






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 						__magic_name__			(   __a      :	Optional[Any]	,		__a      :	List[Any]	,		__a      :	List[Any]						):



     '''simple docstring'''




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

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






def 						__magic_name__			(   __a      :	List[Any]	,		__a      :	int	,		__a      :	Union[str, Any]	,		__a      :	Any=0.9_995						):



     '''simple docstring'''




     if not isinstance(lowerCAmelCase__	,		np.ndarray						):
          UpperCamelCase__				=    True
          UpperCamelCase__				=    va.device
          UpperCamelCase__				=    va.cpu().numpy()
          UpperCamelCase__				=    va.cpu().numpy()

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

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

     return va






def 						__magic_name__			(   __a      :	str	,		__a      :	Optional[Any]						):



     '''simple docstring'''




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






def 						__magic_name__			(   __a      :	Optional[int]	,		__a      :	Tuple						):



     '''simple docstring'''




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




class      __A(    _lowerCAmelCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=None	,      ):
       super().__init__()
       self.register_modules(
           vae=SCREAMING_SNAKE_CASE_	,      text_encoder=SCREAMING_SNAKE_CASE_	,      clip_model=SCREAMING_SNAKE_CASE_	,      tokenizer=SCREAMING_SNAKE_CASE_	,      unet=SCREAMING_SNAKE_CASE_	,      scheduler=SCREAMING_SNAKE_CASE_	,      feature_extractor=SCREAMING_SNAKE_CASE_	,      coca_model=SCREAMING_SNAKE_CASE_	,      coca_tokenizer=SCREAMING_SNAKE_CASE_	,      coca_transform=SCREAMING_SNAKE_CASE_	,      )
       UpperCamelCase__				=    (
           feature_extractor.size
           if isinstance(feature_extractor.size	,      SCREAMING_SNAKE_CASE_						)
           else feature_extractor.size["""shortest_edge"""]
       )
       UpperCamelCase__				=    transforms.Normalize(mean=feature_extractor.image_mean	,      std=feature_extractor.image_std						)
       set_requires_grad(self.text_encoder	,      SCREAMING_SNAKE_CASE_						)
       set_requires_grad(self.clip_model	,      SCREAMING_SNAKE_CASE_						)







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







  def 	UpperCAmelCase_ (self						):
       self.enable_attention_slicing(SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       set_requires_grad(self.vae	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       set_requires_grad(self.vae	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       set_requires_grad(self.unet	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       set_requires_grad(self.unet	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       # get the original timestep using init_timestep
       UpperCamelCase__				=    min(int(num_inference_steps * strength						)	,      SCREAMING_SNAKE_CASE_						)

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

       return timesteps, num_inference_steps - t_start







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=None						):
       if not isinstance(SCREAMING_SNAKE_CASE_	,      torch.Tensor						):
            raise ValueError(F"`image` has to be of type `torch.Tensor` but is {type(SCREAMING_SNAKE_CASE_						)}"						)

       UpperCamelCase__				=    image.to(device=SCREAMING_SNAKE_CASE_	,      dtype=SCREAMING_SNAKE_CASE_						)

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

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

       UpperCamelCase__				=    randn_tensor(init_latents.shape	,      generator=SCREAMING_SNAKE_CASE_	,      device=SCREAMING_SNAKE_CASE_	,      dtype=SCREAMING_SNAKE_CASE_						)

       # get latents
       UpperCamelCase__				=    self.scheduler.add_noise(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    init_latents

       return latents







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    self.coca_transform(SCREAMING_SNAKE_CASE_						).unsqueeze(0						)
       with torch.no_grad(), torch.cuda.amp.autocast():
            UpperCamelCase__				=    self.coca_model.generate(transformed_image.to(device=self.device	,      dtype=self.coca_model.dtype						)						)
       UpperCamelCase__				=    self.coca_tokenizer.decode(generated[0].cpu().numpy()						)
       return generated.split("""<end_of_text>"""						)[0].replace("""<start_of_text>"""	,      """"""						).rstrip(""" .,"""						)







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







  @torch.enable_grad()
  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      ):
       UpperCamelCase__				=    latents.detach().requires_grad_()

       UpperCamelCase__				=    self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)

