




'''simple docstring'''







from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_lowerCamelCase				:		Optional[int]				      =    logging.get_logger(__name__)  # pylint: disable=invalid-name

_lowerCamelCase				:		List[str]				      =    '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'






def 						_lowercase						(      lowercase_		,				lowercase_		,				lowercase_=8    ):



		'''simple docstring'''







		_snake_case      			=			height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		_snake_case      			=			width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor






def 						_lowercase						(      lowercase_		,				lowercase_=512		,				lowercase_=512    ):



		'''simple docstring'''







		_snake_case      			=			pil_image.resize((w, h)		,				resample=Image.BICUBIC		,				reducing_gap=1    )
		_snake_case      			=			np.array(pil_image.convert("RGB"    )    )
		_snake_case      			=			arr.astype(np.floataa    ) / 127.5 - 1
		_snake_case      			=			np.transpose(lowercase_		,				[2, 0, 1]    )
		_snake_case      			=			torch.from_numpy(lowercase_    ).unsqueeze(0    )
		return image





class 	lowerCamelCase_				(			UpperCAmelCase_				):




					'''simple docstring'''






					def __init__(       self,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    ):
							super().__init__()

							self.register_modules(
							    unet=__UpperCamelCase,    scheduler=__UpperCamelCase,    movq=__UpperCamelCase,    )
							_snake_case      			=			2 ** (len(self.movq.config.block_out_channels					) - 1)






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase					):
							# get the original timestep using init_timestep
							_snake_case      			=			min(int(num_inference_steps * strength					),    __UpperCamelCase					)

							_snake_case      			=			max(num_inference_steps - init_timestep,    0					)
							_snake_case      			=			self.scheduler.timesteps[t_start:]

							return timesteps, num_inference_steps - t_start






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase=None					):
							if not isinstance(__UpperCamelCase,    (torch.Tensor, PIL.Image.Image, list)					):
									raise ValueError(
									    f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__UpperCamelCase					)}'''					)

							_snake_case      			=			image.to(device=__UpperCamelCase,    dtype=__UpperCamelCase					)

							_snake_case      			=			batch_size * num_images_per_prompt

							if image.shape[1] == 4:
									_snake_case      			=			image

							else:
									if isinstance(__UpperCamelCase,    __UpperCamelCase					) and len(__UpperCamelCase					) != batch_size:
											raise ValueError(
											    f'''You have passed a list of generators of length {len(__UpperCamelCase					)}, but requested an effective batch'''
											    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''					)

									elif isinstance(__UpperCamelCase,    __UpperCamelCase					):
											_snake_case      			=			[
											    self.movq.encode(image[i : i + 1]					).latent_dist.sample(generator[i]					) for i in range(__UpperCamelCase					)
											]
											_snake_case      			=			torch.cat(__UpperCamelCase,    dim=0					)
									else:
											_snake_case      			=			self.movq.encode(__UpperCamelCase					).latent_dist.sample(__UpperCamelCase					)

									_snake_case      			=			self.movq.config.scaling_factor * init_latents

							_snake_case      			=			torch.cat([init_latents],    dim=0					)

							_snake_case      			=			init_latents.shape
							_snake_case      			=			randn_tensor(__UpperCamelCase,    generator=__UpperCamelCase,    device=__UpperCamelCase,    dtype=__UpperCamelCase					)

							# get latents
							_snake_case      			=			self.scheduler.add_noise(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase					)

							_snake_case      			=			init_latents

							return latents






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase=0					):
							if is_accelerate_available():
									from accelerate import cpu_offload
							else:
									raise ImportError("Please install accelerate via `pip install accelerate`"					)

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

							_snake_case      			=			[
							    self.unet,
							    self.movq,
							]
							for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
											cpu_offload(__UpperCamelCase,    __UpperCamelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase=0					):
							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."					)

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

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

							_snake_case      			=			None
							for cpu_offloaded_model in [self.unet, self.movq]:
									_snake_case			,       _snake_case      			=			cpu_offload_with_hook(__UpperCamelCase,    __UpperCamelCase,    prev_module_hook=__UpperCamelCase					)

