


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


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


__lowerCAmelCase     :			Any      ='\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				(      A__	):






				'''simple docstring'''



				def __init__(	self      :Tuple      ,      lowerCAmelCase__      :UNetaDModel      ,      lowerCAmelCase__      :CMStochasticIterativeScheduler     )		->      None:
										super().__init__()

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

										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		None


				def 		__magic_name__(	self      :str      ,      lowerCAmelCase__      :List[Any]=0     )		->      Optional[Any]:
										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'''     )

										__SCREAMING_SNAKE_CASE						:					int		      =		torch.device(f'''cuda:{gpu_id}'''     )

										if self.device.type != "cpu":
																self.to('''cpu'''      ,      silence_dtype_warnings=lowerCAmelCase__     )
																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(lowerCAmelCase__      ,      lowerCAmelCase__     )

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


				def 		__magic_name__(	self      :Dict      ,      lowerCAmelCase__      :List[str]=0     )		->      Optional[int]:
										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.'''     )

										__SCREAMING_SNAKE_CASE						:					Dict		      =		torch.device(f'''cuda:{gpu_id}'''     )

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

										__SCREAMING_SNAKE_CASE						:					int		      =		None
										for cpu_offloaded_model in [self.unet]:
																__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					str		      =		cpu_offload_with_hook(lowerCAmelCase__      ,      lowerCAmelCase__      ,      prev_module_hook=lowerCAmelCase__     )

										if self.safety_checker is not None:
																__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Any		      =		cpu_offload_with_hook(self.safety_checker      ,      lowerCAmelCase__      ,      prev_module_hook=lowerCAmelCase__     )

										# We'll offload the last model manually.
										__SCREAMING_SNAKE_CASE						:					int		      =		hook


				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def 		__magic_name__(	self      :List[str]     )		->      Optional[int]:
										if not hasattr(self.unet      ,      '''_hf_hook'''     ):
																return self.device
										for module in self.unet.modules():
																if (
																    hasattr(lowerCAmelCase__      ,      '''_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 		__magic_name__(	self      :Dict      ,      lowerCAmelCase__      :Optional[int]      ,      lowerCAmelCase__      :Optional[int]      ,      lowerCAmelCase__      :Tuple      ,      lowerCAmelCase__      :Dict      ,      lowerCAmelCase__      :List[Any]      ,      lowerCAmelCase__      :Union[str, Any]      ,      lowerCAmelCase__      :Union[str, Any]      ,      lowerCAmelCase__      :Optional[int]=None     )		->      Optional[int]:
										__SCREAMING_SNAKE_CASE						:					int		      =		(batch_size, num_channels, height, width)
										if isinstance(lowerCAmelCase__      ,      lowerCAmelCase__     ) and len(lowerCAmelCase__     ) != batch_size:
																raise ValueError(
																    f'''You have passed a list of generators of length {len(lowerCAmelCase__     )}, 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:
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		randn_tensor(lowerCAmelCase__      ,      generator=lowerCAmelCase__      ,      device=lowerCAmelCase__      ,      dtype=lowerCAmelCase__     )
										else:
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		latents.to(device=lowerCAmelCase__      ,      dtype=lowerCAmelCase__     )

										# scale the initial noise by the standard deviation required by the scheduler
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		latents * self.scheduler.init_noise_sigma
										return latents


				def 		__magic_name__(	self      :Any      ,      lowerCAmelCase__      :torch.FloatTensor      ,      lowerCAmelCase__      :str = "pil"     )		->      int:
										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
										__SCREAMING_SNAKE_CASE						:					int		      =		(sample / 2 + 0.5).clamp(0      ,      1     )
										if output_type == "pt":
																return sample

