



'''simple docstring'''
from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


snake_case_				:			List[str]						 =						logging.get_logger(__name__)  # pylint: disable=invalid-name


snake_case_				:			Optional[int]						 =						'\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = "cuda"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample.png")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")\n        ```\n'






class 							lowercase__			(							lowerCAmelCase_     ):





	def __init__(				self      :       Optional[int]				,lowerCamelCase__      :       Optional[Any]				,lowerCamelCase__      :       int				):







							'''simple docstring'''
							super().__init__()

							self.register_modules(
							    unet=__a				,scheduler=__a				,)

							_UpperCamelCase      :	List[str]				       =     None





	def 			UpperCamelCase_						(				self      :       Union[str, Any]				,lowerCamelCase__      :       int=0				):







							'''simple docstring'''





							if is_accelerate_available() and is_accelerate_version('>='				,'0.14.0'				):
													from accelerate import cpu_offload
							else:
													raise ImportError('`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher'				)

							_UpperCamelCase      :	str				       =     torch.device(F'cuda:{gpu_id}'				)

							if self.device.type != "cpu":
													self.to('cpu'				,silence_dtype_warnings=__a				)
													torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

							for cpu_offloaded_model in [self.unet]:
													cpu_offload(__a				,__a				)

							if self.safety_checker is not None:
													cpu_offload(self.safety_checker				,execution_device=__a				,offload_buffers=__a				)





	def 			UpperCamelCase_						(				self      :       Optional[Any]				,lowerCamelCase__      :       Dict=0				):







							'''simple docstring'''





							if is_accelerate_available() and is_accelerate_version('>='				,'0.17.0.dev0'				):
													from accelerate import cpu_offload_with_hook
							else:
													raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'				)

							_UpperCamelCase      :	Tuple				       =     torch.device(F'cuda:{gpu_id}'				)

							if self.device.type != "cpu":
													self.to('cpu'				,silence_dtype_warnings=__a				)
													torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

							_UpperCamelCase      :	List[Any]				       =     None
							for cpu_offloaded_model in [self.unet]:
													_UpperCamelCase      :	List[str]				       =     cpu_offload_with_hook(__a				,__a				,prev_module_hook=__a				)

							if self.safety_checker is not None:
													_UpperCamelCase      :	str				       =     cpu_offload_with_hook(self.safety_checker				,__a				,prev_module_hook=__a				)

							# We'll offload the last model manually.
							_UpperCamelCase      :	List[Any]				       =     hook





	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def 			UpperCamelCase_						(				self      :       Optional[int]				):







							'''simple docstring'''





							if not hasattr(self.unet				,'_hf_hook'				):
													return self.device
							for module in self.unet.modules():
													if (
													    hasattr(__a				,'_hf_hook'				)
													    and hasattr(module._hf_hook				,'execution_device'				)
													    and module._hf_hook.execution_device is not None
													):
																			return torch.device(module._hf_hook.execution_device				)
							return self.device





	def 			UpperCamelCase_						(				self      :       Union[str, Any]				,lowerCamelCase__      :       List[str]				,lowerCamelCase__      :       List[str]				,lowerCamelCase__      :       Optional[Any]				,lowerCamelCase__      :       Optional[int]				,lowerCamelCase__      :       Dict				,lowerCamelCase__      :       Optional[Any]				,lowerCamelCase__      :       Optional[Any]				,lowerCamelCase__      :       Union[str, Any]=None				):







							'''simple docstring'''
							_UpperCamelCase      :	str				       =     (batch_size, num_channels, height, width)
							if isinstance(__a				,__a				) and len(__a				) != batch_size:
													raise ValueError(
													    F'You have passed a list of generators of length {len(__a				)}, but requested an effective batch'
													    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'				)

							if latents is None:
													_UpperCamelCase      :	Optional[Any]				       =     randn_tensor(__a				,generator=__a				,device=__a				,dtype=__a				)
							else:
													_UpperCamelCase      :	Dict				       =     latents.to(device=__a				,dtype=__a				)

							# scale the initial noise by the standard deviation required by the scheduler
							_UpperCamelCase      :	Union[str, Any]				       =     latents * self.scheduler.init_noise_sigma
							return latents





	def 			UpperCamelCase_						(				self      :       Optional[int]				,lowerCamelCase__      :       Union[str, Any]				,lowerCamelCase__      :       Optional[int] = "pil"				):







