






'''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    _snake_case   (				nn.Module					):

					"""simple docstring"""





					def __init__(     self   , UpperCAmelCase__   , UpperCAmelCase__=1.0     )      ->	List[Any]:
										super().__init__()

										a_									=   cut_size
										a_									=   cut_power




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__     )      ->	Any:
										a_							,					a_									=   pixel_values.shape[2:4]
										a_									=   min(UpperCAmelCase__   , UpperCAmelCase__     )
										a_									=   min(UpperCAmelCase__   , UpperCAmelCase__   , self.cut_size     )
										a_									=   []
										for _ in range(UpperCAmelCase__     ):
															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(UpperCAmelCase__   , self.cut_size     )     )
										return torch.cat(UpperCAmelCase__     )



def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase )				->				Optional[Any]:






					"""simple docstring"""







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

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase )				->				Union[str, Any]:






					"""simple docstring"""







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





class    _snake_case   (				snake_case					):

					"""simple docstring"""





					def __init__(     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , )      ->	Tuple:
										super().__init__()
										self.register_modules(
										    vae=UpperCAmelCase__   , text_encoder=UpperCAmelCase__   , clip_model=UpperCAmelCase__   , tokenizer=UpperCAmelCase__   , unet=UpperCAmelCase__   , scheduler=UpperCAmelCase__   , feature_extractor=UpperCAmelCase__   , )

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

										set_requires_grad(self.text_encoder   , UpperCAmelCase__     )
										set_requires_grad(self.clip_model   , UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__ = "auto"     )      ->	Union[str, Any]:







										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(UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Optional[int]:
										self.enable_attention_slicing(UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Optional[Any]:
										set_requires_grad(self.vae   , UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Optional[Any]:
										set_requires_grad(self.vae   , UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										set_requires_grad(self.unet   , UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	int:
										set_requires_grad(self.unet   , UpperCAmelCase__     )




					@torch.enable_grad()
					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__=True   , )      ->	int:
										a_									=   latents.detach().requires_grad_()

										a_									=   self.scheduler.scale_model_input(UpperCAmelCase__   , UpperCAmelCase__     )

										# predict the noise residual
										a_									=   self.unet(UpperCAmelCase__   , UpperCAmelCase__   , encoder_hidden_states=UpperCAmelCase__     ).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(UpperCAmelCase__     )
															a_									=   pred_original_sample * (fac) + latents * (1 - fac)
										elif isinstance(self.scheduler   , UpperCAmelCase__     ):
															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(UpperCAmelCase__     ).sample
										a_									=   (image / 2 + 0.5).clamp(0   , 1     )

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

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

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

										a_									=   -torch.autograd.grad(UpperCAmelCase__   , UpperCAmelCase__     )[0]

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




					@torch.no_grad()
					def __call__(     self   , UpperCAmelCase__   , UpperCAmelCase__ = 512   , UpperCAmelCase__ = 512   , UpperCAmelCase__ = 50   , UpperCAmelCase__ = 7.5   , UpperCAmelCase__ = 1   , UpperCAmelCase__ = 0.0   , UpperCAmelCase__ = 100   , UpperCAmelCase__ = None   , UpperCAmelCase__ = 4   , UpperCAmelCase__ = True   , UpperCAmelCase__ = None   , UpperCAmelCase__ = None   , UpperCAmelCase__ = "pil"   , UpperCAmelCase__ = True   , )      ->	Optional[Any]:







										if isinstance(UpperCAmelCase__   , UpperCAmelCase__     ):
															a_									=   1
										elif isinstance(UpperCAmelCase__   , UpperCAmelCase__     ):
															a_									=   len(UpperCAmelCase__     )
										else:
															raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(UpperCAmelCase__     )}'''     )

