




import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput




def      __magic_name__							(					A						:   Dict,    A						:   Optional[Any]=0.9_99,    A						:   int="cosine",    ):






			'''simple docstring'''







			if alpha_transform_type == "cosine":

						def alpha_bar_fn(A						:   Union[str, Any]  ):
									return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2  ) ** 2

			elif alpha_transform_type == "exp":

						def alpha_bar_fn(A						:   Union[str, Any]  ):
									return math.exp(t * -12.0  )

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

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






class        snake_case__				(_UpperCamelCase       ,			_UpperCamelCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				int									=   [e.name for e in KarrasDiffusionSchedulers]
				SCREAMING_SNAKE_CASE_			:				Union[str, Any]									=   2





				@register_to_config
				def __init__( self							:	Any   , __lowerCamelCase							:	int = 10_00   , __lowerCamelCase							:	float = 0.00_085   , __lowerCamelCase							:	float = 0.012   , __lowerCamelCase							:	str = "linear"   , __lowerCamelCase							:	Optional[Union[np.ndarray, List[float]]] = None   , __lowerCamelCase							:	str = "epsilon"   , __lowerCamelCase							:	str = "linspace"   , __lowerCamelCase							:	int = 0   , )   ->	Dict:

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

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

							#  set all values
							self.set_timesteps(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	str   , __lowerCamelCase							:	List[Any]   , __lowerCamelCase							:	str=None				)   ->	Optional[Any]:

							if schedule_timesteps is None:
										a       =     self.timesteps

							a       =     (schedule_timesteps == timestep).nonzero()

							# The sigma index that is taken for the **very** first `step`
							# is always the second index (or the last index if there is only 1)
							# This way we can ensure we don't accidentally skip a sigma in
							# case we start in the middle of the denoising schedule (e.g. for image-to-image)
							if len(self._index_counter				) == 0:
										a       =     1 if len(__lowerCamelCase				) > 1 else 0
							else:
										a       =     timestep.cpu().item() if torch.is_tensor(__lowerCamelCase				) else timestep
										a       =     self._index_counter[timestep_int]

							return indices[pos].item()





				@property
				def 				__UpperCAmelCase				( self							:	Optional[int]				)   ->	Union[str, Any]:

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

							return (self.sigmas.max() ** 2 + 1) ** 0.5





				def 				__UpperCAmelCase				( self							:	Union[str, Any]   , __lowerCamelCase							:	torch.FloatTensor   , __lowerCamelCase							:	Union[float, torch.FloatTensor]   , )   ->	torch.FloatTensor:
							a       =     self.index_for_timestep(__lowerCamelCase				)

							if self.state_in_first_order:
										a       =     self.sigmas[step_index]
							else:
										a       =     self.sigmas_interpol[step_index - 1]

							a       =     sample / ((sigma**2 + 1) ** 0.5)
							return sample





				def 				__UpperCAmelCase				( self							:	Union[str, Any]   , __lowerCamelCase							:	int   , __lowerCamelCase							:	Union[str, torch.device] = None   , __lowerCamelCase							:	Optional[int] = None   , )   ->	Any:
							a       =     num_inference_steps

							a       =     num_train_timesteps or self.config.num_train_timesteps

							# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
							if self.config.timestep_spacing == "linspace":
										a       =     np.linspace(0   , num_train_timesteps - 1   , __lowerCamelCase   , dtype=__lowerCamelCase				)[::-1].copy()
							elif self.config.timestep_spacing == "leading":
										a       =     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
										a       =     (np.arange(0   , __lowerCamelCase				) * step_ratio).round()[::-1].copy().astype(__lowerCamelCase				)
										timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
										a       =     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
										a       =     (np.arange(__lowerCamelCase   , 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'."""				)

							a       =     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
							a       =     torch.from_numpy(np.log(__lowerCamelCase				)				).to(__lowerCamelCase				)

							a       =     np.interp(__lowerCamelCase   , np.arange(0   , len(__lowerCamelCase				)				)   , __lowerCamelCase				)
							a       =     np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)
							a       =     torch.from_numpy(__lowerCamelCase				).to(device=__lowerCamelCase				)