							'''simple docstring'''





							if output_type not in ["pt", "np", "pil"]:
													raise ValueError(
													    F'output_type={output_type} is not supported. Make sure to choose one of [\'pt\', \'np\', or \'pil\']'				)

							# Equivalent to diffusers.VaeImageProcessor.denormalize
							_UpperCamelCase      :	List[Any]				       =     (sample / 2 + 0.5).clamp(0				,1				)
							if output_type == "pt":
													return sample

							# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
							_UpperCamelCase      :	str				       =     sample.cpu().permute(0				,2				,3				,1				).numpy()
							if output_type == "np":
													return sample

							# Output_type must be 'pil'
							_UpperCamelCase      :	Union[str, Any]				       =     self.numpy_to_pil(__a				)
							return sample





	def 			UpperCamelCase_						(				self      :       List[str]				,lowerCamelCase__      :       List[str]				,lowerCamelCase__      :       List[Any]				,lowerCamelCase__      :       int=None				):







							'''simple docstring'''





							if self.unet.config.num_class_embeds is not None:
													if isinstance(__a				,__a				):
																			_UpperCamelCase      :	Tuple				       =     torch.tensor(__a				,dtype=torch.int				)
													elif isinstance(__a				,__a				):
																			assert batch_size == 1, "Batch size must be 1 if classes is an int"
																			_UpperCamelCase      :	int				       =     torch.tensor([class_labels]				,dtype=torch.int				)
													elif class_labels is None:
																			# Randomly generate batch_size class labels
																			# TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
																			_UpperCamelCase      :	str				       =     torch.randint(0				,self.unet.config.num_class_embeds				,size=(batch_size,)				)
													_UpperCamelCase      :	str				       =     class_labels.to(__a				)
							else:
													_UpperCamelCase      :	Optional[int]				       =     None
							return class_labels





	def 			UpperCamelCase_						(				self      :       Tuple				,lowerCamelCase__      :       Tuple				,lowerCamelCase__      :       Tuple				,lowerCamelCase__      :       List[Any]				,lowerCamelCase__      :       List[str]				,lowerCamelCase__      :       Dict				,lowerCamelCase__      :       Optional[int]				):







							'''simple docstring'''





							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:
													logger.warning(
													    F'Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;'
													    ' `timesteps` will be used over `num_inference_steps`.'				)

							if latents is not None:
													_UpperCamelCase      :	Optional[Any]				       =     (batch_size, 3, img_size, img_size)
													if latents.shape != expected_shape:
																			raise ValueError(F'The shape of latents is {latents.shape} but is expected to be {expected_shape}.'				)

							if (callback_steps is None) or (
							    callback_steps is not None and (not isinstance(__a				,__a				) or callback_steps <= 0)
							):
													raise ValueError(
													    F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
													    F' {type(__a				)}.'				)





	@torch.no_grad()
	@replace_example_docstring(__a				)
	def __call__(				self      :       Tuple				,lowerCamelCase__      :       int = 1				,lowerCamelCase__      :       Tuple = None				,lowerCamelCase__      :       int = 1				,lowerCamelCase__      :       List[str] = None				,lowerCamelCase__      :       Optional[int] = None				,lowerCamelCase__      :       Optional[int] = None				,lowerCamelCase__      :       Optional[Any] = "pil"				,lowerCamelCase__      :       str = True				,lowerCamelCase__      :       Tuple = None				,lowerCamelCase__      :       Dict = 1				,):







							'''simple docstring'''
							_UpperCamelCase      :	Dict				       =     self.unet.config.sample_size
							_UpperCamelCase      :	Union[str, Any]				       =     self._execution_device

							# 1. Check inputs
							self.check_inputs(__a				,__a				,__a				,__a				,__a				,__a				)

							# 2. Prepare image latents
							# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
							_UpperCamelCase      :	Union[str, Any]				       =     self.prepare_latents(
							    batch_size=__a				,num_channels=self.unet.config.in_channels				,height=__a				,width=__a				,dtype=self.unet.dtype				,device=__a				,generator=__a				,latents=__a				,)

							# 3. Handle class_labels for class-conditional models
							_UpperCamelCase      :	Optional[Any]				       =     self.prepare_class_labels(__a				,__a				,class_labels=__a				)

							# 4. Prepare timesteps
							if timesteps is not None:
													self.scheduler.set_timesteps(timesteps=__a				,device=__a				)
													_UpperCamelCase      :	Union[str, Any]				       =     self.scheduler.timesteps
													_UpperCamelCase      :	Tuple				       =     len(__a				)
							else:
													self.scheduler.set_timesteps(__a				)
													_UpperCamelCase      :	Tuple				       =     self.scheduler.timesteps

