



'''simple docstring'''





import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    "The `inpainting.py` script is outdated. Please use directly `from diffusers import"
    " StableDiffusionInpaintPipeline` instead."
)








'''simple docstring'''





import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



     if isinstance(__A    ,      torch.Tensor):
          return image
     elif isinstance(__A    ,      PIL.Image.Image):
          _a           =    [image]

     if isinstance(image[0]    ,      PIL.Image.Image):
          _a           =    [np.array(i.resize((w, h)    ,      resample=PIL_INTERPOLATION['''lanczos''']))[None, :] for i in image]
          _a           =    np.concatenate(__A    ,      axis=0)
          _a           =    np.array(__A).astype(np.floataa) / 2_55.0
          _a           =    image.transpose(0    ,      3    ,      1    ,      2)
          _a           =    2.0 * image - 1.0
          _a           =    torch.from_numpy(__A)
     elif isinstance(image[0]    ,      torch.Tensor):
          _a           =    torch.cat(__A    ,      dim=0)
     return image




def lowerCAmelCase    (__A    ,      __A    ,      __A    ,      __A=0.99_95):



     """simple docstring"""



     if not isinstance(__A    ,      np.ndarray):
          _a           =    True
          _a           =    va.device
          _a           =    va.cpu().numpy()
          _a           =    va.cpu().numpy()

     _a           =    np.sum(va * va / (np.linalg.norm(__A) * np.linalg.norm(__A)))
     if np.abs(__A) > DOT_THRESHOLD:
          _a           =    (1 - t) * va + t * va
     else:
          _a           =    np.arccos(__A)
          _a           =    np.sin(__A)
          _a           =    theta_a * t
          _a           =    np.sin(__A)
          _a           =    np.sin(theta_a - theta_t) / sin_theta_a
          _a           =    sin_theta_t / sin_theta_a
          _a           =    sa * va + sa * va

     if inputs_are_torch:
          _a           =    torch.from_numpy(__A).to(__A)

     return va




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



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




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



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






class __A     (    A		):



 '''simple docstring'''







 def __init__(self					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  A=None					,  A=None					,  A=None					,  )       ->	str:


      """simple docstring"""



      super().__init__()
      self.register_modules(
          vae=A					,  text_encoder=A					,  clip_model=A					,  tokenizer=A					,  unet=A					,  scheduler=A					,  feature_extractor=A					,  coca_model=A					,  coca_tokenizer=A					,  coca_transform=A					,  )
      _a           =    (
          feature_extractor.size
          if isinstance(feature_extractor.size					,  A			)
          else feature_extractor.size['''shortest_edge''']
      )
      _a           =    transforms.Normalize(mean=feature_extractor.image_mean					,  std=feature_extractor.image_std			)
      set_requires_grad(self.text_encoder					,  A			)
      set_requires_grad(self.clip_model					,  A			)



 def    a__    (self					,  A = "auto"			)       ->	Union[str, Any]:


      """simple docstring"""



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



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      self.enable_attention_slicing(A			)



 def    a__    (self			)       ->	int:


      """simple docstring"""



      set_requires_grad(self.vae					,  A			)



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


      """simple docstring"""



      set_requires_grad(self.vae					,  A			)



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      set_requires_grad(self.unet					,  A			)



 def    a__    (self			)       ->	str:


      """simple docstring"""



      set_requires_grad(self.unet					,  A			)



 def    a__    (self					,  A					,  A					,  A			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    min(int(num_inference_steps * strength			)					,  A			)

      _a           =    max(num_inference_steps - init_timestep					,  0			)
      _a           =    self.scheduler.timesteps[t_start:]

      return timesteps, num_inference_steps - t_start



 def    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A=None			)       ->	List[str]:


      """simple docstring"""



      if not isinstance(A					,  torch.Tensor			):
           raise ValueError(f'''`image` has to be of type `torch.Tensor` but is {type(A			)}'''			)

      _a           =    image.to(device=A					,  dtype=A			)

      if isinstance(A					,  A			):
           _a           =    [
               self.vae.encode(image[i : i + 1]			).latent_dist.sample(generator[i]			) for i in range(A			)
           ]
           _a           =    torch.cat(A					,  dim=0			)
      else:
           _a           =    self.vae.encode(A			).latent_dist.sample(A			)

      # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
      _a           =    0.18215 * init_latents
      _a           =    init_latents.repeat_interleave(A					,  dim=0			)

      _a           =    randn_tensor(init_latents.shape					,  generator=A					,  device=A					,  dtype=A			)

      # get latents
      _a           =    self.scheduler.add_noise(A					,  A					,  A			)
      _a           =    init_latents

      return latents



 def    a__    (self					,  A			)       ->	Tuple:


      """simple docstring"""



      _a           =    self.coca_transform(A			).unsqueeze(0			)
      with torch.no_grad(), torch.cuda.amp.autocast():
           _a           =    self.coca_model.generate(transformed_image.to(device=self.device					,  dtype=self.coca_model.dtype			)			)
      _a           =    self.coca_tokenizer.decode(generated[0].cpu().numpy()			)
      return generated.split('''<end_of_text>'''			)[0].replace('''<start_of_text>'''					,  ''''''			).rstrip(''' .,'''			)



 def    a__    (self					,  A					,  A			)       ->	List[Any]:


      """simple docstring"""



