"""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 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: int    ,_lowerCamelCase		: Dict    ,_lowerCamelCase		: Optional[int]			) ->   str:
  if isinstance(_lowerCamelCase    ,torch.Tensor			):
    return image
  elif isinstance(_lowerCamelCase    ,PIL.Image.Image			):
    _lowerCAmelCase		: List[Any]						    =  [image]

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





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: int    ,_lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: str    ,_lowerCamelCase		: Any=0.99_95			) ->   List[str]:
  if not isinstance(_lowerCamelCase    ,np.ndarray			):
    _lowerCAmelCase		: Optional[Any]						    =  True
    _lowerCAmelCase		: Dict						    =  va.device
    _lowerCAmelCase		: Optional[Any]						    =  va.cpu().numpy()
    _lowerCAmelCase		: Optional[int]						    =  va.cpu().numpy()

  _lowerCAmelCase		: Tuple						    =  np.sum(va * va / (np.linalg.norm(_lowerCamelCase			) * np.linalg.norm(_lowerCamelCase			))			)
  if np.abs(_lowerCamelCase			) > DOT_THRESHOLD:
    _lowerCAmelCase		: List[Any]						    =  (1 - t) * va + t * va
  else:
    _lowerCAmelCase		: Tuple						    =  np.arccos(_lowerCamelCase			)
    _lowerCAmelCase		: int						    =  np.sin(_lowerCamelCase			)
    _lowerCAmelCase		: Union[str, Any]						    =  theta_a * t
    _lowerCAmelCase		: int						    =  np.sin(_lowerCamelCase			)
    _lowerCAmelCase		: Tuple						    =  np.sin(theta_a - theta_t			) / sin_theta_a
    _lowerCAmelCase		: str						    =  sin_theta_t / sin_theta_a
    _lowerCAmelCase		: str						    =  sa * va + sa * va

  if inputs_are_torch:
    _lowerCAmelCase		: str						    =  torch.from_numpy(_lowerCamelCase			).to(_lowerCamelCase			)

  return va





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: List[Any]    ,_lowerCamelCase		: Optional[Any]			) ->   Union[str, Any]:
  _lowerCAmelCase		: Union[str, Any]						    =  F.normalize(_lowerCamelCase    ,dim=-1			)
  _lowerCAmelCase		: List[str]						    =  F.normalize(_lowerCamelCase    ,dim=-1			)
  return (x - y).norm(dim=-1			).div(2			).arcsin().pow(2			).mul(2			)





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: List[Any]    ,_lowerCamelCase		: Optional[int]			) ->   Dict:
  for param in model.parameters():
    _lowerCAmelCase		: Optional[Any]						    =  value






class 		__A							(				SCREAMING_SNAKE_CASE_	):






     def __init__(	self       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__=None       ,  a__=None       ,  a__=None       ,  ):
       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__       ,  )
       _lowerCAmelCase		: Dict						    =  (
           feature_extractor.size
           if isinstance(feature_extractor.size       ,  a__						)
           else feature_extractor.size["""shortest_edge"""]
       )
       _lowerCAmelCase		: Optional[int]						    =  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"						):
       if slice_size == "auto":
         # half the attention head size is usually a good trade-off between
         # speed and memory
         _lowerCAmelCase		: Any						    =  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__						)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__						):
       # get the original timestep using init_timestep
       _lowerCAmelCase		: List[str]						    =  min(int(num_inference_steps * strength						)       ,  a__						)

       _lowerCAmelCase		: str						    =  max(num_inference_steps - init_timestep       ,  0						)
       _lowerCAmelCase		: Optional[int]						    =  self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__=None						):
       if not isinstance(a__       ,  torch.Tensor						):
         raise ValueError(F"`image` has to be of type `torch.Tensor` but is {type(a__						)}"						)

       _lowerCAmelCase		: Any						    =  image.to(device=a__       ,  dtype=a__						)

