





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__ (					lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






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

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




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__=0.9995  ):






   '''simple docstring'''






   if not isinstance(_lowerCamelCase  ,    np.ndarray  ):
      UpperCAmelCase_     :   str											=True
      UpperCAmelCase_     :   int											=va.device
      UpperCAmelCase_     :   List[Any]											=va.cpu().numpy()
      UpperCAmelCase_     :   Dict											=va.cpu().numpy()

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

   if inputs_are_torch:
      UpperCAmelCase_     :   Optional[int]											=torch.from_numpy(_lowerCamelCase  ).to(_lowerCamelCase  )

   return va




def     a__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_     :   Tuple											=F.normalize(_lowerCamelCase  ,    dim=-1  )
   UpperCAmelCase_     :   Any											=F.normalize(_lowerCamelCase  ,    dim=-1  )
   return (x - y).norm(dim=-1  ).div(2  ).arcsin().pow(2  ).mul(2  )




def     a__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






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





class      A       (	__lowercase						):



      def __init__(	self:						Union[str, Any]		,  _lowerCAmelCase:						Dict		,  _lowerCAmelCase:						Optional[Any]		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						List[Any]		,  _lowerCAmelCase:						Optional[int]		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						Union[str, Any]		,  _lowerCAmelCase:						Union[str, Any]=None		,  _lowerCAmelCase:						Union[str, Any]=None		,  _lowerCAmelCase:						Any=None		,  )						->    Dict:
         '''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_     :   Tuple											=(
             feature_extractor.size
             if isinstance(feature_extractor.size		,  UpperCamelCase__       )
             else feature_extractor.size["shortest_edge"]
         )
         UpperCAmelCase_     :   Optional[Any]											=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:						Union[str, Any]		,  _lowerCAmelCase:						Union[str, Any] = "auto"       )						->    List[str]:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						int       )						->    str:
         '''simple docstring'''

         self.enable_attention_slicing(UpperCamelCase__       )



      def 	lowerCAmelCase__    (	self:						int       )						->    Union[str, Any]:
         '''simple docstring'''

         set_requires_grad(self.vae		,  UpperCamelCase__       )



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    List[Any]:
         '''simple docstring'''

         set_requires_grad(self.vae		,  UpperCamelCase__       )



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Optional[int]:
         '''simple docstring'''

         set_requires_grad(self.unet		,  UpperCamelCase__       )



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Union[str, Any]:
         '''simple docstring'''

         set_requires_grad(self.unet		,  UpperCamelCase__       )



      def 	lowerCAmelCase__    (	self:						int		,  _lowerCAmelCase:						Any		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						Any       )						->    List[str]:
         '''simple docstring'''

         UpperCAmelCase_     :   Tuple											=min(int(num_inference_steps * strength       )		,  UpperCamelCase__       )

         UpperCAmelCase_     :   List[str]											=max(num_inference_steps - init_timestep		,  0       )
         UpperCAmelCase_     :   Optional[Any]											=self.scheduler.timesteps[t_start:]

         return timesteps, num_inference_steps - t_start



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						Dict		,  _lowerCAmelCase:						Optional[Any]		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						Optional[Any]=None       )						->    int:
         '''simple docstring'''

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

         UpperCAmelCase_     :   Union[str, Any]											=image.to(device=UpperCamelCase__		,  dtype=UpperCamelCase__       )

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

         # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
         UpperCAmelCase_     :   int											=0.1_82_15 * init_latents
         UpperCAmelCase_     :   Optional[int]											=init_latents.repeat_interleave(UpperCamelCase__		,  dim=0       )

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

         # get latents
         UpperCAmelCase_     :   Tuple											=self.scheduler.add_noise(UpperCamelCase__		,  UpperCamelCase__		,  UpperCamelCase__       )
         UpperCAmelCase_     :   int											=init_latents

         return latents



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						Optional[int]       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_     :   List[Any]											=self.coca_transform(UpperCamelCase__       ).unsqueeze(0       )
         with torch.no_grad(), torch.cuda.amp.autocast():
            UpperCAmelCase_     :   Any											=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:						List[str]		,  _lowerCAmelCase:						Optional[Any]		,  _lowerCAmelCase:						str       )						->    Dict:
         '''simple docstring'''

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



      @torch.enable_grad()
      def 	lowerCAmelCase__    (	self:						Dict		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						Any		,  _lowerCAmelCase:						Optional[int]		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						List[Any]		,  _lowerCAmelCase:						List[str]		,  )						->    List[str]:
         '''simple docstring'''

         UpperCAmelCase_     :   Union[str, Any]											=latents.detach().requires_grad_()

         UpperCAmelCase_     :   Tuple											=self.scheduler.scale_model_input(UpperCamelCase__		,  UpperCamelCase__       )

