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


_SCREAMING_SNAKE_CASE     :   List[Any]							 =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :   List[str]							 =      {
    'ZinengTang/tvlt-base': 'https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json',
}







class 			UpperCamelCase__		(     snake_case_ ):







	'''simple docstring'''
	_lowerCAmelCase											=					"tvlt"





	def __init__(      self					,							UpperCamelCase__=224					,							UpperCamelCase__=2048					,							UpperCamelCase__=128					,							UpperCamelCase__=[16, 16]					,							UpperCamelCase__=[16, 16]					,							UpperCamelCase__=3					,							UpperCamelCase__=1					,							UpperCamelCase__=8					,							UpperCamelCase__=768					,							UpperCamelCase__=12					,							UpperCamelCase__=12					,							UpperCamelCase__=3072					,							UpperCamelCase__="gelu"					,							UpperCamelCase__=0.0					,							UpperCamelCase__=0.0					,							UpperCamelCase__=0.0_2					,							UpperCamelCase__=1e-6					,							UpperCamelCase__=True					,							UpperCamelCase__=False					,							UpperCamelCase__=16					,							UpperCamelCase__=512					,							UpperCamelCase__=8					,							UpperCamelCase__=2048					,							UpperCamelCase__=0.7_5					,							UpperCamelCase__=0.1_5					,							UpperCamelCase__="frame-level"					,							UpperCamelCase__=True					,							UpperCamelCase__=True					,							UpperCamelCase__="classification"					,							**UpperCamelCase__					,							):
					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}"		)

					A__	:						Tuple								=				image_size
					A__	:						Dict								=				spectrogram_length
					A__	:						int								=				frequency_length
					A__	:						List[Any]								=				image_patch_size
					A__	:						Dict								=				audio_patch_size
					A__	:						List[str]								=				num_image_channels
					A__	:						Optional[Any]								=				num_audio_channels
					A__	:						Optional[int]								=				num_frames

					A__	:						List[str]								=				hidden_size
					A__	:						Optional[Any]								=				num_hidden_layers
					A__	:						Optional[Any]								=				num_attention_heads
					A__	:						Any								=				intermediate_size
					A__	:						List[str]								=				hidden_act
					A__	:						Optional[int]								=				hidden_dropout_prob
					A__	:						int								=				attention_probs_dropout_prob
					A__	:						str								=				initializer_range
					A__	:						str								=				layer_norm_eps
					A__	:						Tuple								=				qkv_bias
					A__	:						str								=				use_mean_pooling

					A__	:						Optional[int]								=				decoder_num_attention_heads
					A__	:						Any								=				decoder_hidden_size
					A__	:						Dict								=				decoder_num_hidden_layers
					A__	:						Dict								=				decoder_intermediate_size
					A__	:						int								=				pixel_mask_ratio
					A__	:						str								=				audio_mask_ratio
					A__	:						Dict								=				audio_mask_type

					A__	:						int								=				task_matching
					A__	:						Dict								=				task_mae
					A__	:						Union[str, Any]								=				loss_type






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


_SCREAMING_SNAKE_CASE   						=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   						=		{
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}
class SCREAMING_SNAKE_CASE_    (       snake_case_       ):
						__magic_name__:      Optional[Any]			   =							"nystromformer"






						def __init__(   self   :   List[str]		, _A   :   Optional[Any]=30000		, _A   :   str=768		, _A   :   Optional[int]=12		, _A   :   Dict=12		, _A   :   Any=3072		, _A   :   str="gelu_new"		, _A   :   Optional[Any]=0.1		, _A   :   int=0.1		, _A   :   Dict=510		, _A   :   List[Any]=2		, _A   :   Optional[Any]=64		, _A   :   Union[str, Any]=64		, _A   :   List[Any]=65		, _A   :   Dict=False		, _A   :   str=0.0_2		, _A   :   Dict=1E-5		, _A   :   List[Any]=1		, _A   :   Optional[int]=0		, _A   :   List[Any]=2		, **_A   :   int		, )				->     int:







							"""simple docstring"""


							snake_case_		:      Union[str, Any]           =						vocab_size
							snake_case_		:      str           =						max_position_embeddings
							snake_case_		:      Tuple           =						hidden_size
							snake_case_		:      Union[str, Any]           =						num_hidden_layers
							snake_case_		:      str           =						num_attention_heads
							snake_case_		:      Optional[Any]           =						intermediate_size
							snake_case_		:      str           =						hidden_act
							snake_case_		:      str           =						hidden_dropout_prob
							snake_case_		:      Optional[Any]           =						attention_probs_dropout_prob
							snake_case_		:      Union[str, Any]           =						initializer_range
							snake_case_		:      Union[str, Any]           =						type_vocab_size
							snake_case_		:      Dict           =						segment_means_seq_len
							snake_case_		:      Tuple           =						num_landmarks
							snake_case_		:      Any           =						conv_kernel_size
							snake_case_		:      List[str]           =						inv_coeff_init_option
							snake_case_		:      str           =						layer_norm_eps
							super().__init__(pad_token_id=_A		, bos_token_id=_A		, eos_token_id=_A		, **_A						)

