code: 
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowerCAmelCase__ 	=				logging.get_logger(__name__)
  
  lowerCAmelCase__ 	=				{
      'ZinengTang/tvlt-base': 'https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json',
  }
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       a):
  
  
  
  
       '''simple docstring'''
       __SCREAMING_SNAKE_CASE						    =				"tvlt"
  
  
       def __init__(		self				,		__lowerCamelCase=2_2_4				,		__lowerCamelCase=2_0_4_8				,		__lowerCamelCase=1_2_8				,		__lowerCamelCase=[1_6, 1_6]				,		__lowerCamelCase=[1_6, 1_6]				,		__lowerCamelCase=3				,		__lowerCamelCase=1				,		__lowerCamelCase=8				,		__lowerCamelCase=7_6_8				,		__lowerCamelCase=1_2				,		__lowerCamelCase=1_2				,		__lowerCamelCase=3_0_7_2				,		__lowerCamelCase="gelu"				,		__lowerCamelCase=0.0				,		__lowerCamelCase=0.0				,		__lowerCamelCase=0.0_2				,		__lowerCamelCase=1e-6				,		__lowerCamelCase=True				,		__lowerCamelCase=False				,		__lowerCamelCase=1_6				,		__lowerCamelCase=5_1_2				,		__lowerCamelCase=8				,		__lowerCamelCase=2_0_4_8				,		__lowerCamelCase=0.7_5				,		__lowerCamelCase=0.1_5				,		__lowerCamelCase="frame-level"				,		__lowerCamelCase=True				,		__lowerCamelCase=True				,		__lowerCamelCase="classification"				,		**__lowerCamelCase				,		)    ->       List[str]:
              super().__init__(**__lowerCamelCase)
  
              if audio_mask_type not in ("frame-level", "patch_level"):
                     raise ValueError(
                         "audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') "
                         F"got {audio_mask_type}")
  
              _A   : int       					=				image_size
              _A   : List[str]       					=				spectrogram_length
              _A   : Any       					=				frequency_length
              _A   : Optional[Any]       					=				image_patch_size
              _A   : Tuple       					=				audio_patch_size
              _A   : List[Any]       					=				num_image_channels
              _A   : List[str]       					=				num_audio_channels
              _A   : Optional[Any]       					=				num_frames
  
              _A   : Optional[int]       					=				hidden_size
              _A   : Any       					=				num_hidden_layers
              _A   : Dict       					=				num_attention_heads
              _A   : str       					=				intermediate_size
              _A   : Optional[int]       					=				hidden_act
              _A   : Tuple       					=				hidden_dropout_prob
              _A   : Union[str, Any]       					=				attention_probs_dropout_prob
              _A   : Optional[Any]       					=				initializer_range
              _A   : Tuple       					=				layer_norm_eps
              _A   : Optional[Any]       					=				qkv_bias
              _A   : List[Any]       					=				use_mean_pooling
  
              _A   : Dict       					=				decoder_num_attention_heads
              _A   : Union[str, Any]       					=				decoder_hidden_size
              _A   : Dict       					=				decoder_num_hidden_layers
              _A   : Tuple       					=				decoder_intermediate_size
              _A   : Dict       					=				pixel_mask_ratio
              _A   : Union[str, Any]       					=				audio_mask_ratio
              _A   : List[str]       					=				audio_mask_type
  
              _A   : List[Any]       					=				task_matching
              _A   : Union[str, Any]       					=				task_mae
              _A   : Optional[Any]       					=				loss_type
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_torch_available,
      is_vision_available,
  )
  
  
  lowerCAmelCase__   	=				{
      'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
  }
  
  try:
      if not is_vision_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				['SegformerFeatureExtractor']
      lowerCAmelCase__   	=				['SegformerImageProcessor']
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				[
          'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'SegformerDecodeHead',
          'SegformerForImageClassification',
          'SegformerForSemanticSegmentation',
          'SegformerLayer',
          'SegformerModel',
          'SegformerPreTrainedModel',
      ]
  
  try:
      if not is_tf_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				[
          'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFSegformerDecodeHead',
          'TFSegformerForImageClassification',
          'TFSegformerForSemanticSegmentation',
          'TFSegformerModel',
          'TFSegformerPreTrainedModel',
      ]
  
  
  if TYPE_CHECKING:
      from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig
  
      try:
          if not is_vision_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .feature_extraction_segformer import SegformerFeatureExtractor
          from .image_processing_segformer import SegformerImageProcessor
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_segformer import (
              SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              SegformerDecodeHead,
              SegformerForImageClassification,
              SegformerForSemanticSegmentation,
              SegformerLayer,
              SegformerModel,
              SegformerPreTrainedModel,
          )
      try:
          if not is_tf_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_tf_segformer import (
              TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFSegformerDecodeHead,
              TFSegformerForImageClassification,
              TFSegformerForSemanticSegmentation,
              TFSegformerModel,
              TFSegformerPreTrainedModel,
          )
  
  else:
      import sys
  
      lowerCAmelCase__   	=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  