



'''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__ :   List[str]				,				lowerCamelCase__ :   Optional[int]				,				lowerCamelCase__ :   List[str]					):


			'''simple docstring'''







			if isinstance(__snake_case				,				torch.Tensor					):
						return image
			elif isinstance(__snake_case				,				PIL.Image.Image					):
						A:   Union[str, Any]								= [image]

			if isinstance(image[0]				,				PIL.Image.Image					):
						A:   str								= [np.array(i.resize((w, h)				,				resample=PIL_INTERPOLATION["""lanczos"""]					)					)[None, :] for i in image]
						A:   Tuple								= np.concatenate(__snake_case				,				axis=0					)
						A:   Tuple								= np.array(__snake_case					).astype(np.floataa					) / 255.0
						A:   int								= image.transpose(0				,				3				,				1				,				2					)
						A:   Union[str, Any]								= 2.0 * image - 1.0
						A:   Any								= torch.from_numpy(__snake_case					)
			elif isinstance(image[0]				,				torch.Tensor					):
						A:   Union[str, Any]								= torch.cat(__snake_case				,				dim=0					)
			return image



def 		_SCREAMING_SNAKE_CASE				(					lowerCamelCase__ :   List[Any]				,				lowerCamelCase__ :   List[str]				,				lowerCamelCase__ :   Optional[Any]				,				lowerCamelCase__ :   Optional[Any]=0.99_95					):


			'''simple docstring'''







			if not isinstance(__snake_case				,				np.ndarray					):
						A:   Tuple								= True
						A:   Optional[Any]								= va.device
						A:   Union[str, Any]								= va.cpu().numpy()
						A:   Any								= va.cpu().numpy()

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

			if inputs_are_torch:
						A:   str								= torch.from_numpy(__snake_case					).to(__snake_case					)

			return va



def 		_SCREAMING_SNAKE_CASE				(					lowerCamelCase__ :   Union[str, Any]				,				lowerCamelCase__ :   int					):


			'''simple docstring'''







			A:   List[str]								= F.normalize(__snake_case				,				dim=-1					)
			A:   Optional[Any]								= F.normalize(__snake_case				,				dim=-1					)
			return (x - y).norm(dim=-1					).div(2					).arcsin().pow(2					).mul(2					)



def 		_SCREAMING_SNAKE_CASE				(					lowerCamelCase__ :   Dict				,				lowerCamelCase__ :   str					):


			'''simple docstring'''







			for param in model.parameters():
						A:   Dict								= value


class 	SCREAMING_SNAKE_CASE__					( _UpperCamelCase       ):
	"""simple docstring"""


	def __init__(		self       , A       , A       , A       , A       , A       , A       , A       , A=None       , A=None       , A=None       , )			->				Optional[Any]:
				super().__init__()
				self.register_modules(
				    vae=UpperCamelCase__       , text_encoder=UpperCamelCase__       , clip_model=UpperCamelCase__       , tokenizer=UpperCamelCase__       , unet=UpperCamelCase__       , scheduler=UpperCamelCase__       , feature_extractor=UpperCamelCase__       , coca_model=UpperCamelCase__       , coca_tokenizer=UpperCamelCase__       , coca_transform=UpperCamelCase__       , )
				A:   Union[str, Any]								= (
				    feature_extractor.size
				    if isinstance(feature_extractor.size       , UpperCamelCase__      )
				    else feature_extractor.size["""shortest_edge"""]
				)
				A:   List[str]								= transforms.Normalize(mean=feature_extractor.image_mean       , std=feature_extractor.image_std      )
				set_requires_grad(self.text_encoder       , UpperCamelCase__      )
				set_requires_grad(self.clip_model       , UpperCamelCase__      )


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


	def   a__			(		self      )			->				Tuple:
				self.enable_attention_slicing(UpperCamelCase__      )


	def   a__			(		self      )			->				Union[str, Any]:
				set_requires_grad(self.vae       , UpperCamelCase__      )


	def   a__			(		self      )			->				str:
				set_requires_grad(self.vae       , UpperCamelCase__      )


	def   a__			(		self      )			->				int:
				set_requires_grad(self.unet       , UpperCamelCase__      )


	def   a__			(		self      )			->				Any:
				set_requires_grad(self.unet       , UpperCamelCase__      )


	def   a__			(		self       , A       , A       , A      )			->				Dict:
				# get the original timestep using init_timestep
				A:   int								= min(int(num_inference_steps * strength      )       , UpperCamelCase__      )