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

       # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
       _lowerCAmelCase		: List[str]						    =  0.1_8_2_1_5 * init_latents
       _lowerCAmelCase		: Optional[int]						    =  init_latents.repeat_interleave(a__       ,  dim=0						)

       _lowerCAmelCase		: Union[str, Any]						    =  randn_tensor(init_latents.shape       ,  generator=a__       ,  device=a__       ,  dtype=a__						)

       # get latents
       _lowerCAmelCase		: Optional[int]						    =  self.scheduler.add_noise(a__       ,  a__       ,  a__						)
       _lowerCAmelCase		: int						    =  init_latents

       return latents






     def 					__A						(	self       ,  a__						):
       _lowerCAmelCase		: List[str]						    =  self.coca_transform(a__						).unsqueeze(0						)
       with torch.no_grad(), torch.cuda.amp.autocast():
         _lowerCAmelCase		: str						    =  self.coca_model.generate(transformed_image.to(device=self.device       ,  dtype=self.coca_model.dtype						)						)
       _lowerCAmelCase		: Optional[Any]						    =  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__						):
       _lowerCAmelCase		: str						    =  self.feature_extractor.preprocess(a__						)
       _lowerCAmelCase		: str						    =  torch.from_numpy(clip_image_input["""pixel_values"""][0]						).unsqueeze(0						).to(self.device						).half()
       _lowerCAmelCase		: Tuple						    =  self.clip_model.get_image_features(a__						)
       _lowerCAmelCase		: Dict						    =  image_embeddings_clip / image_embeddings_clip.norm(p=2       ,  dim=-1       ,  keepdim=a__						)
       _lowerCAmelCase		: Optional[int]						    =  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__       ,  ):
       _lowerCAmelCase		: str						    =  latents.detach().requires_grad_()

       _lowerCAmelCase		: List[str]						    =  self.scheduler.scale_model_input(a__       ,  a__						)

       # predict the noise residual
       _lowerCAmelCase		: Any						    =  self.unet(a__       ,  a__       ,  encoder_hidden_states=a__						).sample

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

         _lowerCAmelCase		: Union[str, Any]						    =  torch.sqrt(a__						)
         _lowerCAmelCase		: int						    =  pred_original_sample * (fac) + latents * (1 - fac)
       elif isinstance(self.scheduler       ,  a__						):
         _lowerCAmelCase		: Optional[Any]						    =  self.scheduler.sigmas[index]
         _lowerCAmelCase		: Dict						    =  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
       _lowerCAmelCase		: Union[str, Any]						    =  1 / 0.1_8_2_1_5 * sample
       _lowerCAmelCase		: int						    =  self.vae.decode(a__						).sample
       _lowerCAmelCase		: int						    =  (image / 2 + 0.5).clamp(0       ,  1						)

       _lowerCAmelCase		: Any						    =  transforms.Resize(self.feature_extractor_size						)(a__						)
       _lowerCAmelCase		: str						    =  self.normalize(a__						).to(latents.dtype						)

       _lowerCAmelCase		: Union[str, Any]						    =  self.clip_model.get_image_features(a__						)
       _lowerCAmelCase		: Optional[int]						    =  image_embeddings_clip / image_embeddings_clip.norm(p=2       ,  dim=-1       ,  keepdim=a__						)

       _lowerCAmelCase		: Optional[Any]						    =  spherical_dist_loss(a__       ,  a__						).mean() * clip_guidance_scale

       _lowerCAmelCase		: Union[str, Any]						    =  -torch.autograd.grad(a__       ,  a__						)[0]

       if isinstance(self.scheduler       ,  a__						):
         _lowerCAmelCase		: Any						    =  latents.detach() + grads * (sigma**2)
         _lowerCAmelCase		: Dict						    =  noise_pred_original
       else:
         _lowerCAmelCase		: Optional[int]						    =  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       ,  ):
       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:
         _lowerCAmelCase		: Any						    =  [generator] + [None] * (batch_size - 1)

