

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 )    ->       Union[str, Any]:



    '''simple docstring'''






    if isinstance(__UpperCAmelCase,					torch.Tensor ):
        return image
    elif isinstance(__UpperCAmelCase,					PIL.Image.Image ):
        lowerCAmelCase :	int				=					[image]

    if isinstance(image[0],					PIL.Image.Image ):
        lowerCAmelCase :	Optional[Any]				=					[np.array(i.resize((w, h),					resample=PIL_INTERPOLATION['lanczos'] ) )[None, :] for i in image]
        lowerCAmelCase :	Optional[Any]				=					np.concatenate(__UpperCAmelCase,					axis=0 )
        lowerCAmelCase :	Dict				=					np.array(__UpperCAmelCase ).astype(np.floataa ) / 255.0
        lowerCAmelCase :	str				=					image.transpose(0,					3,					1,					2 )
        lowerCAmelCase :	Optional[Any]				=					2.0 * image - 1.0
        lowerCAmelCase :	Optional[int]				=					torch.from_numpy(__UpperCAmelCase )
    elif isinstance(image[0],					torch.Tensor ):
        lowerCAmelCase :	int				=					torch.cat(__UpperCAmelCase,					dim=0 )
    return image
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase,					_UpperCAmelCase=0.9_9_9_5 )    ->       List[Any]:



    '''simple docstring'''






    if not isinstance(__UpperCAmelCase,					np.ndarray ):
        lowerCAmelCase :	int				=					True
        lowerCAmelCase :	str				=					va.device
        lowerCAmelCase :	int				=					va.cpu().numpy()
        lowerCAmelCase :	int				=					va.cpu().numpy()

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

    if inputs_are_torch:
        lowerCAmelCase :	Dict				=					torch.from_numpy(__UpperCAmelCase ).to(__UpperCAmelCase )

    return va
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase,					_UpperCAmelCase )    ->       Union[str, Any]:



    '''simple docstring'''






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



    '''simple docstring'''






    for param in model.parameters():
        lowerCAmelCase :	List[str]				=					value






class       __A (							_SCREAMING_SNAKE_CASE       ):






    def __init__(		self	:			Any			, UpperCAmelCase_	:			AutoencoderKL			, UpperCAmelCase_	:			CLIPTextModel			, UpperCAmelCase_	:			CLIPModel			, UpperCAmelCase_	:			CLIPTokenizer			, UpperCAmelCase_	:			UNetaDConditionModel			, UpperCAmelCase_	:			Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]			, UpperCAmelCase_	:			CLIPFeatureExtractor			, UpperCAmelCase_	:			Tuple=None			, UpperCAmelCase_	:			Union[str, Any]=None			, UpperCAmelCase_	:			Union[str, Any]=None			, ):
        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			, )
        lowerCAmelCase :	List[str]				=					(
            feature_extractor.size
            if isinstance(feature_extractor.size			, _lowerCAmelCase			)
            else feature_extractor.size['shortest_edge']
        )
        lowerCAmelCase :	Optional[Any]				=					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 		lowercase__       (		self	:			Union[str, Any]			, UpperCAmelCase_	:			Optional[Union[str, int]] = "auto"			):
        if slice_size == "auto":
            # half the attention head size is usually a good trade-off between
            # speed and memory
            lowerCAmelCase :	List[str]				=					self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(_lowerCAmelCase			)






    def 		lowercase__       (		self	:			List[Any]			):
        self.enable_attention_slicing(_lowerCAmelCase			)






    def 		lowercase__       (		self	:			Optional[Any]			):
        set_requires_grad(self.vae			, _lowerCAmelCase			)






    def 		lowercase__       (		self	:			Optional[int]			):
        set_requires_grad(self.vae			, _lowerCAmelCase			)






    def 		lowercase__       (		self	:			int			):
        set_requires_grad(self.unet			, _lowerCAmelCase			)






    def 		lowercase__       (		self	:			List[str]			):
        set_requires_grad(self.unet			, _lowerCAmelCase			)






    def 		lowercase__       (		self	:			Optional[int]			, UpperCAmelCase_	:			Union[str, Any]			, UpperCAmelCase_	:			Optional[int]			, UpperCAmelCase_	:			Any			):
        # get the original timestep using init_timestep
        lowerCAmelCase :	str				=					min(int(num_inference_steps * strength			)			, _lowerCAmelCase			)

