


'''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  lowercase_					(   nn.Module       ):


    def __init__(					self  , a  , a=1.0   ):
          super().__init__()

          UpperCamelCase__  =    cut_size
          UpperCamelCase__  =    cut_power


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







def _UpperCamelCase	(					__A				,      __A   )							->		int:


      '''simple docstring'''



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







def _UpperCamelCase	(					__A				,      __A   )							->		Union[str, Any]:


      '''simple docstring'''



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



class  lowercase_					(   a__       ):


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

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

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


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


    def        __a							(					self   ):
          self.enable_attention_slicing(a   )


    def        __a							(					self   ):
          set_requires_grad(self.vae  , a   )


    def        __a							(					self   ):
          set_requires_grad(self.vae  , a   )


    def        __a							(					self   ):
          set_requires_grad(self.unet  , a   )


    def        __a							(					self   ):
          set_requires_grad(self.unet  , a   )


    @torch.enable_grad()
    def        __a							(					self  , a  , a  , a  , a  , a  , a  , a  , a  , a=True  , ):
          UpperCamelCase__  =    latents.detach().requires_grad_()

          UpperCamelCase__  =    self.scheduler.scale_model_input(a  , a   )

          # predict the noise residual
          UpperCamelCase__  =    self.unet(a  , a  , encoder_hidden_states=a   ).sample

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

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

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

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

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

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

          UpperCamelCase__  =    -torch.autograd.grad(a  , a   )[0]

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


    @torch.no_grad()
    def __call__(					self  , a  , a = 5_12  , a = 5_12  , a = 50  , a = 7.5  , a = 1  , a = 0.0  , a = 1_00  , a = None  , a = 4  , a = True  , a = None  , a = None  , a = "pil"  , a = True  , ):
          if isinstance(a  , a   ):
                UpperCamelCase__  =    1
          elif isinstance(a  , a   ):
                UpperCamelCase__  =    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
          UpperCamelCase__  =    self.tokenizer(
              a  , padding="max_length"  , max_length=self.tokenizer.model_max_length  , truncation=a  , return_tensors="pt"  , )
          UpperCamelCase__  =    self.text_encoder(text_input.input_ids.to(self.device   )   )[0]
          # duplicate text embeddings for each generation per prompt
          UpperCamelCase__  =    text_embeddings.repeat_interleave(a  , dim=0   )

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

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

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

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

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

                # predict the noise residual
                UpperCamelCase__  =    self.unet(a  , a  , encoder_hidden_states=a   ).sample

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

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

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

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

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

          if output_type == "pil":
                UpperCamelCase__  =    self.numpy_to_pil(a   )

          if not return_dict:
                return (image, None)

          return StableDiffusionPipelineOutput(images=a  , nsfw_content_detected=a   )









'''simple docstring'''







# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

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


a__  : Any				=	{
    'configuration_tvlt': ['TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TvltConfig'],
    'feature_extraction_tvlt': ['TvltFeatureExtractor'],
    'processing_tvlt': ['TvltProcessor'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__  : int				=	[
      'TVLT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TvltModel',
      'TvltForPreTraining',
      'TvltForAudioVisualClassification',
      'TvltPreTrainedModel',
  ]

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__  : List[Any]				=	['TvltImageProcessor']


if TYPE_CHECKING:
  from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
  from .processing_tvlt import TvltProcessor
  from .feature_extraction_tvlt import TvltFeatureExtractor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tvlt import (
        TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TvltForAudioVisualClassification,
        TvltForPreTraining,
        TvltModel,
        TvltPreTrainedModel,
    )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_tvlt import TvltImageProcessor


else:
  import sys

  a__  : Any				=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





