

import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
					import torch


lowerCAmelCase_     :   Tuple 			=			logging.get_logger(__name__)





class 			snake_case_     :







			'''simple docstring'''

			def __init__(  self				:       Dict							,     _UpperCamelCase				:       List[str]							,     _UpperCamelCase				:       Tuple=2_1							,     _UpperCamelCase				:       Dict=[]							,     _UpperCamelCase				:       Union[str, Any]=None							)   ->List[Any]:
									snake_case_     =			model
									snake_case_     =			trace_batch_nums
									snake_case_     =			abort_after_batch_num

									# keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
									snake_case_     =			collections.deque([]							,     _UpperCamelCase							)
									snake_case_     =			[]
									snake_case_     =			0
									snake_case_     =			0
									snake_case_     =			False
									snake_case_     =			'''                 '''

									self.analyse_model()

									self.register_forward_hook()







			def        snake_case__(  self				:       str							,     _UpperCamelCase				:       Any=None							)   ->Dict:
									if frame is not None:
															self.expand_frame(_UpperCamelCase							)
									self.frames.append('''\n'''.join(self.frame							)							)
									snake_case_     =			[]  # start a new frame







			def        snake_case__(  self				:       Optional[int]							,     _UpperCamelCase				:       List[Any]							)   ->Union[str, Any]:
									self.frame.append(_UpperCamelCase							)







			def        snake_case__(  self				:       int							)   ->str:
									print('''\n'''.join(self.frames							)							)
									snake_case_     =			[]







			def        snake_case__(  self				:       List[Any]							)   ->Dict:
									snake_case_     =			[]







			def        snake_case__(  self				:       Union[str, Any]							)   ->str:
									print(f'''\nDetected inf/nan during batch_number={self.batch_number}'''							)
									print(f'''Last {len(self.frames							)} forward frames:'''							)
									print(f'''{'abs min':8} {'abs max':8} metadata'''							)
									print('''\n'''.join(self.frames							)							)
									print('''\n\n'''							)
									snake_case_     =			[]







			def        snake_case__(  self				:       Any							)   ->List[Any]:
									# extract the fully qualified module names, to be able to report at run time. e.g.:
									# encoder.block.2.layer.0.SelfAttention.o
									#
									# for shared weights only the first shared module name will be registered
									snake_case_     =			{m: name for name, m in self.model.named_modules()}
									# self.longest_module_name = max(len(v) for v in self.module_names.values())







			def        snake_case__(  self				:       List[Any]							,     _UpperCamelCase				:       Union[str, Any]							,     _UpperCamelCase				:       Dict							)   ->Dict:
									if torch.is_tensor(_UpperCamelCase							):
															self.expand_frame(get_abs_min_max(_UpperCamelCase							,     _UpperCamelCase							)							)
															if detect_overflow(_UpperCamelCase							,     _UpperCamelCase							):
																					snake_case_     =			True
									elif var is None:
															self.expand_frame(f'''{'None':>17} {ctx}'''							)
									else:
															self.expand_frame(f'''{'not a tensor':>17} {ctx}'''							)







			def        snake_case__(  self				:       Any							)   ->int:
									self.expand_frame(f'''\n\n{self.prefix} *** Starting batch number={self.batch_number} ***'''							)
									self.expand_frame(f'''{'abs min':8} {'abs max':8} metadata'''							)







			def        snake_case__(  self				:       Dict							)   ->Union[str, Any]:
									self.expand_frame(f'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''							)







			def        snake_case__(  self				:       Any							,     _UpperCamelCase				:       Any							,     _UpperCamelCase				:       Union[str, Any]							,     _UpperCamelCase				:       List[Any]							)   ->List[str]:
									self.expand_frame(f'''{self.prefix} {self.module_names[module]} {module.__class__.__name__}'''							)

									# params
									for name, p in module.named_parameters(recurse=_UpperCamelCase							):
															self.analyse_variable(_UpperCamelCase							,     _UpperCamelCase							)

									# inputs
									if isinstance(_UpperCamelCase							,     _UpperCamelCase							):
															for i, x in enumerate(_UpperCamelCase							):
																					self.analyse_variable(_UpperCamelCase							,     f'''input[{i}]'''							)
									else:
															self.analyse_variable(_UpperCamelCase							,     '''input'''							)