							# compute up and down sigmas
							a       =     sigmas.roll(-1				)
							a       =     0.0
							a       =     (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
							a       =     (sigmas_next**2 - sigmas_up**2) ** 0.5
							a       =     0.0

							# compute interpolated sigmas
							a       =     sigmas.log().lerp(sigmas_down.log()   , 0.5				).exp()
							a       =     0.0

							# set sigmas
							a       =     torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2				), sigmas[-1:]]				)
							a       =     torch.cat(
							    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2				), sigmas_interpol[-1:]]				)
							a       =     torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2				), sigmas_up[-1:]]				)
							a       =     torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2				), sigmas_down[-1:]]				)

							if str(__lowerCamelCase				).startswith("mps"				):
										# mps does not support float64
										a       =     torch.from_numpy(__lowerCamelCase				).to(__lowerCamelCase   , dtype=torch.floataa				)
							else:
										a       =     torch.from_numpy(__lowerCamelCase				).to(__lowerCamelCase				)

							a       =     self.sigma_to_t(__lowerCamelCase				).to(__lowerCamelCase   , dtype=timesteps.dtype				)
							a       =     torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])   , dim=-1				).flatten()

							a       =     torch.cat([timesteps[:1], interleaved_timesteps]				)

							a       =     None

							# for exp beta schedules, such as the one for `pipeline_shap_e.py`
							# we need an index counter
							a       =     defaultdict(__lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Dict   , __lowerCamelCase							:	Dict				)   ->	Tuple:
							a       =     sigma.log()

							# get distribution
							a       =     log_sigma - self.log_sigmas[:, None]

							# get sigmas range
							a       =     dists.ge(0				).cumsum(dim=0				).argmax(dim=0				).clamp(max=self.log_sigmas.shape[0] - 2				)
							a       =     low_idx + 1

							a       =     self.log_sigmas[low_idx]
							a       =     self.log_sigmas[high_idx]

							# interpolate sigmas
							a       =     (low - log_sigma) / (low - high)
							a       =     w.clamp(0   , 1				)

							# transform interpolation to time range
							a       =     (1 - w) * low_idx + w * high_idx
							a       =     t.view(sigma.shape				)
							return t





				@property
				def 				__UpperCAmelCase				( self							:	Any				)   ->	int:
							return self.sample is None





				def 				__UpperCAmelCase				( self							:	Tuple   , __lowerCamelCase							:	Union[torch.FloatTensor, np.ndarray]   , __lowerCamelCase							:	Union[float, torch.FloatTensor]   , __lowerCamelCase							:	Union[torch.FloatTensor, np.ndarray]   , __lowerCamelCase							:	Optional[torch.Generator] = None   , __lowerCamelCase							:	bool = True   , )   ->	Union[SchedulerOutput, Tuple]:
							a       =     self.index_for_timestep(__lowerCamelCase				)

							# advance index counter by 1
							a       =     timestep.cpu().item() if torch.is_tensor(__lowerCamelCase				) else timestep
							self._index_counter[timestep_int] += 1

							if self.state_in_first_order:
										a       =     self.sigmas[step_index]
										a       =     self.sigmas_interpol[step_index]
										a       =     self.sigmas_up[step_index]
										a       =     self.sigmas_down[step_index - 1]
							else:
										# 2nd order / KPDM2's method
										a       =     self.sigmas[step_index - 1]
										a       =     self.sigmas_interpol[step_index - 1]
										a       =     self.sigmas_up[step_index - 1]
										a       =     self.sigmas_down[step_index - 1]

