





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	:   Dict					,			UpperCAmelCase	:   Dict					,			UpperCAmelCase	:   List[Any]=1.0			):
           super().__init__()

           lowerCAmelCase_					:	Optional[int]						=   cut_size
           lowerCAmelCase_					:	List[str]						=   cut_power



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






def 			__UpperCamelCase				(			lowercase__	:       str		,    lowercase__	:       str		)	->      Optional[Any]:
    '''simple docstring'''





    lowerCAmelCase_					:	List[Any]						=   F.normalize(lowercase__		,    dim=-1		)
    lowerCAmelCase_					:	Tuple						=   F.normalize(lowercase__		,    dim=-1		)
    return (x - y).norm(dim=-1		).div(2		).arcsin().pow(2		).mul(2		)
def 			__UpperCamelCase				(			lowercase__	:       Union[str, Any]		,    lowercase__	:       Optional[int]		)	->      int:
    '''simple docstring'''





    for param in model.parameters():
        lowerCAmelCase_					:	str						=   value

class 		__a							(	__UpperCamelCase      ):



       def __init__(     self	:   str					,			UpperCAmelCase	:   AutoencoderKL					,			UpperCAmelCase	:   CLIPTextModel					,			UpperCAmelCase	:   CLIPModel					,			UpperCAmelCase	:   CLIPTokenizer					,			UpperCAmelCase	:   UNetaDConditionModel					,			UpperCAmelCase	:   Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]					,			UpperCAmelCase	:   CLIPImageProcessor					,			):
           super().__init__()
           self.register_modules(
               vae=UpperCAmelCase					,			text_encoder=UpperCAmelCase					,			clip_model=UpperCAmelCase					,			tokenizer=UpperCAmelCase					,			unet=UpperCAmelCase					,			scheduler=UpperCAmelCase					,			feature_extractor=UpperCAmelCase					,			)

           lowerCAmelCase_					:	Optional[int]						=   transforms.Normalize(mean=feature_extractor.image_mean					,			std=feature_extractor.image_std			)
           lowerCAmelCase_					:	Dict						=   (
               feature_extractor.size
               if isinstance(feature_extractor.size					,			UpperCAmelCase			)
               else feature_extractor.size["""shortest_edge"""]
           )
           lowerCAmelCase_					:	Union[str, Any]						=   MakeCutouts(self.cut_out_size			)

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



       def  A							(     self	:   Optional[int]					,			UpperCAmelCase	:   Optional[Union[str, int]] = "auto"			):
           if slice_size == "auto":
               # half the attention head size is usually a good trade-off between
               # speed and memory
               lowerCAmelCase_					:	str						=   self.unet.config.attention_head_dim // 2
           self.unet.set_attention_slice(UpperCAmelCase			)



       def  A							(     self	:   Optional[int]			):
           self.enable_attention_slicing(UpperCAmelCase			)



       def  A							(     self	:   Optional[int]			):
           set_requires_grad(self.vae					,			UpperCAmelCase			)



       def  A							(     self	:   Union[str, Any]			):
           set_requires_grad(self.vae					,			UpperCAmelCase			)



       def  A							(     self	:   Any			):
           set_requires_grad(self.unet					,			UpperCAmelCase			)



       def  A							(     self	:   Dict			):
           set_requires_grad(self.unet					,			UpperCAmelCase			)



       @torch.enable_grad()
       def  A							(     self	:   Any					,			UpperCAmelCase	:   Tuple					,			UpperCAmelCase	:   Optional[Any]					,			UpperCAmelCase	:   Tuple					,			UpperCAmelCase	:   str					,			UpperCAmelCase	:   str					,			UpperCAmelCase	:   Any					,			UpperCAmelCase	:   List[str]					,			UpperCAmelCase	:   Optional[int]					,			UpperCAmelCase	:   List[str]=True					,			):
           lowerCAmelCase_					:	Any						=   latents.detach().requires_grad_()

           lowerCAmelCase_					:	Dict						=   self.scheduler.scale_model_input(UpperCAmelCase					,			UpperCAmelCase			)