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

										# Output_type must be 'pil'
										__SCREAMING_SNAKE_CASE						:					Dict		      =		self.numpy_to_pil(lowerCAmelCase__     )
										return sample


				def 		__magic_name__(	self      :str      ,      lowerCAmelCase__      :Any      ,      lowerCAmelCase__      :List[Any]      ,      lowerCAmelCase__      :Tuple=None     )		->      Optional[int]:
										if self.unet.config.num_class_embeds is not None:
																if isinstance(lowerCAmelCase__      ,      lowerCAmelCase__     ):
																						__SCREAMING_SNAKE_CASE						:					Tuple		      =		torch.tensor(lowerCAmelCase__      ,      dtype=torch.int     )
																elif isinstance(lowerCAmelCase__      ,      lowerCAmelCase__     ):
																						assert batch_size == 1, "Batch size must be 1 if classes is an int"
																						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		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
																						__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		torch.randint(0      ,      self.unet.config.num_class_embeds      ,      size=(batch_size,)     )
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		class_labels.to(lowerCAmelCase__     )
										else:
																__SCREAMING_SNAKE_CASE						:					Any		      =		None
										return class_labels


				def 		__magic_name__(	self      :Optional[int]      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :Dict      ,      lowerCAmelCase__      :Any      ,      lowerCAmelCase__      :int      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :Any     )		->      Union[str, Any]:
										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:
																__SCREAMING_SNAKE_CASE						:					Union[str, 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(lowerCAmelCase__      ,      lowerCAmelCase__     ) or callback_steps <= 0)
										):
																raise ValueError(
																    f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
																    f''' {type(lowerCAmelCase__     )}.'''     )



				@torch.no_grad()
				@replace_example_docstring(lowerCAmelCase__     )
				def __call__(	self      :str      ,      lowerCAmelCase__      :int = 1      ,      lowerCAmelCase__      :Optional[Union[torch.Tensor, List[int], int]] = None      ,      lowerCAmelCase__      :int = 1      ,      lowerCAmelCase__      :List[int] = None      ,      lowerCAmelCase__      :Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,      lowerCAmelCase__      :Optional[torch.FloatTensor] = None      ,      lowerCAmelCase__      :Optional[str] = "pil"      ,      lowerCAmelCase__      :bool = True      ,      lowerCAmelCase__      :Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,      lowerCAmelCase__      :int = 1      ,      )		->      Any:
										__SCREAMING_SNAKE_CASE						:					int		      =		self.unet.config.sample_size
										__SCREAMING_SNAKE_CASE						:					int		      =		self._execution_device

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

										# 2. Prepare image latents
										# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
										__SCREAMING_SNAKE_CASE						:					Dict		      =		self.prepare_latents(
										    batch_size=lowerCAmelCase__      ,      num_channels=self.unet.config.in_channels      ,      height=lowerCAmelCase__      ,      width=lowerCAmelCase__      ,      dtype=self.unet.dtype      ,      device=lowerCAmelCase__      ,      generator=lowerCAmelCase__      ,      latents=lowerCAmelCase__      ,      )

										# 3. Handle class_labels for class-conditional models
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.prepare_class_labels(lowerCAmelCase__      ,      lowerCAmelCase__      ,      class_labels=lowerCAmelCase__     )

										# 4. Prepare timesteps
										if timesteps is not None:
																self.scheduler.set_timesteps(timesteps=lowerCAmelCase__      ,      device=lowerCAmelCase__     )
																__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		self.scheduler.timesteps
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		len(lowerCAmelCase__     )
										else:
																self.scheduler.set_timesteps(lowerCAmelCase__     )
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		self.scheduler.timesteps

										# 5. Denoising loop
										# Multistep sampling: implements Algorithm 1 in the paper
										with self.progress_bar(total=lowerCAmelCase__     ) as progress_bar:
																for i, t in enumerate(lowerCAmelCase__     ):
																						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.scheduler.scale_model_input(lowerCAmelCase__      ,      lowerCAmelCase__     )
																						__SCREAMING_SNAKE_CASE						:					Dict		      =		self.unet(lowerCAmelCase__      ,      lowerCAmelCase__      ,      class_labels=lowerCAmelCase__      ,      return_dict=lowerCAmelCase__     )[0]

																						__SCREAMING_SNAKE_CASE						:					int		      =		self.scheduler.step(lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      generator=lowerCAmelCase__     )[0]

