


'''simple docstring'''
from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
)


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_pt_objects import *  # noqa F403
else:
     from .consistency_models import ConsistencyModelPipeline
     from .dance_diffusion import DanceDiffusionPipeline
     from .ddim import DDIMPipeline
     from .ddpm import DDPMPipeline
     from .dit import DiTPipeline
     from .latent_diffusion import LDMSuperResolutionPipeline
     from .latent_diffusion_uncond import LDMPipeline
     from .pipeline_utils import AudioPipelineOutput, DiffusionPipeline, ImagePipelineOutput
     from .pndm import PNDMPipeline
     from .repaint import RePaintPipeline
     from .score_sde_ve import ScoreSdeVePipeline
     from .stochastic_karras_ve import KarrasVePipeline

try:
     if not (is_torch_available() and is_librosa_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
     from .audio_diffusion import AudioDiffusionPipeline, Mel

try:
     if not (is_torch_available() and is_transformers_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
     from .alt_diffusion import AltDiffusionImgaImgPipeline, AltDiffusionPipeline
     from .audioldm import AudioLDMPipeline
     from .controlnet import (
         StableDiffusionControlNetImgaImgPipeline,
         StableDiffusionControlNetInpaintPipeline,
         StableDiffusionControlNetPipeline,
     )
     from .deepfloyd_if import (
         IFImgaImgPipeline,
         IFImgaImgSuperResolutionPipeline,
         IFInpaintingPipeline,
         IFInpaintingSuperResolutionPipeline,
         IFPipeline,
         IFSuperResolutionPipeline,
     )
     from .kandinsky import (
         KandinskyImgaImgPipeline,
         KandinskyInpaintPipeline,
         KandinskyPipeline,
         KandinskyPriorPipeline,
     )
     from .kandinskya_a import (
         KandinskyVaaControlnetImgaImgPipeline,
         KandinskyVaaControlnetPipeline,
         KandinskyVaaImgaImgPipeline,
         KandinskyVaaInpaintPipeline,
         KandinskyVaaPipeline,
         KandinskyVaaPriorEmbaEmbPipeline,
         KandinskyVaaPriorPipeline,
     )
     from .latent_diffusion import LDMTextToImagePipeline
     from .paint_by_example import PaintByExamplePipeline
     from .semantic_stable_diffusion import SemanticStableDiffusionPipeline
     from .shap_e import ShapEImgaImgPipeline, ShapEPipeline
     from .stable_diffusion import (
         CycleDiffusionPipeline,
         StableDiffusionAttendAndExcitePipeline,
         StableDiffusionDepthaImgPipeline,
         StableDiffusionDiffEditPipeline,
         StableDiffusionImageVariationPipeline,
         StableDiffusionImgaImgPipeline,
         StableDiffusionInpaintPipeline,
         StableDiffusionInpaintPipelineLegacy,
         StableDiffusionInstructPixaPixPipeline,
         StableDiffusionLatentUpscalePipeline,
         StableDiffusionLDMaDPipeline,
         StableDiffusionModelEditingPipeline,
         StableDiffusionPanoramaPipeline,
         StableDiffusionParadigmsPipeline,
         StableDiffusionPipeline,
         StableDiffusionPixaPixZeroPipeline,
         StableDiffusionSAGPipeline,
         StableDiffusionUpscalePipeline,
         StableUnCLIPImgaImgPipeline,
         StableUnCLIPPipeline,
     )
     from .stable_diffusion_safe import StableDiffusionPipelineSafe
     from .text_to_video_synthesis import TextToVideoSDPipeline, TextToVideoZeroPipeline, VideoToVideoSDPipeline
     from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline
     from .unidiffuser import ImageTextPipelineOutput, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder
     from .versatile_diffusion import (
         VersatileDiffusionDualGuidedPipeline,
         VersatileDiffusionImageVariationPipeline,
         VersatileDiffusionPipeline,
         VersatileDiffusionTextToImagePipeline,
     )
     from .vq_diffusion import VQDiffusionPipeline


try:
     if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
     from .stable_diffusion_xl import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
     if not is_onnx_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_onnx_objects import *  # noqa F403
else:
     from .onnx_utils import OnnxRuntimeModel

try:
     if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
     from .stable_diffusion import (
         OnnxStableDiffusionImgaImgPipeline,
         OnnxStableDiffusionInpaintPipeline,
         OnnxStableDiffusionInpaintPipelineLegacy,
         OnnxStableDiffusionPipeline,
         OnnxStableDiffusionUpscalePipeline,
         StableDiffusionOnnxPipeline,
     )

try:
     if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
     from .stable_diffusion import StableDiffusionKDiffusionPipeline

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_flax_objects import *  # noqa F403
else:
     from .pipeline_flax_utils import FlaxDiffusionPipeline


try:
     if not (is_flax_available() and is_transformers_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
     from .controlnet import FlaxStableDiffusionControlNetPipeline
     from .stable_diffusion import (
         FlaxStableDiffusionImgaImgPipeline,
         FlaxStableDiffusionInpaintPipeline,
         FlaxStableDiffusionPipeline,
     )
try:
     if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
     from .spectrogram_diffusion import MidiProcessor, SpectrogramDiffusionPipeline