							# 5. Denoising loop
							# Multistep sampling: implements Algorithm 1 in the paper
							with self.progress_bar(total=__a				) as progress_bar:
													for i, t in enumerate(__a				):
																			_UpperCamelCase      :	Optional[int]				       =     self.scheduler.scale_model_input(__a				,__a				)
																			_UpperCamelCase      :	List[str]				       =     self.unet(__a				,__a				,class_labels=__a				,return_dict=__a				)[0]

																			_UpperCamelCase      :	int				       =     self.scheduler.step(__a				,__a				,__a				,generator=__a				)[0]

																			# call the callback, if provided
																			progress_bar.update()
																			if callback is not None and i % callback_steps == 0:
																									callback(__a				,__a				,__a				)

        # 6. Post-process image sample
							_UpperCamelCase      :	Tuple				       =     self.postprocess_image(__a				,output_type=__a				)

							# Offload last model to CPU
							if hasattr(self				,'final_offload_hook'				) and self.final_offload_hook is not None:
													self.final_offload_hook.offload()

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=__a				)







'''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 A__					(  UpperCAmelCase_						,						UpperCAmelCase_=0.999						,						UpperCAmelCase_="cosine"						,						):
						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

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

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

						_UpperCamelCase      :	Union[str, Any]				       =     []
						for i in range(UpperCAmelCase_							):
												_UpperCamelCase      :	Any				       =     i / num_diffusion_timesteps
												_UpperCamelCase      :	Any				       =     (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(UpperCAmelCase_							) / alpha_bar_fn(UpperCAmelCase_							)						,						UpperCAmelCase_							)							)
						return torch.tensor(UpperCAmelCase_						,						dtype=torch.floataa							)






class 							lowercase__			(							lowercase		,      lowercase     ):
	lowercase__						       =    [e.name for e in KarrasDiffusionSchedulers]
	lowercase__						       =    2





	@register_to_config
	def __init__(				self      :       List[str]				,lowerCamelCase__      :       int = 1000				,lowerCamelCase__      :       float = 0.0_0_0_8_5				,lowerCamelCase__      :       float = 0.0_1_2				,lowerCamelCase__      :       str = "linear"				,lowerCamelCase__      :       Optional[Union[np.ndarray, List[float]]] = None				,lowerCamelCase__      :       str = "epsilon"				,lowerCamelCase__      :       str = "linspace"				,lowerCamelCase__      :       int = 0				,):







							'''simple docstring'''
							if trained_betas is not None:
													_UpperCamelCase      :	Tuple				       =     torch.tensor(lowerCamelCase__				,dtype=torch.floataa				)
							elif beta_schedule == "linear":
													_UpperCamelCase      :	int				       =     torch.linspace(lowerCamelCase__				,lowerCamelCase__				,lowerCamelCase__				,dtype=torch.floataa				)
							elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													_UpperCamelCase      :	Dict				       =     (
													    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      :	Dict				       =     betas_for_alpha_bar(lowerCamelCase__				)
							else:
													raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'				)

							_UpperCamelCase      :	Tuple				       =     1.0 - self.betas
							_UpperCamelCase      :	Any				       =     torch.cumprod(self.alphas				,dim=0				)

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





	def 			UpperCamelCase_						(				self      :       Optional[Any]				,lowerCamelCase__      :       Any				,lowerCamelCase__      :       Optional[int]=None				):







							'''simple docstring'''
							if schedule_timesteps is None:
													_UpperCamelCase      :	Optional[Any]				       =     self.timesteps

							_UpperCamelCase      :	List[str]				       =     (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:
													_UpperCamelCase      :	List[Any]				       =     1 if len(lowerCamelCase__				) > 1 else 0
							else:
													_UpperCamelCase      :	Optional[Any]				       =     timestep.cpu().item() if torch.is_tensor(lowerCamelCase__				) else timestep
													_UpperCamelCase      :	str				       =     self._index_counter[timestep_int]

							return indices[pos].item()





	@property
	def 			UpperCamelCase_						(				self      :       List[str]				):







							'''simple docstring'''
							# standard deviation of the initial noise distribution
							if self.config.timestep_spacing in ["linspace", "trailing"]:
													return self.sigmas.max()

