"""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					,       __lowerCamelCase					,       __lowerCamelCase=1.0	):




         '''simple docstring'''



         super().__init__()

         __A					:					str								=		cut_size
         __A					:					Tuple								=		cut_power

    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



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







def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Any  ,       _SCREAMING_SNAKE_CASE				:				int						)    ->     str:







     '''simple docstring'''


     __A					:					str								=		F.normalize(_SCREAMING_SNAKE_CASE  ,       dim=-1						)
     __A					:					Optional[Any]								=		F.normalize(_SCREAMING_SNAKE_CASE  ,       dim=-1						)
     return (x - y).norm(dim=-1						).div(2						).arcsin().pow(2						).mul(2						)




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Dict  ,       _SCREAMING_SNAKE_CASE				:				Any						)    ->     Optional[int]:







     '''simple docstring'''


     for param in model.parameters():
          __A					:					Any								=		value






class 							__snake_case(					A_	):


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       ):




         '''simple docstring'''



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

         __A					:					List[Any]								=		transforms.Normalize(mean=feature_extractor.image_mean					,       std=feature_extractor.image_std	)
         __A					:					Any								=		(
             feature_extractor.size
             if isinstance(feature_extractor.size					,       A_	)
             else feature_extractor.size["shortest_edge"]
         )
         __A					:					int								=		MakeCutouts(self.cut_out_size	)

         set_requires_grad(self.text_encoder					,       A_	)
         set_requires_grad(self.clip_model					,       A_	)
    def 						_a							(  self					,       __lowerCamelCase = "auto"	):




         '''simple docstring'''



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




         '''simple docstring'''



         self.enable_attention_slicing(A_	)
    def 						_a							(  self	):




         '''simple docstring'''



         set_requires_grad(self.vae					,       A_	)
    def 						_a							(  self	):




         '''simple docstring'''



         set_requires_grad(self.vae					,       A_	)
    def 						_a							(  self	):




         '''simple docstring'''



         set_requires_grad(self.unet					,       A_	)
    def 						_a							(  self	):




         '''simple docstring'''



         set_requires_grad(self.unet					,       A_	)
    @torch.enable_grad()
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase=True					,       ):




         '''simple docstring'''



         __A					:					int								=		latents.detach().requires_grad_()

         __A					:					List[str]								=		self.scheduler.scale_model_input(A_					,       A_	)

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

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

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

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

         if use_cutouts:
              __A					:					str								=		self.make_cutouts(A_					,       A_	)
         else:
              __A					:					List[str]								=		transforms.Resize(self.cut_out_size	)(A_	)
         __A					:					str								=		self.normalize(A_	).to(latents.dtype	)

         __A					:					Optional[int]								=		self.clip_model.get_image_features(A_	)
         __A					:					int								=		image_embeddings_clip / image_embeddings_clip.norm(p=2					,       dim=-1					,       keepdim=A_	)

         if use_cutouts:
              __A					:					Any								=		spherical_dist_loss(A_					,       A_	)
              __A					:					Dict								=		dists.view([num_cutouts, sample.shape[0], -1]	)
              __A					:					List[Any]								=		dists.sum(2	).mean(0	).sum() * clip_guidance_scale
         else:
              __A					:					List[Any]								=		spherical_dist_loss(A_					,       A_	).mean() * clip_guidance_scale

         __A					:					List[Any]								=		-torch.autograd.grad(A_					,       A_	)[0]

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

    @torch.no_grad()
    def __call__(  self					,       __lowerCamelCase					,       __lowerCamelCase = 512					,       __lowerCamelCase = 512					,       __lowerCamelCase = 50					,       __lowerCamelCase = 7.5					,       __lowerCamelCase = 1					,       __lowerCamelCase = 0.0					,       __lowerCamelCase = 100					,       __lowerCamelCase = None					,       __lowerCamelCase = 4					,       __lowerCamelCase = True					,       __lowerCamelCase = None					,       __lowerCamelCase = None					,       __lowerCamelCase = "pil"					,       __lowerCamelCase = True					,       ):




         '''simple docstring'''



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

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

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

         # set timesteps
         __A					:					int								=		"offset" in set(inspect.signature(self.scheduler.set_timesteps	).parameters.keys()	)
         __A					:					int								=		{}
         if accepts_offset:
              __A					:					Dict								=		1

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

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

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

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

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

              # predict the noise residual
              __A					:					Optional[Any]								=		self.unet(A_					,       A_					,       encoder_hidden_states=A_	).sample

              # perform classifier free guidance
              if do_classifier_free_guidance:
                   __A					:					List[Any]								=		noise_pred.chunk(2	)
                   __A					:					Optional[int]								=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

              # perform clip guidance
              if clip_guidance_scale > 0:
                   __A					:					Any								=		(
                       text_embeddings.chunk(2	)[1] if do_classifier_free_guidance else text_embeddings
                   )
                   __A					:					Union[str, Any]								=		self.cond_fn(
                       A_					,       A_					,       A_					,       A_					,       A_					,       A_					,       A_					,       A_					,       A_					,       )

              # compute the previous noisy sample x_t -> x_t-1
              __A					:					Tuple								=		self.scheduler.step(A_					,       A_					,       A_					,       **A_	).prev_sample

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

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

         if output_type == "pil":
              __A					:					Optional[Any]								=		self.numpy_to_pil(A_	)

         if not return_dict:
              return (image, None)

         return StableDiffusionPipelineOutput(images=A_					,       nsfw_content_detected=A_	)






def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   Optional[int]:

     """simple docstring"""







     if power < 0:
          return -1
     base %= mod
     _lowerCAmelCase :       Tuple	   =     1

     while power > 0:
          if power & 1:
               _lowerCAmelCase :       Optional[Any]	   =     (result * base) % mod
          _lowerCAmelCase :       int	   =     power >> 1
          _lowerCAmelCase :       Optional[int]	   =     (base * base) % mod

     return result







def 						_snake_case		(     )							->   Optional[int]:

     """simple docstring"""







     print(modular_exponential(3		,				200		,				13					)					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  main()