'''simple docstring'''





import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a		:	Dict       =      logging.get_logger(__name__)

a		:	Optional[int]       =      {
    '''facebook/hubert-base-ls960''': '''https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json''',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}




class      UpperCamelCase       (	snake_case__       ):
  a     :int          =					'hubert'



  def __init__(			self		,     __lowerCAmelCase=32		,     __lowerCAmelCase=768		,     __lowerCAmelCase=12		,     __lowerCAmelCase=12		,     __lowerCAmelCase=3072		,     __lowerCAmelCase="gelu"		,     __lowerCAmelCase=0.1		,     __lowerCAmelCase=0.1		,     __lowerCAmelCase=0.1		,     __lowerCAmelCase=True		,     __lowerCAmelCase=0.0		,     __lowerCAmelCase=0.1		,     __lowerCAmelCase=0.1		,     __lowerCAmelCase=0.02		,     __lowerCAmelCase=1e-5		,     __lowerCAmelCase="group"		,     __lowerCAmelCase="gelu"		,     __lowerCAmelCase=(512, 512, 512, 512, 512, 512, 512)		,     __lowerCAmelCase=(5, 2, 2, 2, 2, 2, 2)		,     __lowerCAmelCase=(10, 3, 3, 3, 3, 2, 2)		,     __lowerCAmelCase=False		,     __lowerCAmelCase=128		,     __lowerCAmelCase=16		,     __lowerCAmelCase=False		,     __lowerCAmelCase=True		,     __lowerCAmelCase=0.05		,     __lowerCAmelCase=10		,     __lowerCAmelCase=2		,     __lowerCAmelCase=0.0		,     __lowerCAmelCase=10		,     __lowerCAmelCase=0		,     __lowerCAmelCase="sum"		,     __lowerCAmelCase=False		,     __lowerCAmelCase=False		,     __lowerCAmelCase=256		,     __lowerCAmelCase=0		,     __lowerCAmelCase=1		,     __lowerCAmelCase=2		,     **__lowerCAmelCase		,     ):







      '''simple docstring'''


      super().__init__(**__lowerCAmelCase		,     pad_token_id=__lowerCAmelCase		,     bos_token_id=__lowerCAmelCase		,     eos_token_id=__lowerCAmelCase )
      _a				:							List[Any]							       =      hidden_size
      _a				:							Union[str, Any]							       =      feat_extract_norm
      _a				:							Any							       =      feat_extract_activation
      _a				:							Tuple							       =      list(__lowerCAmelCase )
      _a				:							List[Any]							       =      list(__lowerCAmelCase )
      _a				:							Any							       =      list(__lowerCAmelCase )
      _a				:							Optional[int]							       =      conv_bias
      _a				:							Tuple							       =      num_conv_pos_embeddings
      _a				:							Optional[int]							       =      num_conv_pos_embedding_groups
      _a				:							Tuple							       =      len(self.conv_dim )
      _a				:							int							       =      num_hidden_layers
      _a				:							Optional[Any]							       =      intermediate_size
      _a				:							Dict							       =      hidden_act
      _a				:							Tuple							       =      num_attention_heads
      _a				:							Tuple							       =      hidden_dropout
      _a				:							str							       =      attention_dropout
      _a				:							Union[str, Any]							       =      activation_dropout
      _a				:							int							       =      feat_proj_layer_norm
      _a				:							List[Any]							       =      feat_proj_dropout
      _a				:							Any							       =      final_dropout
      _a				:							Dict							       =      layerdrop
      _a				:							List[str]							       =      layer_norm_eps
      _a				:							Any							       =      initializer_range
      _a				:							List[str]							       =      vocab_size
      _a				:							int							       =      do_stable_layer_norm
      _a				:							str							       =      use_weighted_layer_sum
      _a				:							Optional[Any]							       =      classifier_proj_size

      if (
          (len(self.conv_stride ) != self.num_feat_extract_layers)
          or (len(self.conv_kernel ) != self.num_feat_extract_layers)
          or (len(self.conv_dim ) != self.num_feat_extract_layers)
      ):
          raise ValueError(
              """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
              """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
              f''' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'''
              f''' `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )

      # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
      _a				:							str							       =      apply_spec_augment
      _a				:							List[Any]							       =      mask_time_prob
      _a				:							List[Any]							       =      mask_time_length
      _a				:							List[Any]							       =      mask_time_min_masks
      _a				:							Union[str, Any]							       =      mask_feature_prob
      _a				:							str							       =      mask_feature_length
      _a				:							Any							       =      mask_feature_min_masks

      # ctc loss
      _a				:							Tuple							       =      ctc_loss_reduction
      _a				:							Optional[int]							       =      ctc_zero_infinity



  @property
  def     a_							(			self ):







      '''simple docstring'''


      return functools.reduce(operator.mul		,     self.conv_stride		,     1 )


