

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


__snake_case							:     List[str] 						=	logging.get_logger(__name__)

__snake_case							:     Any 						=	{
    # pcqm4mv1 now deprecated
    """graphormer-base""": """https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json""",
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}



class    A__(a_    ):
						"""simple docstring"""






						_A  :							Optional[int]   			=	'''graphormer'''
						_A  :							Optional[int]   			=	['''past_key_values''']





						def __init__(  self       ,     _lowercase = 1       ,     _lowercase = 512 * 9       ,     _lowercase = 512 * 3       ,     _lowercase = 512       ,     _lowercase = 512       ,     _lowercase = 512       ,     _lowercase = 128       ,     _lowercase = 5       ,     _lowercase = 1_024       ,     _lowercase = "multi_hop"       ,     _lowercase = 512       ,     _lowercase = False       ,     _lowercase = 12       ,     _lowercase = 768       ,     _lowercase = 768       ,     _lowercase = 32       ,     _lowercase = 0.1       ,     _lowercase = 0.1       ,     _lowercase = 0.0       ,     _lowercase = False       ,     _lowercase = False       ,     _lowercase = False       ,     _lowercase = "gelu"       ,     _lowercase = None       ,     _lowercase = False       ,     _lowercase = 0       ,     _lowercase = False       ,     _lowercase = 0.0       ,     _lowercase = 8       ,     _lowercase = None       ,     _lowercase = None       ,     _lowercase = True       ,     _lowercase = True       ,     _lowercase=0       ,     _lowercase=1       ,     _lowercase=2       ,     **_lowercase       ,     )      ->				List[str]:
									a_		:		Tuple				    =				num_classes
									a_		:		Any				    =				num_atoms
									a_		:		str				    =				num_in_degree
									a_		:		Optional[Any]				    =				num_out_degree
									a_		:		Union[str, Any]				    =				num_edges
									a_		:		List[Any]				    =				num_spatial
									a_		:		Any				    =				num_edge_dis
									a_		:		Union[str, Any]				    =				edge_type
									a_		:		Optional[Any]				    =				multi_hop_max_dist
									a_		:		Any				    =				spatial_pos_max
									a_		:		Any				    =				max_nodes
									a_		:		Optional[Any]				    =				num_hidden_layers
									a_		:		Union[str, Any]				    =				embedding_dim
									a_		:		Optional[Any]				    =				embedding_dim
									a_		:		Union[str, Any]				    =				ffn_embedding_dim
									a_		:		Optional[Any]				    =				num_attention_heads
									a_		:		List[Any]				    =				dropout
									a_		:		int				    =				attention_dropout
									a_		:		str				    =				layerdrop
									a_		:		Dict				    =				encoder_normalize_before
									a_		:		Optional[int]				    =				pre_layernorm
									a_		:		str				    =				apply_graphormer_init
									a_		:		Optional[int]				    =				activation_fn
									a_		:		List[str]				    =				embed_scale
									a_		:		Tuple				    =				freeze_embeddings
									a_		:		Tuple				    =				num_trans_layers_to_freeze
									a_		:		str				    =				share_input_output_embed
									a_		:		Any				    =				traceable
									a_		:		str				    =				q_noise
									a_		:		Union[str, Any]				    =				qn_block_size

									# These parameters are here for future extensions
									# atm, the model only supports self attention
									a_		:		List[Any]				    =				kdim
									a_		:		Any				    =				vdim
									a_		:		Tuple				    =				self_attention
									a_		:		Optional[int]				    =				bias

									super().__init__(
									    pad_token_id=_lowercase       ,     bos_token_id=_lowercase       ,     eos_token_id=_lowercase       ,     **_lowercase       ,     )





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


__snake_case							:     Optional[Any] 						=	logging.get_logger(__name__)

__snake_case							:     str 						=	{
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}



class    A__(a_    ):
						"""simple docstring"""






						_A  :							Any   			=	'''tvlt'''





						def __init__(  self       ,     _lowercase=224       ,     _lowercase=2_048       ,     _lowercase=128       ,     _lowercase=[16, 16]       ,     _lowercase=[16, 16]       ,     _lowercase=3       ,     _lowercase=1       ,     _lowercase=8       ,     _lowercase=768       ,     _lowercase=12       ,     _lowercase=12       ,     _lowercase=3_072       ,     _lowercase="gelu"       ,     _lowercase=0.0       ,     _lowercase=0.0       ,     _lowercase=0.0_2       ,     _lowercase=1e-6       ,     _lowercase=True       ,     _lowercase=False       ,     _lowercase=16       ,     _lowercase=512       ,     _lowercase=8       ,     _lowercase=2_048       ,     _lowercase=0.7_5       ,     _lowercase=0.1_5       ,     _lowercase="frame-level"       ,     _lowercase=True       ,     _lowercase=True       ,     _lowercase="classification"       ,     **_lowercase       ,     )      ->				Tuple:
									super().__init__(**_lowercase  )

									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_		:		str				    =				spectrogram_length
									a_		:		int				    =				frequency_length
									a_		:		str				    =				image_patch_size
									a_		:		int				    =				audio_patch_size
									a_		:		Any				    =				num_image_channels
									a_		:		str				    =				num_audio_channels
									a_		:		int				    =				num_frames

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

									a_		:		List[Any]				    =				decoder_num_attention_heads
									a_		:		Optional[Any]				    =				decoder_hidden_size
									a_		:		Dict				    =				decoder_num_hidden_layers
									a_		:		Optional[Any]				    =				decoder_intermediate_size
									a_		:		Dict				    =				pixel_mask_ratio
									a_		:		int				    =				audio_mask_ratio
									a_		:		Optional[Any]				    =				audio_mask_type

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


