


'''simple docstring'''
import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

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


def 			a      (			__a ,					__a ,					__a   )  ->	List[str]:



					'''simple docstring'''






					if isinstance(__a ,					torch.Tensor   ):
										return image
					elif isinstance(__a ,					PIL.Image.Image   ):
										UpperCamelCase__   :Tuple												= [image]

					if isinstance(image[0] ,					PIL.Image.Image   ):
										UpperCamelCase__   :str												= [np.array(i.resize((w, h) ,					resample=PIL_INTERPOLATION['''lanczos''']   )   )[None, :] for i in image]
										UpperCamelCase__   :Optional[int]												= np.concatenate(__a ,					axis=0   )
										UpperCamelCase__   :List[str]												= np.array(__a   ).astype(np.floataa   ) / 2_5_5.0
										UpperCamelCase__   :int												= image.transpose(0 ,					3 ,					1 ,					2   )
										UpperCamelCase__   :List[str]												= 2.0 * image - 1.0
										UpperCamelCase__   :int												= torch.from_numpy(__a   )
					elif isinstance(image[0] ,					torch.Tensor   ):
										UpperCamelCase__   :int												= torch.cat(__a ,					dim=0   )
					return image


def 			a      (			__a ,					__a ,					__a ,					__a=0.9_9_9_5   )  ->	Dict:



					'''simple docstring'''






					if not isinstance(__a ,					np.ndarray   ):
										UpperCamelCase__   :str												= True
										UpperCamelCase__   :Optional[int]												= va.device
										UpperCamelCase__   :List[str]												= va.cpu().numpy()
										UpperCamelCase__   :int												= va.cpu().numpy()

					UpperCamelCase__   :List[str]												= np.sum(va * va / (np.linalg.norm(__a   ) * np.linalg.norm(__a   ))   )
					if np.abs(__a   ) > DOT_THRESHOLD:
										UpperCamelCase__   :List[str]												= (1 - t) * va + t * va
					else:
										UpperCamelCase__   :Union[str, Any]												= np.arccos(__a   )
										UpperCamelCase__   :Optional[int]												= np.sin(__a   )
										UpperCamelCase__   :Dict												= theta_a * t
										UpperCamelCase__   :Optional[Any]												= np.sin(__a   )
										UpperCamelCase__   :Union[str, Any]												= np.sin(theta_a - theta_t   ) / sin_theta_a
										UpperCamelCase__   :str												= sin_theta_t / sin_theta_a
										UpperCamelCase__   :Optional[int]												= sa * va + sa * va

					if inputs_are_torch:
										UpperCamelCase__   :List[str]												= torch.from_numpy(__a   ).to(__a   )

					return va


def 			a      (			__a ,					__a   )  ->	int:



					'''simple docstring'''






					UpperCamelCase__   :str												= F.normalize(__a ,					dim=-1   )
					UpperCamelCase__   :Any												= F.normalize(__a ,					dim=-1   )
					return (x - y).norm(dim=-1   ).div(2   ).arcsin().pow(2   ).mul(2   )


def 			a      (			__a ,					__a   )  ->	Any:



					'''simple docstring'''






					for param in model.parameters():
										UpperCamelCase__   :Any												= value







class    lowercase	(							A__					):

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_=None    ,   UpperCamelCase_=None    ,   UpperCamelCase_=None    ,   ):

							'''simple docstring'''


							super().__init__()
							self.register_modules(
							    vae=UpperCamelCase_    ,   text_encoder=UpperCamelCase_    ,   clip_model=UpperCamelCase_    ,   tokenizer=UpperCamelCase_    ,   unet=UpperCamelCase_    ,   scheduler=UpperCamelCase_    ,   feature_extractor=UpperCamelCase_    ,   coca_model=UpperCamelCase_    ,   coca_tokenizer=UpperCamelCase_    ,   coca_transform=UpperCamelCase_    ,   )
							UpperCamelCase__   :List[Any]												= (
							    feature_extractor.size
							    if isinstance(feature_extractor.size    ,   UpperCamelCase_							)
							    else feature_extractor.size['''shortest_edge''']
							)
							UpperCamelCase__   :List[str]												= transforms.Normalize(mean=feature_extractor.image_mean    ,   std=feature_extractor.image_std							)
							set_requires_grad(self.text_encoder    ,   UpperCamelCase_							)
							set_requires_grad(self.clip_model    ,   UpperCamelCase_							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_ = "auto"							):

							'''simple docstring'''