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

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

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

         UpperCAmelCase_     :   Any											=transforms.Resize(self.feature_extractor_size       )(UpperCamelCase__       )
         UpperCAmelCase_     :   Union[str, Any]											=self.normalize(UpperCamelCase__       ).to(latents.dtype       )

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

         UpperCAmelCase_     :   str											=spherical_dist_loss(UpperCamelCase__		,  UpperCamelCase__       ).mean() * clip_guidance_scale

         UpperCAmelCase_     :   Optional[Any]											=-torch.autograd.grad(UpperCamelCase__		,  UpperCamelCase__       )[0]

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






      @torch.no_grad()
      def __call__(	self:						Tuple		,  _lowerCAmelCase:						List[Any]		,  _lowerCAmelCase:						List[str]		,  _lowerCAmelCase:						int = None		,  _lowerCAmelCase:						Optional[int] = None		,  _lowerCAmelCase:						List[str] = 512		,  _lowerCAmelCase:						Tuple = 512		,  _lowerCAmelCase:						Optional[int] = 0.6		,  _lowerCAmelCase:						Optional[Any] = 50		,  _lowerCAmelCase:						Tuple = 7.5		,  _lowerCAmelCase:						int = 1		,  _lowerCAmelCase:						Dict = 0.0		,  _lowerCAmelCase:						Tuple = 100		,  _lowerCAmelCase:						int = None		,  _lowerCAmelCase:						Optional[Any] = "pil"		,  _lowerCAmelCase:						Optional[int] = True		,  _lowerCAmelCase:						Optional[int] = 0.8		,  _lowerCAmelCase:						str = 0.1		,  _lowerCAmelCase:						str = 0.1		,  )						->    Union[str, Any]:
         '''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_     :   str											=[generator] + [None] * (batch_size - 1)

         UpperCAmelCase_     :   Dict											=[
             ("model", self.coca_model is None),
             ("tokenizer", self.coca_tokenizer is None),
             ("transform", self.coca_transform is None),
         ]
         UpperCAmelCase_     :   List[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_     :   Dict											=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_     :   Optional[Any]											=self.get_image_description(UpperCamelCase__       )

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

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

         UpperCAmelCase_     :   Tuple											=slerp(UpperCamelCase__		,  UpperCamelCase__		,  UpperCamelCase__       )

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

         # set timesteps
         UpperCAmelCase_     :   List[Any]											="offset" in set(inspect.signature(self.scheduler.set_timesteps       ).parameters.keys()       )
         UpperCAmelCase_     :   Optional[int]											={}
         if accepts_offset:
            UpperCAmelCase_     :   Optional[Any]											=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_     :   Optional[Any]											=self.get_timesteps(UpperCamelCase__		,  UpperCamelCase__		,  self.device       )
         UpperCAmelCase_     :   Tuple											=timesteps[:1].repeat(UpperCamelCase__       )

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

         UpperCAmelCase_     :   Tuple											=preprocess(UpperCamelCase__		,  UpperCamelCase__		,  UpperCamelCase__       )
         UpperCAmelCase_     :   Union[str, Any]											=self.prepare_latents(
             UpperCamelCase__		,  UpperCamelCase__		,  UpperCamelCase__		,  text_embeddings.dtype		,  self.device		,  UpperCamelCase__       )

         UpperCAmelCase_     :   Tuple											=slerp(UpperCamelCase__		,  UpperCamelCase__		,  UpperCamelCase__       )

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

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

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

         with self.progress_bar(total=UpperCamelCase__       ):
            for i, t in enumerate(UpperCamelCase__       ):
               # expand the latents if we are doing classifier free guidance
               UpperCAmelCase_     :   Dict											=torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents
               UpperCAmelCase_     :   Union[str, Any]											=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[Any]											=noise_pred.chunk(2       )
                  UpperCAmelCase_     :   Tuple											=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

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

               # compute the previous noisy sample x_t -> x_t-1
               UpperCAmelCase_     :   str											=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_     :   Optional[Any]											=1 / 0.1_82_15 * latents
         UpperCAmelCase_     :   Optional[Any]											=self.vae.decode(UpperCamelCase__       ).sample

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

         if output_type == "pil":
            UpperCAmelCase_     :   List[Any]											=self.numpy_to_pil(UpperCamelCase__       )

         if not return_dict:
            return (image, None)

         return StableDiffusionPipelineOutput(images=UpperCamelCase__		,  nsfw_content_detected=UpperCamelCase__       )











from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

__lowercase  :   Optional[int]						      =TypeVar("""T""")




def     a__ (					lowercase__  ):






   '''simple docstring'''






   return (position - 1) // 2




def     a__ (					lowercase__  ):






   '''simple docstring'''






   return (2 * position) + 1




def     a__ (					lowercase__  ):






   '''simple docstring'''






   return (2 * position) + 2

class      A       (	Generic[T]						):



      def __init__(	self:						List[str]       )						->    None:
         '''simple docstring'''

