"""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     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase	,       __UpperCAmelCase							)				-> Dict:
				if isinstance(UpperCamelCase__	,       torch.Tensor							):
								return image
				elif isinstance(UpperCamelCase__	,       PIL.Image.Image							):
								SCREAMING_SNAKE_CASE__			=					[image]

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




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase	,       __UpperCAmelCase	,       __UpperCAmelCase=0.9_995							)				-> Optional[Any]:
				if not isinstance(UpperCamelCase__	,       np.ndarray							):
								SCREAMING_SNAKE_CASE__			=					True
								SCREAMING_SNAKE_CASE__			=					va.device
								SCREAMING_SNAKE_CASE__			=					va.cpu().numpy()
								SCREAMING_SNAKE_CASE__			=					va.cpu().numpy()

				SCREAMING_SNAKE_CASE__			=					np.sum(va * va / (np.linalg.norm(UpperCamelCase__							) * np.linalg.norm(UpperCamelCase__							))							)
				if np.abs(UpperCamelCase__							) > DOT_THRESHOLD:
								SCREAMING_SNAKE_CASE__			=					(1 - t) * va + t * va
				else:
								SCREAMING_SNAKE_CASE__			=					np.arccos(UpperCamelCase__							)
								SCREAMING_SNAKE_CASE__			=					np.sin(UpperCamelCase__							)
								SCREAMING_SNAKE_CASE__			=					theta_a * t
								SCREAMING_SNAKE_CASE__			=					np.sin(UpperCamelCase__							)
								SCREAMING_SNAKE_CASE__			=					np.sin(theta_a - theta_t							) / sin_theta_a
								SCREAMING_SNAKE_CASE__			=					sin_theta_t / sin_theta_a
								SCREAMING_SNAKE_CASE__			=					sa * va + sa * va

				if inputs_are_torch:
								SCREAMING_SNAKE_CASE__			=					torch.from_numpy(UpperCamelCase__							).to(UpperCamelCase__							)

				return va




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase							)				-> Union[str, Any]:
				SCREAMING_SNAKE_CASE__			=					F.normalize(UpperCamelCase__	,       dim=-1							)
				SCREAMING_SNAKE_CASE__			=					F.normalize(UpperCamelCase__	,       dim=-1							)
				return (x - y).norm(dim=-1							).div(2							).arcsin().pow(2							).mul(2							)




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase							)				-> Optional[int]:
				for param in model.parameters():
								SCREAMING_SNAKE_CASE__			=					value

class lowerCamelCase		(UpperCamelCase__   ):






					'''simple docstring'''


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



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



					def       lowerCAmelCase_       (    self      :					Union[str, Any]		)				->							Tuple:
									self.enable_attention_slicing(__lowerCamelCase		)



					def       lowerCAmelCase_       (    self      :					Tuple		)				->							str:
									set_requires_grad(self.vae ,			__lowerCamelCase		)



					def       lowerCAmelCase_       (    self      :					List[Any]		)				->							Optional[int]:
									set_requires_grad(self.vae ,			__lowerCamelCase		)



					def       lowerCAmelCase_       (    self      :					Any		)				->							List[Any]:
									set_requires_grad(self.unet ,			__lowerCamelCase		)



					def       lowerCAmelCase_       (    self      :					List[str]		)				->							Union[str, Any]:
									set_requires_grad(self.unet ,			__lowerCamelCase		)



					def       lowerCAmelCase_       (    self      :					Dict ,			_snake_case      :					List[Any] ,			_snake_case      :					str ,			_snake_case      :					str		)				->							str:
									# get the original timestep using init_timestep
									SCREAMING_SNAKE_CASE__			=					min(int(num_inference_steps * strength		) ,			__lowerCamelCase		)

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

									return timesteps, num_inference_steps - t_start



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

									SCREAMING_SNAKE_CASE__			=					image.to(device=__lowerCamelCase ,			dtype=__lowerCamelCase		)

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

									# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
									SCREAMING_SNAKE_CASE__			=					0.1_8215 * init_latents
									SCREAMING_SNAKE_CASE__			=					init_latents.repeat_interleave(__lowerCamelCase ,			dim=0		)

