



import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


lowerCAmelCase					:				Optional[Any]   	=[nn.LayerNorm]

lowerCAmelCase					:				Optional[Any]   	=logging.get_logger(__name__)

lowerCAmelCase					:				Optional[Any]   	=version.parse(version.parse(torch.__version__).base_version)

lowerCAmelCase					:				Optional[int]   	=parsed_torch_version_base >= version.parse('2.0')
lowerCAmelCase					:				Dict   	=parsed_torch_version_base >= version.parse('1.12')
lowerCAmelCase					:				str   	=parsed_torch_version_base >= version.parse('1.11')
lowerCAmelCase					:				Dict   	=parsed_torch_version_base < version.parse('1.11')







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,self  ):


						'''simple docstring'''




						from torch import _softmax_backward_data

						if is_torch_less_than_1_11:
												return _softmax_backward_data(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,parent.dim			,self  )
						else:
												return _softmax_backward_data(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,parent.dim			,self.dtype  )







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__ = 0  ):


						'''simple docstring'''




						lowerCAmelCase	:   Dict					     =					index.to(layer.weight.device  )
						lowerCAmelCase	:   Tuple					     =					layer.weight.index_select(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ).clone().detach()
						if layer.bias is not None:
												if dim == 1:
																		lowerCAmelCase	:   int					     =					layer.bias.clone().detach()
												else:
																		lowerCAmelCase	:   Any					     =					layer.bias[index].clone().detach()
						lowerCAmelCase	:   List[str]					     =					list(layer.weight.size()  )
						lowerCAmelCase	:   Optional[Any]					     =					len(SCREAMING_SNAKE_CASE__  )
						lowerCAmelCase	:   Optional[Any]					     =					nn.Linear(new_size[1]			,new_size[0]			,bias=layer.bias is not None  ).to(layer.weight.device  )
						lowerCAmelCase	:   int					     =					False
						new_layer.weight.copy_(W.contiguous()  )
						lowerCAmelCase	:   Optional[int]					     =					True
						if layer.bias is not None:
												lowerCAmelCase	:   Any					     =					False
												new_layer.bias.copy_(b.contiguous()  )
												lowerCAmelCase	:   Any					     =					True
						return new_layer






class 	_a			(      nn.Module  ):






			def __init__(    self    ,  lowercase_    ,  lowercase_ )	->					Union[str, Any]:
									super().__init__()
									lowerCAmelCase	:   int					     =					nf
									lowerCAmelCase	:   Any					     =					nn.Parameter(torch.empty(lowercase_    ,  lowercase_ ) )
									lowerCAmelCase	:   List[Any]					     =					nn.Parameter(torch.zeros(lowercase_ ) )
									nn.init.normal_(self.weight    ,  std=0.0_2 )






			def 							_snake_case   (    self    ,  lowercase_ )	->					int:
									lowerCAmelCase	:   List[Any]					     =					x.size()[:-1] + (self.nf,)
									lowerCAmelCase	:   Optional[int]					     =					torch.addmm(self.bias    ,  x.view(-1    ,  x.size(-1 ) )    ,  self.weight )
									lowerCAmelCase	:   Optional[int]					     =					x.view(lowercase_ )
									return x







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__ = 1  ):


						'''simple docstring'''




						lowerCAmelCase	:   Optional[int]					     =					index.to(layer.weight.device  )
						lowerCAmelCase	:   Optional[int]					     =					layer.weight.index_select(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ).clone().detach()
						if dim == 0:
												lowerCAmelCase	:   str					     =					layer.bias.clone().detach()
						else:
												lowerCAmelCase	:   List[Any]					     =					layer.bias[index].clone().detach()
						lowerCAmelCase	:   Any					     =					list(layer.weight.size()  )
						lowerCAmelCase	:   Dict					     =					len(SCREAMING_SNAKE_CASE__  )
						lowerCAmelCase	:   List[str]					     =					ConvaD(new_size[1]			,new_size[0]  ).to(layer.weight.device  )
						lowerCAmelCase	:   Union[str, Any]					     =					False
						new_layer.weight.copy_(W.contiguous()  )
						lowerCAmelCase	:   List[str]					     =					True
						lowerCAmelCase	:   Optional[Any]					     =					False
						new_layer.bias.copy_(b.contiguous()  )
						lowerCAmelCase	:   Dict					     =					True
						return new_layer