							# currently only gamma=0 is supported. This usually works best anyways.
							# We can support gamma in the future but then need to scale the timestep before
							# passing it to the model which requires a change in API
							a       =     0
							a       =     sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

							a       =     model_output.device
							a       =     randn_tensor(model_output.shape   , dtype=model_output.dtype   , device=__lowerCamelCase   , generator=__lowerCamelCase				)

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
										a       =     sigma_hat if self.state_in_first_order else sigma_interpol
										a       =     sample - sigma_input * model_output
							elif self.config.prediction_type == "v_prediction":
										a       =     sigma_hat if self.state_in_first_order else sigma_interpol
										a       =     model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
										    sample / (sigma_input**2 + 1)
										)
							elif self.config.prediction_type == "sample":
										raise NotImplementedError("prediction_type not implemented yet: sample"				)
							else:
										raise ValueError(
										    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""				)

							if self.state_in_first_order:
										# 2. Convert to an ODE derivative for 1st order
										a       =     (sample - pred_original_sample) / sigma_hat
										# 3. delta timestep
										a       =     sigma_interpol - sigma_hat

										# store for 2nd order step
										a       =     sample
										a       =     dt
										a       =     sample + derivative * dt
							else:
										# DPM-Solver-2
										# 2. Convert to an ODE derivative for 2nd order
										a       =     (sample - pred_original_sample) / sigma_interpol
										# 3. delta timestep
										a       =     sigma_down - sigma_hat

										a       =     self.sample
										a       =     None

										a       =     sample + derivative * dt
										a       =     prev_sample + noise * sigma_up

							if not return_dict:
										return (prev_sample,)

							return SchedulerOutput(prev_sample=__lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Dict   , __lowerCamelCase							:	torch.FloatTensor   , __lowerCamelCase							:	torch.FloatTensor   , __lowerCamelCase							:	torch.FloatTensor   , )   ->	torch.FloatTensor:
							a       =     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
										a       =     self.timesteps.to(original_samples.device   , dtype=torch.floataa				)
										a       =     timesteps.to(original_samples.device   , dtype=torch.floataa				)
							else:
										a       =     self.timesteps.to(original_samples.device				)
										a       =     timesteps.to(original_samples.device				)

							a       =     [self.index_for_timestep(__lowerCamelCase   , __lowerCamelCase				) for t in timesteps]

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

							a       =     original_samples + noise * sigma
							return noisy_samples





				def __len__( self							:	List[Any]				)   ->	int:
							return self.config.num_train_timesteps











"""simple docstring"""







import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__=0.9_99					,   lowerCamelCase__="cosine"					,   )			->				int:
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(lowerCamelCase__      ):
																					return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2      ) ** 2

							elif alpha_transform_type == "exp":

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

							else:
														raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""      )

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






class   UpperCAmelCase__		(   UpperCamelCase ,UpperCamelCase  ):
		lowerCAmelCase_							:						int							=  [e.name for e in KarrasDiffusionSchedulers]
		lowerCAmelCase_							:						Union[str, Any]							=  2



		@register_to_config
		def __init__(			self	:     Any   ,				snake_case	:     int = 1_000   ,				snake_case	:     float = 0.00085   ,				snake_case	:     float = 0.012   ,				snake_case	:     str = "linear"   ,				snake_case	:     Optional[Union[np.ndarray, List[float]]] = None   ,				snake_case	:     str = "epsilon"   ,				snake_case	:     str = "linspace"   ,				snake_case	:     int = 0   ,				)      ->    Dict:






									'''simple docstring'''

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

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

									#  set all values
									self.set_timesteps(snake_case   ,				snake_case   ,				snake_case					)



		def        A_		(			self	:     str   ,				snake_case	:     List[Any]   ,				snake_case	:     str=None					)      ->    Optional[Any]:






									'''simple docstring'''

									if schedule_timesteps is None:
																A    =       self.timesteps

									A    =       (schedule_timesteps == timestep).nonzero()