							# We'll offload the last model manually.
							_snake_case      			=			hook






					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							if not hasattr(self.unet,    "_hf_hook"					):
									return self.device
							for module in self.unet.modules():
									if (
									    hasattr(__UpperCamelCase,    "_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






					@torch.no_grad()
					@replace_example_docstring(__UpperCamelCase					)
					def __call__(       self,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase = 512,    __UpperCamelCase = 512,    __UpperCamelCase = 100,    __UpperCamelCase = 4.0,    __UpperCamelCase = 0.3,    __UpperCamelCase = 1,    __UpperCamelCase = None,    __UpperCamelCase = "pil",    __UpperCamelCase = True,    ):
							_snake_case      			=			self._execution_device

							_snake_case      			=			guidance_scale > 1.0

							if isinstance(__UpperCamelCase,    __UpperCamelCase					):
									_snake_case      			=			torch.cat(__UpperCamelCase,    dim=0					)
							if isinstance(__UpperCamelCase,    __UpperCamelCase					):
									_snake_case      			=			torch.cat(__UpperCamelCase,    dim=0					)
							if isinstance(__UpperCamelCase,    __UpperCamelCase					):
									_snake_case      			=			torch.cat(__UpperCamelCase,    dim=0					)

							_snake_case      			=			image_embeds.shape[0]

							if do_classifier_free_guidance:
									_snake_case      			=			image_embeds.repeat_interleave(__UpperCamelCase,    dim=0					)
									_snake_case      			=			negative_image_embeds.repeat_interleave(__UpperCamelCase,    dim=0					)
									_snake_case      			=			hint.repeat_interleave(__UpperCamelCase,    dim=0					)

							_snake_case      			=			torch.cat([negative_image_embeds, image_embeds],    dim=0					).to(dtype=self.unet.dtype,    device=__UpperCamelCase					)
							_snake_case      			=			torch.cat([hint, hint],    dim=0					).to(dtype=self.unet.dtype,    device=__UpperCamelCase					)

							if not isinstance(__UpperCamelCase,    __UpperCamelCase					):
									_snake_case      			=			[image]
							if not all(isinstance(__UpperCamelCase,    (PIL.Image.Image, torch.Tensor)					) for i in image					):
									raise ValueError(
									    f'''Input is in incorrect format: {[type(__UpperCamelCase					) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''					)

							_snake_case      			=			torch.cat([prepare_image(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase					) for i in image],    dim=0					)
							_snake_case      			=			image.to(dtype=image_embeds.dtype,    device=__UpperCamelCase					)

							_snake_case      			=			self.movq.encode(__UpperCamelCase					)["latents"]
							_snake_case      			=			latents.repeat_interleave(__UpperCamelCase,    dim=0					)
							self.scheduler.set_timesteps(__UpperCamelCase,    device=__UpperCamelCase					)
							_snake_case			,       _snake_case      			=			self.get_timesteps(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase					)
							_snake_case      			=			timesteps[:1].repeat(batch_size * num_images_per_prompt					)
							_snake_case			,       _snake_case      			=			downscale_height_and_width(__UpperCamelCase,    __UpperCamelCase,    self.movq_scale_factor					)
							_snake_case      			=			self.prepare_latents(
							    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    image_embeds.dtype,    __UpperCamelCase,    __UpperCamelCase					)
							for i, t in enumerate(self.progress_bar(__UpperCamelCase					)					):
									# expand the latents if we are doing classifier free guidance
									_snake_case      			=			torch.cat([latents] * 2					) if do_classifier_free_guidance else latents