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__ = None  ):


						'''simple docstring'''




						if isinstance(SCREAMING_SNAKE_CASE__			,nn.Linear  ):
												return prune_linear_layer(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,dim=0 if dim is None else dim  )
						elif isinstance(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):
												return prune_convad_layer(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,dim=1 if dim is None else dim  )
						else:
												raise ValueError(F"""Can't prune layer of class {layer.__class__}"""  )







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,*SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						assert len(SCREAMING_SNAKE_CASE__  ) > 0, F"""{input_tensors} has to be a tuple/list of tensors"""

						# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
						lowerCAmelCase	:   Any					     =					len(inspect.signature(SCREAMING_SNAKE_CASE__  ).parameters  )
						if num_args_in_forward_chunk_fn != len(SCREAMING_SNAKE_CASE__  ):
												raise ValueError(
												    F"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(SCREAMING_SNAKE_CASE__  )} input """
												    """tensors are given"""  )

						if chunk_size > 0:
												lowerCAmelCase	:   Union[str, Any]					     =					input_tensors[0].shape[chunk_dim]
												for input_tensor in input_tensors:
																		if input_tensor.shape[chunk_dim] != tensor_shape:
																								raise ValueError(
																								    F"""All input tenors have to be of the same shape: {tensor_shape}, """
																								    F"""found shape {input_tensor.shape[chunk_dim]}"""  )

												if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
																		raise ValueError(
																		    F"""The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk """
																		    F"""size {chunk_size}"""  )

												lowerCAmelCase	:   List[str]					     =					input_tensors[0].shape[chunk_dim] // chunk_size

												# chunk input tensor into tuples
												lowerCAmelCase	:   int					     =					tuple(input_tensor.chunk(SCREAMING_SNAKE_CASE__			,dim=SCREAMING_SNAKE_CASE__  ) for input_tensor in input_tensors  )
												# apply forward fn to every tuple
												lowerCAmelCase	:   List[str]					     =					tuple(forward_fn(*SCREAMING_SNAKE_CASE__  ) for input_tensors_chunk in zip(*SCREAMING_SNAKE_CASE__  )  )
												# concatenate output at same dimension
												return torch.cat(SCREAMING_SNAKE_CASE__			,dim=SCREAMING_SNAKE_CASE__  )

						return forward_fn(*SCREAMING_SNAKE_CASE__  )







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						lowerCAmelCase	:   Optional[int]					     =					torch.ones(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  )
						lowerCAmelCase	:   Tuple					     =					set(SCREAMING_SNAKE_CASE__  ) - already_pruned_heads  # Convert to set and remove already pruned heads
						for head in heads:
												# Compute how many pruned heads are before the head and move the index accordingly
												lowerCAmelCase	:   Union[str, Any]					     =					head - sum(1 if h < head else 0 for h in already_pruned_heads  )
												lowerCAmelCase	:   int					     =					0
						lowerCAmelCase	:   Optional[Any]					     =					mask.view(-1  ).contiguous().eq(1  )
						lowerCAmelCase	:   torch.LongTensor					     =					torch.arange(len(SCREAMING_SNAKE_CASE__  )  )[mask].long()
						return heads, index







def  _UpperCAmelCase  (				*SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__ = None  ):


						'''simple docstring'''




						return torch.meshgrid(*SCREAMING_SNAKE_CASE__			,indexing=SCREAMING_SNAKE_CASE__  )







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						return tensor.device, storage_ptr(SCREAMING_SNAKE_CASE__  ), storage_size(SCREAMING_SNAKE_CASE__  )






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 	_a			(      nn.Module  ):






			def __init__(    self    ,  lowercase_    ,  lowercase_=1.0 )	->					Optional[int]:
									super().__init__()

									lowerCAmelCase	:   Tuple					     =					cut_size
									lowerCAmelCase	:   str					     =					cut_power






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







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						lowerCAmelCase	:   str					     =					F.normalize(SCREAMING_SNAKE_CASE__			,dim=-1  )
						lowerCAmelCase	:   Tuple					     =					F.normalize(SCREAMING_SNAKE_CASE__			,dim=-1  )
						return (x - y).norm(dim=-1  ).div(2  ).arcsin().pow(2  ).mul(2  )