           # predict the noise residual
           lowerCAmelCase_					:	Tuple						=   self.unet(UpperCAmelCase					,			UpperCAmelCase					,			encoder_hidden_states=UpperCAmelCase			).sample

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

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

           lowerCAmelCase_					:	Tuple						=   1 / self.vae.config.scaling_factor * sample
           lowerCAmelCase_					:	List[str]						=   self.vae.decode(UpperCAmelCase			).sample
           lowerCAmelCase_					:	List[str]						=   (image / 2 + 0.5).clamp(0					,			1			)

           if use_cutouts:
               lowerCAmelCase_					:	Union[str, Any]						=   self.make_cutouts(UpperCAmelCase					,			UpperCAmelCase			)
           else:
               lowerCAmelCase_					:	Optional[int]						=   transforms.Resize(self.cut_out_size			)(UpperCAmelCase			)
           lowerCAmelCase_					:	Tuple						=   self.normalize(UpperCAmelCase			).to(latents.dtype			)

           lowerCAmelCase_					:	List[Any]						=   self.clip_model.get_image_features(UpperCAmelCase			)
           lowerCAmelCase_					:	int						=   image_embeddings_clip / image_embeddings_clip.norm(p=2					,			dim=-1					,			keepdim=UpperCAmelCase			)

           if use_cutouts:
               lowerCAmelCase_					:	Tuple						=   spherical_dist_loss(UpperCAmelCase					,			UpperCAmelCase			)
               lowerCAmelCase_					:	Optional[Any]						=   dists.view([num_cutouts, sample.shape[0], -1]			)
               lowerCAmelCase_					:	Tuple						=   dists.sum(2			).mean(0			).sum() * clip_guidance_scale
           else:
               lowerCAmelCase_					:	str						=   spherical_dist_loss(UpperCAmelCase					,			UpperCAmelCase			).mean() * clip_guidance_scale

           lowerCAmelCase_					:	int						=   -torch.autograd.grad(UpperCAmelCase					,			UpperCAmelCase			)[0]

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



       @torch.no_grad()
       def __call__(     self	:   int					,			UpperCAmelCase	:   Union[str, List[str]]					,			UpperCAmelCase	:   Optional[int] = 5_12					,			UpperCAmelCase	:   Optional[int] = 5_12					,			UpperCAmelCase	:   Optional[int] = 50					,			UpperCAmelCase	:   Optional[float] = 7.5					,			UpperCAmelCase	:   Optional[int] = 1					,			UpperCAmelCase	:   float = 0.0					,			UpperCAmelCase	:   Optional[float] = 1_00					,			UpperCAmelCase	:   Optional[Union[str, List[str]]] = None					,			UpperCAmelCase	:   Optional[int] = 4					,			UpperCAmelCase	:   Optional[bool] = True					,			UpperCAmelCase	:   Optional[torch.Generator] = None					,			UpperCAmelCase	:   Optional[torch.FloatTensor] = None					,			UpperCAmelCase	:   Optional[str] = "pil"					,			UpperCAmelCase	:   bool = True					,			):
           if isinstance(UpperCAmelCase					,			UpperCAmelCase			):
               lowerCAmelCase_					:	Dict						=   1
           elif isinstance(UpperCAmelCase					,			UpperCAmelCase			):
               lowerCAmelCase_					:	List[Any]						=   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
           lowerCAmelCase_					:	List[Any]						=   self.tokenizer(
               UpperCAmelCase					,			padding="""max_length"""					,			max_length=self.tokenizer.model_max_length					,			truncation=UpperCAmelCase					,			return_tensors="""pt"""					,			)
           lowerCAmelCase_					:	Any						=   self.text_encoder(text_input.input_ids.to(self.device			)			)[0]
           # duplicate text embeddings for each generation per prompt
           lowerCAmelCase_					:	int						=   text_embeddings.repeat_interleave(UpperCAmelCase					,			dim=0			)