        lowerCAmelCase :	Union[str, Any]				=					max(num_inference_steps - init_timestep			, 0			)
        lowerCAmelCase :	str				=					self.scheduler.timesteps[t_start:]

        return timesteps, num_inference_steps - t_start






    def 		lowercase__       (		self	:			Union[str, Any]			, UpperCAmelCase_	:			Optional[Any]			, UpperCAmelCase_	:			List[Any]			, UpperCAmelCase_	:			Optional[Any]			, UpperCAmelCase_	:			int			, UpperCAmelCase_	:			Any			, UpperCAmelCase_	:			Optional[Any]=None			):
        if not isinstance(_lowerCAmelCase			, torch.Tensor			):
            raise ValueError(f"`image` has to be of type `torch.Tensor` but is {type(_lowerCAmelCase			)}"			)

        lowerCAmelCase :	str				=					image.to(device=_lowerCAmelCase			, dtype=_lowerCAmelCase			)

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

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
        lowerCAmelCase :	Any				=					0.1_82_15 * init_latents
        lowerCAmelCase :	Union[str, Any]				=					init_latents.repeat_interleave(_lowerCAmelCase			, dim=0			)

        lowerCAmelCase :	Union[str, Any]				=					randn_tensor(init_latents.shape			, generator=_lowerCAmelCase			, device=_lowerCAmelCase			, dtype=_lowerCAmelCase			)

        # get latents
        lowerCAmelCase :	Union[str, Any]				=					self.scheduler.add_noise(_lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			)
        lowerCAmelCase :	Dict				=					init_latents

        return latents






    def 		lowercase__       (		self	:			Tuple			, UpperCAmelCase_	:			int			):
        lowerCAmelCase :	int				=					self.coca_transform(_lowerCAmelCase			).unsqueeze(0			)
        with torch.no_grad(), torch.cuda.amp.autocast():
            lowerCAmelCase :	Union[str, Any]				=					self.coca_model.generate(transformed_image.to(device=self.device			, dtype=self.coca_model.dtype			)			)
        lowerCAmelCase :	Optional[Any]				=					self.coca_tokenizer.decode(generated[0].cpu().numpy()			)
        return generated.split('<end_of_text>'			)[0].replace('<start_of_text>'			, ''			).rstrip(' .,'			)






    def 		lowercase__       (		self	:			Dict			, UpperCAmelCase_	:			Optional[int]			, UpperCAmelCase_	:			List[str]			):
        lowerCAmelCase :	Union[str, Any]				=					self.feature_extractor.preprocess(_lowerCAmelCase			)
        lowerCAmelCase :	Any				=					torch.from_numpy(clip_image_input['pixel_values'][0]			).unsqueeze(0			).to(self.device			).half()
        lowerCAmelCase :	Optional[int]				=					self.clip_model.get_image_features(_lowerCAmelCase			)
        lowerCAmelCase :	Optional[Any]				=					image_embeddings_clip / image_embeddings_clip.norm(p=2			, dim=-1			, keepdim=_lowerCAmelCase			)
        lowerCAmelCase :	Any				=					image_embeddings_clip.repeat_interleave(_lowerCAmelCase			, dim=0			)
        return image_embeddings_clip






    @torch.enable_grad()
    def 		lowercase__       (		self	:			Optional[Any]			, UpperCAmelCase_	:			str			, UpperCAmelCase_	:			int			, UpperCAmelCase_	:			Union[str, Any]			, UpperCAmelCase_	:			int			, UpperCAmelCase_	:			Optional[int]			, UpperCAmelCase_	:			Optional[int]			, UpperCAmelCase_	:			List[str]			, ):
        lowerCAmelCase :	Optional[Any]				=					latents.detach().requires_grad_()

        lowerCAmelCase :	int				=					self.scheduler.scale_model_input(_lowerCAmelCase			, _lowerCAmelCase			)