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						for param in model.parameters():
												lowerCAmelCase	:   Optional[int]					     =					value






class 	_a			(      snake_case_  ):






			def __init__(    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  )	->					Optional[Any]:
									super().__init__()
									self.register_modules(
									    vae=lowercase_    ,  text_encoder=lowercase_    ,  clip_model=lowercase_    ,  tokenizer=lowercase_    ,  unet=lowercase_    ,  scheduler=lowercase_    ,  feature_extractor=lowercase_    ,  )

									lowerCAmelCase	:   Optional[Any]					     =					transforms.Normalize(mean=feature_extractor.image_mean    ,  std=feature_extractor.image_std )
									lowerCAmelCase	:   Dict					     =					(
									    feature_extractor.size
									    if isinstance(feature_extractor.size    ,  lowercase_ )
									    else feature_extractor.size["""shortest_edge"""]
									)
									lowerCAmelCase	:   str					     =					MakeCutouts(self.cut_out_size )

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






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






			def 							_snake_case   (    self )	->					str:
									self.enable_attention_slicing(lowercase_ )






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






			def 							_snake_case   (    self )	->					List[Any]:
									set_requires_grad(self.vae    ,  lowercase_ )






			def 							_snake_case   (    self )	->					Union[str, Any]:
									set_requires_grad(self.unet    ,  lowercase_ )






			def 							_snake_case   (    self )	->					Dict:
									set_requires_grad(self.unet    ,  lowercase_ )






			@torch.enable_grad()
			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_=True    ,  )	->					Dict:
									lowerCAmelCase	:   Optional[int]					     =					latents.detach().requires_grad_()

									lowerCAmelCase	:   Optional[int]					     =					self.scheduler.scale_model_input(lowercase_    ,  lowercase_ )

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

									if isinstance(self.scheduler    ,  (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
															lowerCAmelCase	:   Any					     =					self.scheduler.alphas_cumprod[timestep]
															lowerCAmelCase	:   Union[str, Any]					     =					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
															lowerCAmelCase	:   Tuple					     =					(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

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

									lowerCAmelCase	:   Dict					     =					1 / self.vae.config.scaling_factor * sample
									lowerCAmelCase	:   List[str]					     =					self.vae.decode(lowercase_ ).sample
									lowerCAmelCase	:   Any					     =					(image / 2 + 0.5).clamp(0    ,  1 )

									if use_cutouts:
															lowerCAmelCase	:   Union[str, Any]					     =					self.make_cutouts(lowercase_    ,  lowercase_ )
									else:
															lowerCAmelCase	:   Union[str, Any]					     =					transforms.Resize(self.cut_out_size )(lowercase_ )
									lowerCAmelCase	:   Any					     =					self.normalize(lowercase_ ).to(latents.dtype )

									lowerCAmelCase	:   Union[str, Any]					     =					self.clip_model.get_image_features(lowercase_ )
									lowerCAmelCase	:   Dict					     =					image_embeddings_clip / image_embeddings_clip.norm(p=2    ,  dim=-1    ,  keepdim=lowercase_ )

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

									lowerCAmelCase	:   Optional[Any]					     =					-torch.autograd.grad(lowercase_    ,  lowercase_ )[0]

									if isinstance(self.scheduler    ,  lowercase_ ):
															lowerCAmelCase	:   int					     =					latents.detach() + grads * (sigma**2)
															lowerCAmelCase	:   Any					     =					noise_pred_original
									else:
															lowerCAmelCase	:   Any					     =					noise_pred_original - torch.sqrt(lowercase_ ) * grads
									return noise_pred, latents






