


'''simple docstring'''






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 			a  (  nn.Module							):
 def __init__(			self   ,					__magic_name__   ,					__magic_name__=1.0    )       ->				Optional[Any]:
      super().__init__()

      _a         =				cut_size
      _a         =				cut_power




 def 				__UpperCAmelCase		(			self   ,					__magic_name__   ,					__magic_name__    )       ->				Tuple:
      _a   , _a         =				pixel_values.shape[2:4]
      _a         =				min(__magic_name__   ,					__magic_name__    )
      _a         =				min(__magic_name__   ,					__magic_name__   ,					self.cut_size    )
      _a         =				[]
      for _ in range(__magic_name__    ):
           _a         =				int(torch.rand([]    ) ** self.cut_power * (max_size - min_size) + min_size    )
           _a         =				torch.randint(0   ,					sideX - size + 1   ,					()    )
           _a         =				torch.randint(0   ,					sideY - size + 1   ,					()    )
           _a         =				pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
           cutouts.append(F.adaptive_avg_poolad(__magic_name__   ,					self.cut_size    )    )
      return torch.cat(__magic_name__    )






def 						_A					(lowerCAmelCase__	:int					,    lowerCAmelCase__	:int		) ->	Union[str, Any]:




     '''simple docstring'''



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






def 						_A					(lowerCAmelCase__	:Tuple					,    lowerCAmelCase__	:Dict		) ->	List[str]:




     '''simple docstring'''



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






class 			a  (  _SCREAMING_SNAKE_CASE							):
 def __init__(			self   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					)       ->				str:
      super().__init__()
      self.register_modules(
          vae=__magic_name__   ,					text_encoder=__magic_name__   ,					clip_model=__magic_name__   ,					tokenizer=__magic_name__   ,					unet=__magic_name__   ,					scheduler=__magic_name__   ,					feature_extractor=__magic_name__   ,					)

      _a         =				transforms.Normalize(mean=feature_extractor.image_mean   ,					std=feature_extractor.image_std    )
      _a         =				(
          feature_extractor.size
          if isinstance(feature_extractor.size   ,					__magic_name__    )
          else feature_extractor.size['shortest_edge']
      )
      _a         =				MakeCutouts(self.cut_out_size    )

      set_requires_grad(self.text_encoder   ,					__magic_name__    )
      set_requires_grad(self.clip_model   ,					__magic_name__    )
 def 				__UpperCAmelCase		(			self   ,					__magic_name__ = "auto"    )       ->				Dict:
      if slice_size == "auto":
           # half the attention head size is usually a good trade-off between
           # speed and memory
           _a         =				self.unet.config.attention_head_dim // 2
      self.unet.set_attention_slice(__magic_name__    )
 def 				__UpperCAmelCase		(			self    )       ->				Tuple:
      self.enable_attention_slicing(__magic_name__    )
 def 				__UpperCAmelCase		(			self    )       ->				int:
      set_requires_grad(self.vae   ,					__magic_name__    )
 def 				__UpperCAmelCase		(			self    )       ->				Tuple:
      set_requires_grad(self.vae   ,					__magic_name__    )
 def 				__UpperCAmelCase		(			self    )       ->				Union[str, Any]:
      set_requires_grad(self.unet   ,					__magic_name__    )
 def 				__UpperCAmelCase		(			self    )       ->				List[Any]:
      set_requires_grad(self.unet   ,					__magic_name__    )
 @torch.enable_grad()
 def 				__UpperCAmelCase		(			self   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__=True   ,					)       ->				Optional[int]:
      _a         =				latents.detach().requires_grad_()

      _a         =				self.scheduler.scale_model_input(__magic_name__   ,					__magic_name__    )

      # predict the noise residual
      _a         =				self.unet(__magic_name__   ,					__magic_name__   ,					encoder_hidden_states=__magic_name__    ).sample

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

           _a         =				torch.sqrt(__magic_name__    )
           _a         =				pred_original_sample * (fac) + latents * (1 - fac)
      elif isinstance(self.scheduler   ,					__magic_name__    ):
           _a         =				self.scheduler.sigmas[index]
           _a         =				latents - sigma * noise_pred
      else:
           raise ValueError(f'scheduler type {type(self.scheduler    )} not supported'    )

      _a         =				1 / self.vae.config.scaling_factor * sample
      _a         =				self.vae.decode(__magic_name__    ).sample
      _a         =				(image / 2 + 0.5).clamp(0   ,					1    )

      if use_cutouts:
           _a         =				self.make_cutouts(__magic_name__   ,					__magic_name__    )
      else:
           _a         =				transforms.Resize(self.cut_out_size    )(__magic_name__    )
      _a         =				self.normalize(__magic_name__    ).to(latents.dtype    )