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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							self.enable_attention_slicing(UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							set_requires_grad(self.vae    ,   UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							set_requires_grad(self.vae    ,   UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							set_requires_grad(self.unet    ,   UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							set_requires_grad(self.unet    ,   UpperCamelCase_							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :int												= min(int(num_inference_steps * strength							)    ,   UpperCamelCase_							)

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

							return timesteps, num_inference_steps - t_start




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_=None							):

							'''simple docstring'''


							if not isinstance(UpperCamelCase_    ,   torch.Tensor							):
												raise ValueError(F'''`image` has to be of type `torch.Tensor` but is {type(UpperCamelCase_							)}'''							)

							UpperCamelCase__   :List[str]												= image.to(device=UpperCamelCase_    ,   dtype=UpperCamelCase_							)

							if isinstance(UpperCamelCase_    ,   UpperCamelCase_							):
												UpperCamelCase__   :int												= [
												    self.vae.encode(image[i : i + 1]							).latent_dist.sample(generator[i]							) for i in range(UpperCamelCase_							)
												]
												UpperCamelCase__   :str												= torch.cat(UpperCamelCase_    ,   dim=0							)
							else:
												UpperCamelCase__   :Tuple												= self.vae.encode(UpperCamelCase_							).latent_dist.sample(UpperCamelCase_							)

							# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
							UpperCamelCase__   :str												= 0.18215 * init_latents
							UpperCamelCase__   :Optional[Any]												= init_latents.repeat_interleave(UpperCamelCase_    ,   dim=0							)

							UpperCamelCase__   :Any												= randn_tensor(init_latents.shape    ,   generator=UpperCamelCase_    ,   device=UpperCamelCase_    ,   dtype=UpperCamelCase_							)

							# get latents
							UpperCamelCase__   :Tuple												= self.scheduler.add_noise(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							)
							UpperCamelCase__   :List[Any]												= init_latents

							return latents




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Any												= self.coca_transform(UpperCamelCase_							).unsqueeze(0							)
							with torch.no_grad(), torch.cuda.amp.autocast():
												UpperCamelCase__   :List[str]												= self.coca_model.generate(transformed_image.to(device=self.device    ,   dtype=self.coca_model.dtype							)							)
							UpperCamelCase__   :int												= self.coca_tokenizer.decode(generated[0].cpu().numpy()							)
							return generated.split('''<end_of_text>'''							)[0].replace('''<start_of_text>'''    ,   ''''''							).rstrip(''' .,'''							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Union[str, Any]												= self.feature_extractor.preprocess(UpperCamelCase_							)
							UpperCamelCase__   :Any												= torch.from_numpy(clip_image_input['''pixel_values'''][0]							).unsqueeze(0							).to(self.device							).half()
							UpperCamelCase__   :str												= self.clip_model.get_image_features(UpperCamelCase_							)
							UpperCamelCase__   :List[str]												= image_embeddings_clip / image_embeddings_clip.norm(p=2    ,   dim=-1    ,   keepdim=UpperCamelCase_							)
							UpperCamelCase__   :Union[str, Any]												= image_embeddings_clip.repeat_interleave(UpperCamelCase_    ,   dim=0							)
							return image_embeddings_clip




		@torch.enable_grad()
		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   ):

							'''simple docstring'''


							UpperCamelCase__   :List[str]												= latents.detach().requires_grad_()

							UpperCamelCase__   :List[Any]												= self.scheduler.scale_model_input(UpperCamelCase_    ,   UpperCamelCase_							)

							# predict the noise residual
							UpperCamelCase__   :List[Any]												= self.unet(UpperCamelCase_    ,   UpperCamelCase_    ,   encoder_hidden_states=UpperCamelCase_							).sample

							if isinstance(self.scheduler    ,   (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)							):
												UpperCamelCase__   :Union[str, Any]												= 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__   :List[Any]												= (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

												UpperCamelCase__   :Any												= torch.sqrt(UpperCamelCase_							)
												UpperCamelCase__   :Dict												= pred_original_sample * (fac) + latents * (1 - fac)
							elif isinstance(self.scheduler    ,   UpperCamelCase_							):
												UpperCamelCase__   :Any												= self.scheduler.sigmas[index]
												UpperCamelCase__   :Union[str, Any]												= latents - sigma * noise_pred
							else:
												raise ValueError(F'''scheduler type {type(self.scheduler							)} not supported'''							)

							# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
							UpperCamelCase__   :Dict												= 1 / 0.18215 * sample
							UpperCamelCase__   :List[Any]												= self.vae.decode(UpperCamelCase_							).sample
							UpperCamelCase__   :Union[str, Any]												= (image / 2 + 0.5).clamp(0    ,   1							)

							UpperCamelCase__   :List[Any]												= transforms.Resize(self.feature_extractor_size							)(UpperCamelCase_							)
							UpperCamelCase__   :Optional[int]												= self.normalize(UpperCamelCase_							).to(latents.dtype							)

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

							UpperCamelCase__   :Any												= spherical_dist_loss(UpperCamelCase_    ,   UpperCamelCase_							).mean() * clip_guidance_scale

							UpperCamelCase__   :str												= -torch.autograd.grad(UpperCamelCase_    ,   UpperCamelCase_							)[0]

							if isinstance(self.scheduler    ,   UpperCamelCase_							):
												UpperCamelCase__   :List[Any]												= latents.detach() + grads * (sigma**2)
												UpperCamelCase__   :Optional[int]												= noise_pred_original
							else:
												UpperCamelCase__   :Tuple												= noise_pred_original - torch.sqrt(UpperCamelCase_							) * grads
							return noise_pred, latents




		@torch.no_grad()
		def __call__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = 512    ,   UpperCamelCase_ = 512    ,   UpperCamelCase_ = 0.6    ,   UpperCamelCase_ = 50    ,   UpperCamelCase_ = 7.5    ,   UpperCamelCase_ = 1    ,   UpperCamelCase_ = 0.0    ,   UpperCamelCase_ = 100    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = "pil"    ,   UpperCamelCase_ = True    ,   UpperCamelCase_ = 0.8    ,   UpperCamelCase_ = 0.1    ,   UpperCamelCase_ = 0.1    ,   ):

							'''simple docstring'''


							if isinstance(UpperCamelCase_    ,   UpperCamelCase_							) and len(UpperCamelCase_							) != batch_size:
												raise ValueError(F'''You have passed {batch_size} batch_size, but only {len(UpperCamelCase_							)} generators.'''							)

							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}.'''							)

							if isinstance(UpperCamelCase_    ,   torch.Generator							) and batch_size > 1:
												UpperCamelCase__   :List[Any]												= [generator] + [None] * (batch_size - 1)

							UpperCamelCase__   :Union[str, Any]												= [
							    ('''model''', self.coca_model is None),
							    ('''tokenizer''', self.coca_tokenizer is None),
							    ('''transform''', self.coca_transform is None),
							]
							UpperCamelCase__   :Union[str, Any]												= [x[0] for x in coca_is_none if x[1]]
							UpperCamelCase__   :Union[str, Any]												= ''', '''.join(UpperCamelCase_							)
							# generate prompts with coca model if prompt is None
							if content_prompt is None:
												if len(UpperCamelCase_							):
																	raise ValueError(
																	    F'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
																	    F'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''							)
												UpperCamelCase__   :List[str]												= self.get_image_description(UpperCamelCase_							)
							if style_prompt is None:
												if len(UpperCamelCase_							):
																	raise ValueError(
																	    F'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
																	    F''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''							)
												UpperCamelCase__   :List[Any]												= self.get_image_description(UpperCamelCase_							)

							# get prompt text embeddings for content and style
							UpperCamelCase__   :Union[str, Any]												= self.tokenizer(
							    UpperCamelCase_    ,   padding='''max_length'''    ,   max_length=self.tokenizer.model_max_length    ,   truncation=UpperCamelCase_    ,   return_tensors='''pt'''    ,   )
							UpperCamelCase__   :Any												= self.text_encoder(content_text_input.input_ids.to(self.device							)							)[0]

							UpperCamelCase__   :Any												= self.tokenizer(
							    UpperCamelCase_    ,   padding='''max_length'''    ,   max_length=self.tokenizer.model_max_length    ,   truncation=UpperCamelCase_    ,   return_tensors='''pt'''    ,   )
							UpperCamelCase__   :Tuple												= self.text_encoder(style_text_input.input_ids.to(self.device							)							)[0]

							UpperCamelCase__   :str												= slerp(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							)

							# duplicate text embeddings for each generation per prompt
							UpperCamelCase__   :List[str]												= text_embeddings.repeat_interleave(UpperCamelCase_    ,   dim=0							)

							# set timesteps
							UpperCamelCase__   :Dict												= '''offset''' in set(inspect.signature(self.scheduler.set_timesteps							).parameters.keys()							)
							UpperCamelCase__   :Any												= {}
							if accepts_offset:
												UpperCamelCase__   :Tuple												= 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
							self.scheduler.timesteps.to(self.device							)

