

'''simple docstring'''


import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput



class        lowercase    (	nn.Module			):


							def __init__(      self     ,      _snake_case     ,      _snake_case=1.0)				->					int:
								super().__init__()

								UpperCAmelCase_		:   Tuple													=       cut_size
								UpperCAmelCase_		:   Any													=       cut_power





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case)				->					int:
								UpperCAmelCase_						,     UpperCAmelCase_		:   Optional[int]													=       pixel_values.shape[2:4]
								UpperCAmelCase_		:   Optional[int]													=       min(_snake_case     ,      _snake_case)
								UpperCAmelCase_		:   Optional[Any]													=       min(_snake_case     ,      _snake_case     ,      self.cut_size)
								UpperCAmelCase_		:   Union[str, Any]													=       []
								for _ in range(_snake_case):
									UpperCAmelCase_		:   Optional[int]													=       int(torch.rand([]) ** self.cut_power * (max_size - min_size) + min_size)
									UpperCAmelCase_		:   Optional[Any]													=       torch.randint(0     ,      sideX - size + 1     ,      ())
									UpperCAmelCase_		:   List[Any]													=       torch.randint(0     ,      sideY - size + 1     ,      ())
									UpperCAmelCase_		:   List[Any]													=       pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
									cutouts.append(F.adaptive_avg_poolad(_snake_case     ,      self.cut_size))
								return torch.cat(_snake_case)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase	)     ->     int:
	UpperCAmelCase_		:   Union[str, Any]													=       F.normalize(UpperCamelCase ,dim=-1	)
	UpperCAmelCase_		:   str													=       F.normalize(UpperCamelCase ,dim=-1	)
	return (x - y).norm(dim=-1	).div(2	).arcsin().pow(2	).mul(2	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase	)     ->     int:
	for param in model.parameters():
		UpperCAmelCase_		:   Dict													=       value







class        lowercase    (	a_			):


							def __init__(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      )				->					int:
								super().__init__()
								self.register_modules(
								    vae=_snake_case     ,      text_encoder=_snake_case     ,      clip_model=_snake_case     ,      tokenizer=_snake_case     ,      unet=_snake_case     ,      scheduler=_snake_case     ,      feature_extractor=_snake_case     ,      )

								UpperCAmelCase_		:   Dict													=       transforms.Normalize(mean=feature_extractor.image_mean     ,      std=feature_extractor.image_std)
								UpperCAmelCase_		:   Any													=       (
								    feature_extractor.size
								    if isinstance(feature_extractor.size     ,      _snake_case)
								    else feature_extractor.size['shortest_edge']
								)
								UpperCAmelCase_		:   Any													=       MakeCutouts(self.cut_out_size)

								set_requires_grad(self.text_encoder     ,      _snake_case)
								set_requires_grad(self.clip_model     ,      _snake_case)


							def     _snake_case				(      self     ,      _snake_case = "auto")				->					str:
								if slice_size == "auto":
									# half the attention head size is usually a good trade-off between
									# speed and memory
									UpperCAmelCase_		:   Optional[int]													=       self.unet.config.attention_head_dim // 2
								self.unet.set_attention_slice(_snake_case)


							def     _snake_case				(      self)				->					Dict:
								self.enable_attention_slicing(_snake_case)


							def     _snake_case				(      self)				->					Tuple:
								set_requires_grad(self.vae     ,      _snake_case)


							def     _snake_case				(      self)				->					Tuple:
								set_requires_grad(self.vae     ,      _snake_case)


							def     _snake_case				(      self)				->					Optional[Any]:
								set_requires_grad(self.unet     ,      _snake_case)


							def     _snake_case				(      self)				->					Optional[int]:
								set_requires_grad(self.unet     ,      _snake_case)


							@torch.enable_grad()
							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case=True     ,      )				->					str:
								UpperCAmelCase_		:   str													=       latents.detach().requires_grad_()

								UpperCAmelCase_		:   Tuple													=       self.scheduler.scale_model_input(_snake_case     ,      _snake_case)

								# predict the noise residual
								UpperCAmelCase_		:   Dict													=       self.unet(_snake_case     ,      _snake_case     ,      encoder_hidden_states=_snake_case).sample

								if isinstance(self.scheduler     ,      (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)):
									UpperCAmelCase_		:   int													=       self.scheduler.alphas_cumprod[timestep]
									UpperCAmelCase_		:   Optional[int]													=       1 - alpha_prod_t
									# compute predicted original sample from predicted noise also called
									# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
									UpperCAmelCase_		:   Union[str, Any]													=       (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

									UpperCAmelCase_		:   Tuple													=       torch.sqrt(_snake_case)
									UpperCAmelCase_		:   List[str]													=       pred_original_sample * (fac) + latents * (1 - fac)
								elif isinstance(self.scheduler     ,      _snake_case):
									UpperCAmelCase_		:   str													=       self.scheduler.sigmas[index]
									UpperCAmelCase_		:   Optional[int]													=       latents - sigma * noise_pred
								else:
									raise ValueError(F"""scheduler type {type(self.scheduler)} not supported""")