									# outputs
									if isinstance(_UpperCamelCase							,     _UpperCamelCase							):
															for i, x in enumerate(_UpperCamelCase							):
																					# possibly a tuple of tuples
																					if isinstance(_UpperCamelCase							,     _UpperCamelCase							):
																											for j, y in enumerate(_UpperCamelCase							):
																																	self.analyse_variable(_UpperCamelCase							,     f'''output[{i}][{j}]'''							)
																					else:
																											self.analyse_variable(_UpperCamelCase							,     f'''output[{i}]'''							)
									else:
															self.analyse_variable(_UpperCamelCase							,     '''output'''							)

									self.save_frame()







			def        snake_case__(  self				:       Optional[Any]							)   ->Tuple:
									self.model.apply(self._register_forward_hook							)







			def        snake_case__(  self				:       Optional[int]							,     _UpperCamelCase				:       str							)   ->List[Any]:
									module.register_forward_hook(self.forward_hook							)







			def        snake_case__(  self				:       int							,     _UpperCamelCase				:       Any							,     _UpperCamelCase				:       Any							,     _UpperCamelCase				:       Any							)   ->int:
									# - input is a tuple of packed inputs (could be non-Tensors)
									# - output could be a Tensor or a tuple of Tensors and non-Tensors

									snake_case_     =			False

									snake_case_     =			True if self.batch_number in self.trace_batch_nums else False
									if trace_mode:
															self.reset_saved_frames()

									if self.total_calls == 0:
															self.batch_start_frame()
									self.total_calls += 1

									# count batch numbers - the very first forward hook of the batch will be called when the
									# batch completes - i.e. it gets called very last - we know this batch has finished
									if module == self.model:
															self.batch_number += 1
															snake_case_     =			True

									self.create_frame(_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							)

									# if last_frame_of_batch:
									#     self.batch_end_frame()

									if trace_mode:
															self.trace_frames()

									if last_frame_of_batch:
															self.batch_start_frame()

									if self.detected_overflow and not trace_mode:
															self.dump_saved_frames()

															# now we can abort, as it's pointless to continue running
															raise ValueError(
															    '''DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. '''
															    '''Please scroll up above this traceback to see the activation values prior to this event.'''							)

									# abort after certain batch if requested to do so
									if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
															raise ValueError(
															    f'''DebugUnderflowOverflow: aborting after {self.batch_number} batches due to'''
															    f''' `abort_after_batch_num={self.abort_after_batch_num}` arg'''							)






def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						snake_case_     =			var.abs()
						return F'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						snake_case_     =			False
						if torch.isnan(SCREAMING_SNAKE_CASE__ ).any().item():
												snake_case_     =			True
												print(F'''{ctx} has nans''' )
						if torch.isinf(SCREAMING_SNAKE_CASE__ ).any().item():
												snake_case_     =			True
												print(F'''{ctx} has infs''' )

						# if needed to monitor large elements can enable the following
						if 0:  # and detected:
												snake_case_     =			var[torch.ge(var.abs()						,	100 )]
												if naaa.numel() > 0:
																		print(F'''{ctx}:  n100={naaa.numel()}''' )
												snake_case_     =			var[torch.ge(var.abs()						,	1000 )]
												if naaaa.numel() > 0:
																		print(F'''{ctx}: n1000={naaaa.numel()}''' )
												snake_case_     =			var[torch.ge(var.abs()						,	10000 )]
												if naaaaa.numel() > 0:
																		print(F'''{ctx}: n10000={naaaaa.numel()}''' )

						if 0:
												print(F'''min={var.min():9.2e} max={var.max():9.2e}''' )

						if 0:
												print(F'''min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})''' )

						return detected





class 			snake_case_     (	__A      ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     List[str]		    =       "underflow_overflow"
			SCREAMING_SNAKE_CASE  :     Optional[int]		    =       "tpu_metrics_debug"


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 			snake_case_     (	nn.Module      ):







			'''simple docstring'''

			def __init__(  self				:       Optional[Any]							,     _UpperCamelCase				:       Dict							,     _UpperCamelCase				:       List[str]=1.0							)   ->Tuple:
									super().__init__()

									snake_case_     =			cut_size
									snake_case_     =			cut_power







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






def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						snake_case_     =			F.normalize(SCREAMING_SNAKE_CASE__						,	dim=-1 )
						snake_case_     =			F.normalize(SCREAMING_SNAKE_CASE__						,	dim=-1 )
						return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						for param in model.parameters():
												snake_case_     =			value





class 			snake_case_     (	__A      ):







