


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       __lowerCAmelCase       (	nn.Module   ):






  """simple docstring"""





  def __init__(       self    :    List[Any]      ,			_lowerCAmelCase    :    List[str]      ,			_lowerCAmelCase    :    Optional[int]=1.0					)				->	Tuple:




     """simple docstring"""
     super().__init__()

     snake_case_         =   cut_size
     snake_case_         =   cut_power



  def    lowerCAmelCase__					(       self    :    Optional[int]      ,			_lowerCAmelCase    :    Dict      ,			_lowerCAmelCase    :    Optional[int]					)				->	List[str]:




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






def      _lowerCAmelCase  (							lowerCAmelCase_							:Dict						,  lowerCAmelCase_							:Dict							)->List[Any]:
   '''simple docstring'''






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



def      _lowerCAmelCase  (							lowerCAmelCase_							:int						,  lowerCAmelCase_							:str							)->Tuple:
   '''simple docstring'''






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



class       __lowerCAmelCase       (	a   ):






  """simple docstring"""





  def __init__(       self    :    Optional[int]      ,			_lowerCAmelCase    :    AutoencoderKL      ,			_lowerCAmelCase    :    CLIPTextModel      ,			_lowerCAmelCase    :    CLIPModel      ,			_lowerCAmelCase    :    CLIPTokenizer      ,			_lowerCAmelCase    :    UNetaDConditionModel      ,			_lowerCAmelCase    :    Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]      ,			_lowerCAmelCase    :    CLIPImageProcessor      ,			)				->	int:




     """simple docstring"""
     super().__init__()
     self.register_modules(
         vae=_lowerCAmelCase      ,			text_encoder=_lowerCAmelCase      ,			clip_model=_lowerCAmelCase      ,			tokenizer=_lowerCAmelCase      ,			unet=_lowerCAmelCase      ,			scheduler=_lowerCAmelCase      ,			feature_extractor=_lowerCAmelCase      ,			)

     snake_case_         =   transforms.Normalize(mean=feature_extractor.image_mean      ,			std=feature_extractor.image_std					)
     snake_case_         =   (
         feature_extractor.size
         if isinstance(feature_extractor.size      ,			_lowerCAmelCase					)
         else feature_extractor.size["shortest_edge"]
     )
     snake_case_         =   MakeCutouts(self.cut_out_size					)

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



  def    lowerCAmelCase__					(       self    :    str      ,			_lowerCAmelCase    :    Optional[Union[str, int]] = "auto"					)				->	str:




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



  def    lowerCAmelCase__					(       self    :    Optional[int]					)				->	Tuple:




     """simple docstring"""
     self.enable_attention_slicing(_lowerCAmelCase					)



  def    lowerCAmelCase__					(       self    :    Dict					)				->	List[Any]:




     """simple docstring"""
     set_requires_grad(self.vae      ,			_lowerCAmelCase					)



  def    lowerCAmelCase__					(       self    :    List[Any]					)				->	List[Any]:




     """simple docstring"""
     set_requires_grad(self.vae      ,			_lowerCAmelCase					)



  def    lowerCAmelCase__					(       self    :    int					)				->	Dict:




     """simple docstring"""
     set_requires_grad(self.unet      ,			_lowerCAmelCase					)



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




     """simple docstring"""
     set_requires_grad(self.unet      ,			_lowerCAmelCase					)



  @torch.enable_grad()
  def    lowerCAmelCase__					(       self    :    Optional[Any]      ,			_lowerCAmelCase    :    Dict      ,			_lowerCAmelCase    :    Dict      ,			_lowerCAmelCase    :    Optional[int]      ,			_lowerCAmelCase    :    Union[str, Any]      ,			_lowerCAmelCase    :    Any      ,			_lowerCAmelCase    :    Dict      ,			_lowerCAmelCase    :    Optional[Any]      ,			_lowerCAmelCase    :    Optional[Any]      ,			_lowerCAmelCase    :    Any=True      ,			)				->	Any:




     """simple docstring"""
     snake_case_         =   latents.detach().requires_grad_()

     snake_case_         =   self.scheduler.scale_model_input(_lowerCAmelCase      ,			_lowerCAmelCase					)

     # predict the noise residual
     snake_case_         =   self.unet(_lowerCAmelCase      ,			_lowerCAmelCase      ,			encoder_hidden_states=_lowerCAmelCase					).sample

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

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

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

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

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

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

     snake_case_         =   -torch.autograd.grad(_lowerCAmelCase      ,			_lowerCAmelCase					)[0]

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



  @torch.no_grad()
  def __call__(       self    :    Optional[Any]      ,			_lowerCAmelCase    :    Union[str, List[str]]      ,			_lowerCAmelCase    :    Optional[int] = 5_1_2      ,			_lowerCAmelCase    :    Optional[int] = 5_1_2      ,			_lowerCAmelCase    :    Optional[int] = 5_0      ,			_lowerCAmelCase    :    Optional[float] = 7.5      ,			_lowerCAmelCase    :    Optional[int] = 1      ,			_lowerCAmelCase    :    float = 0.0      ,			_lowerCAmelCase    :    Optional[float] = 1_0_0      ,			_lowerCAmelCase    :    Optional[Union[str, List[str]]] = None      ,			_lowerCAmelCase    :    Optional[int] = 4      ,			_lowerCAmelCase    :    Optional[bool] = True      ,			_lowerCAmelCase    :    Optional[torch.Generator] = None      ,			_lowerCAmelCase    :    Optional[torch.FloatTensor] = None      ,			_lowerCAmelCase    :    Optional[str] = "pil"      ,			_lowerCAmelCase    :    bool = True      ,			)				->	Optional[int]:




     """simple docstring"""
     if isinstance(_lowerCAmelCase      ,			_lowerCAmelCase					):
        snake_case_         =   1
     elif isinstance(_lowerCAmelCase      ,			_lowerCAmelCase					):
        snake_case_         =   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
     snake_case_         =   self.tokenizer(
         _lowerCAmelCase      ,			padding="max_length"      ,			max_length=self.tokenizer.model_max_length      ,			truncation=_lowerCAmelCase      ,			return_tensors="pt"      ,			)
     snake_case_         =   self.text_encoder(text_input.input_ids.to(self.device					)					)[0]
     # duplicate text embeddings for each generation per prompt
     snake_case_         =   text_embeddings.repeat_interleave(_lowerCAmelCase      ,			dim=0					)

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

     # set timesteps
     snake_case_         =   "offset" in set(inspect.signature(self.scheduler.set_timesteps					).parameters.keys()					)
     snake_case_         =   {}
     if accepts_offset:
        snake_case_         =   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
     snake_case_         =   self.scheduler.timesteps.to(self.device					)

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

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

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

        # predict the noise residual
        snake_case_         =   self.unet(_lowerCAmelCase      ,			_lowerCAmelCase      ,			encoder_hidden_states=_lowerCAmelCase					).sample

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

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

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

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

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

     if output_type == "pil":
        snake_case_         =   self.numpy_to_pil(_lowerCAmelCase					)

     if not return_dict:
        return (image, None)

     return StableDiffusionPipelineOutput(images=_lowerCAmelCase      ,			nsfw_content_detected=_lowerCAmelCase					)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE		:str											=       {'''configuration_cvt''': ['''CVT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CvtConfig''']}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE		:Any											=       [
       '''CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''CvtForImageClassification''',
       '''CvtModel''',
       '''CvtPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE		:Any											=       [
       '''TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFCvtForImageClassification''',
       '''TFCvtModel''',
       '''TFCvtPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_cvt import (
          CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
          CvtForImageClassification,
          CvtModel,
          CvtPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_cvt import (
          TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFCvtForImageClassification,
          TFCvtModel,
          TFCvtPreTrainedModel,
      )


else:
   import sys

   SCREAMING_SNAKE_CASE		:str											=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