									SCREAMING_SNAKE_CASE__			=					randn_tensor(init_latents.shape ,			generator=__lowerCamelCase ,			device=__lowerCamelCase ,			dtype=__lowerCamelCase		)

									# get latents
									SCREAMING_SNAKE_CASE__			=					self.scheduler.add_noise(__lowerCamelCase ,			__lowerCamelCase ,			__lowerCamelCase		)
									SCREAMING_SNAKE_CASE__			=					init_latents

									return latents



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



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



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

									SCREAMING_SNAKE_CASE__			=					self.scheduler.scale_model_input(__lowerCamelCase ,			__lowerCamelCase		)

									# predict the noise residual
									SCREAMING_SNAKE_CASE__			=					self.unet(__lowerCamelCase ,			__lowerCamelCase ,			encoder_hidden_states=__lowerCamelCase		).sample

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

													SCREAMING_SNAKE_CASE__			=					torch.sqrt(__lowerCamelCase		)
													SCREAMING_SNAKE_CASE__			=					pred_original_sample * (fac) + latents * (1 - fac)
									elif isinstance(self.scheduler ,			__lowerCamelCase		):
													SCREAMING_SNAKE_CASE__			=					self.scheduler.sigmas[index]
													SCREAMING_SNAKE_CASE__			=					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__			=					1 / 0.1_8215 * sample
									SCREAMING_SNAKE_CASE__			=					self.vae.decode(__lowerCamelCase		).sample
									SCREAMING_SNAKE_CASE__			=					(image / 2 + 0.5).clamp(0 ,			1		)

									SCREAMING_SNAKE_CASE__			=					transforms.Resize(self.feature_extractor_size		)(__lowerCamelCase		)
									SCREAMING_SNAKE_CASE__			=					self.normalize(__lowerCamelCase		).to(latents.dtype		)

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

									SCREAMING_SNAKE_CASE__			=					spherical_dist_loss(__lowerCamelCase ,			__lowerCamelCase		).mean() * clip_guidance_scale

									SCREAMING_SNAKE_CASE__			=					-torch.autograd.grad(__lowerCamelCase ,			__lowerCamelCase		)[0]

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



					@torch.no_grad()
					def __call__(    self      :					Optional[Any] ,			_snake_case      :					int ,			_snake_case      :					Optional[Any] ,			_snake_case      :					Optional[Any] = None ,			_snake_case      :					Any = None ,			_snake_case      :					List[Any] = 512 ,			_snake_case      :					Tuple = 512 ,			_snake_case      :					int = 0.6 ,			_snake_case      :					List[str] = 50 ,			_snake_case      :					Any = 7.5 ,			_snake_case      :					Dict = 1 ,			_snake_case      :					Optional[int] = 0.0 ,			_snake_case      :					Optional[int] = 100 ,			_snake_case      :					List[Any] = None ,			_snake_case      :					Dict = "pil" ,			_snake_case      :					Any = True ,			_snake_case      :					Tuple = 0.8 ,			_snake_case      :					str = 0.1 ,			_snake_case      :					List[Any] = 0.1 ,			)				->							Optional[int]:
									if isinstance(__lowerCamelCase ,			__lowerCamelCase		) and len(__lowerCamelCase		) != batch_size:
													raise ValueError(F"""You have passed {batch_size} batch_size, but only {len(__lowerCamelCase		)} 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(__lowerCamelCase ,			torch.Generator		) and batch_size > 1:
													SCREAMING_SNAKE_CASE__			=					[generator] + [None] * (batch_size - 1)

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

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

									SCREAMING_SNAKE_CASE__			=					self.tokenizer(
									    __lowerCamelCase ,			padding="max_length" ,			max_length=self.tokenizer.model_max_length ,			truncation=__lowerCamelCase ,			return_tensors="pt" ,			)
									SCREAMING_SNAKE_CASE__			=					self.text_encoder(style_text_input.input_ids.to(self.device		)		)[0]

									SCREAMING_SNAKE_CASE__			=					slerp(__lowerCamelCase ,			__lowerCamelCase ,			__lowerCamelCase		)

									# duplicate text embeddings for each generation per prompt
									SCREAMING_SNAKE_CASE__			=					text_embeddings.repeat_interleave(__lowerCamelCase ,			dim=0		)

