code: 
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  snake_case				:	List[Any]							   =						logging.get_logger(__name__)
  
  snake_case				:	str							   =						{
      "ZinengTang/tvlt-base": "https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json",
  }
  
  
  
  
  
  class    _snake_case      (  snake_case       ):
  			UpperCamelCase__					   =					'tvlt'
  
  
  
  
  
  
  
  			def __init__(       self		,     _a=224		,     _a=2_048		,     _a=128		,     _a=[16, 16]		,     _a=[16, 16]		,     _a=3		,     _a=1		,     _a=8		,     _a=768		,     _a=12		,     _a=12		,     _a=3_072		,     _a="gelu"		,     _a=0.0		,     _a=0.0		,     _a=0.02		,     _a=1e-6		,     _a=True		,     _a=False		,     _a=16		,     _a=512		,     _a=8		,     _a=2_048		,     _a=0.75		,     _a=0.15		,     _a="frame-level"		,     _a=True		,     _a=True		,     _a="classification"		,     **_a		,     ):
  				super().__init__(**_a	)
  
  				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}'''	)
  
  				__magic_name__	:	List[str]			     =  image_size
  				__magic_name__	:	Optional[int]			     =  spectrogram_length
  				__magic_name__	:	str			     =  frequency_length
  				__magic_name__	:	List[str]			     =  image_patch_size
  				__magic_name__	:	Dict			     =  audio_patch_size
  				__magic_name__	:	Any			     =  num_image_channels
  				__magic_name__	:	Any			     =  num_audio_channels
  				__magic_name__	:	Optional[Any]			     =  num_frames
  
  				__magic_name__	:	Union[str, Any]			     =  hidden_size
  				__magic_name__	:	Optional[Any]			     =  num_hidden_layers
  				__magic_name__	:	Tuple			     =  num_attention_heads
  				__magic_name__	:	Tuple			     =  intermediate_size
  				__magic_name__	:	int			     =  hidden_act
  				__magic_name__	:	Optional[int]			     =  hidden_dropout_prob
  				__magic_name__	:	Union[str, Any]			     =  attention_probs_dropout_prob
  				__magic_name__	:	Union[str, Any]			     =  initializer_range
  				__magic_name__	:	Optional[Any]			     =  layer_norm_eps
  				__magic_name__	:	Optional[int]			     =  qkv_bias
  				__magic_name__	:	List[Any]			     =  use_mean_pooling
  
  				__magic_name__	:	List[str]			     =  decoder_num_attention_heads
  				__magic_name__	:	List[Any]			     =  decoder_hidden_size
  				__magic_name__	:	Dict			     =  decoder_num_hidden_layers
  				__magic_name__	:	List[Any]			     =  decoder_intermediate_size
  				__magic_name__	:	Optional[int]			     =  pixel_mask_ratio
  				__magic_name__	:	int			     =  audio_mask_ratio
  				__magic_name__	:	str			     =  audio_mask_type
  
  				__magic_name__	:	Any			     =  task_matching
  				__magic_name__	:	Any			     =  task_mae
  				__magic_name__	:	Dict			     =  loss_type
  
style_context: 
  from __future__ import annotations
  
  snake_case				:	List[str]							   =						"Alexander Joslin"
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						int     )       ->	int:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	if number <= 0:
  		__magic_name__	:	int			     =  F'''n must be greater than 0. Got n = {number}'''
  		raise ValueError(_snake_case     )
  
  	__magic_name__	:	List[str]			     =  [number + 1] * (number + 1)
  
  	# starting position
  	__magic_name__	:	Any			     =  0
  	for i in range(1   ,     _snake_case     ):
  		__magic_name__	:	Optional[int]			     =  min(table[i + 1]   ,     table[i] + 1     )
  		# check if out of bounds
  		if i * 2 <= number:
  			__magic_name__	:	str			     =  min(table[i * 2]   ,     table[i] + 1     )
  		# check if out of bounds
  		if i * 3 <= number:
  			__magic_name__	:	Any			     =  min(table[i * 3]   ,     table[i] + 1     )
  	return table[number]
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  