									_snake_case      			=			{"image_embeds": image_embeds, "hint": hint}
									_snake_case      			=			self.unet(
									    sample=__UpperCamelCase,    timestep=__UpperCamelCase,    encoder_hidden_states=__UpperCamelCase,    added_cond_kwargs=__UpperCamelCase,    return_dict=__UpperCamelCase,    )[0]

									if do_classifier_free_guidance:
											_snake_case			,       _snake_case      			=			noise_pred.split(latents.shape[1],    dim=1					)
											_snake_case			,       _snake_case      			=			noise_pred.chunk(2					)
											_snake_case			,       _snake_case      			=			variance_pred.chunk(2					)
											_snake_case      			=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
											_snake_case      			=			torch.cat([noise_pred, variance_pred_text],    dim=1					)

									if not (
									    hasattr(self.scheduler.config,    "variance_type"					)
									    and self.scheduler.config.variance_type in ["learned", "learned_range"]
									):
											_snake_case			,       _snake_case      			=			noise_pred.split(latents.shape[1],    dim=1					)

									# compute the previous noisy sample x_t -> x_t-1

									_snake_case      			=			self.scheduler.step(
									    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    generator=__UpperCamelCase,    )[0]

							# post-processing
							_snake_case      			=			self.movq.decode(__UpperCamelCase,    force_not_quantize=__UpperCamelCase					)["sample"]

							if output_type not in ["pt", "np", "pil"]:
									raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''					)

							if output_type in ["np", "pil"]:
									_snake_case      			=			image * 0.5 + 0.5
									_snake_case      			=			image.clamp(0,    1					)
									_snake_case      			=			image.cpu().permute(0,    2,    3,    1					).float().numpy()

							if output_type == "pil":
									_snake_case      			=			self.numpy_to_pil(__UpperCamelCase					)

							if not return_dict:
									return (image,)

							return ImagePipelineOutput(images=__UpperCamelCase					)











'''simple docstring'''







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				:		Optional[Any]				      =    logging.get_logger(__name__)  # pylint: disable=invalid-name





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 	lowerCamelCase_				(			UpperCAmelCase_				):




					'''simple docstring'''






					lowercase__       =					42
					lowercase__       =					None






def 						_lowercase						(      lowercase_		,				lowercase_=0.999		,				lowercase_="cosine"		,				):



		'''simple docstring'''







		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}'''    )

		_snake_case      			=			[]
		for i in range(lowercase_    ):
				_snake_case      			=			i / num_diffusion_timesteps
				_snake_case      			=			(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 	lowerCamelCase_				(			UpperCAmelCase_,			UpperCAmelCase_				):




					'''simple docstring'''






					lowercase__       =					[e.name for e in KarrasDiffusionSchedulers]
					lowercase__       =					1






					@register_to_config
					def __init__(       self,    __UpperCamelCase = 1_000,    __UpperCamelCase = 0.00_01,    __UpperCamelCase = 0.02,    __UpperCamelCase = "linear",    __UpperCamelCase = None,    __UpperCamelCase = "epsilon",    __UpperCamelCase = "linspace",    __UpperCamelCase = 0,    ):
							if trained_betas is not None:
									_snake_case      			=			torch.tensor(__UpperCamelCase,    dtype=torch.floataa					)
							elif beta_schedule == "linear":
									_snake_case      			=			torch.linspace(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    dtype=torch.floataa					)
							elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									_snake_case      			=			(
									    torch.linspace(beta_start**0.5,    beta_end**0.5,    __UpperCamelCase,    dtype=torch.floataa					) ** 2
									)
							elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									_snake_case      			=			betas_for_alpha_bar(__UpperCamelCase					)
							else:
									raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''					)

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

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

							# setable values
							_snake_case      			=			None
							_snake_case      			=			np.linspace(0,    num_train_timesteps - 1,    __UpperCamelCase,    dtype=__UpperCamelCase					)[::-1].copy()
							_snake_case      			=			torch.from_numpy(__UpperCamelCase					)
							_snake_case      			=			False






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# 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 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase					):
							if isinstance(__UpperCamelCase,    torch.Tensor					):
									_snake_case      			=			timestep.to(self.timesteps.device					)
							_snake_case      			=			(self.timesteps == timestep).nonzero().item()
							_snake_case      			=			self.sigmas[step_index]
							_snake_case      			=			sample / ((sigma**2 + 1) ** 0.5)
							_snake_case      			=			True
							return sample






