code: 
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  _A							    =	logging.get_logger(__name__)
  
  _A							    =	{
      """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
  }
  
  
  class 							_lowerCAmelCase		(     lowerCAmelCase__	):
  				lowerCamelCase__									=    'tvlt'
  
  
  
  
  				def __init__( self		,		snake_case_=224		,		snake_case_=2_048		,		snake_case_=128		,		snake_case_=[16, 16]		,		snake_case_=[16, 16]		,		snake_case_=3		,		snake_case_=1		,		snake_case_=8		,		snake_case_=768		,		snake_case_=12		,		snake_case_=12		,		snake_case_=3_072		,		snake_case_="gelu"		,		snake_case_=0.0		,		snake_case_=0.0		,		snake_case_=0.02		,		snake_case_=1E-6		,		snake_case_=True		,		snake_case_=False		,		snake_case_=16		,		snake_case_=512		,		snake_case_=8		,		snake_case_=2_048		,		snake_case_=0.75		,		snake_case_=0.15		,		snake_case_="frame-level"		,		snake_case_=True		,		snake_case_=True		,		snake_case_="classification"		,		**snake_case_		,		)    ->      Any:
  											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}'							)
  
  											SCREAMING_SNAKE_CASE			: int	     =image_size
  											SCREAMING_SNAKE_CASE			: List[str]	     =spectrogram_length
  											SCREAMING_SNAKE_CASE			: str	     =frequency_length
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =image_patch_size
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =audio_patch_size
  											SCREAMING_SNAKE_CASE			: List[str]	     =num_image_channels
  											SCREAMING_SNAKE_CASE			: Tuple	     =num_audio_channels
  											SCREAMING_SNAKE_CASE			: List[Any]	     =num_frames
  
  											SCREAMING_SNAKE_CASE			: Dict	     =hidden_size
  											SCREAMING_SNAKE_CASE			: Tuple	     =num_hidden_layers
  											SCREAMING_SNAKE_CASE			: List[Any]	     =num_attention_heads
  											SCREAMING_SNAKE_CASE			: Any	     =intermediate_size
  											SCREAMING_SNAKE_CASE			: Any	     =hidden_act
  											SCREAMING_SNAKE_CASE			: Dict	     =hidden_dropout_prob
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =attention_probs_dropout_prob
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =initializer_range
  											SCREAMING_SNAKE_CASE			: int	     =layer_norm_eps
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =qkv_bias
  											SCREAMING_SNAKE_CASE			: int	     =use_mean_pooling
  
  											SCREAMING_SNAKE_CASE			: int	     =decoder_num_attention_heads
  											SCREAMING_SNAKE_CASE			: int	     =decoder_hidden_size
  											SCREAMING_SNAKE_CASE			: int	     =decoder_num_hidden_layers
  											SCREAMING_SNAKE_CASE			: Dict	     =decoder_intermediate_size
  											SCREAMING_SNAKE_CASE			: int	     =pixel_mask_ratio
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =audio_mask_ratio
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =audio_mask_type
  
  											SCREAMING_SNAKE_CASE			: Tuple	     =task_matching
  											SCREAMING_SNAKE_CASE			: str	     =task_mae
  											SCREAMING_SNAKE_CASE			: str	     =loss_type
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  _A							    =	{"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}
  
  try:
  							if not is_torch_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
  							    """RegNetForImageClassification""",
  							    """RegNetModel""",
  							    """RegNetPreTrainedModel""",
  							]
  
  try:
  							if not is_tf_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
  							    """TFRegNetForImageClassification""",
  							    """TFRegNetModel""",
  							    """TFRegNetPreTrainedModel""",
  							]
  
  try:
  							if not is_flax_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """FlaxRegNetForImageClassification""",
  							    """FlaxRegNetModel""",
  							    """FlaxRegNetPreTrainedModel""",
  							]
  
  
  if TYPE_CHECKING:
  							from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig
  
  							try:
  														if not is_torch_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_regnet import (
  														    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
  														    RegNetForImageClassification,
  														    RegNetModel,
  														    RegNetPreTrainedModel,
  														)
  
  							try:
  														if not is_tf_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_tf_regnet import (
  														    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
  														    TFRegNetForImageClassification,
  														    TFRegNetModel,
  														    TFRegNetPreTrainedModel,
  														)
  
  							try:
  														if not is_flax_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_flax_regnet import (
  														    FlaxRegNetForImageClassification,
  														    FlaxRegNetModel,
  														    FlaxRegNetPreTrainedModel,
  														)
  
  
  else:
  							import sys
  
  							_A							    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)
  
  
  
  
  
  
  