code: 
  
  
  '''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
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  from typing import Callable, Optional, Union
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  A__	:      Any				=						logging.get_logger(__name__)
  
  A__	:      str				=						{
      """microsoft/prophetnet-large-uncased""": (
          """https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"""
      ),
  }
  
  
  
  
  
  class      UpperCAmelCase_						(_UpperCAmelCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Dict							  =    'prophetnet'
      lowerCamelCase						:      List[str]							  =    ['past_key_values']
      lowerCamelCase						:      List[str]							  =    {
          'num_attention_heads': 'num_encoder_attention_heads',
      }
  
  
  
  
  
  
  
      def __init__(							self    ,  SCREAMING_SNAKE_CASE_ = 0.1    ,  SCREAMING_SNAKE_CASE_ = "gelu"    ,  SCREAMING_SNAKE_CASE_ = 3_05_22    ,  SCREAMING_SNAKE_CASE_ = 10_24    ,  SCREAMING_SNAKE_CASE_ = 40_96    ,  SCREAMING_SNAKE_CASE_ = 12    ,  SCREAMING_SNAKE_CASE_ = 16    ,  SCREAMING_SNAKE_CASE_ = 40_96    ,  SCREAMING_SNAKE_CASE_ = 12    ,  SCREAMING_SNAKE_CASE_ = 16    ,  SCREAMING_SNAKE_CASE_ = 0.1    ,  SCREAMING_SNAKE_CASE_ = 0.1    ,  SCREAMING_SNAKE_CASE_ = 5_12    ,  SCREAMING_SNAKE_CASE_ = 0.0_2    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = 0    ,  SCREAMING_SNAKE_CASE_ = 2    ,  SCREAMING_SNAKE_CASE_ = 32    ,  SCREAMING_SNAKE_CASE_ = 1_28    ,  SCREAMING_SNAKE_CASE_ = False    ,  SCREAMING_SNAKE_CASE_ = 0.0    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = 0    ,  SCREAMING_SNAKE_CASE_ = 1    ,  SCREAMING_SNAKE_CASE_ = 2    ,  **SCREAMING_SNAKE_CASE_    ,  )       ->  List[Any]:
           __lowerCamelCase     :			List[Any]              =	vocab_size
           __lowerCamelCase     :			Union[str, Any]              =	hidden_size
           __lowerCamelCase     :			Tuple              =	encoder_ffn_dim
           __lowerCamelCase     :			Any              =	num_encoder_layers
           __lowerCamelCase     :			int              =	num_encoder_attention_heads
           __lowerCamelCase     :			Optional[Any]              =	decoder_ffn_dim
           __lowerCamelCase     :			str              =	num_decoder_layers
           __lowerCamelCase     :			int              =	num_decoder_attention_heads
           __lowerCamelCase     :			Union[str, Any]              =	max_position_embeddings
           __lowerCamelCase     :			Optional[Any]              =	init_std  # Normal(0, this parameter)
           __lowerCamelCase     :			Union[str, Any]              =	activation_function
  
           # parameters for prophetnet
           __lowerCamelCase     :			int              =	ngram
           __lowerCamelCase     :			Optional[int]              =	num_buckets
           __lowerCamelCase     :			str              =	relative_max_distance
           __lowerCamelCase     :			Optional[Any]              =	disable_ngram_loss
           __lowerCamelCase     :			str              =	eps
  
           # 3 Types of Dropout
           __lowerCamelCase     :			Optional[Any]              =	attention_dropout
           __lowerCamelCase     :			str              =	activation_dropout
           __lowerCamelCase     :			Optional[Any]              =	dropout
  
           __lowerCamelCase     :			Optional[Any]              =	use_cache
  
           super().__init__(
               pad_token_id=SCREAMING_SNAKE_CASE_    ,  bos_token_id=SCREAMING_SNAKE_CASE_    ,  eos_token_id=SCREAMING_SNAKE_CASE_    ,  is_encoder_decoder=SCREAMING_SNAKE_CASE_    ,  add_cross_attention=SCREAMING_SNAKE_CASE_    ,  decoder_start_token_id=SCREAMING_SNAKE_CASE_    ,  **SCREAMING_SNAKE_CASE_    ,  )
  
  
  
  
  
  
  
      @property
      def    lowercase_       (							self			)       ->  int:
           return self.num_encoder_layers + self.num_decoder_layers
  
  
  
  
  
  
  
      @num_hidden_layers.setter
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Union[str, Any]:
           raise NotImplementedError(
               'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
               ' `num_decoder_layers`.'			)
  
  
  
  
  
  
  