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





	def 			UpperCamelCase_						(				self      :       Optional[Any]				,lowerCamelCase__      :       torch.FloatTensor				,lowerCamelCase__      :       Union[float, torch.FloatTensor]				,):







							'''simple docstring'''
							_UpperCamelCase      :	int				       =     self.index_for_timestep(lowerCamelCase__				)

							if self.state_in_first_order:
													_UpperCamelCase      :	str				       =     self.sigmas[step_index]
							else:
													_UpperCamelCase      :	List[Any]				       =     self.sigmas_interpol[step_index - 1]

							_UpperCamelCase      :	Optional[Any]				       =     sample / ((sigma**2 + 1) ** 0.5)
							return sample





	def 			UpperCamelCase_						(				self      :       Tuple				,lowerCamelCase__      :       int				,lowerCamelCase__      :       Union[str, torch.device] = None				,lowerCamelCase__      :       Optional[int] = None				,):







							'''simple docstring'''
							_UpperCamelCase      :	int				       =     num_inference_steps

							_UpperCamelCase      :	List[str]				       =     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":
													_UpperCamelCase      :	Optional[int]				       =     np.linspace(0				,num_train_timesteps - 1				,lowerCamelCase__				,dtype=lowerCamelCase__				)[::-1].copy()
							elif self.config.timestep_spacing == "leading":
													_UpperCamelCase      :	Optional[Any]				       =     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      :	Tuple				       =     (np.arange(0				,lowerCamelCase__				) * step_ratio).round()[::-1].copy().astype(lowerCamelCase__				)
													timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
													_UpperCamelCase      :	Dict				       =     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      :	int				       =     (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\'.'				)

							_UpperCamelCase      :	List[str]				       =     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
							_UpperCamelCase      :	str				       =     torch.from_numpy(np.log(lowerCamelCase__				)				).to(lowerCamelCase__				)

							_UpperCamelCase      :	int				       =     np.interp(lowerCamelCase__				,np.arange(0				,len(lowerCamelCase__				)				)				,lowerCamelCase__				)
							_UpperCamelCase      :	Any				       =     np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)
							_UpperCamelCase      :	int				       =     torch.from_numpy(lowerCamelCase__				).to(device=lowerCamelCase__				)

							# compute up and down sigmas
							_UpperCamelCase      :	Dict				       =     sigmas.roll(-1				)
							_UpperCamelCase      :	Tuple				       =     0.0
							_UpperCamelCase      :	Optional[Any]				       =     (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
							_UpperCamelCase      :	List[str]				       =     (sigmas_next**2 - sigmas_up**2) ** 0.5
							_UpperCamelCase      :	Tuple				       =     0.0

							# compute interpolated sigmas
							_UpperCamelCase      :	Any				       =     sigmas.log().lerp(sigmas_down.log()				,0.5				).exp()
							_UpperCamelCase      :	Tuple				       =     0.0

							# set sigmas
							_UpperCamelCase      :	Tuple				       =     torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2				), sigmas[-1:]]				)
							_UpperCamelCase      :	Tuple				       =     torch.cat(
							    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2				), sigmas_interpol[-1:]]				)
							_UpperCamelCase      :	Optional[int]				       =     torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2				), sigmas_up[-1:]]				)
							_UpperCamelCase      :	List[str]				       =     torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2				), sigmas_down[-1:]]				)

							if str(lowerCamelCase__				).startswith('mps'				):
													# mps does not support float64
													_UpperCamelCase      :	int				       =     torch.from_numpy(lowerCamelCase__				).to(lowerCamelCase__				,dtype=torch.floataa				)
							else:
													_UpperCamelCase      :	Dict				       =     torch.from_numpy(lowerCamelCase__				).to(lowerCamelCase__				)

							_UpperCamelCase      :	List[str]				       =     self.sigma_to_t(lowerCamelCase__				).to(lowerCamelCase__				,dtype=timesteps.dtype				)
							_UpperCamelCase      :	List[Any]				       =     torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])				,dim=-1				).flatten()

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

							_UpperCamelCase      :	Optional[int]				       =     None

							# for exp beta schedules, such as the one for `pipeline_shap_e.py`
							# we need an index counter
							_UpperCamelCase      :	Optional[Any]				       =     defaultdict(lowerCamelCase__				)





	def 			UpperCamelCase_						(				self      :       List[Any]				,lowerCamelCase__      :       Optional[Any]				):