       _lowerCAmelCase		: Tuple						    =  [
           ("""model""", self.coca_model is None),
           ("""tokenizer""", self.coca_tokenizer is None),
           ("""transform""", self.coca_transform is None),
       ]
       _lowerCAmelCase		: Tuple						    =  [x[0] for x in coca_is_none if x[1]]
       _lowerCAmelCase		: Optional[int]						    =  """, """.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."						)
         _lowerCAmelCase		: Optional[int]						    =  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."						)
         _lowerCAmelCase		: str						    =  self.get_image_description(a__						)

       # get prompt text embeddings for content and style
       _lowerCAmelCase		: Any						    =  self.tokenizer(
           a__       ,  padding="""max_length"""       ,  max_length=self.tokenizer.model_max_length       ,  truncation=a__       ,  return_tensors="""pt"""       ,  )
       _lowerCAmelCase		: Optional[Any]						    =  self.text_encoder(content_text_input.input_ids.to(self.device						)						)[0]

       _lowerCAmelCase		: Tuple						    =  self.tokenizer(
           a__       ,  padding="""max_length"""       ,  max_length=self.tokenizer.model_max_length       ,  truncation=a__       ,  return_tensors="""pt"""       ,  )
       _lowerCAmelCase		: str						    =  self.text_encoder(style_text_input.input_ids.to(self.device						)						)[0]

       _lowerCAmelCase		: Optional[Any]						    =  slerp(a__       ,  a__       ,  a__						)

       # duplicate text embeddings for each generation per prompt
       _lowerCAmelCase		: Union[str, Any]						    =  text_embeddings.repeat_interleave(a__       ,  dim=0						)

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

       _lowerCAmelCase    ,			_lowerCAmelCase		: str						    =  self.get_timesteps(a__       ,  a__       ,  self.device						)
       _lowerCAmelCase		: str						    =  timesteps[:1].repeat(a__						)

       # Preprocess image
       _lowerCAmelCase		: Optional[Any]						    =  preprocess(a__       ,  a__       ,  a__						)
       _lowerCAmelCase		: List[str]						    =  self.prepare_latents(
           a__       ,  a__       ,  a__       ,  text_embeddings.dtype       ,  self.device       ,  a__						)

       _lowerCAmelCase		: Tuple						    =  preprocess(a__       ,  a__       ,  a__						)
       _lowerCAmelCase		: Any						    =  self.prepare_latents(
           a__       ,  a__       ,  a__       ,  text_embeddings.dtype       ,  self.device       ,  a__						)

       _lowerCAmelCase		: Union[str, Any]						    =  slerp(a__       ,  a__       ,  a__						)

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

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

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

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

           # predict the noise residual
           _lowerCAmelCase		: Tuple						    =  self.unet(a__       ,  a__       ,  encoder_hidden_states=a__						).sample

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

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

           # compute the previous noisy sample x_t -> x_t-1
           _lowerCAmelCase		: List[str]						    =  self.scheduler.step(a__       ,  a__       ,  a__       ,  **a__						).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
       _lowerCAmelCase		: int						    =  1 / 0.1_8_2_1_5 * latents
       _lowerCAmelCase		: Dict						    =  self.vae.decode(a__						).sample

       _lowerCAmelCase		: Dict						    =  (image / 2 + 0.5).clamp(0       ,  1						)
       _lowerCAmelCase		: List[Any]						    =  image.cpu().permute(0       ,  2       ,  3       ,  1						).numpy()

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

       if not return_dict:
         return (image, None)

       return StableDiffusionPipelineOutput(images=a__       ,  nsfw_content_detected=a__						)





"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_a   :	Tuple 					=     {'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a   :	str 					=     [
         'WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST',
         'WavLMForAudioFrameClassification',
         'WavLMForCTC',
         'WavLMForSequenceClassification',
         'WavLMForXVector',
         'WavLMModel',
         'WavLMPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_wavlm import (
              WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              WavLMForAudioFrameClassification,
              WavLMForCTC,
              WavLMForSequenceClassification,
              WavLMForXVector,
              WavLMModel,
              WavLMPreTrainedModel,
          )

else:
     import sys

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