							UpperCamelCase__					,				UpperCamelCase__   :int												= self.get_timesteps(UpperCamelCase_    ,   UpperCamelCase_    ,   self.device							)
							UpperCamelCase__   :Optional[Any]												= timesteps[:1].repeat(UpperCamelCase_							)

							# Preprocess image
							UpperCamelCase__   :Any												= preprocess(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							)
							UpperCamelCase__   :Dict												= self.prepare_latents(
							    UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   text_embeddings.dtype    ,   self.device    ,   UpperCamelCase_							)

							UpperCamelCase__   :Optional[int]												= preprocess(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							)
							UpperCamelCase__   :Optional[Any]												= self.prepare_latents(
							    UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   text_embeddings.dtype    ,   self.device    ,   UpperCamelCase_							)

							UpperCamelCase__   :int												= slerp(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							)

							if clip_guidance_scale > 0:
												UpperCamelCase__   :Optional[Any]												= self.get_clip_image_embeddings(UpperCamelCase_    ,   UpperCamelCase_							)
												UpperCamelCase__   :List[str]												= self.get_clip_image_embeddings(UpperCamelCase_    ,   UpperCamelCase_							)
												UpperCamelCase__   :Dict												= slerp(
												    UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							)

							# 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__   :Any												= guidance_scale > 1.0
							# get unconditional embeddings for classifier free guidance
							if do_classifier_free_guidance:
												UpperCamelCase__   :str												= content_text_input.input_ids.shape[-1]
												UpperCamelCase__   :List[str]												= self.tokenizer(['''''']    ,   padding='''max_length'''    ,   max_length=UpperCamelCase_    ,   return_tensors='''pt'''							)
												UpperCamelCase__   :Optional[int]												= self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]
												# duplicate unconditional embeddings for each generation per prompt
												UpperCamelCase__   :Optional[Any]												= 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
												UpperCamelCase__   :Optional[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__   :Optional[int]												= (batch_size, self.unet.config.in_channels, height // 8, width // 8)
							UpperCamelCase__   :Dict												= text_embeddings.dtype
							if latents is None:
												if self.device.type == "mps":
																	# randn does not work reproducibly on mps
																	UpperCamelCase__   :Any												= torch.randn(UpperCamelCase_    ,   generator=UpperCamelCase_    ,   device='''cpu'''    ,   dtype=UpperCamelCase_							).to(
																	    self.device							)
												else:
																	UpperCamelCase__   :int												= 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}'''							)
												UpperCamelCase__   :Union[str, Any]												= latents.to(self.device							)

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

							# check if the scheduler accepts generator
							UpperCamelCase__   :int												= '''generator''' in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
							if accepts_generator:
												UpperCamelCase__   :List[Any]												= generator

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

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

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

																	# perform clip guidance
																	if clip_guidance_scale > 0:
																						UpperCamelCase__   :Dict												= (
																						    text_embeddings.chunk(2							)[1] if do_classifier_free_guidance else text_embeddings
																						)
																						UpperCamelCase__					,				UpperCamelCase__   :Optional[int]												= self.cond_fn(
																						    UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   )

																	# compute the previous noisy sample x_t -> x_t-1
																	UpperCamelCase__   :Dict												= self.scheduler.step(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   **UpperCamelCase_							).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
							UpperCamelCase__   :Any												= 1 / 0.18215 * latents
							UpperCamelCase__   :Union[str, Any]												= self.vae.decode(UpperCamelCase_							).sample

							UpperCamelCase__   :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__   :Tuple												= self.numpy_to_pil(UpperCamelCase_							)

							if not return_dict:
												return (image, None)

							return StableDiffusionPipelineOutput(images=UpperCamelCase_    ,   nsfw_content_detected=UpperCamelCase_							)



'''simple docstring'''
import re

from filelock import FileLock


try:
		import nltk

		__snake_case							      =     True
except (ImportError, ModuleNotFoundError):
		__snake_case							      =     False

if NLTK_AVAILABLE:
		with FileLock('''.lock''') as lock:
				nltk.download('''punkt''', quiet=True)


def 			a      (			__a   )  ->	str:



					'''simple docstring'''






					re.sub('''<n>''' ,					'''''' ,					__a   )  # remove pegasus newline char
					assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
					return "\n".join(nltk.sent_tokenize(__a   )   )