





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       UpperCAmelCase							(     _lowerCamelCase      :  int			,   _lowerCamelCase      :  List[str]			,   _lowerCamelCase      :  int     ):





						'''simple docstring'''


						if isinstance(snake_case_			,   torch.Tensor     ):
												return image
						elif isinstance(snake_case_			,   PIL.Image.Image     ):
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					[image]

						if isinstance(image[0]			,   PIL.Image.Image     ):
												SCREAMING_SNAKE_CASE__     :				Any   =					[np.array(i.resize((w, h)			,   resample=PIL_INTERPOLATION["lanczos"]     )     )[None, :] for i in image]
												SCREAMING_SNAKE_CASE__     :				Dict   =					np.concatenate(snake_case_			,   axis=0     )
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					np.array(snake_case_     ).astype(np.floataa     ) / 255.0
												SCREAMING_SNAKE_CASE__     :				Dict   =					image.transpose(0			,   3			,   1			,   2     )
												SCREAMING_SNAKE_CASE__     :				List[Any]   =					2.0 * image - 1.0
												SCREAMING_SNAKE_CASE__     :				Optional[int]   =					torch.from_numpy(snake_case_     )
						elif isinstance(image[0]			,   torch.Tensor     ):
												SCREAMING_SNAKE_CASE__     :				Optional[int]   =					torch.cat(snake_case_			,   dim=0     )
						return image







def       UpperCAmelCase							(     _lowerCamelCase      :  Dict			,   _lowerCamelCase      :  Union[str, Any]			,   _lowerCamelCase      :  Union[str, Any]			,   _lowerCamelCase      :  Any=0.9_9_9_5     ):





						'''simple docstring'''


						if not isinstance(snake_case_			,   np.ndarray     ):
												SCREAMING_SNAKE_CASE__     :				List[str]   =					True
												SCREAMING_SNAKE_CASE__     :				Any   =					va.device
												SCREAMING_SNAKE_CASE__     :				str   =					va.cpu().numpy()
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					va.cpu().numpy()

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

						if inputs_are_torch:
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					torch.from_numpy(snake_case_     ).to(snake_case_     )

						return va







def       UpperCAmelCase							(     _lowerCamelCase      :  Union[str, Any]			,   _lowerCamelCase      :  Optional[Any]     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					F.normalize(snake_case_			,   dim=-1     )
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					F.normalize(snake_case_			,   dim=-1     )
						return (x - y).norm(dim=-1     ).div(2     ).arcsin().pow(2     ).mul(2     )







def       UpperCAmelCase							(     _lowerCamelCase      :  Dict			,   _lowerCamelCase      :  List[Any]     ):





						'''simple docstring'''


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



class    _a     (				UpperCamelCase_							):






							"""simple docstring"""

							def __init__(      self				:     Union[str, Any]							,	a				:     int							,	a				:     Any							,	a				:     str							,	a				:     List[str]							,	a				:     Tuple							,	a				:     Tuple							,	a				:     Optional[int]							,	a				:     List[str]=None							,	a				:     Union[str, Any]=None							,	a				:     Optional[int]=None							,	)	->str:
													super().__init__()
													self.register_modules(
													    vae=a							,	text_encoder=a							,	clip_model=a							,	tokenizer=a							,	unet=a							,	scheduler=a							,	feature_extractor=a							,	coca_model=a							,	coca_tokenizer=a							,	coca_transform=a							,	)
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					(
													    feature_extractor.size
													    if isinstance(feature_extractor.size							,	a    )
													    else feature_extractor.size['''shortest_edge''']
													)
													SCREAMING_SNAKE_CASE__     :				Any   =					transforms.Normalize(mean=feature_extractor.image_mean							,	std=feature_extractor.image_std    )
													set_requires_grad(self.text_encoder							,	a    )
													set_requires_grad(self.clip_model							,	a    )

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

							def      A_  (      self				:     Dict    )	->int:
													self.enable_attention_slicing(a    )

							def      A_  (      self				:     Any    )	->str:
													set_requires_grad(self.vae							,	a    )

							def      A_  (      self				:     int    )	->Any:
													set_requires_grad(self.vae							,	a    )

							def      A_  (      self				:     Optional[int]    )	->Optional[Any]:
													set_requires_grad(self.unet							,	a    )

							def      A_  (      self				:     Optional[int]    )	->Union[str, Any]:
													set_requires_grad(self.unet							,	a    )

							def      A_  (      self				:     int							,	a				:     Dict							,	a				:     Dict							,	a				:     int    )	->Optional[int]:
													# get the original timestep using init_timestep
													SCREAMING_SNAKE_CASE__     :				int   =					min(int(num_inference_steps * strength    )							,	a    )