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

        # 6. Post-process image sample
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		self.postprocess_image(lowerCAmelCase__      ,      output_type=lowerCAmelCase__     )

										# 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=lowerCAmelCase__     )




import math
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 KarrasDiffusionSchedulers, SchedulerMixin


__lowerCAmelCase     :			str      =logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 	_lowercase				(      A__	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      torch.FloatTensor
				SCREAMING_SNAKE_CASE__    :      Optional[torch.FloatTensor]      =		None




def 	_UpperCamelCase							(				lowercase__   ,	lowercase__=0.999   ,	lowercase__="cosine"   ,	):
						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

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

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

						__SCREAMING_SNAKE_CASE						:					List[str]		      =		[]
						for i in range(lowercase__				):
												__SCREAMING_SNAKE_CASE						:					List[Any]		      =		i / num_diffusion_timesteps
												__SCREAMING_SNAKE_CASE						:					Dict		      =		(i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(lowercase__				) / alpha_bar_fn(lowercase__				)   ,	lowercase__				)				)
						return torch.tensor(lowercase__   ,	dtype=torch.floataa				)
class 	_lowercase				(      A__						,      A__	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      int      =		[e.name for e in KarrasDiffusionSchedulers]
				SCREAMING_SNAKE_CASE__    :      Optional[int]      =		1


				@register_to_config
				def __init__(	self      :Tuple      ,      lowerCAmelCase__      :int = 1_000      ,      lowerCAmelCase__      :float = 0.0001      ,      lowerCAmelCase__      :float = 0.02      ,      lowerCAmelCase__      :str = "linear"      ,      lowerCAmelCase__      :Optional[Union[np.ndarray, List[float]]] = None      ,      lowerCAmelCase__      :str = "epsilon"      ,      lowerCAmelCase__      :str = "linear"      ,      lowerCAmelCase__      :Optional[bool] = False      ,      lowerCAmelCase__      :str = "linspace"      ,      lowerCAmelCase__      :int = 0      ,      )		->      Optional[int]:
										if trained_betas is not None:
																__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		torch.tensor(lowerCAmelCase__      ,      dtype=torch.floataa     )
										elif beta_schedule == "linear":
																__SCREAMING_SNAKE_CASE						:					str		      =		torch.linspace(lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      dtype=torch.floataa     )
										elif beta_schedule == "scaled_linear":
																# this schedule is very specific to the latent diffusion model.
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		(
																    torch.linspace(beta_start**0.5      ,      beta_end**0.5      ,      lowerCAmelCase__      ,      dtype=torch.floataa     ) ** 2
																)
										elif beta_schedule == "squaredcos_cap_v2":
																# Glide cosine schedule
																__SCREAMING_SNAKE_CASE						:					Dict		      =		betas_for_alpha_bar(lowerCAmelCase__     )
										else:
																raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''     )

										__SCREAMING_SNAKE_CASE						:					Dict		      =		1.0 - self.betas
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		torch.cumprod(self.alphas      ,      dim=0     )

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
										__SCREAMING_SNAKE_CASE						:					Dict		      =		np.concatenate([sigmas[::-1], [0.0]]     ).astype(np.floataa     )
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		torch.from_numpy(lowerCAmelCase__     )

										# setable values
										__SCREAMING_SNAKE_CASE						:					str		      =		None
										__SCREAMING_SNAKE_CASE						:					int		      =		np.linspace(0      ,      num_train_timesteps - 1      ,      lowerCAmelCase__      ,      dtype=lowerCAmelCase__     )[::-1].copy()
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		torch.from_numpy(lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					str		      =		False
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		use_karras_sigmas


				@property
				def 		__magic_name__(	self      :int     )		->      Union[str, Any]:
										# 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 		__magic_name__(	self      :Dict      ,      lowerCAmelCase__      :torch.FloatTensor      ,      lowerCAmelCase__      :Union[float, torch.FloatTensor]     )		->      torch.FloatTensor:
										if isinstance(lowerCAmelCase__      ,      torch.Tensor     ):
																__SCREAMING_SNAKE_CASE						:					Any		      =		timestep.to(self.timesteps.device     )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		(self.timesteps == timestep).nonzero().item()
										__SCREAMING_SNAKE_CASE						:					Any		      =		self.sigmas[step_index]

