code: 
  
  
  
  
  
  
  import inspect
  from typing import List, Optional, Union
  
  import torch
  from torch import nn
  from torch.nn import functional as F
  from torchvision import transforms
  from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer
  
  from diffusers import (
      AutoencoderKL,
      DDIMScheduler,
      DiffusionPipeline,
      DPMSolverMultistepScheduler,
      LMSDiscreteScheduler,
      PNDMScheduler,
      UNetaDConditionModel,
  )
  from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      nn.Module				):
  
  
  
        """simple docstring"""
        def __init__(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_=1.0				)       ->   Any:
            super().__init__()
  
            UpperCamelCase :Dict										=     cut_size
            UpperCamelCase :Any										=     cut_power
  
  
  
  
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   Any:
            UpperCamelCase							,     UpperCamelCase :str										=     pixel_values.shape[2:4]
            UpperCamelCase :List[str]										=     min(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :List[Any]										=     min(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       self.cut_size				)
            UpperCamelCase :Tuple										=     []
            for _ in range(SCREAMING_SNAKE_CASE_				):
                UpperCamelCase :List[Any]										=     int(torch.rand([]				) ** self.cut_power * (max_size - min_size) + min_size				)
                UpperCamelCase :Dict										=     torch.randint(0					,       sideX - size + 1					,       ()				)
                UpperCamelCase :Union[str, Any]										=     torch.randint(0					,       sideY - size + 1					,       ()				)
                UpperCamelCase :Optional[int]										=     pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
                cutouts.append(F.adaptive_avg_poolad(SCREAMING_SNAKE_CASE_					,       self.cut_size				)				)
            return torch.cat(SCREAMING_SNAKE_CASE_				)
  
  
  
  
  
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			Optional[int]  ,						SCREAMING_SNAKE_CASE__						:			int ):
      UpperCamelCase :Dict										=     F.normalize(SCREAMING_SNAKE_CASE__  ,						dim=-1 )
      UpperCamelCase :Optional[Any]										=     F.normalize(SCREAMING_SNAKE_CASE__  ,						dim=-1 )
      return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			Any  ,						SCREAMING_SNAKE_CASE__						:			Tuple ):
      for param in model.parameters():
          UpperCamelCase :List[str]										=     value
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      lowercase				):
  
  
  
        """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_					,       )       ->   int:
            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_					,       )
  
            UpperCamelCase :Union[str, Any]										=     transforms.Normalize(mean=feature_extractor.image_mean					,       std=feature_extractor.image_std				)
            UpperCamelCase :List[Any]										=     (
                feature_extractor.size
                if isinstance(feature_extractor.size					,       SCREAMING_SNAKE_CASE_				)
                else feature_extractor.size['''shortest_edge''']
            )
            UpperCamelCase :Optional[int]										=     MakeCutouts(self.cut_out_size				)
  
            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"				)       ->   Optional[int]:
            if slice_size == "auto":
                # half the attention head size is usually a good trade-off between
                # speed and memory
                UpperCamelCase :Union[str, Any]										=     self.unet.config.attention_head_dim // 2
            self.unet.set_attention_slice(SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self				)       ->   List[str]:
            self.enable_attention_slicing(SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self				)       ->   Dict:
            set_requires_grad(self.vae					,       SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self				)       ->   Optional[int]:
            set_requires_grad(self.vae					,       SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self				)       ->   str:
            set_requires_grad(self.unet					,       SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self				)       ->   Any:
            set_requires_grad(self.unet					,       SCREAMING_SNAKE_CASE_				)
        @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_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_=True					,       )       ->   Any:
            UpperCamelCase :Optional[int]										=     latents.detach().requires_grad_()
  
            UpperCamelCase :Optional[int]										=     self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
  
            # predict the noise residual
            UpperCamelCase :Optional[int]										=     self.unet(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       encoder_hidden_states=SCREAMING_SNAKE_CASE_				).sample
  
            if isinstance(self.scheduler					,       (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)				):
                UpperCamelCase :Dict										=     self.scheduler.alphas_cumprod[timestep]
                UpperCamelCase :List[str]										=     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 :List[str]										=     (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
  
                UpperCamelCase :List[str]										=     torch.sqrt(SCREAMING_SNAKE_CASE_				)
                UpperCamelCase :str										=     pred_original_sample * (fac) + latents * (1 - fac)
            elif isinstance(self.scheduler					,       SCREAMING_SNAKE_CASE_				):
                UpperCamelCase :List[Any]										=     self.scheduler.sigmas[index]
                UpperCamelCase :Optional[Any]										=     latents - sigma * noise_pred
            else:
                raise ValueError(F'''scheduler type {type(self.scheduler				)} not supported'''				)
  
