





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,
)


_a									=				logging.get_logger(__name__)  # pylint: disable=invalid-name

_a									=				"\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 lowerCAmelCase__(__snake_case	,__snake_case	,__snake_case=8							)    ->				List[str]:







	'''simple docstring'''







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





def lowerCAmelCase__(__snake_case	,__snake_case=512	,__snake_case=512							)    ->				List[str]:







	'''simple docstring'''







	lowerCamelCase__        =		pil_image.resize((w, h)	,resample=Image.BICUBIC	,reducing_gap=1							)
	lowerCamelCase__        =		np.array(pil_image.convert('''RGB'''							)							)
	lowerCamelCase__        =		arr.astype(np.floataa							) / 1_2_7.5 - 1
	lowerCamelCase__        =		np.transpose(__snake_case	,[2, 0, 1]							)
	lowerCamelCase__        =		torch.from_numpy(__snake_case							).unsqueeze(0							)
	return image



class  __A   (   lowerCAmelCase__			):

			'''simple docstring'''







			def __init__(   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  ):



				'''simple docstring'''







				super().__init__()

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





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__        =		min(int(num_inference_steps * strength							)	,  _A							)

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

				return timesteps, num_inference_steps - t_start





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase=None							):



				'''simple docstring'''







				if not isinstance(_A	,  (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(_A							)}'							)

				lowerCamelCase__        =		image.to(device=_A	,  dtype=_A							)

				lowerCamelCase__        =		batch_size * num_images_per_prompt

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

				else:
					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.'							)

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

					lowerCamelCase__        =		self.movq.config.scaling_factor * init_latents

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

				lowerCamelCase__        =		init_latents.shape
				lowerCamelCase__        =		randn_tensor(_A	,  generator=_A	,  device=_A	,  dtype=_A							)

				# get latents
				lowerCamelCase__        =		self.scheduler.add_noise(_A	,  _A	,  _A							)

				lowerCamelCase__        =		init_latents

				return latents





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase=0							):



				'''simple docstring'''







				if is_accelerate_available():
					from accelerate import cpu_offload
				else:
					raise ImportError('''Please install accelerate via `pip install accelerate`'''							)

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

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





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase=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.'''							)

				lowerCamelCase__        =		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)

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

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





			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def 		__lowerCamelCase    (   self							):



				'''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





			@torch.no_grad()
			@replace_example_docstring(_A							)
			def __call__(   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase = 5_1_2	,  __lowerCAmelCase = 5_1_2	,  __lowerCAmelCase = 1_0_0	,  __lowerCAmelCase = 4.0	,  __lowerCAmelCase = 0.3	,  __lowerCAmelCase = 1	,  __lowerCAmelCase = None	,  __lowerCAmelCase = "pil"	,  __lowerCAmelCase = True	,  ):



				'''simple docstring'''







				lowerCamelCase__        =		self._execution_device

				lowerCamelCase__        =		guidance_scale > 1.0

				if isinstance(_A	,  _A							):
					lowerCamelCase__        =		torch.cat(_A	,  dim=0							)
				if isinstance(_A	,  _A							):
					lowerCamelCase__        =		torch.cat(_A	,  dim=0							)
				if isinstance(_A	,  _A							):
					lowerCamelCase__        =		torch.cat(_A	,  dim=0							)

				lowerCamelCase__        =		image_embeds.shape[0]

				if do_classifier_free_guidance:
					lowerCamelCase__        =		image_embeds.repeat_interleave(_A	,  dim=0							)
					lowerCamelCase__        =		negative_image_embeds.repeat_interleave(_A	,  dim=0							)
					lowerCamelCase__        =		hint.repeat_interleave(_A	,  dim=0							)

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

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

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

				lowerCamelCase__        =		self.movq.encode(_A							)['''latents''']
				lowerCamelCase__        =		latents.repeat_interleave(_A	,  dim=0							)
				self.scheduler.set_timesteps(_A	,  device=_A							)
				lowerCamelCase__        =		self.get_timesteps(_A	,  _A	,  _A							)
				lowerCamelCase__        =		timesteps[:1].repeat(batch_size * num_images_per_prompt							)
				lowerCamelCase__        =		downscale_height_and_width(_A	,  _A	,  self.movq_scale_factor							)
				lowerCamelCase__        =		self.prepare_latents(
				    _A	,  _A	,  _A	,  _A	,  image_embeds.dtype	,  _A	,  _A							)
				for i, t in enumerate(self.progress_bar(_A							)							):
					# expand the latents if we are doing classifier free guidance
					lowerCamelCase__        =		torch.cat([latents] * 2							) if do_classifier_free_guidance else latents

					lowerCamelCase__        =		{'''image_embeds''': image_embeds, '''hint''': hint}
					lowerCamelCase__        =		self.unet(
					    sample=_A	,  timestep=_A	,  encoder_hidden_states=_A	,  added_cond_kwargs=_A	,  return_dict=_A	,  )[0]