      _a           =    self.feature_extractor.preprocess(A			)
      _a           =    torch.from_numpy(clip_image_input['''pixel_values'''][0]			).unsqueeze(0			).to(self.device			).half()
      _a           =    self.clip_model.get_image_features(A			)
      _a           =    image_embeddings_clip / image_embeddings_clip.norm(p=2					,  dim=-1					,  keepdim=A			)
      _a           =    image_embeddings_clip.repeat_interleave(A					,  dim=0			)
      return image_embeddings_clip



 @torch.enable_grad()
 def    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  )       ->	Union[str, Any]:


      """simple docstring"""



      _a           =    latents.detach().requires_grad_()

      _a           =    self.scheduler.scale_model_input(A					,  A			)

      # predict the noise residual
      _a           =    self.unet(A					,  A					,  encoder_hidden_states=A			).sample

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

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

      # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
      _a           =    1 / 0.18215 * sample
      _a           =    self.vae.decode(A			).sample
      _a           =    (image / 2 + 0.5).clamp(0					,  1			)

      _a           =    transforms.Resize(self.feature_extractor_size			)(A			)
      _a           =    self.normalize(A			).to(latents.dtype			)

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

      _a           =    spherical_dist_loss(A					,  A			).mean() * clip_guidance_scale

      _a           =    -torch.autograd.grad(A					,  A			)[0]

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



 @torch.no_grad()
 def __call__(self					,  A					,  A					,  A = None					,  A = None					,  A = 512					,  A = 512					,  A = 0.6					,  A = 50					,  A = 7.5					,  A = 1					,  A = 0.0					,  A = 100					,  A = None					,  A = "pil"					,  A = True					,  A = 0.8					,  A = 0.1					,  A = 0.1					,  )       ->	str:


      """simple docstring"""



      if isinstance(A					,  A			) and len(A			) != batch_size:
           raise ValueError(f'''You have passed {batch_size} batch_size, but only {len(A			)} generators.'''			)

      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}.'''			)

      if isinstance(A					,  torch.Generator			) and batch_size > 1:
           _a           =    [generator] + [None] * (batch_size - 1)

      _a           =    [
          ('''model''', self.coca_model is None),
          ('''tokenizer''', self.coca_tokenizer is None),
          ('''transform''', self.coca_transform is None),
      ]
      _a           =    [x[0] for x in coca_is_none if x[1]]
      _a           =    ''', '''.join(A			)
      # generate prompts with coca model if prompt is None
      if content_prompt is None:
           if len(A			):
                raise ValueError(
                    f'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
                    f'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''			)
           _a           =    self.get_image_description(A			)
      if style_prompt is None:
           if len(A			):
                raise ValueError(
                    f'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
                    f''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''			)
           _a           =    self.get_image_description(A			)

      # get prompt text embeddings for content and style
      _a           =    self.tokenizer(
          A					,  padding='''max_length'''					,  max_length=self.tokenizer.model_max_length					,  truncation=A					,  return_tensors='''pt'''					,  )
      _a           =    self.text_encoder(content_text_input.input_ids.to(self.device			)			)[0]

      _a           =    self.tokenizer(
          A					,  padding='''max_length'''					,  max_length=self.tokenizer.model_max_length					,  truncation=A					,  return_tensors='''pt'''					,  )
      _a           =    self.text_encoder(style_text_input.input_ids.to(self.device			)			)[0]

      _a           =    slerp(A					,  A					,  A			)

      # duplicate text embeddings for each generation per prompt
      _a           =    text_embeddings.repeat_interleave(A					,  dim=0			)

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

      _a    ,       _a           =    self.get_timesteps(A					,  A					,  self.device			)
      _a           =    timesteps[:1].repeat(A			)

      # Preprocess image
      _a           =    preprocess(A					,  A					,  A			)
      _a           =    self.prepare_latents(
          A					,  A					,  A					,  text_embeddings.dtype					,  self.device					,  A			)

      _a           =    preprocess(A					,  A					,  A			)
      _a           =    self.prepare_latents(
          A					,  A					,  A					,  text_embeddings.dtype					,  self.device					,  A			)

      _a           =    slerp(A					,  A					,  A			)

      if clip_guidance_scale > 0:
           _a           =    self.get_clip_image_embeddings(A					,  A			)
           _a           =    self.get_clip_image_embeddings(A					,  A			)
           _a           =    slerp(
               A					,  A					,  A			)

      # 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           =    guidance_scale > 1.0
      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance:
           _a           =    content_text_input.input_ids.shape[-1]
           _a           =    self.tokenizer(['''''']					,  padding='''max_length'''					,  max_length=A					,  return_tensors='''pt'''			)
           _a           =    self.text_encoder(uncond_input.input_ids.to(self.device			)			)[0]
           # duplicate unconditional embeddings for each generation per prompt
           _a           =    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           =    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           =    (batch_size, self.unet.config.in_channels, height // 8, width // 8)
      _a           =    text_embeddings.dtype
      if latents is None:
           if self.device.type == "mps":
                # randn does not work reproducibly on mps
                _a           =    torch.randn(A					,  generator=A					,  device='''cpu'''					,  dtype=A			).to(
                    self.device			)
           else:
                _a           =    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           =    latents.to(self.device			)

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

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

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

                # predict the noise residual
                _a           =    self.unet(A					,  A					,  encoder_hidden_states=A			).sample

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

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

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

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
      _a           =    1 / 0.18215 * latents
      _a           =    self.vae.decode(A			).sample

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

      if output_type == "pil":
           _a           =    self.numpy_to_pil(A			)

      if not return_dict:
           return (image, None)

      return StableDiffusionPipelineOutput(images=A					,  nsfw_content_detected=A			)



