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						):




   def __init__(       self     :							Union[str, Any]			,      _lowercase     :							List[str]			,      _lowercase     :							List[Any]=1.0 ):



     """simple docstring"""






     super().__init__()

     SCREAMING_SNAKE_CASE__     				= cut_size
     SCREAMING_SNAKE_CASE__     				= cut_power




   def       __a		(       self     :							Union[str, Any]			,      _lowercase     :							int			,      _lowercase     :							Optional[int] ):



     """simple docstring"""






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





def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Any					,	__UpperCamelCase       :			Dict      )				->   Dict:







  """simple docstring"""





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



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			int					,	__UpperCamelCase       :			List[Any]      )				->   str:







  """simple docstring"""





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







class __snake_case							(		lowerCamelCase_						):




   def __init__(       self     :							List[str]			,      _lowercase     :							AutoencoderKL			,      _lowercase     :							CLIPTextModel			,      _lowercase     :							CLIPModel			,      _lowercase     :							CLIPTokenizer			,      _lowercase     :							UNetaDConditionModel			,      _lowercase     :							Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]			,      _lowercase     :							CLIPImageProcessor			,      ):



     """simple docstring"""






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

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

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




   def       __a		(       self     :							Tuple			,      _lowercase     :							Optional[Union[str, int]] = "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__     				= self.unet.config.attention_head_dim // 2
     self.unet.set_attention_slice(_lowercase )




   def       __a		(       self     :							Tuple ):



     """simple docstring"""






     self.enable_attention_slicing(_lowercase )




   def       __a		(       self     :							Union[str, Any] ):



     """simple docstring"""






     set_requires_grad(self.vae			,      _lowercase )




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     set_requires_grad(self.vae			,      _lowercase )




   def       __a		(       self     :							Optional[int] ):



     """simple docstring"""






     set_requires_grad(self.unet			,      _lowercase )




   def       __a		(       self     :							Optional[int] ):



     """simple docstring"""






     set_requires_grad(self.unet			,      _lowercase )




   @torch.enable_grad()
   def       __a		(       self     :							List[Any]			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Tuple			,      _lowercase     :							Optional[Any]			,      _lowercase     :							Any			,      _lowercase     :							List[Any]			,      _lowercase     :							Optional[Any]			,      _lowercase     :							str			,      _lowercase     :							List[Any]			,      _lowercase     :							Optional[Any]=True			,      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= latents.detach().requires_grad_()

     SCREAMING_SNAKE_CASE__     				= self.scheduler.scale_model_input(_lowercase			,      _lowercase )

     # predict the noise residual
     SCREAMING_SNAKE_CASE__     				= self.unet(_lowercase			,      _lowercase			,      encoder_hidden_states=_lowercase ).sample

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

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

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

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

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

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

     SCREAMING_SNAKE_CASE__     				= -torch.autograd.grad(_lowercase			,      _lowercase )[0]

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




   @torch.no_grad()
   def __call__(       self     :							int			,      _lowercase     :							Union[str, List[str]]			,      _lowercase     :							Optional[int] = 5_12			,      _lowercase     :							Optional[int] = 5_12			,      _lowercase     :							Optional[int] = 50			,      _lowercase     :							Optional[float] = 7.5			,      _lowercase     :							Optional[int] = 1			,      _lowercase     :							float = 0.0			,      _lowercase     :							Optional[float] = 1_00			,      _lowercase     :							Optional[Union[str, List[str]]] = None			,      _lowercase     :							Optional[int] = 4			,      _lowercase     :							Optional[bool] = True			,      _lowercase     :							Optional[torch.Generator] = None			,      _lowercase     :							Optional[torch.FloatTensor] = None			,      _lowercase     :							Optional[str] = "pil"			,      _lowercase     :							bool = True			,      ):



     """simple docstring"""






     if isinstance(_lowercase			,      _lowercase ):
       SCREAMING_SNAKE_CASE__     				= 1
     elif isinstance(_lowercase			,      _lowercase ):
       SCREAMING_SNAKE_CASE__     				= len(_lowercase )
     else:
       raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(_lowercase )}""" )

     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__     				= self.tokenizer(
         _lowercase			,      padding="""max_length"""			,      max_length=self.tokenizer.model_max_length			,      truncation=_lowercase			,      return_tensors="""pt"""			,      )
     SCREAMING_SNAKE_CASE__     				= self.text_encoder(text_input.input_ids.to(self.device ) )[0]
     # duplicate text embeddings for each generation per prompt
     SCREAMING_SNAKE_CASE__     				= text_embeddings.repeat_interleave(_lowercase			,      dim=0 )

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

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

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

     # Some schedulers like PNDM have timesteps as arrays
     # It's more optimized to move all timesteps to correct device beforehand
     SCREAMING_SNAKE_CASE__     				= self.scheduler.timesteps.to(self.device )

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

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

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

       # predict the noise residual
       SCREAMING_SNAKE_CASE__     				= self.unet(_lowercase			,      _lowercase			,      encoder_hidden_states=_lowercase ).sample

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

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

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

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

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

     if output_type == "pil":
       SCREAMING_SNAKE_CASE__     				= self.numpy_to_pil(_lowercase )

     if not return_dict:
       return (image, None)

     return StableDiffusionPipelineOutput(images=_lowercase			,      nsfw_content_detected=_lowercase )

from typing import TYPE_CHECKING

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


__lowerCamelCase     :					Optional[int]				  =						{'''configuration_vilt''': ['''VILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViltConfig''']}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase     :					Dict				  =						['''ViltFeatureExtractor''']
   __lowerCamelCase     :					Dict				  =						['''ViltImageProcessor''']
   __lowerCamelCase     :					str				  =						['''ViltProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase     :					Any				  =						[
       '''VILT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ViltForImageAndTextRetrieval''',
       '''ViltForImagesAndTextClassification''',
       '''ViltForTokenClassification''',
       '''ViltForMaskedLM''',
       '''ViltForQuestionAnswering''',
       '''ViltLayer''',
       '''ViltModel''',
       '''ViltPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_vilt import ViltFeatureExtractor
      from .image_processing_vilt import ViltImageProcessor
      from .processing_vilt import ViltProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vilt import (
          VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViltForImageAndTextRetrieval,
          ViltForImagesAndTextClassification,
          ViltForMaskedLM,
          ViltForQuestionAnswering,
          ViltForTokenClassification,
          ViltLayer,
          ViltModel,
          ViltPreTrainedModel,
      )


else:
   import sys

   __lowerCamelCase     :					List[str]				  =						_LazyModule(__name__, globals()['''__file__'''], _import_structure)