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

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

            lowerCAmelCase :	Tuple				=					torch.sqrt(_lowerCAmelCase			)
            lowerCAmelCase :	Dict				=					pred_original_sample * (fac) + latents * (1 - fac)
        elif isinstance(self.scheduler			, _lowerCAmelCase			):
            lowerCAmelCase :	int				=					self.scheduler.sigmas[index]
            lowerCAmelCase :	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
        lowerCAmelCase :	Optional[Any]				=					1 / 0.1_82_15 * sample
        lowerCAmelCase :	Any				=					self.vae.decode(_lowerCAmelCase			).sample
        lowerCAmelCase :	Optional[int]				=					(image / 2 + 0.5).clamp(0			, 1			)

        lowerCAmelCase :	Optional[Any]				=					transforms.Resize(self.feature_extractor_size			)(_lowerCAmelCase			)
        lowerCAmelCase :	Tuple				=					self.normalize(_lowerCAmelCase			).to(latents.dtype			)

        lowerCAmelCase :	Optional[int]				=					self.clip_model.get_image_features(_lowerCAmelCase			)
        lowerCAmelCase :	List[Any]				=					image_embeddings_clip / image_embeddings_clip.norm(p=2			, dim=-1			, keepdim=_lowerCAmelCase			)

        lowerCAmelCase :	Optional[Any]				=					spherical_dist_loss(_lowerCAmelCase			, _lowerCAmelCase			).mean() * clip_guidance_scale

        lowerCAmelCase :	Optional[Any]				=					-torch.autograd.grad(_lowerCAmelCase			, _lowerCAmelCase			)[0]

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






    @torch.no_grad()
    def __call__(		self	:			int			, UpperCAmelCase_	:			Union[torch.FloatTensor, PIL.Image.Image]			, UpperCAmelCase_	:			Union[torch.FloatTensor, PIL.Image.Image]			, UpperCAmelCase_	:			Optional[str] = None			, UpperCAmelCase_	:			Optional[str] = None			, UpperCAmelCase_	:			Optional[int] = 512			, UpperCAmelCase_	:			Optional[int] = 512			, UpperCAmelCase_	:			float = 0.6			, UpperCAmelCase_	:			Optional[int] = 50			, UpperCAmelCase_	:			Optional[float] = 7.5			, UpperCAmelCase_	:			Optional[int] = 1			, UpperCAmelCase_	:			float = 0.0			, UpperCAmelCase_	:			Optional[float] = 100			, UpperCAmelCase_	:			Optional[torch.Generator] = None			, UpperCAmelCase_	:			Optional[str] = "pil"			, UpperCAmelCase_	:			bool = True			, UpperCAmelCase_	:			float = 0.8			, UpperCAmelCase_	:			float = 0.1			, UpperCAmelCase_	:			float = 0.1			, ):
        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:
            lowerCAmelCase :	Union[str, Any]				=					[generator] + [None] * (batch_size - 1)

        lowerCAmelCase :	Any				=					[
            ('model', self.coca_model is None),
            ('tokenizer', self.coca_tokenizer is None),
            ('transform', self.coca_transform is None),
        ]
        lowerCAmelCase :	Any				=					[x[0] for x in coca_is_none if x[1]]
        lowerCAmelCase :	int				=					', '.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."			)
            lowerCAmelCase :	List[str]				=					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."			)
            lowerCAmelCase :	List[Any]				=					self.get_image_description(_lowerCAmelCase			)

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

        lowerCAmelCase :	Dict				=					self.tokenizer(
            _lowerCAmelCase			, padding='max_length'			, max_length=self.tokenizer.model_max_length			, truncation=_lowerCAmelCase			, return_tensors='pt'			, )
        lowerCAmelCase :	Dict				=					self.text_encoder(style_text_input.input_ids.to(self.device			)			)[0]

        lowerCAmelCase :	Optional[Any]				=					slerp(_lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			)

        # duplicate text embeddings for each generation per prompt
        lowerCAmelCase :	Any				=					text_embeddings.repeat_interleave(_lowerCAmelCase			, dim=0			)

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

        lowerCAmelCase	,     lowerCAmelCase :	Tuple				=					self.get_timesteps(_lowerCAmelCase			, _lowerCAmelCase			, self.device			)
        lowerCAmelCase :	List[str]				=					timesteps[:1].repeat(_lowerCAmelCase			)