								UpperCAmelCase_		:   Any													=       1 / self.vae.config.scaling_factor * sample
								UpperCAmelCase_		:   Dict													=       self.vae.decode(_snake_case).sample
								UpperCAmelCase_		:   Optional[int]													=       (image / 2 + 0.5).clamp(0     ,      1)

								if use_cutouts:
									UpperCAmelCase_		:   Optional[int]													=       self.make_cutouts(_snake_case     ,      _snake_case)
								else:
									UpperCAmelCase_		:   Optional[int]													=       transforms.Resize(self.cut_out_size)(_snake_case)
								UpperCAmelCase_		:   Any													=       self.normalize(_snake_case).to(latents.dtype)

								UpperCAmelCase_		:   Dict													=       self.clip_model.get_image_features(_snake_case)
								UpperCAmelCase_		:   List[str]													=       image_embeddings_clip / image_embeddings_clip.norm(p=2     ,      dim=-1     ,      keepdim=_snake_case)

								if use_cutouts:
									UpperCAmelCase_		:   List[Any]													=       spherical_dist_loss(_snake_case     ,      _snake_case)
									UpperCAmelCase_		:   Optional[int]													=       dists.view([num_cutouts, sample.shape[0], -1])
									UpperCAmelCase_		:   Optional[int]													=       dists.sum(2).mean(0).sum() * clip_guidance_scale
								else:
									UpperCAmelCase_		:   List[Any]													=       spherical_dist_loss(_snake_case     ,      _snake_case).mean() * clip_guidance_scale

								UpperCAmelCase_		:   Union[str, Any]													=       -torch.autograd.grad(_snake_case     ,      _snake_case)[0]

								if isinstance(self.scheduler     ,      _snake_case):
									UpperCAmelCase_		:   Union[str, Any]													=       latents.detach() + grads * (sigma**2)
									UpperCAmelCase_		:   List[str]													=       noise_pred_original
								else:
									UpperCAmelCase_		:   Optional[Any]													=       noise_pred_original - torch.sqrt(_snake_case) * grads
								return noise_pred, latents





							@torch.no_grad()
							def __call__(      self     ,      _snake_case     ,      _snake_case = 512     ,      _snake_case = 512     ,      _snake_case = 50     ,      _snake_case = 7.5     ,      _snake_case = 1     ,      _snake_case = 0.0     ,      _snake_case = 100     ,      _snake_case = None     ,      _snake_case = 4     ,      _snake_case = True     ,      _snake_case = None     ,      _snake_case = None     ,      _snake_case = "pil"     ,      _snake_case = True     ,      )				->					List[Any]:
								if isinstance(_snake_case     ,      _snake_case):
									UpperCAmelCase_		:   str													=       1
								elif isinstance(_snake_case     ,      _snake_case):
									UpperCAmelCase_		:   Optional[int]													=       len(_snake_case)
								else:
									raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(_snake_case)}""")

								if height % 8 != 0 or width % 8 != 0:
									raise ValueError(F"""`height` and `width` have to be divisible by 8 but are {height} and {width}.""")

								# get prompt text embeddings
								UpperCAmelCase_		:   int													=       self.tokenizer(
								    _snake_case     ,      padding='max_length'     ,      max_length=self.tokenizer.model_max_length     ,      truncation=_snake_case     ,      return_tensors='pt'     ,      )
								UpperCAmelCase_		:   Tuple													=       self.text_encoder(text_input.input_ids.to(self.device))[0]
								# duplicate text embeddings for each generation per prompt
								UpperCAmelCase_		:   Dict													=       text_embeddings.repeat_interleave(_snake_case     ,      dim=0)

								if clip_guidance_scale > 0:
									if clip_prompt is not None:
										UpperCAmelCase_		:   int													=       self.tokenizer(
										    _snake_case     ,      padding='max_length'     ,      max_length=self.tokenizer.model_max_length     ,      truncation=_snake_case     ,      return_tensors='pt'     ,      ).input_ids.to(self.device)
									else:
										UpperCAmelCase_		:   Any													=       text_input.input_ids.to(self.device)
									UpperCAmelCase_		:   Optional[int]													=       self.clip_model.get_text_features(_snake_case)
									UpperCAmelCase_		:   List[str]													=       text_embeddings_clip / text_embeddings_clip.norm(p=2     ,      dim=-1     ,      keepdim=_snake_case)
									# duplicate text embeddings clip for each generation per prompt
									UpperCAmelCase_		:   str													=       text_embeddings_clip.repeat_interleave(_snake_case     ,      dim=0)

								# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
								# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
								# corresponds to doing no classifier free guidance.
								UpperCAmelCase_		:   List[str]													=       guidance_scale > 1.0
								# get unconditional embeddings for classifier free guidance
								if do_classifier_free_guidance:
									UpperCAmelCase_		:   List[Any]													=       text_input.input_ids.shape[-1]
									UpperCAmelCase_		:   Any													=       self.tokenizer(['']     ,      padding='max_length'     ,      max_length=_snake_case     ,      return_tensors='pt')
									UpperCAmelCase_		:   List[str]													=       self.text_encoder(uncond_input.input_ids.to(self.device))[0]
									# duplicate unconditional embeddings for each generation per prompt
									UpperCAmelCase_		:   List[Any]													=       uncond_embeddings.repeat_interleave(_snake_case     ,      dim=0)

									# For classifier free guidance, we need to do two forward passes.
									# Here we concatenate the unconditional and text embeddings into a single batch
									# to avoid doing two forward passes
									UpperCAmelCase_		:   int													=       torch.cat([uncond_embeddings, text_embeddings])

								# get the initial random noise unless the user supplied it

								# Unlike in other pipelines, latents need to be generated in the target device
								# for 1-to-1 results reproducibility with the CompVis implementation.
								# However this currently doesn't work in `mps`.
								UpperCAmelCase_		:   Any													=       (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
								UpperCAmelCase_		:   List[Any]													=       text_embeddings.dtype
								if latents is None:
									if self.device.type == "mps":
										# randn does not work reproducibly on mps
										UpperCAmelCase_		:   List[str]													=       torch.randn(_snake_case     ,      generator=_snake_case     ,      device='cpu'     ,      dtype=_snake_case).to(
										    self.device)
									else:
										UpperCAmelCase_		:   Optional[int]													=       torch.randn(_snake_case     ,      generator=_snake_case     ,      device=self.device     ,      dtype=_snake_case)
								else:
									if latents.shape != latents_shape:
										raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""")
									UpperCAmelCase_		:   Union[str, Any]													=       latents.to(self.device)

								# set timesteps
								UpperCAmelCase_		:   List[Any]													=       'offset' in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
								UpperCAmelCase_		:   str													=       {}
								if accepts_offset:
									UpperCAmelCase_		:   Optional[Any]													=       1

								self.scheduler.set_timesteps(_snake_case     ,      **_snake_case)

								# Some schedulers like PNDM have timesteps as arrays
								# It's more optimized to move all timesteps to correct device beforehand
								UpperCAmelCase_		:   Union[str, Any]													=       self.scheduler.timesteps.to(self.device)

								# scale the initial noise by the standard deviation required by the scheduler
								UpperCAmelCase_		:   str													=       latents * self.scheduler.init_noise_sigma

								# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
								# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
								# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
								# and should be between [0, 1]
								UpperCAmelCase_		:   Optional[Any]													=       'eta' in set(inspect.signature(self.scheduler.step).parameters.keys())
								UpperCAmelCase_		:   List[Any]													=       {}
								if accepts_eta:
									UpperCAmelCase_		:   Tuple													=       eta

								# check if the scheduler accepts generator
								UpperCAmelCase_		:   str													=       'generator' in set(inspect.signature(self.scheduler.step).parameters.keys())
								if accepts_generator:
									UpperCAmelCase_		:   Tuple													=       generator

								for i, t in enumerate(self.progress_bar(_snake_case)):
									# expand the latents if we are doing classifier free guidance
									UpperCAmelCase_		:   List[str]													=       torch.cat([latents] * 2) if do_classifier_free_guidance else latents
									UpperCAmelCase_		:   List[str]													=       self.scheduler.scale_model_input(_snake_case     ,      _snake_case)

									# predict the noise residual
									UpperCAmelCase_		:   str													=       self.unet(_snake_case     ,      _snake_case     ,      encoder_hidden_states=_snake_case).sample

									# perform classifier free guidance
									if do_classifier_free_guidance:
										UpperCAmelCase_						,     UpperCAmelCase_		:   int													=       noise_pred.chunk(2)
										UpperCAmelCase_		:   List[Any]													=       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

									# perform clip guidance
									if clip_guidance_scale > 0:
										UpperCAmelCase_		:   Union[str, Any]													=       (
										    text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings
										)
										UpperCAmelCase_						,     UpperCAmelCase_		:   str													=       self.cond_fn(
										    _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      )

									# compute the previous noisy sample x_t -> x_t-1
									UpperCAmelCase_		:   str													=       self.scheduler.step(_snake_case     ,      _snake_case     ,      _snake_case     ,      **_snake_case).prev_sample

								# scale and decode the image latents with vae
								UpperCAmelCase_		:   Union[str, Any]													=       1 / self.vae.config.scaling_factor * latents
								UpperCAmelCase_		:   Optional[Any]													=       self.vae.decode(_snake_case).sample

								UpperCAmelCase_		:   List[Any]													=       (image / 2 + 0.5).clamp(0     ,      1)
								UpperCAmelCase_		:   Union[str, Any]													=       image.cpu().permute(0     ,      2     ,      3     ,      1).numpy()

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

								if not return_dict:
									return (image, None)

								return StableDiffusionPipelineOutput(images=_snake_case     ,      nsfw_content_detected=_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