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

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

					lowerCamelCase__        =		self.scheduler.step(
					    _A	,  _A	,  _A	,  generator=_A	,  )[0]

				# post-processing
				lowerCamelCase__        =		self.movq.decode(_A	,  force_not_quantize=_A							)['''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"]:
					lowerCamelCase__        =		image * 0.5 + 0.5
					lowerCamelCase__        =		image.clamp(0	,  1							)
					lowerCamelCase__        =		image.cpu().permute(0	,  2	,  3	,  1							).float().numpy()

				if output_type == "pil":
					lowerCamelCase__        =		self.numpy_to_pil(_A							)

				if not return_dict:
					return (image,)

				return ImagePipelineOutput(images=_A							)










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


_a									=				logging.get_logger(__name__)  # pylint: disable=invalid-name






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

			'''simple docstring'''







			lowerCAmelCase_												=						42
			lowerCAmelCase_												=						None





def lowerCAmelCase__(__snake_case	,__snake_case=0.9_9_9	,__snake_case="cosine"	,)    ->				Tuple:







	'''simple docstring'''







	if alpha_transform_type == "cosine":

		def alpha_bar_fn(__snake_case							):
			return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2							) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(__snake_case							):
			return math.exp(t * -1_2.0							)

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

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






class  __A   (   lowerCAmelCase      ,    lowerCAmelCase			):

			'''simple docstring'''







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





			@register_to_config
			def __init__(   self	,  __lowerCAmelCase = 1_0_0_0	,  __lowerCAmelCase = 0.0001	,  __lowerCAmelCase = 0.02	,  __lowerCAmelCase = "linear"	,  __lowerCAmelCase = None	,  __lowerCAmelCase = "epsilon"	,  __lowerCAmelCase = "linspace"	,  __lowerCAmelCase = 0	,  ):



				'''simple docstring'''







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

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

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

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





			@property
			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







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

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





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase							):



				'''simple docstring'''







				if isinstance(__lowerCAmelCase	,  torch.Tensor							):
					lowerCamelCase__        =		timestep.to(self.timesteps.device							)
				lowerCamelCase__        =		(self.timesteps == timestep).nonzero().item()
				lowerCamelCase__        =		self.sigmas[step_index]
				lowerCamelCase__        =		sample / ((sigma**2 + 1) ** 0.5)
				lowerCamelCase__        =		True
				return sample





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase = None							):



				'''simple docstring'''







				lowerCamelCase__        =		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":
					lowerCamelCase__        =		np.linspace(0	,  self.config.num_train_timesteps - 1	,  __lowerCAmelCase	,  dtype=__lowerCAmelCase							)[
					    ::-1
					].copy()
				elif self.config.timestep_spacing == "leading":
					lowerCamelCase__        =		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
					lowerCamelCase__        =		(np.arange(0	,  __lowerCAmelCase							) * step_ratio).round()[::-1].copy().astype(__lowerCAmelCase							)
					timesteps += self.config.steps_offset
				elif self.config.timestep_spacing == "trailing":
					lowerCamelCase__        =		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
					lowerCamelCase__        =		(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\'.'							)

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





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase = None	,  __lowerCAmelCase = True	,  ):



				'''simple docstring'''







				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							):
					lowerCamelCase__        =		timestep.to(self.timesteps.device							)

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

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

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

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

				lowerCamelCase__        =		sigma_down - sigma

				lowerCamelCase__        =		sample + derivative * dt

				lowerCamelCase__        =		model_output.device
				lowerCamelCase__        =		randn_tensor(model_output.shape	,  dtype=model_output.dtype	,  device=__lowerCAmelCase	,  generator=__lowerCAmelCase							)

				lowerCamelCase__        =		prev_sample + noise * sigma_up

				if not return_dict:
					return (prev_sample,)

				return EulerAncestralDiscreteSchedulerOutput(
				    prev_sample=__lowerCAmelCase	,  pred_original_sample=__lowerCAmelCase							)





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  ):



				'''simple docstring'''







				lowerCamelCase__        =		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
					lowerCamelCase__        =		self.timesteps.to(original_samples.device	,  dtype=torch.floataa							)
					lowerCamelCase__        =		timesteps.to(original_samples.device	,  dtype=torch.floataa							)
				else:
					lowerCamelCase__        =		self.timesteps.to(original_samples.device							)
					lowerCamelCase__        =		timesteps.to(original_samples.device							)

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

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

				lowerCamelCase__        =		original_samples + noise * sigma
				return noisy_samples





			def __len__(   self							):



				'''simple docstring'''







				return self.config.num_train_timesteps