            UpperCamelCase :Tuple										=     1 / self.vae.config.scaling_factor * sample
            UpperCamelCase :List[Any]										=     self.vae.decode(SCREAMING_SNAKE_CASE_				).sample
            UpperCamelCase :List[Any]										=     (image / 2 + 0.5).clamp(0					,       1				)
  
            if use_cutouts:
                UpperCamelCase :int										=     self.make_cutouts(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
            else:
                UpperCamelCase :Union[str, Any]										=     transforms.Resize(self.cut_out_size				)(SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :Dict										=     self.normalize(SCREAMING_SNAKE_CASE_				).to(latents.dtype				)
  
            UpperCamelCase :List[str]										=     self.clip_model.get_image_features(SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :int										=     image_embeddings_clip / image_embeddings_clip.norm(p=2					,       dim=-1					,       keepdim=SCREAMING_SNAKE_CASE_				)
  
            if use_cutouts:
                UpperCamelCase :List[str]										=     spherical_dist_loss(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
                UpperCamelCase :Dict										=     dists.view([num_cutouts, sample.shape[0], -1]				)
                UpperCamelCase :List[str]										=     dists.sum(2				).mean(0				).sum() * clip_guidance_scale
            else:
                UpperCamelCase :int										=     spherical_dist_loss(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				).mean() * clip_guidance_scale
  
            UpperCamelCase :List[str]										=     -torch.autograd.grad(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)[0]
  
            if isinstance(self.scheduler					,       SCREAMING_SNAKE_CASE_				):
                UpperCamelCase :List[str]										=     latents.detach() + grads * (sigma**2)
                UpperCamelCase :List[Any]										=     noise_pred_original
            else:
                UpperCamelCase :Optional[int]										=     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_ = 512					,       SCREAMING_SNAKE_CASE_ = 512					,       SCREAMING_SNAKE_CASE_ = 50					,       SCREAMING_SNAKE_CASE_ = 7.5					,       SCREAMING_SNAKE_CASE_ = 1					,       SCREAMING_SNAKE_CASE_ = 0.0					,       SCREAMING_SNAKE_CASE_ = 100					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = 4					,       SCREAMING_SNAKE_CASE_ = True					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = "pil"					,       SCREAMING_SNAKE_CASE_ = True					,       )       ->   List[str]:
            if isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                UpperCamelCase :Dict										=     1
            elif isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                UpperCamelCase :str										=     len(SCREAMING_SNAKE_CASE_				)
            else:
                raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_				)}'''				)
  
            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}.'''				)
  
            # get prompt text embeddings
            UpperCamelCase :Optional[Any]										=     self.tokenizer(
                SCREAMING_SNAKE_CASE_					,       padding='''max_length'''					,       max_length=self.tokenizer.model_max_length					,       truncation=SCREAMING_SNAKE_CASE_					,       return_tensors='''pt'''					,       )
            UpperCamelCase :Any										=     self.text_encoder(text_input.input_ids.to(self.device				)				)[0]
            # duplicate text embeddings for each generation per prompt
            UpperCamelCase :Dict										=     text_embeddings.repeat_interleave(SCREAMING_SNAKE_CASE_					,       dim=0				)
  
            if clip_guidance_scale > 0:
                if clip_prompt is not None:
                    UpperCamelCase :List[str]										=     self.tokenizer(
                        SCREAMING_SNAKE_CASE_					,       padding='''max_length'''					,       max_length=self.tokenizer.model_max_length					,       truncation=SCREAMING_SNAKE_CASE_					,       return_tensors='''pt'''					,       ).input_ids.to(self.device				)
                else:
                    UpperCamelCase :Optional[Any]										=     text_input.input_ids.to(self.device				)
                UpperCamelCase :Union[str, Any]										=     self.clip_model.get_text_features(SCREAMING_SNAKE_CASE_				)
                UpperCamelCase :List[Any]										=     text_embeddings_clip / text_embeddings_clip.norm(p=2					,       dim=-1					,       keepdim=SCREAMING_SNAKE_CASE_				)
                # duplicate text embeddings clip for each generation per prompt
                UpperCamelCase :Tuple										=     text_embeddings_clip.repeat_interleave(SCREAMING_SNAKE_CASE_					,       dim=0				)
  