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

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

									if clip_guidance_scale > 0:
															if clip_prompt is not None:
																					lowerCAmelCase	:   int					     =					self.tokenizer(
																					    lowercase_    ,  padding="""max_length"""    ,  max_length=self.tokenizer.model_max_length    ,  truncation=lowercase_    ,  return_tensors="""pt"""    ,  ).input_ids.to(self.device )
															else:
																					lowerCAmelCase	:   Tuple					     =					text_input.input_ids.to(self.device )
															lowerCAmelCase	:   Dict					     =					self.clip_model.get_text_features(lowercase_ )
															lowerCAmelCase	:   Tuple					     =					text_embeddings_clip / text_embeddings_clip.norm(p=2    ,  dim=-1    ,  keepdim=lowercase_ )
															# duplicate text embeddings clip for each generation per prompt
															lowerCAmelCase	:   Any					     =					text_embeddings_clip.repeat_interleave(lowercase_    ,  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.
									lowerCAmelCase	:   Optional[Any]					     =					guidance_scale > 1.0
									# get unconditional embeddings for classifier free guidance
									if do_classifier_free_guidance:
															lowerCAmelCase	:   Optional[Any]					     =					text_input.input_ids.shape[-1]
															lowerCAmelCase	:   Dict					     =					self.tokenizer([""""""]    ,  padding="""max_length"""    ,  max_length=lowercase_    ,  return_tensors="""pt""" )
															lowerCAmelCase	:   str					     =					self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
															# duplicate unconditional embeddings for each generation per prompt
															lowerCAmelCase	:   Optional[int]					     =					uncond_embeddings.repeat_interleave(lowercase_    ,  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
															lowerCAmelCase	:   str					     =					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`.
									lowerCAmelCase	:   Optional[int]					     =					(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
									lowerCAmelCase	:   int					     =					text_embeddings.dtype
									if latents is None:
															if self.device.type == "mps":
																					# randn does not work reproducibly on mps
																					lowerCAmelCase	:   str					     =					torch.randn(lowercase_    ,  generator=lowercase_    ,  device="""cpu"""    ,  dtype=lowercase_ ).to(
																					    self.device )
															else:
																					lowerCAmelCase	:   Optional[int]					     =					torch.randn(lowercase_    ,  generator=lowercase_    ,  device=self.device    ,  dtype=lowercase_ )
									else:
															if latents.shape != latents_shape:
																					raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" )
															lowerCAmelCase	:   Any					     =					latents.to(self.device )

									# set timesteps
									lowerCAmelCase	:   List[str]					     =					"""offset""" in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
									lowerCAmelCase	:   int					     =					{}
									if accepts_offset:
															lowerCAmelCase	:   List[str]					     =					1

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

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

									# scale the initial noise by the standard deviation required by the scheduler
									lowerCAmelCase	:   List[Any]					     =					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]
									lowerCAmelCase	:   List[str]					     =					"""eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
									lowerCAmelCase	:   int					     =					{}
									if accepts_eta:
															lowerCAmelCase	:   Any					     =					eta

									# check if the scheduler accepts generator
									lowerCAmelCase	:   Union[str, Any]					     =					"""generator""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
									if accepts_generator:
															lowerCAmelCase	:   Dict					     =					generator

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

															# predict the noise residual
															lowerCAmelCase	:   Optional[Any]					     =					self.unet(lowercase_    ,  lowercase_    ,  encoder_hidden_states=lowercase_ ).sample

															# perform classifier free guidance
															if do_classifier_free_guidance:
																					lowerCAmelCase     ,	lowerCAmelCase	:   int					     =					noise_pred.chunk(2 )
																					lowerCAmelCase	:   str					     =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

															# perform clip guidance
															if clip_guidance_scale > 0:
																					lowerCAmelCase	:   Any					     =					(
																					    text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
																					)
																					lowerCAmelCase     ,	lowerCAmelCase	:   List[Any]					     =					self.cond_fn(
																					    lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  )

															# compute the previous noisy sample x_t -> x_t-1
															lowerCAmelCase	:   List[Any]					     =					self.scheduler.step(lowercase_    ,  lowercase_    ,  lowercase_    ,  **lowercase_ ).prev_sample

									# scale and decode the image latents with vae
									lowerCAmelCase	:   Dict					     =					1 / self.vae.config.scaling_factor * latents
									lowerCAmelCase	:   Optional[int]					     =					self.vae.decode(lowercase_ ).sample

									lowerCAmelCase	:   Any					     =					(image / 2 + 0.5).clamp(0    ,  1 )
									lowerCAmelCase	:   str					     =					image.cpu().permute(0    ,  2    ,  3    ,  1 ).numpy()

									if output_type == "pil":
															lowerCAmelCase	:   Tuple					     =					self.numpy_to_pil(lowercase_ )

									if not return_dict:
															return (image, None)

									return StableDiffusionPipelineOutput(images=lowercase_    ,  nsfw_content_detected=lowercase_ )