				A:   Tuple								= max(num_inference_steps - init_timestep       , 0      )
				A:   List[Any]								= self.scheduler.timesteps[t_start:]

				return timesteps, num_inference_steps - t_start


	def   a__			(		self       , A       , A       , A       , A       , A       , A=None      )			->				Any:
				if not isinstance(UpperCamelCase__       , torch.Tensor      ):
							raise ValueError(f'`image` has to be of type `torch.Tensor` but is {type(UpperCamelCase__      )}'      )

				A:   List[Any]								= image.to(device=UpperCamelCase__       , dtype=UpperCamelCase__      )

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

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

				A:   Tuple								= randn_tensor(init_latents.shape       , generator=UpperCamelCase__       , device=UpperCamelCase__       , dtype=UpperCamelCase__      )

				# get latents
				A:   List[str]								= self.scheduler.add_noise(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__      )
				A:   List[Any]								= init_latents

				return latents


	def   a__			(		self       , A      )			->				List[str]:
				A:   Any								= self.coca_transform(UpperCamelCase__      ).unsqueeze(0      )
				with torch.no_grad(), torch.cuda.amp.autocast():
							A:   Union[str, Any]								= self.coca_model.generate(transformed_image.to(device=self.device       , dtype=self.coca_model.dtype      )      )
				A:   List[str]								= 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[str]:
				A:   str								= self.feature_extractor.preprocess(UpperCamelCase__      )
				A:   Optional[int]								= torch.from_numpy(clip_image_input["""pixel_values"""][0]      ).unsqueeze(0      ).to(self.device      ).half()
				A:   Any								= self.clip_model.get_image_features(UpperCamelCase__      )
				A:   Dict								= image_embeddings_clip / image_embeddings_clip.norm(p=2       , dim=-1       , keepdim=UpperCamelCase__      )
				A:   Union[str, Any]								= image_embeddings_clip.repeat_interleave(UpperCamelCase__       , dim=0      )
				return image_embeddings_clip


	@torch.enable_grad()
	def   a__			(		self       , A       , A       , A       , A       , A       , A       , A       , )			->				int:
				A:   Any								= latents.detach().requires_grad_()

				A:   str								= self.scheduler.scale_model_input(UpperCamelCase__       , UpperCamelCase__      )

				# predict the noise residual
				A:   Dict								= self.unet(UpperCamelCase__       , UpperCamelCase__       , encoder_hidden_states=UpperCamelCase__      ).sample

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

							A:   Dict								= torch.sqrt(UpperCamelCase__      )
							A:   str								= pred_original_sample * (fac) + latents * (1 - fac)
				elif isinstance(self.scheduler       , UpperCamelCase__      ):
							A:   List[str]								= self.scheduler.sigmas[index]
							A:   Optional[Any]								= 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:   Any								= 1 / 0.18215 * sample
				A:   Tuple								= self.vae.decode(UpperCamelCase__      ).sample
				A:   Union[str, Any]								= (image / 2 + 0.5).clamp(0       , 1      )

				A:   Dict								= transforms.Resize(self.feature_extractor_size      )(UpperCamelCase__      )
				A:   Dict								= self.normalize(UpperCamelCase__      ).to(latents.dtype      )

				A:   List[str]								= self.clip_model.get_image_features(UpperCamelCase__      )
				A:   List[str]								= image_embeddings_clip / image_embeddings_clip.norm(p=2       , dim=-1       , keepdim=UpperCamelCase__      )

				A:   int								= spherical_dist_loss(UpperCamelCase__       , UpperCamelCase__      ).mean() * clip_guidance_scale

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

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





	@torch.no_grad()
	def __call__(		self       , A       , A       , A = None       , A = None       , A = 5_12       , A = 5_12       , A = 0.6       , A = 50       , A = 7.5       , A = 1       , A = 0.0       , A = 1_00       , A = None       , A = "pil"       , A = True       , A = 0.8       , A = 0.1       , A = 0.1       , )			->				Tuple:
				if isinstance(UpperCamelCase__       , UpperCamelCase__      ) and len(UpperCamelCase__      ) != batch_size:
							raise ValueError(f'You have passed {batch_size} batch_size, but only {len(UpperCamelCase__      )} 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(UpperCamelCase__       , torch.Generator      ) and batch_size > 1:
							A:   Optional[Any]								= [generator] + [None] * (batch_size - 1)