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

										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		True
										return sample


				def 		__magic_name__(	self      :Dict      ,      lowerCAmelCase__      :int      ,      lowerCAmelCase__      :Union[str, torch.device] = None     )		->      int:
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		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":
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		np.linspace(0      ,      self.config.num_train_timesteps - 1      ,      lowerCAmelCase__      ,      dtype=lowerCAmelCase__     )[
																    ::-1
																].copy()
										elif self.config.timestep_spacing == "leading":
																__SCREAMING_SNAKE_CASE						:					List[str]		      =		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
																__SCREAMING_SNAKE_CASE						:					List[str]		      =		(np.arange(0      ,      lowerCAmelCase__     ) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase__     )
																timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		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
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		(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\'.'''     )

										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
										__SCREAMING_SNAKE_CASE						:					int		      =		np.log(lowerCAmelCase__     )

										if self.config.interpolation_type == "linear":
																__SCREAMING_SNAKE_CASE						:					str		      =		np.interp(lowerCAmelCase__      ,      np.arange(0      ,      len(lowerCAmelCase__     )     )      ,      lowerCAmelCase__     )
										elif self.config.interpolation_type == "log_linear":
																__SCREAMING_SNAKE_CASE						:					Any		      =		torch.linspace(np.log(sigmas[-1]     )      ,      np.log(sigmas[0]     )      ,      num_inference_steps + 1     ).exp()
										else:
																raise ValueError(
																    f'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
																    ''' \'linear\' or \'log_linear\''''     )

										if self.use_karras_sigmas:
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		self._convert_to_karras(in_sigmas=lowerCAmelCase__      ,      num_inference_steps=self.num_inference_steps     )
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		np.array([self._sigma_to_t(lowerCAmelCase__      ,      lowerCAmelCase__     ) for sigma in sigmas]     )

										__SCREAMING_SNAKE_CASE						:					Tuple		      =		np.concatenate([sigmas, [0.0]]     ).astype(np.floataa     )
										__SCREAMING_SNAKE_CASE						:					Any		      =		torch.from_numpy(lowerCAmelCase__     ).to(device=lowerCAmelCase__     )
										if str(lowerCAmelCase__     ).startswith('''mps'''     ):
																# mps does not support float64
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		torch.from_numpy(lowerCAmelCase__     ).to(lowerCAmelCase__      ,      dtype=torch.floataa     )
										else:
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		torch.from_numpy(lowerCAmelCase__     ).to(device=lowerCAmelCase__     )


				def 		__magic_name__(	self      :Optional[int]      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :Union[str, Any]     )		->      int:
										# get log sigma
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		np.log(lowerCAmelCase__     )

										# get distribution
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		log_sigma - log_sigmas[:, np.newaxis]

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

										__SCREAMING_SNAKE_CASE						:					List[str]		      =		log_sigmas[low_idx]
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		log_sigmas[high_idx]

										# interpolate sigmas
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		(low - log_sigma) / (low - high)
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		np.clip(lowerCAmelCase__      ,      0      ,      1     )

										# transform interpolation to time range
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		(1 - w) * low_idx + w * high_idx
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		t.reshape(sigma.shape     )
										return t


				def 		__magic_name__(	self      :Optional[int]      ,      lowerCAmelCase__      :torch.FloatTensor      ,      lowerCAmelCase__      :Union[str, Any]     )		->      torch.FloatTensor:
										__SCREAMING_SNAKE_CASE						:					float		      =		in_sigmas[-1].item()
										__SCREAMING_SNAKE_CASE						:					float		      =		in_sigmas[0].item()

