


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   __lowercase						(nn.Module     ):





							"""simple docstring"""







							def __init__(			self ,	lowerCAmelCase__ ,	lowerCAmelCase__=1.0					):





										"""simple docstring"""




										super().__init__()

										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				cut_size
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				cut_power






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    int 		=				pixel_values.shape[2:4]
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				min(lowerCAmelCase__ ,	lowerCAmelCase__					)
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				min(lowerCAmelCase__ ,	lowerCAmelCase__ ,	self.cut_size					)
										SCREAMING_SNAKE_CASE_		:    Any 		=				[]
										for _ in range(lowerCAmelCase__					):
													SCREAMING_SNAKE_CASE_		:    List[Any] 		=				int(torch.rand([]					) ** self.cut_power * (max_size - min_size) + min_size					)
													SCREAMING_SNAKE_CASE_		:    int 		=				torch.randint(0 ,	sideX - size + 1 ,	()					)
													SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				torch.randint(0 ,	sideY - size + 1 ,	()					)
													SCREAMING_SNAKE_CASE_		:    int 		=				pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
													cutouts.append(F.adaptive_avg_poolad(lowerCAmelCase__ ,	self.cut_size					)					)
										return torch.cat(lowerCAmelCase__					)







def 	a__ (							A__,			A__							):
			SCREAMING_SNAKE_CASE_		:    Any 		=				F.normalize(A__,			dim=-1							)
			SCREAMING_SNAKE_CASE_		:    str 		=				F.normalize(A__,			dim=-1							)
			return (x - y).norm(dim=-1							).div(2							).arcsin().pow(2							).mul(2							)






def 	a__ (							A__,			A__							):
			for param in model.parameters():
						SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				value







class   __lowercase						(__SCREAMING_SNAKE_CASE     ):





							"""simple docstring"""







							def __init__(			self ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	):





										"""simple docstring"""




										super().__init__()
										self.register_modules(
										    vae=lowerCAmelCase__ ,	text_encoder=lowerCAmelCase__ ,	clip_model=lowerCAmelCase__ ,	tokenizer=lowerCAmelCase__ ,	unet=lowerCAmelCase__ ,	scheduler=lowerCAmelCase__ ,	feature_extractor=lowerCAmelCase__ ,	)

										SCREAMING_SNAKE_CASE_		:    str 		=				transforms.Normalize(mean=feature_extractor.image_mean ,	std=feature_extractor.image_std					)
										SCREAMING_SNAKE_CASE_		:    str 		=				(
										    feature_extractor.size
										    if isinstance(feature_extractor.size ,	lowerCAmelCase__					)
										    else feature_extractor.size['shortest_edge']
										)
										SCREAMING_SNAKE_CASE_		:    Any 		=				MakeCutouts(self.cut_out_size					)

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






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ = "auto"					):





										"""simple docstring"""




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






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										self.enable_attention_slicing(lowerCAmelCase__					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										set_requires_grad(self.vae ,	lowerCAmelCase__					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										set_requires_grad(self.vae ,	lowerCAmelCase__					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										set_requires_grad(self.unet ,	lowerCAmelCase__					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										set_requires_grad(self.unet ,	lowerCAmelCase__					)






							@torch.enable_grad()
							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__=True ,	):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Dict 		=				latents.detach().requires_grad_()

										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				self.scheduler.scale_model_input(lowerCAmelCase__ ,	lowerCAmelCase__					)

										# predict the noise residual
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				self.unet(lowerCAmelCase__ ,	lowerCAmelCase__ ,	encoder_hidden_states=lowerCAmelCase__					).sample

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

													SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				torch.sqrt(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE_		:    Any 		=				pred_original_sample * (fac) + latents * (1 - fac)
										elif isinstance(self.scheduler ,	lowerCAmelCase__					):
													SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				self.scheduler.sigmas[index]
													SCREAMING_SNAKE_CASE_		:    Dict 		=				latents - sigma * noise_pred
										else:
													raise ValueError(F'''scheduler type {type(self.scheduler					)} not supported'''					)

										SCREAMING_SNAKE_CASE_		:    List[str] 		=				1 / self.vae.config.scaling_factor * sample
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				self.vae.decode(lowerCAmelCase__					).sample
										SCREAMING_SNAKE_CASE_		:    int 		=				(image / 2 + 0.5).clamp(0 ,	1					)

										if use_cutouts:
													SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				self.make_cutouts(lowerCAmelCase__ ,	lowerCAmelCase__					)
										else:
													SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				transforms.Resize(self.cut_out_size					)(lowerCAmelCase__					)
										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				self.normalize(lowerCAmelCase__					).to(latents.dtype					)

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

										if use_cutouts:
													SCREAMING_SNAKE_CASE_		:    int 		=				spherical_dist_loss(lowerCAmelCase__ ,	lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE_		:    Dict 		=				dists.view([num_cutouts, sample.shape[0], -1]					)
													SCREAMING_SNAKE_CASE_		:    Any 		=				dists.sum(2					).mean(0					).sum() * clip_guidance_scale
										else:
													SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				spherical_dist_loss(lowerCAmelCase__ ,	lowerCAmelCase__					).mean() * clip_guidance_scale

										SCREAMING_SNAKE_CASE_		:    str 		=				-torch.autograd.grad(lowerCAmelCase__ ,	lowerCAmelCase__					)[0]

										if isinstance(self.scheduler ,	lowerCAmelCase__					):
													SCREAMING_SNAKE_CASE_		:    List[str] 		=				latents.detach() + grads * (sigma**2)
													SCREAMING_SNAKE_CASE_		:    Any 		=				noise_pred_original
										else:
													SCREAMING_SNAKE_CASE_		:    Any 		=				noise_pred_original - torch.sqrt(lowerCAmelCase__					) * grads
										return noise_pred, latents






