
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,
)


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

__magic_name__  :  Dict     			=   '\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__   :      Union[str, Any]				,					snake_case__   :      List[Any]				,					snake_case__   :      Tuple=8 )->			int:
    A_								=					height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    A_								=					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__   :      Any				,					snake_case__   :      int=512				,					snake_case__   :      Any=512 )->			Dict:
    A_								=					pil_image.resize((w, h)				,					resample=Image.BICUBIC				,					reducing_gap=1 )
    A_								=					np.array(pil_image.convert("RGB" ) )
    A_								=					arr.astype(np.floataa ) / 1_2_7.5 - 1
    A_								=					np.transpose(snake_case__				,					[2, 0, 1] )
    A_								=					torch.from_numpy(snake_case__ ).unsqueeze(0 )
    return image



class 							lowerCamelCase       (		a_					):



  """simple docstring"""





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

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

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

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

      return timesteps, num_inference_steps - t_start

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase=None					):
      if not isinstance(_snake_case						,    (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(_snake_case					)}'					)

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

      A_								=					batch_size * num_images_per_prompt

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

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

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

          A_								=					self.movq.config.scaling_factor * init_latents

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

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

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

      A_								=					init_latents

      return latents

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

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

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

  def 					lowercase_						(					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."					)

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

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

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

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

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

      A_								=					guidance_scale > 1.0

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

      A_								=					image_embeds.shape[0]

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

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

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

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

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

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

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

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

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

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

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

      if not return_dict:
          return (image,)

      return ImagePipelineOutput(images=_snake_case					)









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



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class        lowercase    (	a_			):
							_lowerCamelCase		:   torch.FloatTensor
							_lowerCamelCase		:   Optional[torch.FloatTensor]= None







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase=0.999 ,UpperCamelCase="cosine" ,)     ->     List[str]:
	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_		:   Optional[int]													=       []
	for i in range(UpperCamelCase	):
		UpperCAmelCase_		:   Optional[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    (	a_,       a_			):
							_lowerCamelCase		:   List[str]= [e.name for e in KarrasDiffusionSchedulers]
							_lowerCamelCase		:   Optional[int]= 1


							@register_to_config
							def __init__(      self     ,      _snake_case = 1000     ,      _snake_case = 0.0_001     ,      _snake_case = 0.02     ,      _snake_case = "linear"     ,      _snake_case = None     ,      _snake_case = "epsilon"     ,      _snake_case = "linspace"     ,      _snake_case = 0     ,      )				->					List[str]:
								if trained_betas is not None:
									UpperCAmelCase_		:   List[str]													=       torch.tensor(_snake_case     ,      dtype=torch.floataa)
								elif beta_schedule == "linear":
									UpperCAmelCase_		:   List[Any]													=       torch.linspace(_snake_case     ,      _snake_case     ,      _snake_case     ,      dtype=torch.floataa)
								elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									UpperCAmelCase_		:   List[Any]													=       (
									    torch.linspace(beta_start**0.5     ,      beta_end**0.5     ,      _snake_case     ,      dtype=torch.floataa) ** 2
									)
								elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									UpperCAmelCase_		:   int													=       betas_for_alpha_bar(_snake_case)
								else:
									raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""")

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

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

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


							@property
							def     _snake_case				(      self)				->					Optional[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     _snake_case				(      self     ,      _snake_case     ,      _snake_case)				->					torch.FloatTensor:
								if isinstance(_snake_case     ,      torch.Tensor):
									UpperCAmelCase_		:   List[Any]													=       timestep.to(self.timesteps.device)
								UpperCAmelCase_		:   int													=       (self.timesteps == timestep).nonzero().item()
								UpperCAmelCase_		:   Union[str, Any]													=       self.sigmas[step_index]
								UpperCAmelCase_		:   Union[str, Any]													=       sample / ((sigma**2 + 1) ** 0.5)
								UpperCAmelCase_		:   Optional[Any]													=       True
								return sample


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

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


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case = None     ,      _snake_case = True     ,      )				->					Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

								if (
								    isinstance(_snake_case     ,      _snake_case)
								    or isinstance(_snake_case     ,      torch.IntTensor)
								    or isinstance(_snake_case     ,      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(_snake_case     ,      torch.Tensor):
									UpperCAmelCase_		:   Optional[int]													=       timestep.to(self.timesteps.device)

								UpperCAmelCase_		:   List[Any]													=       (self.timesteps == timestep).nonzero().item()
								UpperCAmelCase_		:   int													=       self.sigmas[step_index]

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

								UpperCAmelCase_		:   Dict													=       self.sigmas[step_index]
								UpperCAmelCase_		:   Union[str, Any]													=       self.sigmas[step_index + 1]
								UpperCAmelCase_		:   str													=       (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
								UpperCAmelCase_		:   Any													=       (sigma_to**2 - sigma_up**2) ** 0.5

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

								UpperCAmelCase_		:   Tuple													=       sigma_down - sigma

								UpperCAmelCase_		:   List[str]													=       sample + derivative * dt

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

								UpperCAmelCase_		:   Union[str, Any]													=       prev_sample + noise * sigma_up

								if not return_dict:
									return (prev_sample,)

								return EulerAncestralDiscreteSchedulerOutput(
								    prev_sample=_snake_case     ,      pred_original_sample=_snake_case)


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      )				->					torch.FloatTensor:
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								UpperCAmelCase_		:   List[str]													=       self.sigmas.to(device=original_samples.device     ,      dtype=original_samples.dtype)
								if original_samples.device.type == "mps" and torch.is_floating_point(_snake_case):
									# mps does not support float64
									UpperCAmelCase_		:   str													=       self.timesteps.to(original_samples.device     ,      dtype=torch.floataa)
									UpperCAmelCase_		:   List[Any]													=       timesteps.to(original_samples.device     ,      dtype=torch.floataa)
								else:
									UpperCAmelCase_		:   List[Any]													=       self.timesteps.to(original_samples.device)
									UpperCAmelCase_		:   str													=       timesteps.to(original_samples.device)

								UpperCAmelCase_		:   Optional[Any]													=       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

								UpperCAmelCase_		:   int													=       original_samples + noise * sigma
								return noisy_samples





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