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

													return timesteps, num_inference_steps - t_start

							def      A_  (      self				:     List[str]							,	a				:     Dict							,	a				:     List[Any]							,	a				:     List[str]							,	a				:     int							,	a				:     List[Any]							,	a				:     Dict=None    )	->Any:
													if not isinstance(a							,	torch.Tensor    ):
																			raise ValueError(f"""`image` has to be of type `torch.Tensor` but is {type(a    )}"""    )

													SCREAMING_SNAKE_CASE__     :				int   =					image.to(device=a							,	dtype=a    )

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

													# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
													SCREAMING_SNAKE_CASE__     :				str   =					0.1_8215 * init_latents
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					init_latents.repeat_interleave(a							,	dim=0    )

													SCREAMING_SNAKE_CASE__     :				List[str]   =					randn_tensor(init_latents.shape							,	generator=a							,	device=a							,	dtype=a    )

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

													return latents

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

							def      A_  (      self				:     str							,	a				:     Optional[int]							,	a				:     List[Any]    )	->List[str]:
													SCREAMING_SNAKE_CASE__     :				str   =					self.feature_extractor.preprocess(a    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					torch.from_numpy(clip_image_input["pixel_values"][0]    ).unsqueeze(0    ).to(self.device    ).half()
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.clip_model.get_image_features(a    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					image_embeddings_clip / image_embeddings_clip.norm(p=2							,	dim=-1							,	keepdim=a    )
													SCREAMING_SNAKE_CASE__     :				str   =					image_embeddings_clip.repeat_interleave(a							,	dim=0    )
													return image_embeddings_clip

							@torch.enable_grad()
							def      A_  (      self				:     Optional[Any]							,	a				:     Optional[int]							,	a				:     Optional[int]							,	a				:     str							,	a				:     Dict							,	a				:     Dict							,	a				:     List[Any]							,	a				:     Union[str, Any]							,	)	->Tuple:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					latents.detach().requires_grad_()

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.scheduler.scale_model_input(a							,	a    )

													# predict the noise residual
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					self.unet(a							,	a							,	encoder_hidden_states=a    ).sample

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

																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					torch.sqrt(a    )
																			SCREAMING_SNAKE_CASE__     :				int   =					pred_original_sample * (fac) + latents * (1 - fac)
													elif isinstance(self.scheduler							,	a    ):
																			SCREAMING_SNAKE_CASE__     :				Dict   =					self.scheduler.sigmas[index]
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					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
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					1 / 0.1_8215 * sample
													SCREAMING_SNAKE_CASE__     :				Tuple   =					self.vae.decode(a    ).sample
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					(image / 2 + 0.5).clamp(0							,	1    )

													SCREAMING_SNAKE_CASE__     :				int   =					transforms.Resize(self.feature_extractor_size    )(a    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					self.normalize(a    ).to(latents.dtype    )

													SCREAMING_SNAKE_CASE__     :				int   =					self.clip_model.get_image_features(a    )
													SCREAMING_SNAKE_CASE__     :				Dict   =					image_embeddings_clip / image_embeddings_clip.norm(p=2							,	dim=-1							,	keepdim=a    )

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					spherical_dist_loss(a							,	a    ).mean() * clip_guidance_scale

													SCREAMING_SNAKE_CASE__     :				Tuple   =					-torch.autograd.grad(a							,	a    )[0]

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






							@torch.no_grad()
							def __call__(      self				:     Optional[int]							,	a				:     str							,	a				:     Optional[int]							,	a				:     Dict = None							,	a				:     Union[str, Any] = None							,	a				:     Dict = 5_12							,	a				:     Tuple = 5_12							,	a				:     Dict = 0.6							,	a				:     List[str] = 50							,	a				:     Tuple = 7.5							,	a				:     Dict = 1							,	a				:     int = 0.0							,	a				:     Optional[Any] = 1_00							,	a				:     Dict = None							,	a				:     Any = "pil"							,	a				:     str = True							,	a				:     Dict = 0.8							,	a				:     Any = 0.1							,	a				:     Union[str, Any] = 0.1							,	)	->Optional[Any]:
													if isinstance(a							,	a    ) and len(a    ) != batch_size:
																			raise ValueError(f"""You have passed {batch_size} batch_size, but only {len(a    )} 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(a							,	torch.Generator    ) and batch_size > 1:
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					[generator] + [None] * (batch_size - 1)