            # 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 :Dict										=     guidance_scale > 1.0
            # get unconditional embeddings for classifier free guidance
            if do_classifier_free_guidance:
                UpperCamelCase :List[Any]										=     text_input.input_ids.shape[-1]
                UpperCamelCase :List[Any]										=     self.tokenizer(['''''']					,       padding='''max_length'''					,       max_length=SCREAMING_SNAKE_CASE_					,       return_tensors='''pt'''				)
                UpperCamelCase :List[str]										=     self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]
                # duplicate unconditional embeddings for each generation per prompt
                UpperCamelCase :Dict										=     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 :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`.
            UpperCamelCase :Union[str, Any]										=     (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
            UpperCamelCase :List[str]										=     text_embeddings.dtype
            if latents is None:
                if self.device.type == "mps":
                    # randn does not work reproducibly on mps
                    UpperCamelCase :List[str]										=     torch.randn(SCREAMING_SNAKE_CASE_					,       generator=SCREAMING_SNAKE_CASE_					,       device='''cpu'''					,       dtype=SCREAMING_SNAKE_CASE_				).to(
                        self.device				)
                else:
                    UpperCamelCase :Union[str, Any]										=     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 :Optional[int]										=     latents.to(self.device				)
  
            # set timesteps
            UpperCamelCase :int										=     '''offset''' in set(inspect.signature(self.scheduler.set_timesteps				).parameters.keys()				)
            UpperCamelCase :Any										=     {}
            if accepts_offset:
                UpperCamelCase :List[Any]										=     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
            UpperCamelCase :List[str]										=     self.scheduler.timesteps.to(self.device				)
  
            # scale the initial noise by the standard deviation required by the scheduler
            UpperCamelCase :Tuple										=     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 :Optional[Any]										=     '''eta''' in set(inspect.signature(self.scheduler.step				).parameters.keys()				)
            UpperCamelCase :Tuple										=     {}
            if accepts_eta:
                UpperCamelCase :Any										=     eta
  
            # check if the scheduler accepts generator
            UpperCamelCase :Dict										=     '''generator''' in set(inspect.signature(self.scheduler.step				).parameters.keys()				)
            if accepts_generator:
                UpperCamelCase :Optional[int]										=     generator
  
            for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_				)				):
                # expand the latents if we are doing classifier free guidance
                UpperCamelCase :Tuple										=     torch.cat([latents] * 2				) if do_classifier_free_guidance else latents
                UpperCamelCase :str										=     self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
  
                # predict the noise residual
                UpperCamelCase :Tuple										=     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							,     UpperCamelCase :List[str]										=     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 :int										=     (
                        text_embeddings.chunk(2				)[1] if do_classifier_free_guidance else text_embeddings
                    )
                    UpperCamelCase							,     UpperCamelCase :Optional[int]										=     self.cond_fn(
                        SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       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 :int										=     self.scheduler.step(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				).prev_sample
  
            # scale and decode the image latents with vae
            UpperCamelCase :List[Any]										=     1 / self.vae.config.scaling_factor * latents
            UpperCamelCase :Union[str, Any]										=     self.vae.decode(SCREAMING_SNAKE_CASE_				).sample
  
            UpperCamelCase :str										=     (image / 2 + 0.5).clamp(0					,       1				)
            UpperCamelCase :Optional[Any]										=     image.cpu().permute(0					,       2					,       3					,       1				).numpy()
  
            if output_type == "pil":
                UpperCamelCase :List[str]										=     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_				)
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from __future__ import annotations
  
  
  
  
  
  
  
  class     UpperCAmelCase_		:
  
  
  
        """simple docstring"""
        def __init__(       self					,       SCREAMING_SNAKE_CASE_				)       ->   None:
            UpperCamelCase :Tuple										=     value
            UpperCamelCase :Node | None										=     None
            UpperCamelCase :Node | None										=     None
  
  
  
  
  
  
  
  class     UpperCAmelCase_		:
  
  
  
        """simple docstring"""
        UpperCamelCase_       :      int
        def __init__(       self				)       ->   None:
            UpperCamelCase :List[Any]										=     0
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   None:
            if node is None:
                return
  
            if path_sum == self.target:
                self.paths += 1
  
            if node.left:
                self.depth_first_search(node.left					,       path_sum + node.left.value				)
            if node.right:
                self.depth_first_search(node.right					,       path_sum + node.right.value				)
  
  
  
  
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_ = None				)       ->   int:
            if node is None:
                return 0
            if target is not None:
                UpperCamelCase :Tuple										=     target
  
            self.depth_first_search(SCREAMING_SNAKE_CASE_					,       node.value				)
            self.path_sum(node.left				)
            self.path_sum(node.right				)
  
            return self.paths
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()
  
  
  
  
  
  