										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(
										    UpperCAmelCase__   , padding='max_length'   , max_length=self.tokenizer.model_max_length   , truncation=UpperCAmelCase__   , 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(UpperCAmelCase__   , dim=0     )

										if clip_guidance_scale > 0:
															if clip_prompt is not None:
																				a_									=   self.tokenizer(
																				    UpperCAmelCase__   , padding='max_length'   , max_length=self.tokenizer.model_max_length   , truncation=UpperCAmelCase__   , return_tensors='pt'   , ).input_ids.to(self.device     )
															else:
																				a_									=   text_input.input_ids.to(self.device     )
															a_									=   self.clip_model.get_text_features(UpperCAmelCase__     )
															a_									=   text_embeddings_clip / text_embeddings_clip.norm(p=2   , dim=-1   , keepdim=UpperCAmelCase__     )
															# duplicate text embeddings clip for each generation per prompt
															a_									=   text_embeddings_clip.repeat_interleave(UpperCAmelCase__   , 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=UpperCAmelCase__   , 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(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
															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(UpperCAmelCase__   , generator=UpperCAmelCase__   , device='cpu'   , dtype=UpperCAmelCase__     ).to(
																				    self.device     )
															else:
																				a_									=   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}'''     )
															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(UpperCAmelCase__   , **UpperCAmelCase__     )

										# 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(UpperCAmelCase__     )     ):
															# 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(UpperCAmelCase__   , UpperCAmelCase__     )

															# predict the noise residual
															a_									=   self.unet(UpperCAmelCase__   , UpperCAmelCase__   , encoder_hidden_states=UpperCAmelCase__     ).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(
																				    UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , )

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

										# scale and decode the image latents with vae
										a_									=   1 / self.vae.config.scaling_factor * latents
										a_									=   self.vae.decode(UpperCAmelCase__     ).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(UpperCAmelCase__     )

										if not return_dict:
															return (image, None)

										return StableDiffusionPipelineOutput(images=UpperCAmelCase__   , nsfw_content_detected=UpperCAmelCase__     )









'''simple docstring'''
class    _snake_case   :

					"""simple docstring"""





					def __init__(     self   , UpperCAmelCase__     )      ->	None:
										a_									=   data
										a_									=   42
										a_									=   42



def 							a	(   _UpperCAmelCase )				->				None:






					"""simple docstring"""







					a_									=   0
					a_									=   x

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase )				->				None:






					"""simple docstring"""







					a_							,					a_									=   find_set(_UpperCAmelCase ), find_set(_UpperCAmelCase )
					if x == y:
										return

					elif x.rank > y.rank:
										a_									=   x
					else:
										a_									=   y
										if x.rank == y.rank:
															y.rank += 1

def 							a	(   _UpperCAmelCase )				->				Node:






					"""simple docstring"""







					if x != x.parent:
										a_									=   find_set(x.parent )
					return x.parent

def 							a	(   _UpperCAmelCase )				->				set:






					"""simple docstring"""







					a_									=   ({0, 1, 2}, {3, 4, 5})
					for s in sets:
										if node.data in s:
															return s
					a_									=   F'''{node.data} is not in {sets}'''
					raise ValueError(_UpperCAmelCase )

def 							a	(   )				->				None:






					"""simple docstring"""







					a_									=   [Node(_UpperCAmelCase ) for i in range(6 )]
					for v in vertex:
										make_set(_UpperCAmelCase )

					union_set(vertex[0]					,  vertex[1] )
					union_set(vertex[1]					,  vertex[2] )
					union_set(vertex[3]					,  vertex[4] )
					union_set(vertex[3]					,  vertex[5] )

					for nodea in vertex:
										for nodea in vertex:
															if find_python_set(_UpperCAmelCase ).isdisjoint(find_python_set(_UpperCAmelCase ) ):
																				assert find_set(_UpperCAmelCase ) != find_set(_UpperCAmelCase )
															else:
																				assert find_set(_UpperCAmelCase ) == find_set(_UpperCAmelCase )


if __name__ == "__main__":
						test_disjoint_set()