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					[
													    ('''model''', self.coca_model is None),
													    ('''tokenizer''', self.coca_tokenizer is None),
													    ('''transform''', self.coca_transform is None),
													]
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					[x[0] for x in coca_is_none if x[1]]
													SCREAMING_SNAKE_CASE__     :				Dict   =					''', '''.join(a    )
													# generate prompts with coca model if prompt is None
													if content_prompt is None:
																			if len(a    ):
																									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."""    )
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.get_image_description(a    )
													if style_prompt is None:
																			if len(a    ):
																									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."""    )
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.get_image_description(a    )

													# get prompt text embeddings for content and style
													SCREAMING_SNAKE_CASE__     :				Any   =					self.tokenizer(
													    a							,	padding="max_length"							,	max_length=self.tokenizer.model_max_length							,	truncation=a							,	return_tensors="pt"							,	)
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.text_encoder(content_text_input.input_ids.to(self.device    )    )[0]

													SCREAMING_SNAKE_CASE__     :				str   =					self.tokenizer(
													    a							,	padding="max_length"							,	max_length=self.tokenizer.model_max_length							,	truncation=a							,	return_tensors="pt"							,	)
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					self.text_encoder(style_text_input.input_ids.to(self.device    )    )[0]

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					slerp(a							,	a							,	a    )

													# duplicate text embeddings for each generation per prompt
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					text_embeddings.repeat_interleave(a							,	dim=0    )

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

													self.scheduler.set_timesteps(a							,	**a    )
													# 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    )

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.get_timesteps(a							,	a							,	self.device    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					timesteps[:1].repeat(a    )

													# Preprocess image
													SCREAMING_SNAKE_CASE__     :				Dict   =					preprocess(a							,	a							,	a    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.prepare_latents(
													    a							,	a							,	a							,	text_embeddings.dtype							,	self.device							,	a    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					preprocess(a							,	a							,	a    )
													SCREAMING_SNAKE_CASE__     :				int   =					self.prepare_latents(
													    a							,	a							,	a							,	text_embeddings.dtype							,	self.device							,	a    )

													SCREAMING_SNAKE_CASE__     :				str   =					slerp(a							,	a							,	a    )

													if clip_guidance_scale > 0:
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.get_clip_image_embeddings(a							,	a    )
																			SCREAMING_SNAKE_CASE__     :				Any   =					self.get_clip_image_embeddings(a							,	a    )
																			SCREAMING_SNAKE_CASE__     :				Dict   =					slerp(
																			    a							,	a							,	a    )

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

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

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

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

																									# predict the noise residual
																									SCREAMING_SNAKE_CASE__     :				Any   =					self.unet(a							,	a							,	encoder_hidden_states=a    ).sample

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

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

																									# compute the previous noisy sample x_t -> x_t-1
																									SCREAMING_SNAKE_CASE__     :				int   =					self.scheduler.step(a							,	a							,	a							,	**a    ).prev_sample

																									 # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					1 / 0.1_8215 * latents
													SCREAMING_SNAKE_CASE__     :				Tuple   =					self.vae.decode(a    ).sample

													SCREAMING_SNAKE_CASE__     :				Tuple   =					(image / 2 + 0.5).clamp(0							,	1    )
													SCREAMING_SNAKE_CASE__     :				int   =					image.cpu().permute(0							,	2							,	3							,	1    ).numpy()

													if output_type == "pil":
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					self.numpy_to_pil(a    )

													if not return_dict:
																			return (image, None)

													return StableDiffusionPipelineOutput(images=a							,	nsfw_content_detected=a    )










def       UpperCAmelCase							(     _lowerCamelCase      :  int = 1_000     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Dict   =					-1
						SCREAMING_SNAKE_CASE__     :				str   =					0
						for a in range(1			,   n // 3     ):
												# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
												SCREAMING_SNAKE_CASE__     :				Tuple   =					(n * n - 2 * a * n) // (2 * n - 2 * a)
												SCREAMING_SNAKE_CASE__     :				Dict   =					n - a - b
												if c * c == (a * a + b * b):
																		SCREAMING_SNAKE_CASE__     :				str   =					a * b * c
																		if candidate >= product:
																								SCREAMING_SNAKE_CASE__     :				List[str]   =					candidate
						return product


if __name__ == "__main__":
				print(f"{solution() = }")