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

      if use_cutouts:
           _a         =				spherical_dist_loss(__magic_name__   ,					__magic_name__    )
           _a         =				dists.view([num_cutouts, sample.shape[0], -1]    )
           _a         =				dists.sum(2    ).mean(0    ).sum() * clip_guidance_scale
      else:
           _a         =				spherical_dist_loss(__magic_name__   ,					__magic_name__    ).mean() * clip_guidance_scale

      _a         =				-torch.autograd.grad(__magic_name__   ,					__magic_name__    )[0]

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




 @torch.no_grad()
 def __call__(			self   ,					__magic_name__   ,					__magic_name__ = 5_12   ,					__magic_name__ = 5_12   ,					__magic_name__ = 50   ,					__magic_name__ = 7.5   ,					__magic_name__ = 1   ,					__magic_name__ = 0.0   ,					__magic_name__ = 1_00   ,					__magic_name__ = None   ,					__magic_name__ = 4   ,					__magic_name__ = True   ,					__magic_name__ = None   ,					__magic_name__ = None   ,					__magic_name__ = "pil"   ,					__magic_name__ = True   ,					)       ->				Optional[Any]:
      if isinstance(__magic_name__   ,					__magic_name__    ):
           _a         =				1
      elif isinstance(__magic_name__   ,					__magic_name__    ):
           _a         =				len(__magic_name__    )
      else:
           raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(__magic_name__    )}'    )

      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
      _a         =				self.tokenizer(
          __magic_name__   ,					padding='max_length'   ,					max_length=self.tokenizer.model_max_length   ,					truncation=__magic_name__   ,					return_tensors='pt'   ,					)
      _a         =				self.text_encoder(text_input.input_ids.to(self.device    )    )[0]
      # duplicate text embeddings for each generation per prompt
      _a         =				text_embeddings.repeat_interleave(__magic_name__   ,					dim=0    )

      if clip_guidance_scale > 0:
           if clip_prompt is not None:
                _a         =				self.tokenizer(
                    __magic_name__   ,					padding='max_length'   ,					max_length=self.tokenizer.model_max_length   ,					truncation=__magic_name__   ,					return_tensors='pt'   ,					).input_ids.to(self.device    )
           else:
                _a         =				text_input.input_ids.to(self.device    )
           _a         =				self.clip_model.get_text_features(__magic_name__    )
           _a         =				text_embeddings_clip / text_embeddings_clip.norm(p=2   ,					dim=-1   ,					keepdim=__magic_name__    )
           # duplicate text embeddings clip for each generation per prompt
           _a         =				text_embeddings_clip.repeat_interleave(__magic_name__   ,					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.
      _a         =				guidance_scale > 1.0
      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance:
           _a         =				text_input.input_ids.shape[-1]
           _a         =				self.tokenizer(['']   ,					padding='max_length'   ,					max_length=__magic_name__   ,					return_tensors='pt'    )
           _a         =				self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]
           # duplicate unconditional embeddings for each generation per prompt
           _a         =				uncond_embeddings.repeat_interleave(__magic_name__   ,					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
           _a         =				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`.
      _a         =				(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
      _a         =				text_embeddings.dtype
      if latents is None:
           if self.device.type == "mps":
                # randn does not work reproducibly on mps
                _a         =				torch.randn(__magic_name__   ,					generator=__magic_name__   ,					device='cpu'   ,					dtype=__magic_name__    ).to(
                    self.device    )
           else:
                _a         =				torch.randn(__magic_name__   ,					generator=__magic_name__   ,					device=self.device   ,					dtype=__magic_name__    )
      else:
           if latents.shape != latents_shape:
                raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'    )
           _a         =				latents.to(self.device    )

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

      self.scheduler.set_timesteps(__magic_name__   ,					**__magic_name__    )

      # Some schedulers like PNDM have timesteps as arrays
      # It's more optimized to move all timesteps to correct device beforehand
      _a         =				self.scheduler.timesteps.to(self.device    )

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

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

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

           # predict the noise residual
           _a         =				self.unet(__magic_name__   ,					__magic_name__   ,					encoder_hidden_states=__magic_name__    ).sample

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

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

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

      # scale and decode the image latents with vae
      _a         =				1 / self.vae.config.scaling_factor * latents
      _a         =				self.vae.decode(__magic_name__    ).sample

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

      if output_type == "pil":
           _a         =				self.numpy_to_pil(__magic_name__    )

      if not return_dict:
           return (image, None)

      return StableDiffusionPipelineOutput(images=__magic_name__   ,					nsfw_content_detected=__magic_name__    )





'''simple docstring'''






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
 from .image_encoder import PaintByExampleImageEncoder
 from .pipeline_paint_by_example import PaintByExamplePipeline

