									return noisy_samples






		def __len__(    self		)				-> Dict:




									"""simple docstring"""





									return self.config.num_train_timesteps




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class 	__a   (       unittest.TestCase    ):
		# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
		_a			:						List[Any]    			=			(ScoreSdeVeScheduler,)
		_a			:						Any    			=			()
		@property
		def 			UpperCAmelCase__       (    self		)				-> List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=    4
									_UpperCAmelCase					=    3
									_UpperCAmelCase					=    8
									_UpperCAmelCase					=    8

									_UpperCAmelCase					=    torch.rand((batch_size, num_channels, height, width)		)

									return sample
		@property
		def 			UpperCAmelCase__       (    self		)				-> Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    4
									_UpperCAmelCase					=    3
									_UpperCAmelCase					=    8
									_UpperCAmelCase					=    8

									_UpperCAmelCase					=    batch_size * num_channels * height * width
									_UpperCAmelCase					=    torch.arange(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    sample.reshape(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    sample / num_elems
									_UpperCAmelCase					=    sample.permute(3    ,					0    ,					1    ,					2		)

									return sample
		def 			UpperCAmelCase__       (    self		)				-> str:




									"""simple docstring"""





									def model(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					*_SCREAMING_SNAKE_CASE		):
																return sample * t / (t + 1)

									return model
		def 			UpperCAmelCase__       (    self    ,					**_SCREAMING_SNAKE_CASE		)				-> Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    {
									    'num_train_timesteps': 2000,
									    'snr': 0.15,
									    'sigma_min': 0.01,
									    'sigma_max': 1348,
									    'sampling_eps': 1e-5,
									}

									config.update(**_SCREAMING_SNAKE_CASE		)
									return config
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE=0    ,					**_SCREAMING_SNAKE_CASE		)				-> List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    dict(self.forward_default_kwargs		)

									for scheduler_class in self.scheduler_classes:
																_UpperCAmelCase					=    self.dummy_sample
																_UpperCAmelCase					=    0.1 * sample

																_UpperCAmelCase					=    self.get_scheduler_config(**_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase					=    scheduler_class(**_SCREAMING_SNAKE_CASE		)

																with tempfile.TemporaryDirectory() as tmpdirname:
																							scheduler.save_config(_SCREAMING_SNAKE_CASE		)
																							_UpperCAmelCase					=    scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase					=    scheduler.step_pred(
																    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample
																_UpperCAmelCase					=    new_scheduler.step_pred(
																    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample

																assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"

																_UpperCAmelCase					=    scheduler.step_correct(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample
																_UpperCAmelCase					=    new_scheduler.step_correct(
																    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample

																assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler correction are not identical"
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE=0    ,					**_SCREAMING_SNAKE_CASE		)				-> List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=    dict(self.forward_default_kwargs		)
									kwargs.update(_SCREAMING_SNAKE_CASE		)

									for scheduler_class in self.scheduler_classes:
																_UpperCAmelCase					=    self.dummy_sample
																_UpperCAmelCase					=    0.1 * sample

																_UpperCAmelCase					=    self.get_scheduler_config()
																_UpperCAmelCase					=    scheduler_class(**_SCREAMING_SNAKE_CASE		)

																with tempfile.TemporaryDirectory() as tmpdirname:
																							scheduler.save_config(_SCREAMING_SNAKE_CASE		)
																							_UpperCAmelCase					=    scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase					=    scheduler.step_pred(
																    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample
																_UpperCAmelCase					=    new_scheduler.step_pred(
																    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample

																assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"

																_UpperCAmelCase					=    scheduler.step_correct(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample
																_UpperCAmelCase					=    new_scheduler.step_correct(
																    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample

																assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler correction are not identical"
		def 			UpperCAmelCase__       (    self		)				-> Any:




									"""simple docstring"""





									for timesteps in [10, 100, 1000]:
																self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> List[Any]:




									"""simple docstring"""





									for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]    ,					[1, 100, 1000]		):
																self.check_over_configs(sigma_min=_SCREAMING_SNAKE_CASE    ,					sigma_max=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> str:




									"""simple docstring"""





									for t in [0.1, 0.5, 0.75]:
																self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    dict(self.forward_default_kwargs		)

									_UpperCAmelCase					=    self.scheduler_classes[0]
									_UpperCAmelCase					=    self.get_scheduler_config()
									_UpperCAmelCase					=    scheduler_class(**_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase					=    3

									_UpperCAmelCase					=    self.dummy_model()
									_UpperCAmelCase					=    self.dummy_sample_deter

									scheduler.set_sigmas(_SCREAMING_SNAKE_CASE		)
									scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    torch.manual_seed(0		)

									for i, t in enumerate(scheduler.timesteps		):
																_UpperCAmelCase					=    scheduler.sigmas[i]

																for _ in range(scheduler.config.correct_steps		):
																							with torch.no_grad():
																														_UpperCAmelCase					=    model(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
																							_UpperCAmelCase					=    scheduler.step_correct(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		).prev_sample

																with torch.no_grad():
																							_UpperCAmelCase					=    model(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase					=    scheduler.step_pred(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase	, _UpperCAmelCase					=    output.prev_sample, output.prev_sample_mean

									_UpperCAmelCase					=    torch.sum(torch.abs(_SCREAMING_SNAKE_CASE		)		)
									_UpperCAmelCase					=    torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

									assert np.isclose(result_sum.item()    ,					14372758528.0		)
									assert np.isclose(result_mean.item()    ,					18714530.0		)






		def 			UpperCAmelCase__       (    self		)				-> Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=    dict(self.forward_default_kwargs		)

									_UpperCAmelCase					=    kwargs.pop('num_inference_steps'    ,					_SCREAMING_SNAKE_CASE		)

									for scheduler_class in self.scheduler_classes:
																_UpperCAmelCase					=    self.get_scheduler_config()
																_UpperCAmelCase					=    scheduler_class(**_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase					=    self.dummy_sample
																_UpperCAmelCase					=    0.1 * sample

																if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE    ,					'set_timesteps'		):
																							scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)
																elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE    ,					'set_timesteps'		):
																							_UpperCAmelCase					=    num_inference_steps

																_UpperCAmelCase					=    scheduler.step_pred(_SCREAMING_SNAKE_CASE    ,					0    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample
																_UpperCAmelCase					=    scheduler.step_pred(_SCREAMING_SNAKE_CASE    ,					1    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample

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