			'''simple docstring'''

			def __init__(  self				:       List[Any]							,     _UpperCamelCase				:       AutoencoderKL							,     _UpperCamelCase				:       CLIPTextModel							,     _UpperCamelCase				:       CLIPModel							,     _UpperCamelCase				:       CLIPTokenizer							,     _UpperCamelCase				:       UNetaDConditionModel							,     _UpperCamelCase				:       Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]							,     _UpperCamelCase				:       CLIPImageProcessor							,     )   ->Union[str, Any]:
									super().__init__()
									self.register_modules(
									    vae=_UpperCamelCase							,     text_encoder=_UpperCamelCase							,     clip_model=_UpperCamelCase							,     tokenizer=_UpperCamelCase							,     unet=_UpperCamelCase							,     scheduler=_UpperCamelCase							,     feature_extractor=_UpperCamelCase							,     )

									snake_case_     =			transforms.Normalize(mean=feature_extractor.image_mean							,     std=feature_extractor.image_std							)
									snake_case_     =			(
									    feature_extractor.size
									    if isinstance(feature_extractor.size							,     _UpperCamelCase							)
									    else feature_extractor.size['''shortest_edge''']
									)
									snake_case_     =			MakeCutouts(self.cut_out_size							)

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







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







			def        snake_case__(  self				:       str							)   ->Optional[Any]:
									self.enable_attention_slicing(_UpperCamelCase							)







			def        snake_case__(  self				:       Any							)   ->Union[str, Any]:
									set_requires_grad(self.vae							,     _UpperCamelCase							)







			def        snake_case__(  self				:       Any							)   ->Dict:
									set_requires_grad(self.vae							,     _UpperCamelCase							)







			def        snake_case__(  self				:       Dict							)   ->Optional[int]:
									set_requires_grad(self.unet							,     _UpperCamelCase							)







			def        snake_case__(  self				:       Optional[int]							)   ->List[Any]:
									set_requires_grad(self.unet							,     _UpperCamelCase							)







			@torch.enable_grad()
			def        snake_case__(  self				:       Optional[Any]							,     _UpperCamelCase				:       Tuple							,     _UpperCamelCase				:       str							,     _UpperCamelCase				:       Any							,     _UpperCamelCase				:       Union[str, Any]							,     _UpperCamelCase				:       Union[str, Any]							,     _UpperCamelCase				:       Union[str, Any]							,     _UpperCamelCase				:       Optional[Any]							,     _UpperCamelCase				:       Optional[int]							,     _UpperCamelCase				:       Tuple=True							,     )   ->Union[str, Any]:
									snake_case_     =			latents.detach().requires_grad_()

									snake_case_     =			self.scheduler.scale_model_input(_UpperCamelCase							,     _UpperCamelCase							)

									# predict the noise residual
									snake_case_     =			self.unet(_UpperCamelCase							,     _UpperCamelCase							,     encoder_hidden_states=_UpperCamelCase							).sample

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

															snake_case_     =			torch.sqrt(_UpperCamelCase							)
															snake_case_     =			pred_original_sample * (fac) + latents * (1 - fac)
									elif isinstance(self.scheduler							,     _UpperCamelCase							):
															snake_case_     =			self.scheduler.sigmas[index]
															snake_case_     =			latents - sigma * noise_pred
									else:
															raise ValueError(f'''scheduler type {type(self.scheduler							)} not supported'''							)

									snake_case_     =			1 / self.vae.config.scaling_factor * sample
									snake_case_     =			self.vae.decode(_UpperCamelCase							).sample
									snake_case_     =			(image / 2 + 0.5).clamp(0							,     1							)

									if use_cutouts:
															snake_case_     =			self.make_cutouts(_UpperCamelCase							,     _UpperCamelCase							)
									else:
															snake_case_     =			transforms.Resize(self.cut_out_size							)(_UpperCamelCase							)
									snake_case_     =			self.normalize(_UpperCamelCase							).to(latents.dtype							)