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

									self.scheduler.set_timesteps(__lowerCamelCase ,			**__lowerCamelCase		)
									# 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__	,							SCREAMING_SNAKE_CASE__			=					self.get_timesteps(__lowerCamelCase ,			__lowerCamelCase ,			self.device		)
									SCREAMING_SNAKE_CASE__			=					timesteps[:1].repeat(__lowerCamelCase		)

									# Preprocess image
									SCREAMING_SNAKE_CASE__			=					preprocess(__lowerCamelCase ,			__lowerCamelCase ,			__lowerCamelCase		)
									SCREAMING_SNAKE_CASE__			=					self.prepare_latents(
									    __lowerCamelCase ,			__lowerCamelCase ,			__lowerCamelCase ,			text_embeddings.dtype ,			self.device ,			__lowerCamelCase		)

									SCREAMING_SNAKE_CASE__			=					preprocess(__lowerCamelCase ,			__lowerCamelCase ,			__lowerCamelCase		)
									SCREAMING_SNAKE_CASE__			=					self.prepare_latents(
									    __lowerCamelCase ,			__lowerCamelCase ,			__lowerCamelCase ,			text_embeddings.dtype ,			self.device ,			__lowerCamelCase		)

									SCREAMING_SNAKE_CASE__			=					slerp(__lowerCamelCase ,			__lowerCamelCase ,			__lowerCamelCase		)

									if clip_guidance_scale > 0:
													SCREAMING_SNAKE_CASE__			=					self.get_clip_image_embeddings(__lowerCamelCase ,			__lowerCamelCase		)
													SCREAMING_SNAKE_CASE__			=					self.get_clip_image_embeddings(__lowerCamelCase ,			__lowerCamelCase		)
													SCREAMING_SNAKE_CASE__			=					slerp(
													    __lowerCamelCase ,			__lowerCamelCase ,			__lowerCamelCase		)

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

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

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

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

																	# predict the noise residual
																	SCREAMING_SNAKE_CASE__			=					self.unet(__lowerCamelCase ,			__lowerCamelCase ,			encoder_hidden_states=__lowerCamelCase		).sample

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

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

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

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
									SCREAMING_SNAKE_CASE__			=					1 / 0.1_8215 * latents
									SCREAMING_SNAKE_CASE__			=					self.vae.decode(__lowerCamelCase		).sample

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

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

									if not return_dict:
													return (image, None)

									return StableDiffusionPipelineOutput(images=__lowerCamelCase ,			nsfw_content_detected=__lowerCamelCase		)











from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

a__: Tuple							   =						TypeVar('T')


def       UpperCamelCase__(			UpperCamelCase__     :			int    )->int:
       return (position - 1) // 2


def       UpperCamelCase__(			UpperCamelCase__     :			int    )->int:
       return (2 * position) + 1


def       UpperCamelCase__(			UpperCamelCase__     :			int    )->int:
       return (2 * position) + 2




class        SCREAMING_SNAKE_CASE__					(		Generic[T] ):



 def __init__(				self					):
        A__		   =		[]
        A__		   =		{}
        A__		   =		0



 def __len__(				self					):
        return self.elements



 def __repr__(				self					):
        return str(self.heap					)



 def 							UpperCamelCase     (				self					):
        # Check if the priority queue is empty
        return self.elements == 0



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        # Add an element with given priority to the queue
        self.heap.append((elem, weight)					)
        A__		   =		self.elements
        self.elements += 1
        self._bubble_up(__lowerCamelCase					)



 def 							UpperCamelCase     (				self					):
        # Remove and return the element with lowest weight (highest priority)
        if self.elements > 1:
               self._swap_nodes(0,self.elements - 1					)
        A__   ,      A__		   =		self.heap.pop()
        del self.position_map[elem]
        self.elements -= 1
        if self.elements > 0:
               A__   ,      A__		   =		self.heap[0]
               self._bubble_down(__lowerCamelCase					)
        return elem