							'''simple docstring'''
							# get log sigma
							_UpperCamelCase      :	Union[str, Any]				       =     sigma.log()

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

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

							_UpperCamelCase      :	Optional[int]				       =     self.log_sigmas[low_idx]
							_UpperCamelCase      :	Optional[int]				       =     self.log_sigmas[high_idx]

							# interpolate sigmas
							_UpperCamelCase      :	Optional[int]				       =     (low - log_sigma) / (low - high)
							_UpperCamelCase      :	Any				       =     w.clamp(0				,1				)

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





	@property
	def 			UpperCamelCase_						(				self      :       Any				):







							'''simple docstring'''
							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				,):







							'''simple docstring'''
							_UpperCamelCase      :	Optional[Any]				       =     self.index_for_timestep(lowerCamelCase__				)

							# advance index counter by 1
							_UpperCamelCase      :	int				       =     timestep.cpu().item() if torch.is_tensor(lowerCamelCase__				) else timestep
							self._index_counter[timestep_int] += 1

							if self.state_in_first_order:
													_UpperCamelCase      :	Optional[int]				       =     self.sigmas[step_index]
													_UpperCamelCase      :	str				       =     self.sigmas_interpol[step_index]
													_UpperCamelCase      :	Optional[int]				       =     self.sigmas_up[step_index]
													_UpperCamelCase      :	List[Any]				       =     self.sigmas_down[step_index - 1]
							else:
													# 2nd order / KPDM2's method
													_UpperCamelCase      :	List[Any]				       =     self.sigmas[step_index - 1]
													_UpperCamelCase      :	Dict				       =     self.sigmas_interpol[step_index - 1]
													_UpperCamelCase      :	List[str]				       =     self.sigmas_up[step_index - 1]
													_UpperCamelCase      :	Optional[Any]				       =     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
							_UpperCamelCase      :	int				       =     0
							_UpperCamelCase      :	Union[str, Any]				       =     sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

							_UpperCamelCase      :	Union[str, Any]				       =     model_output.device
							_UpperCamelCase      :	Dict				       =     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":
													_UpperCamelCase      :	Union[str, Any]				       =     sigma_hat if self.state_in_first_order else sigma_interpol
													_UpperCamelCase      :	str				       =     sample - sigma_input * model_output
							elif self.config.prediction_type == "v_prediction":
													_UpperCamelCase      :	Optional[Any]				       =     sigma_hat if self.state_in_first_order else sigma_interpol
													_UpperCamelCase      :	Optional[Any]				       =     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
													_UpperCamelCase      :	Dict				       =     (sample - pred_original_sample) / sigma_hat
													# 3. delta timestep
													_UpperCamelCase      :	Optional[Any]				       =     sigma_interpol - sigma_hat

													# store for 2nd order step
													_UpperCamelCase      :	Union[str, Any]				       =     sample
													_UpperCamelCase      :	Any				       =     dt
													_UpperCamelCase      :	Optional[Any]				       =     sample + derivative * dt
							else:
													# DPM-Solver-2
													# 2. Convert to an ODE derivative for 2nd order
													_UpperCamelCase      :	Tuple				       =     (sample - pred_original_sample) / sigma_interpol
													# 3. delta timestep
													_UpperCamelCase      :	int				       =     sigma_down - sigma_hat

													_UpperCamelCase      :	Dict				       =     self.sample
													_UpperCamelCase      :	Tuple				       =     None

													_UpperCamelCase      :	Dict				       =     sample + derivative * dt
													_UpperCamelCase      :	int				       =     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				,):







							'''simple docstring'''
							# Make sure sigmas and timesteps have the same device and dtype as original_samples
							_UpperCamelCase      :	Optional[int]				       =     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      :	Any				       =     self.timesteps.to(original_samples.device				,dtype=torch.floataa				)
													_UpperCamelCase      :	Dict				       =     timesteps.to(original_samples.device				,dtype=torch.floataa				)
							else:
													_UpperCamelCase      :	List[str]				       =     self.timesteps.to(original_samples.device				)
													_UpperCamelCase      :	str				       =     timesteps.to(original_samples.device				)

							_UpperCamelCase      :	Optional[int]				       =     [self.index_for_timestep(lowerCamelCase__				,lowerCamelCase__				) for t in timesteps]

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

							_UpperCamelCase      :	Dict				       =     original_samples + noise * sigma
							return noisy_samples





	def __len__(				self      :       List[str]				):







							'''simple docstring'''
							return self.config.num_train_timesteps