       # predict the noise residual
       UpperCamelCase__				=    self.unet(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      encoder_hidden_states=SCREAMING_SNAKE_CASE_						).sample

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

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

       UpperCamelCase__				=    transforms.Resize(self.feature_extractor_size						)(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    self.normalize(SCREAMING_SNAKE_CASE_						).to(latents.dtype						)

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

       UpperCamelCase__				=    spherical_dist_loss(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						).mean() * clip_guidance_scale

       UpperCamelCase__				=    -torch.autograd.grad(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)[0]

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







  @torch.no_grad()
  def __call__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = 5_12	,      SCREAMING_SNAKE_CASE_ = 5_12	,      SCREAMING_SNAKE_CASE_ = 0.6	,      SCREAMING_SNAKE_CASE_ = 50	,      SCREAMING_SNAKE_CASE_ = 7.5	,      SCREAMING_SNAKE_CASE_ = 1	,      SCREAMING_SNAKE_CASE_ = 0.0	,      SCREAMING_SNAKE_CASE_ = 1_00	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = "pil"	,      SCREAMING_SNAKE_CASE_ = True	,      SCREAMING_SNAKE_CASE_ = 0.8	,      SCREAMING_SNAKE_CASE_ = 0.1	,      SCREAMING_SNAKE_CASE_ = 0.1	,      ):
       if isinstance(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						) and len(SCREAMING_SNAKE_CASE_						) != batch_size:
            raise ValueError(F"You have passed {batch_size} batch_size, but only {len(SCREAMING_SNAKE_CASE_						)} 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(SCREAMING_SNAKE_CASE_	,      torch.Generator						) and batch_size > 1:
            UpperCamelCase__				=    [generator] + [None] * (batch_size - 1)

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

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

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

       UpperCamelCase__				=    slerp(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)

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

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

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

       # Preprocess image
       UpperCamelCase__				=    preprocess(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    self.prepare_latents(
           SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      text_embeddings.dtype	,      self.device	,      SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    preprocess(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    self.prepare_latents(
           SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      text_embeddings.dtype	,      self.device	,      SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    slerp(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)

       if clip_guidance_scale > 0:
            UpperCamelCase__				=    self.get_clip_image_embeddings(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)
            UpperCamelCase__				=    self.get_clip_image_embeddings(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)
            UpperCamelCase__				=    slerp(
                SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)

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

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

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

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

                 # predict the noise residual
                 UpperCamelCase__				=    self.unet(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      encoder_hidden_states=SCREAMING_SNAKE_CASE_						).sample

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

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

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

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

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

       if output_type == "pil":
            UpperCamelCase__				=    self.numpy_to_pil(SCREAMING_SNAKE_CASE_						)

       if not return_dict:
            return (image, None)

       return StableDiffusionPipelineOutput(images=SCREAMING_SNAKE_CASE_	,      nsfw_content_detected=SCREAMING_SNAKE_CASE_						)







from __future__ import annotations

from collections import Counter
from random import random




class      __A:

  """simple docstring"""







  def __init__(self						):
       UpperCamelCase__				=    {}







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    {}







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       if nodea not in self.connections:
            self.add_node(SCREAMING_SNAKE_CASE_						)
       if nodea not in self.connections:
            self.add_node(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    probability







  def 	UpperCAmelCase_ (self						):
       return list(self.connections						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    0
       UpperCamelCase__				=    random()

       for dest in self.connections[node]:
            current_probability += self.connections[node][dest]
            if current_probability > random_value:
                 return dest
       return ""






def 						__magic_name__			(   __a      :	str	,		__a      :	list[tuple[str, str, float]]	,		__a      :	int						):



     '''simple docstring'''




     UpperCamelCase__				=    MarkovChainGraphUndirectedUnweighted()

     for nodea, nodea, probability in transitions:
          graph.add_transition_probability(__a	,		__a	,		__a						)

     UpperCamelCase__				=    Counter(graph.get_nodes()						)
     UpperCamelCase__				=    start

     for _ in range(__a						):
          UpperCamelCase__				=    graph.transition(__a						)
          visited[node] += 1

     return visited


if __name__ == "__main__":
    import doctest

    doctest.testmod()