									snake_case_     =			self.clip_model.get_image_features(_UpperCamelCase							)
									snake_case_     =			image_embeddings_clip / image_embeddings_clip.norm(p=2							,     dim=-1							,     keepdim=_UpperCamelCase							)

									if use_cutouts:
															snake_case_     =			spherical_dist_loss(_UpperCamelCase							,     _UpperCamelCase							)
															snake_case_     =			dists.view([num_cutouts, sample.shape[0], -1]							)
															snake_case_     =			dists.sum(2							).mean(0							).sum() * clip_guidance_scale
									else:
															snake_case_     =			spherical_dist_loss(_UpperCamelCase							,     _UpperCamelCase							).mean() * clip_guidance_scale

									snake_case_     =			-torch.autograd.grad(_UpperCamelCase							,     _UpperCamelCase							)[0]

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







			@torch.no_grad()
			def __call__(  self				:       Optional[int]							,     _UpperCamelCase				:       Union[str, List[str]]							,     _UpperCamelCase				:       Optional[int] = 5_1_2							,     _UpperCamelCase				:       Optional[int] = 5_1_2							,     _UpperCamelCase				:       Optional[int] = 5_0							,     _UpperCamelCase				:       Optional[float] = 7.5							,     _UpperCamelCase				:       Optional[int] = 1							,     _UpperCamelCase				:       float = 0.0							,     _UpperCamelCase				:       Optional[float] = 1_0_0							,     _UpperCamelCase				:       Optional[Union[str, List[str]]] = None							,     _UpperCamelCase				:       Optional[int] = 4							,     _UpperCamelCase				:       Optional[bool] = True							,     _UpperCamelCase				:       Optional[torch.Generator] = None							,     _UpperCamelCase				:       Optional[torch.FloatTensor] = None							,     _UpperCamelCase				:       Optional[str] = "pil"							,     _UpperCamelCase				:       bool = True							,     )   ->str:
									if isinstance(_UpperCamelCase							,     _UpperCamelCase							):
															snake_case_     =			1
									elif isinstance(_UpperCamelCase							,     _UpperCamelCase							):
															snake_case_     =			len(_UpperCamelCase							)
									else:
															raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(_UpperCamelCase							)}'''							)

									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
									snake_case_     =			self.tokenizer(
									    _UpperCamelCase							,     padding='''max_length'''							,     max_length=self.tokenizer.model_max_length							,     truncation=_UpperCamelCase							,     return_tensors='''pt'''							,     )
									snake_case_     =			self.text_encoder(text_input.input_ids.to(self.device							)							)[0]
									# duplicate text embeddings for each generation per prompt
									snake_case_     =			text_embeddings.repeat_interleave(_UpperCamelCase							,     dim=0							)

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

									# set timesteps
									snake_case_     =			'''offset''' in set(inspect.signature(self.scheduler.set_timesteps							).parameters.keys()							)
									snake_case_     =			{}
									if accepts_offset:
															snake_case_     =			1

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

									# Some schedulers like PNDM have timesteps as arrays
									# It's more optimized to move all timesteps to correct device beforehand
									snake_case_     =			self.scheduler.timesteps.to(self.device							)

									# scale the initial noise by the standard deviation required by the scheduler
									snake_case_     =			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]
									snake_case_     =			'''eta''' in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
									snake_case_     =			{}
									if accepts_eta:
															snake_case_     =			eta

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

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

															# predict the noise residual
															snake_case_     =			self.unet(_UpperCamelCase							,     _UpperCamelCase							,     encoder_hidden_states=_UpperCamelCase							).sample

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

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

															# compute the previous noisy sample x_t -> x_t-1
															snake_case_     =			self.scheduler.step(_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     **_UpperCamelCase							).prev_sample

									# scale and decode the image latents with vae
									snake_case_     =			1 / self.vae.config.scaling_factor * latents
									snake_case_     =			self.vae.decode(_UpperCamelCase							).sample

									snake_case_     =			(image / 2 + 0.5).clamp(0							,     1							)
									snake_case_     =			image.cpu().permute(0							,     2							,     3							,     1							).numpy()

									if output_type == "pil":
															snake_case_     =			self.numpy_to_pil(_UpperCamelCase							)

									if not return_dict:
															return (image, None)

									return StableDiffusionPipelineOutput(images=_UpperCamelCase							,     nsfw_content_detected=_UpperCamelCase							)