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase = None					):
							_snake_case      			=			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":
									_snake_case      			=			np.linspace(0,    self.config.num_train_timesteps - 1,    __UpperCamelCase,    dtype=__UpperCamelCase					)[
									    ::-1
									].copy()
							elif self.config.timestep_spacing == "leading":
									_snake_case      			=			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
									_snake_case      			=			(np.arange(0,    __UpperCamelCase					) * step_ratio).round()[::-1].copy().astype(__UpperCamelCase					)
									timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
									_snake_case      			=			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
									_snake_case      			=			(np.arange(self.config.num_train_timesteps,    0,    -step_ratio					)).round().copy().astype(__UpperCamelCase					)
									timesteps -= 1
							else:
									raise ValueError(
									    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''					)

							_snake_case      			=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
							_snake_case      			=			np.interp(__UpperCamelCase,    np.arange(0,    len(__UpperCamelCase					)					),    __UpperCamelCase					)
							_snake_case      			=			np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
							_snake_case      			=			torch.from_numpy(__UpperCamelCase					).to(device=__UpperCamelCase					)
							if str(__UpperCamelCase					).startswith("mps"					):
									# mps does not support float64
									_snake_case      			=			torch.from_numpy(__UpperCamelCase					).to(__UpperCamelCase,    dtype=torch.floataa					)
							else:
									_snake_case      			=			torch.from_numpy(__UpperCamelCase					).to(device=__UpperCamelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase = None,    __UpperCamelCase = True,    ):

							if (
							    isinstance(__UpperCamelCase,    __UpperCamelCase					)
							    or isinstance(__UpperCamelCase,    torch.IntTensor					)
							    or isinstance(__UpperCamelCase,    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(__UpperCamelCase,    torch.Tensor					):
									_snake_case      			=			timestep.to(self.timesteps.device					)

							_snake_case      			=			(self.timesteps == timestep).nonzero().item()
							_snake_case      			=			self.sigmas[step_index]

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
									_snake_case      			=			sample - sigma * model_output
							elif self.config.prediction_type == "v_prediction":
									# * c_out + input * c_skip
									_snake_case      			=			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**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`'''					)

							_snake_case      			=			self.sigmas[step_index]
							_snake_case      			=			self.sigmas[step_index + 1]
							_snake_case      			=			(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
							_snake_case      			=			(sigma_to**2 - sigma_up**2) ** 0.5

							# 2. Convert to an ODE derivative
							_snake_case      			=			(sample - pred_original_sample) / sigma

							_snake_case      			=			sigma_down - sigma

							_snake_case      			=			sample + derivative * dt

							_snake_case      			=			model_output.device
							_snake_case      			=			randn_tensor(model_output.shape,    dtype=model_output.dtype,    device=__UpperCamelCase,    generator=__UpperCamelCase					)

							_snake_case      			=			prev_sample + noise * sigma_up

							if not return_dict:
									return (prev_sample,)

							return EulerAncestralDiscreteSchedulerOutput(
							    prev_sample=__UpperCamelCase,    pred_original_sample=__UpperCamelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    ):
							# Make sure sigmas and timesteps have the same device and dtype as original_samples
							_snake_case      			=			self.sigmas.to(device=original_samples.device,    dtype=original_samples.dtype					)
							if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCamelCase					):
									# mps does not support float64
									_snake_case      			=			self.timesteps.to(original_samples.device,    dtype=torch.floataa					)
									_snake_case      			=			timesteps.to(original_samples.device,    dtype=torch.floataa					)
							else:
									_snake_case      			=			self.timesteps.to(original_samples.device					)
									_snake_case      			=			timesteps.to(original_samples.device					)

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

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

							_snake_case      			=			original_samples + noise * sigma
							return noisy_samples






					def __len__(       self					):
							return self.config.num_train_timesteps