        # Preprocess image
        lowerCAmelCase :	List[Any]				=					preprocess(_lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			)
        lowerCAmelCase :	Any				=					self.prepare_latents(
            _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, text_embeddings.dtype			, self.device			, _lowerCAmelCase			)

        lowerCAmelCase :	Union[str, Any]				=					preprocess(_lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			)
        lowerCAmelCase :	Any				=					self.prepare_latents(
            _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, text_embeddings.dtype			, self.device			, _lowerCAmelCase			)

        lowerCAmelCase :	List[Any]				=					slerp(_lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			)

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

        # scale the initial noise by the standard deviation required by the scheduler
        lowerCAmelCase :	int				=					latents * self.scheduler.init_noise_sigma

        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]
        lowerCAmelCase :	Any				=					'eta' in set(inspect.signature(self.scheduler.step			).parameters.keys()			)
        lowerCAmelCase :	Union[str, Any]				=					{}
        if accepts_eta:
            lowerCAmelCase :	Optional[int]				=					eta

        # check if the scheduler accepts generator
        lowerCAmelCase :	Dict				=					'generator' in set(inspect.signature(self.scheduler.step			).parameters.keys()			)
        if accepts_generator:
            lowerCAmelCase :	Union[str, Any]				=					generator

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

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

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

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

                # compute the previous noisy sample x_t -> x_t-1
                lowerCAmelCase :	str				=					self.scheduler.step(_lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, **_lowerCAmelCase			).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
        lowerCAmelCase :	str				=					1 / 0.1_82_15 * latents
        lowerCAmelCase :	str				=					self.vae.decode(_lowerCAmelCase			).sample

        lowerCAmelCase :	Union[str, Any]				=					(image / 2 + 0.5).clamp(0			, 1			)
        lowerCAmelCase :	Optional[Any]				=					image.cpu().permute(0			, 2			, 3			, 1			).numpy()

        if output_type == "pil":
            lowerCAmelCase :	Optional[Any]				=					self.numpy_to_pil(_lowerCAmelCase			)

        if not return_dict:
            return (image, None)

        return StableDiffusionPipelineOutput(images=_lowerCAmelCase			, nsfw_content_detected=_lowerCAmelCase			)











class        lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_							=			{}



 def       lowerCAmelCase_  (							self  :		List[str]						):
       print(self.vertex						)
       for i in self.vertex:
             print(_lowerCAmelCase				,				' -> '				,				' -> '.join([str(_lowerCAmelCase						) for j in self.vertex[i]]						)						)



 def       lowerCAmelCase_  (							self  :		List[Any]				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		int						):
       # check if vertex is already present,
       if from_vertex in self.vertex:
             self.vertex[from_vertex].append(_lowerCAmelCase						)
       else:
             # else make a new vertex
             SCREAMING_SNAKE_CASE_							=			[to_vertex]



 def       lowerCAmelCase_  (							self  :		Dict						):
       # visited array for storing already visited nodes
       SCREAMING_SNAKE_CASE_							=			[False] * len(self.vertex						)

       # call the recursive helper function
       for i in range(len(self.vertex						)						):
             if not visited[i]:
                   self.dfs_recursive(_lowerCAmelCase				,				_lowerCAmelCase						)




 def       lowerCAmelCase_  (							self  :		Union[str, Any]				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		list						):
       # mark start vertex as visited
       SCREAMING_SNAKE_CASE_							=			True

       print(_lowerCAmelCase				,				end=' '						)

       # Recur for all the vertices that are adjacent to this node
       for i in self.vertex:
             if not visited[i]:
                   self.dfs_recursive(_lowerCAmelCase				,				_lowerCAmelCase						)


if __name__ == "__main__":
   lowerCamelCase__ :   List[Any]					=							Graph()
   g.add_edge(0, 1)
   g.add_edge(0, 2)
   g.add_edge(1, 2)
   g.add_edge(2, 0)
   g.add_edge(2, 3)
   g.add_edge(3, 3)

   g.print_graph()
   print('DFS:')
   g.dfs()

   # OUTPUT:
   # 0  ->  1 -> 2
   # 1  ->  2
   # 2  ->  0 -> 3
   # 3  ->  3
   # DFS:
   #  0 1 2 3