"""simple docstring"""
import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->List[str]:
  a__:	Any     				=      UNetaDModel(**_SCREAMING_SNAKE_CASE  )
  a__:	Union[str, Any]     				=      checkpoint['all_modules.0.W'].data
  a__:	Optional[Any]     				=      checkpoint['all_modules.0.W'].data
  a__:	int     				=      checkpoint['all_modules.1.weight'].data
  a__:	Dict     				=      checkpoint['all_modules.1.bias'].data

  a__:	Any     				=      checkpoint['all_modules.2.weight'].data
  a__:	List[Any]     				=      checkpoint['all_modules.2.bias'].data

  a__:	Tuple     				=      checkpoint['all_modules.3.weight'].data
  a__:	Optional[int]     				=      checkpoint['all_modules.3.bias'].data

  a__:	int     				=      checkpoint[list(checkpoint.keys()  )[-4]].data
  a__:	Optional[Any]     				=      checkpoint[list(checkpoint.keys()  )[-3]].data
  a__:	Any     				=      checkpoint[list(checkpoint.keys()  )[-2]].data
  a__:	int     				=      checkpoint[list(checkpoint.keys()  )[-1]].data

  a__:	Tuple     				=      4

  def set_attention_weights(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  ):
    a__:	Dict     				=      old_checkpoint[F'all_modules.{index}.NIN_0.W'].data.T
    a__:	Dict     				=      old_checkpoint[F'all_modules.{index}.NIN_1.W'].data.T
    a__:	str     				=      old_checkpoint[F'all_modules.{index}.NIN_2.W'].data.T

    a__:	List[str]     				=      old_checkpoint[F'all_modules.{index}.NIN_0.b'].data
    a__:	Union[str, Any]     				=      old_checkpoint[F'all_modules.{index}.NIN_1.b'].data
    a__:	str     				=      old_checkpoint[F'all_modules.{index}.NIN_2.b'].data

    a__:	Union[str, Any]     				=      old_checkpoint[F'all_modules.{index}.NIN_3.W'].data.T
    a__:	Optional[int]     				=      old_checkpoint[F'all_modules.{index}.NIN_3.b'].data

    a__:	Optional[Any]     				=      old_checkpoint[F'all_modules.{index}.GroupNorm_0.weight'].data
    a__:	Dict     				=      old_checkpoint[F'all_modules.{index}.GroupNorm_0.bias'].data

  def set_resnet_weights(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  ):
    a__:	int     				=      old_checkpoint[F'all_modules.{index}.Conv_0.weight'].data
    a__:	Dict     				=      old_checkpoint[F'all_modules.{index}.Conv_0.bias'].data
    a__:	str     				=      old_checkpoint[F'all_modules.{index}.GroupNorm_0.weight'].data
    a__:	Any     				=      old_checkpoint[F'all_modules.{index}.GroupNorm_0.bias'].data

    a__:	Any     				=      old_checkpoint[F'all_modules.{index}.Conv_1.weight'].data
    a__:	Union[str, Any]     				=      old_checkpoint[F'all_modules.{index}.Conv_1.bias'].data
    a__:	int     				=      old_checkpoint[F'all_modules.{index}.GroupNorm_1.weight'].data
    a__:	Optional[int]     				=      old_checkpoint[F'all_modules.{index}.GroupNorm_1.bias'].data

    a__:	str     				=      old_checkpoint[F'all_modules.{index}.Dense_0.weight'].data
    a__:	Union[str, Any]     				=      old_checkpoint[F'all_modules.{index}.Dense_0.bias'].data

    if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
      a__:	Union[str, Any]     				=      old_checkpoint[F'all_modules.{index}.Conv_2.weight'].data
      a__:	Any     				=      old_checkpoint[F'all_modules.{index}.Conv_2.bias'].data