									# The sigma index that is taken for the **very** first `step`
									# is always the second index (or the last index if there is only 1)
									# This way we can ensure we don't accidentally skip a sigma in
									# case we start in the middle of the denoising schedule (e.g. for image-to-image)
									if len(self._index_counter					) == 0:
																A    =       1 if len(snake_case					) > 1 else 0
									else:
																A    =       timestep.cpu().item() if torch.is_tensor(snake_case					) else timestep
																A    =       self._index_counter[timestep_int]

									return indices[pos].item()



		@property
		def        A_		(			self	:     Optional[int]					)      ->    Union[str, Any]:






									'''simple docstring'''

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

									return (self.sigmas.max() ** 2 + 1) ** 0.5



		def        A_		(			self	:     Union[str, Any]   ,				snake_case	:     torch.FloatTensor   ,				snake_case	:     Union[float, torch.FloatTensor]   ,				)      ->    torch.FloatTensor:






									'''simple docstring'''

									A    =       self.index_for_timestep(snake_case					)

									if self.state_in_first_order:
																A    =       self.sigmas[step_index]
									else:
																A    =       self.sigmas_interpol[step_index - 1]

									A    =       sample / ((sigma**2 + 1) ** 0.5)
									return sample



		def        A_		(			self	:     Union[str, Any]   ,				snake_case	:     int   ,				snake_case	:     Union[str, torch.device] = None   ,				snake_case	:     Optional[int] = None   ,				)      ->    Any:






									'''simple docstring'''

									A    =       num_inference_steps

									A    =       num_train_timesteps or self.config.num_train_timesteps

									# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
									if self.config.timestep_spacing == "linspace":
																A    =       np.linspace(0   ,				num_train_timesteps - 1   ,				snake_case   ,				dtype=snake_case					)[::-1].copy()
									elif self.config.timestep_spacing == "leading":
																A    =       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
																A    =       (np.arange(0   ,				snake_case					) * step_ratio).round()[::-1].copy().astype(snake_case					)
																timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
																A    =       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
																A    =       (np.arange(snake_case   ,				0   ,				-step_ratio					)).round().copy().astype(snake_case					)
																timesteps -= 1
									else:
																raise ValueError(
																    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""					)

									A    =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
									A    =       torch.from_numpy(np.log(snake_case					)					).to(snake_case					)

									A    =       np.interp(snake_case   ,				np.arange(0   ,				len(snake_case					)					)   ,				snake_case					)
									A    =       np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
									A    =       torch.from_numpy(snake_case					).to(device=snake_case					)

									# compute up and down sigmas
									A    =       sigmas.roll(-1					)
									A    =       0.0
									A    =       (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
									A    =       (sigmas_next**2 - sigmas_up**2) ** 0.5
									A    =       0.0

									# compute interpolated sigmas
									A    =       sigmas.log().lerp(sigmas_down.log()   ,				0.5					).exp()
									A    =       0.0

									# set sigmas
									A    =       torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2					), sigmas[-1:]]					)
									A    =       torch.cat(
									    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2					), sigmas_interpol[-1:]]					)
									A    =       torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2					), sigmas_up[-1:]]					)
									A    =       torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2					), sigmas_down[-1:]]					)

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

									A    =       self.sigma_to_t(snake_case					).to(snake_case   ,				dtype=timesteps.dtype					)
									A    =       torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])   ,				dim=-1					).flatten()

									A    =       torch.cat([timesteps[:1], interleaved_timesteps]					)

									A    =       None

									# for exp beta schedules, such as the one for `pipeline_shap_e.py`
									# we need an index counter
									A    =       defaultdict(snake_case					)



		def        A_		(			self	:     Dict   ,				snake_case	:     Dict					)      ->    Tuple:






									'''simple docstring'''

									A    =       sigma.log()

									# get distribution
									A    =       log_sigma - self.log_sigmas[:, None]

									# get sigmas range
									A    =       dists.ge(0					).cumsum(dim=0					).argmax(dim=0					).clamp(max=self.log_sigmas.shape[0] - 2					)
									A    =       low_idx + 1