										__SCREAMING_SNAKE_CASE						:					Tuple		      =		7.0  # 7.0 is the value used in the paper
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		np.linspace(0      ,      1      ,      lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					str		      =		sigma_min ** (1 / rho)
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		sigma_max ** (1 / rho)
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
										return sigmas


				def 		__magic_name__(	self      :Union[str, Any]      ,      lowerCAmelCase__      :torch.FloatTensor      ,      lowerCAmelCase__      :Union[float, torch.FloatTensor]      ,      lowerCAmelCase__      :torch.FloatTensor      ,      lowerCAmelCase__      :float = 0.0      ,      lowerCAmelCase__      :float = 0.0      ,      lowerCAmelCase__      :float = float('''inf'''     )      ,      lowerCAmelCase__      :float = 1.0      ,      lowerCAmelCase__      :Optional[torch.Generator] = None      ,      lowerCAmelCase__      :bool = True      ,      )		->      Union[EulerDiscreteSchedulerOutput, Tuple]:

										if (
										    isinstance(lowerCAmelCase__      ,      lowerCAmelCase__     )
										    or isinstance(lowerCAmelCase__      ,      torch.IntTensor     )
										    or isinstance(lowerCAmelCase__      ,      torch.LongTensor     )
										):
																raise ValueError(
																    (
																        '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
																        ''' `EulerDiscreteScheduler.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(lowerCAmelCase__      ,      torch.Tensor     ):
																__SCREAMING_SNAKE_CASE						:					List[str]		      =		timestep.to(self.timesteps.device     )

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		(self.timesteps == timestep).nonzero().item()
										__SCREAMING_SNAKE_CASE						:					str		      =		self.sigmas[step_index]

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		min(s_churn / (len(self.sigmas     ) - 1)      ,      2**0.5 - 1     ) if s_tmin <= sigma <= s_tmax else 0.0

										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		randn_tensor(
										    model_output.shape      ,      dtype=model_output.dtype      ,      device=model_output.device      ,      generator=lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		noise * s_noise
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		sigma * (gamma + 1)

										if gamma > 0:
																__SCREAMING_SNAKE_CASE						:					str		      =		sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										# NOTE: "original_sample" should not be an expected prediction_type but is left in for
										# backwards compatibility
										if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
																__SCREAMING_SNAKE_CASE						:					Any		      =		model_output
										elif self.config.prediction_type == "epsilon":
																__SCREAMING_SNAKE_CASE						:					Any		      =		sample - sigma_hat * model_output
										elif self.config.prediction_type == "v_prediction":
																# * c_out + input * c_skip
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
										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
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		(sample - pred_original_sample) / sigma_hat

										__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.sigmas[step_index + 1] - sigma_hat

										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		sample + derivative * dt

										if not return_dict:
																return (prev_sample,)

										return EulerDiscreteSchedulerOutput(prev_sample=lowerCAmelCase__      ,      pred_original_sample=lowerCAmelCase__     )


				def 		__magic_name__(	self      :str      ,      lowerCAmelCase__      :torch.FloatTensor      ,      lowerCAmelCase__      :torch.FloatTensor      ,      lowerCAmelCase__      :torch.FloatTensor      ,      )		->      torch.FloatTensor:
										# Make sure sigmas and timesteps have the same device and dtype as original_samples
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		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
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.timesteps.to(original_samples.device      ,      dtype=torch.floataa     )
																__SCREAMING_SNAKE_CASE						:					int		      =		timesteps.to(original_samples.device      ,      dtype=torch.floataa     )
										else:
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.timesteps.to(original_samples.device     )
																__SCREAMING_SNAKE_CASE						:					Dict		      =		timesteps.to(original_samples.device     )

										__SCREAMING_SNAKE_CASE						:					Dict		      =		[(schedule_timesteps == t).nonzero().item() for t in timesteps]

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		sigmas[step_indices].flatten()
										while len(sigma.shape     ) < len(original_samples.shape     ):
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		sigma.unsqueeze(-1     )

										__SCREAMING_SNAKE_CASE						:					Any		      =		original_samples + noise * sigma
										return noisy_samples



				def __len__(	self      :Optional[Any]     )		->      Union[str, Any]:
										return self.config.num_train_timesteps