  for i, block in enumerate(new_model_architecture.downsample_blocks  ):
    a__:	Tuple     				=      hasattr(_SCREAMING_SNAKE_CASE						,	'attentions'  )
    for j in range(len(block.resnets  )  ):
      set_resnet_weights(block.resnets[j]						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
      module_index += 1
      if has_attentions:
        set_attention_weights(block.attentions[j]						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
        module_index += 1

    if hasattr(_SCREAMING_SNAKE_CASE						,	'downsamplers'  ) and block.downsamplers is not None:
      set_resnet_weights(block.resnet_down						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
      module_index += 1
      a__:	Any     				=      checkpoint[F'all_modules.{module_index}.Conv_0.weight'].data
      a__:	str     				=      checkpoint[F'all_modules.{module_index}.Conv_0.bias'].data
      module_index += 1

  set_resnet_weights(new_model_architecture.mid_block.resnets[0]						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
  module_index += 1
  set_attention_weights(new_model_architecture.mid_block.attentions[0]						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
  module_index += 1
  set_resnet_weights(new_model_architecture.mid_block.resnets[1]						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
  module_index += 1

  for i, block in enumerate(new_model_architecture.up_blocks  ):
    a__:	str     				=      hasattr(_SCREAMING_SNAKE_CASE						,	'attentions'  )
    for j in range(len(block.resnets  )  ):
      set_resnet_weights(block.resnets[j]						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
      module_index += 1
    if has_attentions:
      set_attention_weights(
          block.attentions[0]						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )  # why can there only be a single attention layer for up?
      module_index += 1

    if hasattr(_SCREAMING_SNAKE_CASE						,	'resnet_up'  ) and block.resnet_up is not None:
      a__:	List[Any]     				=      checkpoint[F'all_modules.{module_index}.weight'].data
      a__:	Dict     				=      checkpoint[F'all_modules.{module_index}.bias'].data
      module_index += 1
      a__:	Dict     				=      checkpoint[F'all_modules.{module_index}.weight'].data
      a__:	Optional[int]     				=      checkpoint[F'all_modules.{module_index}.bias'].data
      module_index += 1
      set_resnet_weights(block.resnet_up						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
      module_index += 1

  a__:	str     				=      checkpoint[F'all_modules.{module_index}.weight'].data
  a__:	str     				=      checkpoint[F'all_modules.{module_index}.bias'].data
  module_index += 1
  a__:	Union[str, Any]     				=      checkpoint[F'all_modules.{module_index}.weight'].data
  a__:	Tuple     				=      checkpoint[F'all_modules.{module_index}.bias'].data

  return new_model_architecture.state_dict()


if __name__ == "__main__":
 lowercase__   			=      argparse.ArgumentParser()

 parser.add_argument(
     '--checkpoint_path',
     default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin',
     type=str,
     required=False,
     help='Path to the checkpoint to convert.',
 )

 parser.add_argument(
     '--config_file',
     default='/Users/arthurzucker/Work/diffusers/ArthurZ/config.json',
     type=str,
     required=False,
     help='The config json file corresponding to the architecture.',
 )

 parser.add_argument(
     '--dump_path',
     default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt',
     type=str,
     required=False,
     help='Path to the output model.',
 )

 lowercase__   			=      parser.parse_args()

 lowercase__   			=      torch.load(args.checkpoint_path, map_location='cpu')

 with open(args.config_file) as f:
  lowercase__   			=      json.loads(f.read())

 lowercase__   			=      convert_ncsnpp_checkpoint(
     checkpoint,
     config,
 )

 if "sde" in config:
  del config["sde"]

 lowercase__   			=      UNetaDModel(**config)
 model.load_state_dict(converted_checkpoint)

 try:
  lowercase__   			=      ScoreSdeVeScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

  lowercase__   			=      ScoreSdeVePipeline(unet=model, scheduler=scheduler)
  pipe.save_pretrained(args.dump_path)
 except:  # noqa: E722
  model.save_pretrained(args.dump_path)







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_ )