									A    =       self.log_sigmas[low_idx]
									A    =       self.log_sigmas[high_idx]

									# interpolate sigmas
									A    =       (low - log_sigma) / (low - high)
									A    =       w.clamp(0   ,				1					)

									# transform interpolation to time range
									A    =       (1 - w) * low_idx + w * high_idx
									A    =       t.view(sigma.shape					)
									return t



		@property
		def        A_		(			self	:     Any					)      ->    int:






									'''simple docstring'''

									return self.sample is None



		def        A_		(			self	:     Tuple   ,				snake_case	:     Union[torch.FloatTensor, np.ndarray]   ,				snake_case	:     Union[float, torch.FloatTensor]   ,				snake_case	:     Union[torch.FloatTensor, np.ndarray]   ,				snake_case	:     Optional[torch.Generator] = None   ,				snake_case	:     bool = True   ,				)      ->    Union[SchedulerOutput, Tuple]:






									'''simple docstring'''

									A    =       self.index_for_timestep(snake_case					)

									# advance index counter by 1
									A    =       timestep.cpu().item() if torch.is_tensor(snake_case					) else timestep
									self._index_counter[timestep_int] += 1

									if self.state_in_first_order:
																A    =       self.sigmas[step_index]
																A    =       self.sigmas_interpol[step_index]
																A    =       self.sigmas_up[step_index]
																A    =       self.sigmas_down[step_index - 1]
									else:
																# 2nd order / KPDM2's method
																A    =       self.sigmas[step_index - 1]
																A    =       self.sigmas_interpol[step_index - 1]
																A    =       self.sigmas_up[step_index - 1]
																A    =       self.sigmas_down[step_index - 1]

									# currently only gamma=0 is supported. This usually works best anyways.
									# We can support gamma in the future but then need to scale the timestep before
									# passing it to the model which requires a change in API
									A    =       0
									A    =       sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

									A    =       model_output.device
									A    =       randn_tensor(model_output.shape   ,				dtype=model_output.dtype   ,				device=snake_case   ,				generator=snake_case					)

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
																A    =       sigma_hat if self.state_in_first_order else sigma_interpol
																A    =       sample - sigma_input * model_output
									elif self.config.prediction_type == "v_prediction":
																A    =       sigma_hat if self.state_in_first_order else sigma_interpol
																A    =       model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																    sample / (sigma_input**2 + 1)
																)
									elif self.config.prediction_type == "sample":
																raise NotImplementedError('prediction_type not implemented yet: sample'					)
									else:
																raise ValueError(
																    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""					)

									if self.state_in_first_order:
																# 2. Convert to an ODE derivative for 1st order
																A    =       (sample - pred_original_sample) / sigma_hat
																# 3. delta timestep
																A    =       sigma_interpol - sigma_hat

																# store for 2nd order step
																A    =       sample
																A    =       dt
																A    =       sample + derivative * dt
									else:
																# DPM-Solver-2
																# 2. Convert to an ODE derivative for 2nd order
																A    =       (sample - pred_original_sample) / sigma_interpol
																# 3. delta timestep
																A    =       sigma_down - sigma_hat

																A    =       self.sample
																A    =       None

																A    =       sample + derivative * dt
																A    =       prev_sample + noise * sigma_up

									if not return_dict:
																return (prev_sample,)

									return SchedulerOutput(prev_sample=snake_case					)



		def        A_		(			self	:     Dict   ,				snake_case	:     torch.FloatTensor   ,				snake_case	:     torch.FloatTensor   ,				snake_case	:     torch.FloatTensor   ,				)      ->    torch.FloatTensor:






									'''simple docstring'''

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

									A    =       [self.index_for_timestep(snake_case   ,				snake_case					) for t in timesteps]

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

									A    =       original_samples + noise * sigma
									return noisy_samples



		def __len__(			self	:     List[Any]					)      ->    int:






									'''simple docstring'''

									return self.config.num_train_timesteps