							@torch.no_grad()
							def __call__(			self ,	lowerCAmelCase__ ,	lowerCAmelCase__ = 5_1_2 ,	lowerCAmelCase__ = 5_1_2 ,	lowerCAmelCase__ = 5_0 ,	lowerCAmelCase__ = 7.5 ,	lowerCAmelCase__ = 1 ,	lowerCAmelCase__ = 0.0 ,	lowerCAmelCase__ = 1_0_0 ,	lowerCAmelCase__ = None ,	lowerCAmelCase__ = 4 ,	lowerCAmelCase__ = True ,	lowerCAmelCase__ = None ,	lowerCAmelCase__ = None ,	lowerCAmelCase__ = "pil" ,	lowerCAmelCase__ = True ,	):





										"""simple docstring"""




										if isinstance(lowerCAmelCase__ ,	lowerCAmelCase__					):
													SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				1
										elif isinstance(lowerCAmelCase__ ,	lowerCAmelCase__					):
													SCREAMING_SNAKE_CASE_		:    Any 		=				len(lowerCAmelCase__					)
										else:
													raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__					)}'''					)

										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
										SCREAMING_SNAKE_CASE_		:    int 		=				self.tokenizer(
										    lowerCAmelCase__ ,	padding='max_length' ,	max_length=self.tokenizer.model_max_length ,	truncation=lowerCAmelCase__ ,	return_tensors='pt' ,	)
										SCREAMING_SNAKE_CASE_		:    Any 		=				self.text_encoder(text_input.input_ids.to(self.device					)					)[0]
										# duplicate text embeddings for each generation per prompt
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				text_embeddings.repeat_interleave(lowerCAmelCase__ ,	dim=0					)

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

										# set timesteps
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				'offset' in set(inspect.signature(self.scheduler.set_timesteps					).parameters.keys()					)
										SCREAMING_SNAKE_CASE_		:    int 		=				{}
										if accepts_offset:
													SCREAMING_SNAKE_CASE_		:    Any 		=				1

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

										# Some schedulers like PNDM have timesteps as arrays
										# It's more optimized to move all timesteps to correct device beforehand
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				self.scheduler.timesteps.to(self.device					)

										# scale the initial noise by the standard deviation required by the scheduler
										SCREAMING_SNAKE_CASE_		:    str 		=				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]
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				'eta' in set(inspect.signature(self.scheduler.step					).parameters.keys()					)
										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				{}
										if accepts_eta:
													SCREAMING_SNAKE_CASE_		:    List[Any] 		=				eta

										# check if the scheduler accepts generator
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				'generator' in set(inspect.signature(self.scheduler.step					).parameters.keys()					)
										if accepts_generator:
													SCREAMING_SNAKE_CASE_		:    int 		=				generator

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

													# predict the noise residual
													SCREAMING_SNAKE_CASE_		:    List[Any] 		=				self.unet(lowerCAmelCase__ ,	lowerCAmelCase__ ,	encoder_hidden_states=lowerCAmelCase__					).sample

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

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

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

										# scale and decode the image latents with vae
										SCREAMING_SNAKE_CASE_		:    Any 		=				1 / self.vae.config.scaling_factor * latents
										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				self.vae.decode(lowerCAmelCase__					).sample

										SCREAMING_SNAKE_CASE_		:    List[str] 		=				(image / 2 + 0.5).clamp(0 ,	1					)
										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				image.cpu().permute(0 ,	2 ,	3 ,	1					).numpy()

										if output_type == "pil":
													SCREAMING_SNAKE_CASE_		:    List[Any] 		=				self.numpy_to_pil(lowerCAmelCase__					)

										if not return_dict:
													return (image, None)

										return StableDiffusionPipelineOutput(images=lowerCAmelCase__ ,	nsfw_content_detected=lowerCAmelCase__					)









from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class   __lowercase						(__SCREAMING_SNAKE_CASE     ):





							"""simple docstring"""







							_UpperCAmelCase						     =	42
							_UpperCAmelCase						     =	42






							def __init__(			self ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										super().__init__()

										SCREAMING_SNAKE_CASE_		:    str 		=				PNDMScheduler.from_config(scheduler.config					)

										self.register_modules(unet=lowerCAmelCase__ ,	scheduler=lowerCAmelCase__					)






							@torch.no_grad()
							def __call__(			self ,	lowerCAmelCase__ = 1 ,	lowerCAmelCase__ = 5_0 ,	lowerCAmelCase__ = None ,	lowerCAmelCase__ = "pil" ,	lowerCAmelCase__ = True ,	**lowerCAmelCase__ ,	):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Tuple 		=				randn_tensor(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,	generator=lowerCAmelCase__ ,	device=self.device ,	)

										self.scheduler.set_timesteps(lowerCAmelCase__					)
										for t in self.progress_bar(self.scheduler.timesteps					):
													SCREAMING_SNAKE_CASE_		:    Dict 		=				self.unet(lowerCAmelCase__ ,	lowerCAmelCase__					).sample

													SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				self.scheduler.step(lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					).prev_sample

										SCREAMING_SNAKE_CASE_		:    Dict 		=				(image / 2 + 0.5).clamp(0 ,	1					)
										SCREAMING_SNAKE_CASE_		:    Any 		=				image.cpu().permute(0 ,	2 ,	3 ,	1					).numpy()
										if output_type == "pil":
													SCREAMING_SNAKE_CASE_		:    str 		=				self.numpy_to_pil(lowerCAmelCase__					)

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=lowerCAmelCase__					)