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

           # set timesteps
           lowerCAmelCase_					:	List[Any]						=   """offset""" in set(inspect.signature(self.scheduler.set_timesteps			).parameters.keys()			)
           lowerCAmelCase_					:	Optional[int]						=   {}
           if accepts_offset:
               lowerCAmelCase_					:	Optional[int]						=   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
           lowerCAmelCase_					:	Tuple						=   self.scheduler.timesteps.to(self.device			)

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

           # check if the scheduler accepts generator
           lowerCAmelCase_					:	Optional[Any]						=   """generator""" in set(inspect.signature(self.scheduler.step			).parameters.keys()			)
           if accepts_generator:
               lowerCAmelCase_					:	Tuple						=   generator

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

               # predict the noise residual
               lowerCAmelCase_					:	str						=   self.unet(UpperCAmelCase					,			UpperCAmelCase					,			encoder_hidden_states=UpperCAmelCase			).sample

               # perform classifier free guidance
               if do_classifier_free_guidance:
                   lowerCAmelCase_					:	List[str]						=   noise_pred.chunk(2			)
                   lowerCAmelCase_					:	Tuple						=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

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

               # compute the previous noisy sample x_t -> x_t-1
               lowerCAmelCase_					:	Dict						=   self.scheduler.step(UpperCAmelCase					,			UpperCAmelCase					,			UpperCAmelCase					,			**UpperCAmelCase			).prev_sample

           # scale and decode the image latents with vae
           lowerCAmelCase_					:	str						=   1 / self.vae.config.scaling_factor * latents
           lowerCAmelCase_					:	Tuple						=   self.vae.decode(UpperCAmelCase			).sample

           lowerCAmelCase_					:	Dict						=   (image / 2 + 0.5).clamp(0					,			1			)
           lowerCAmelCase_					:	str						=   image.cpu().permute(0					,			2					,			3					,			1			).numpy()

           if output_type == "pil":
               lowerCAmelCase_					:	Union[str, Any]						=   self.numpy_to_pil(UpperCAmelCase			)

           if not return_dict:
               return (image, None)

           return StableDiffusionPipelineOutput(images=UpperCAmelCase					,			nsfw_content_detected=UpperCAmelCase			)









import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest

class 		__a							(	__UpperCamelCase      ):
       __snake_case						:   Optional[Any]      =		(VQDiffusionScheduler,)



       def  A							(     self	:   Optional[int]					,			**UpperCAmelCase	:   List[Any]			):
           lowerCAmelCase_					:	Any						=   {
               """num_vec_classes""": 40_97,
               """num_train_timesteps""": 1_00,
           }

           config.update(**UpperCAmelCase			)
           return config



       def  A							(     self	:   str					,			UpperCAmelCase	:   List[Any]			):
           lowerCAmelCase_					:	Union[str, Any]						=   4
           lowerCAmelCase_					:	str						=   8
           lowerCAmelCase_					:	Optional[Any]						=   8

           lowerCAmelCase_					:	str						=   torch.randint(0					,			UpperCAmelCase					,			(batch_size, height * width)			)

           return sample



       @property
       def  A							(     self	:   Optional[int]			):
           assert False



       def  A							(     self	:   int					,			UpperCAmelCase	:   Dict			):
           def model(UpperCAmelCase	:   Union[str, Any]					,			UpperCAmelCase	:   List[str]					,			*UpperCAmelCase	:   List[Any]			):
               lowerCAmelCase_		,		lowerCAmelCase_					:	Tuple						=   sample.shape
               lowerCAmelCase_					:	int						=   torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)			)
               lowerCAmelCase_					:	str						=   F.log_softmax(logits.double()					,			dim=1			).float()
               return return_value

           return model



       def  A							(     self	:   Union[str, Any]			):
           for timesteps in [2, 5, 1_00, 10_00]:
               self.check_over_configs(num_train_timesteps=UpperCAmelCase			)



       def  A							(     self	:   str			):
           for num_vec_classes in [5, 1_00, 10_00, 40_00]:
               self.check_over_configs(num_vec_classes=UpperCAmelCase			)



       def  A							(     self	:   Optional[Any]			):
           for t in [0, 50, 99]:
               self.check_over_forward(time_step=UpperCAmelCase			)



       def  A							(     self	:   int			):
           pass


