






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_				)













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()