         UpperCAmelCase_											=[]
         UpperCAmelCase_											={}
         UpperCAmelCase_											=0



      def __len__(	self:						Union[str, Any]       )						->    int:
         '''simple docstring'''

         return self.elements



      def __repr__(	self:						Dict       )						->    str:
         '''simple docstring'''

         return str(self.heap       )



      def 	lowerCAmelCase__    (	self:						Any       )						->    bool:
         '''simple docstring'''

         return self.elements == 0



      def 	lowerCAmelCase__    (	self:						Optional[int]		,  _lowerCAmelCase:						T		,  _lowerCAmelCase:						int       )						->    None:
         '''simple docstring'''

         self.heap.append((elem, weight)       )
         UpperCAmelCase_											=self.elements
         self.elements += 1
         self._bubble_up(_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Tuple       )						->    T:
         '''simple docstring'''

         if self.elements > 1:
            self._swap_nodes(0		,  self.elements - 1       )
         UpperCAmelCase_						,       UpperCAmelCase_											=self.heap.pop()
         del self.position_map[elem]
         self.elements -= 1
         if self.elements > 0:
            UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[0]
            self._bubble_down(_lowerCAmelCase       )
         return elem



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						T		,  _lowerCAmelCase:						int       )						->    None:
         '''simple docstring'''

         UpperCAmelCase_											=self.position_map[elem]
         UpperCAmelCase_											=(elem, weight)
         if position > 0:
            UpperCAmelCase_											=get_parent_position(_lowerCAmelCase       )
            UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[parent_position]
            if parent_weight > weight:
               self._bubble_up(_lowerCAmelCase       )
            else:
               self._bubble_down(_lowerCAmelCase       )
         else:
            self._bubble_down(_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Any		,  _lowerCAmelCase:						T       )						->    None:
         '''simple docstring'''

         UpperCAmelCase_											=self.position_map[elem]
         if curr_pos == 0:
            return None
         UpperCAmelCase_											=get_parent_position(_lowerCAmelCase       )
         UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[curr_pos]
         UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[parent_position]
         if parent_weight > weight:
            self._swap_nodes(_lowerCAmelCase		,  _lowerCAmelCase       )
            return self._bubble_up(_lowerCAmelCase       )
         return None



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						T       )						->    None:
         '''simple docstring'''

         UpperCAmelCase_											=self.position_map[elem]
         UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[curr_pos]
         UpperCAmelCase_											=get_child_left_position(_lowerCAmelCase       )
         UpperCAmelCase_											=get_child_right_position(_lowerCAmelCase       )
         if child_left_position < self.elements and child_right_position < self.elements:
            UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[child_left_position]
            UpperCAmelCase_						,       UpperCAmelCase_											=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:
            UpperCAmelCase_						,       UpperCAmelCase_											=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:
            UpperCAmelCase_						,       UpperCAmelCase_											=self.heap[child_right_position]
            if child_right_weight < weight:
               self._swap_nodes(_lowerCAmelCase		,  _lowerCAmelCase       )
               return self._bubble_down(_lowerCAmelCase       )
         return None






      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						int       )						->    None:
         '''simple docstring'''

         UpperCAmelCase_											=self.heap[nodea_pos][0]
         UpperCAmelCase_											=self.heap[nodea_pos][0]
         UpperCAmelCase_						,       UpperCAmelCase_											=(
             self.heap[nodea_pos],
             self.heap[nodea_pos],
         )
         UpperCAmelCase_											=nodea_pos
         UpperCAmelCase_											=nodea_pos

class      A       (	Generic[T]						):



      def __init__(	self:						Tuple       )						->    None:
         '''simple docstring'''

         UpperCAmelCase_											={}
         UpperCAmelCase_											=0



      def __repr__(	self:						List[str]       )						->    str:
         '''simple docstring'''

         return str(self.connections       )



      def __len__(	self:						Optional[Any]       )						->    int:
         '''simple docstring'''

         return self.nodes



      def 	lowerCAmelCase__    (	self:						Union[str, Any]		,  _lowerCAmelCase:						T       )						->    None:
         '''simple docstring'''

         if node not in self.connections:
            UpperCAmelCase_											={}
            self.nodes += 1






      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						T		,  _lowerCAmelCase:						T		,  _lowerCAmelCase:						int       )						->    None:
         '''simple docstring'''

         self.add_node(_lowerCAmelCase       )
         self.add_node(_lowerCAmelCase       )
         UpperCAmelCase_											=weight
         UpperCAmelCase_											=weight




def     a__ (					lowercase__  ,    ):






   '''simple docstring'''






   UpperCAmelCase_											={node: maxsize for node in graph.connections}
   UpperCAmelCase_											={node: None for node in graph.connections}

   UpperCAmelCase_											=MinPriorityQueue()
   for node, weight in dist.items():
      priority_queue.push(lowercase__  ,    lowercase__  )

   if priority_queue.is_empty():
      return dist, parent

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

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