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        # Update the weight of the given key
        A__		   =		self.position_map[elem]
        A__		   =		(elem, weight)
        if position > 0:
               A__		   =		get_parent_position(__lowerCamelCase					)
               A__   ,      A__		   =		self.heap[parent_position]
               if parent_weight > weight:
                      self._bubble_up(__lowerCamelCase					)
               else:
                      self._bubble_down(__lowerCamelCase					)
        else:
               self._bubble_down(__lowerCamelCase					)



 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        # Place a node at the proper position (upward movement) [to be used internally
        # only]
        A__		   =		self.position_map[elem]
        if curr_pos == 0:
               return None
        A__		   =		get_parent_position(__lowerCamelCase					)
        A__   ,      A__		   =		self.heap[curr_pos]
        A__   ,      A__		   =		self.heap[parent_position]
        if parent_weight > weight:
               self._swap_nodes(__lowerCamelCase,__lowerCamelCase					)
               return self._bubble_up(__lowerCamelCase					)
        return None



 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        # Place a node at the proper position (downward movement) [to be used
        # internally only]
        A__		   =		self.position_map[elem]
        A__   ,      A__		   =		self.heap[curr_pos]
        A__		   =		get_child_left_position(__lowerCamelCase					)
        A__		   =		get_child_right_position(__lowerCamelCase					)
        if child_left_position < self.elements and child_right_position < self.elements:
               A__   ,      A__		   =		self.heap[child_left_position]
               A__   ,      A__		   =		self.heap[child_right_position]
               if child_right_weight < child_left_weight and child_right_weight < weight:
                      self._swap_nodes(__lowerCamelCase,__lowerCamelCase					)
                      return self._bubble_down(__lowerCamelCase					)
        if child_left_position < self.elements:
               A__   ,      A__		   =		self.heap[child_left_position]
               if child_left_weight < weight:
                      self._swap_nodes(__lowerCamelCase,__lowerCamelCase					)
                      return self._bubble_down(__lowerCamelCase					)
        else:
               return None
        if child_right_position < self.elements:
               A__   ,      A__		   =		self.heap[child_right_position]
               if child_right_weight < weight:
                      self._swap_nodes(__lowerCamelCase,__lowerCamelCase					)
                      return self._bubble_down(__lowerCamelCase					)
        return None




 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        # Swap the nodes at the given positions
        A__		   =		self.heap[nodea_pos][0]
        A__		   =		self.heap[nodea_pos][0]
        A__   ,      A__		   =		(
            self.heap[nodea_pos],
            self.heap[nodea_pos],
        )
        A__		   =		nodea_pos
        A__		   =		nodea_pos




class        SCREAMING_SNAKE_CASE__					(		Generic[T] ):



 def __init__(				self					):
        A__		   =		{}
        A__		   =		0



 def __repr__(				self					):
        return str(self.connections					)



 def __len__(				self					):
        return self.nodes



 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        # Add a node in the graph if it is not in the graph
        if node not in self.connections:
               A__		   =		{}
               self.nodes += 1




 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					):
        # Add an edge between 2 nodes in the graph
        self.add_node(__lowerCamelCase					)
        self.add_node(__lowerCamelCase					)
        A__		   =		weight
        A__		   =		weight


def       UpperCamelCase__(			UpperCamelCase__     :			GraphUndirectedWeighted[T]					,	)->tuple[dict[T, int], dict[T, T | None]]:
       A__		   =		{node: maxsize for node in graph.connections}
       A__		   =		{node: None for node in graph.connections}

       A__		   =		MinPriorityQueue()
       for node, weight in dist.items():
              priority_queue.push(UpperCamelCase__					,	UpperCamelCase__    )

       if priority_queue.is_empty():
              return dist, parent

       # initialization
       A__		   =		priority_queue.extract_min()
       A__		   =		0
       for neighbour in graph.connections[node]:
              if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                     A__		   =		dist[node] + graph.connections[node][neighbour]
                     priority_queue.update_key(UpperCamelCase__					,	dist[neighbour]    )
                     A__		   =		node

    # running prim's algorithm
       while not priority_queue.is_empty():
              A__		   =		priority_queue.extract_min()
              for neighbour in graph.connections[node]:
                     if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                            A__		   =		dist[node] + graph.connections[node][neighbour]
                            priority_queue.update_key(UpperCamelCase__					,	dist[neighbour]    )
                            A__		   =		node
       return dist, parent