				A:   Union[str, Any]								= [
				    ("""model""", self.coca_model is None),
				    ("""tokenizer""", self.coca_tokenizer is None),
				    ("""transform""", self.coca_transform is None),
				]
				A:   List[Any]								= [x[0] for x in coca_is_none if x[1]]
				A:   str								= """, """.join(UpperCamelCase__      )
				# generate prompts with coca model if prompt is None
				if content_prompt is None:
							if len(UpperCamelCase__      ):
										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:   str								= self.get_image_description(UpperCamelCase__      )
				if style_prompt is None:
							if len(UpperCamelCase__      ):
										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:   List[Any]								= self.get_image_description(UpperCamelCase__      )

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

				A:   Optional[int]								= self.tokenizer(
				    UpperCamelCase__       , padding="""max_length"""       , max_length=self.tokenizer.model_max_length       , truncation=UpperCamelCase__       , return_tensors="""pt"""       , )
				A:   str								= self.text_encoder(style_text_input.input_ids.to(self.device      )      )[0]

				A:   List[Any]								= slerp(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__      )

				# duplicate text embeddings for each generation per prompt
				A:   List[str]								= text_embeddings.repeat_interleave(UpperCamelCase__       , dim=0      )

				# set timesteps
				A:   Union[str, Any]								= """offset""" in set(inspect.signature(self.scheduler.set_timesteps      ).parameters.keys()      )
				A:   Tuple								= {}
				if accepts_offset:
							A:   Dict								= 1

				self.scheduler.set_timesteps(UpperCamelCase__       , **UpperCamelCase__      )
				# 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:   Tuple								= self.get_timesteps(UpperCamelCase__       , UpperCamelCase__       , self.device      )
				A:   List[Any]								= timesteps[:1].repeat(UpperCamelCase__      )

				# Preprocess image
				A:   Any								= preprocess(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__      )
				A:   str								= self.prepare_latents(
				    UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , text_embeddings.dtype       , self.device       , UpperCamelCase__      )

				A:   List[str]								= preprocess(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__      )
				A:   Dict								= self.prepare_latents(
				    UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , text_embeddings.dtype       , self.device       , UpperCamelCase__      )

				A:   List[str]								= slerp(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__      )

				if clip_guidance_scale > 0:
							A:   Union[str, Any]								= self.get_clip_image_embeddings(UpperCamelCase__       , UpperCamelCase__      )
							A:   List[str]								= self.get_clip_image_embeddings(UpperCamelCase__       , UpperCamelCase__      )
							A:   Optional[int]								= slerp(
							    UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__      )

				# 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:   Optional[Any]								= guidance_scale > 1.0
				# get unconditional embeddings for classifier free guidance
				if do_classifier_free_guidance:
							A:   Tuple								= content_text_input.input_ids.shape[-1]
							A:   Any								= self.tokenizer([""""""]       , padding="""max_length"""       , max_length=UpperCamelCase__       , return_tensors="""pt"""      )
							A:   Union[str, Any]								= self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]
							# duplicate unconditional embeddings for each generation per prompt
							A:   List[str]								= uncond_embeddings.repeat_interleave(UpperCamelCase__       , 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:   Any								= 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:   Any								= (batch_size, self.unet.config.in_channels, height // 8, width // 8)
				A:   Optional[int]								= text_embeddings.dtype
				if latents is None:
							if self.device.type == "mps":
										# randn does not work reproducibly on mps
										A:   Dict								= torch.randn(UpperCamelCase__       , generator=UpperCamelCase__       , device="""cpu"""       , dtype=UpperCamelCase__      ).to(
										    self.device      )
							else:
										A:   Optional[Any]								= torch.randn(UpperCamelCase__       , generator=UpperCamelCase__       , device=self.device       , dtype=UpperCamelCase__      )
				else:
							if latents.shape != latents_shape:
										raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'      )
							A:   List[Any]								= latents.to(self.device      )

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

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

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

										# predict the noise residual
										A:   str								= self.unet(UpperCamelCase__       , UpperCamelCase__       , encoder_hidden_states=UpperCamelCase__      ).sample

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

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

										# compute the previous noisy sample x_t -> x_t-1
										A:   Optional[int]								= self.scheduler.step(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , **UpperCamelCase__      ).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
				A:   Tuple								= 1 / 0.18215 * latents
				A:   List[str]								= self.vae.decode(UpperCamelCase__      ).sample

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

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

				if not return_dict:
							return (image, None)

				return StableDiffusionPipelineOutput(images=UpperCamelCase__       , nsfw_content_detected=UpperCamelCase__      )






'''simple docstring'''






from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)



