



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


snake_case     :    Optional[int]						=    logging.get_logger(__name__)

snake_case     :    int						=    {
    'google/pegasus-x-base': 'https://huggingface.co/google/pegasus-x-base/resolve/main/config.json',
    'google/pegasus-x-large': 'https://huggingface.co/google/pegasus-x-large/resolve/main/config.json',
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}




class 				lowerCAmelCase__	(						UpperCamelCase    ):
					__A  :     Optional[Any]     =    'pegasus_x'
					__A  :     List[str]     =    ['past_key_values']
					__A  :     str     =    {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}







					def __init__(							self   :  List[Any]      ,		_A   :  Any=9_6103      ,		_A   :  int=1_6384      ,		_A   :  int=16      ,		_A   :  int=4096      ,		_A   :  List[Any]=16      ,		_A   :  Optional[int]=16      ,		_A   :  int=4096      ,		_A   :  Dict=16      ,		_A   :  Any=0.0      ,		_A   :  str=0.0      ,		_A   :  str=True      ,		_A   :  Tuple=True      ,		_A   :  Optional[Any]="gelu"      ,		_A   :  Any=1024      ,		_A   :  Any=0.1      ,		_A   :  Tuple=0.0      ,		_A   :  str=0.0      ,		_A   :  Union[str, Any]=0.02      ,		_A   :  Optional[Any]=0      ,		_A   :  Union[str, Any]=True      ,		_A   :  List[Any]=0      ,		_A   :  int=1      ,		_A   :  int=1      ,		_A   :  List[str]=32      ,		_A   :  Tuple=512      ,		_A   :  Tuple=True      ,		**_A   :  List[Any]      ,		):
											A__						:				List[Any]         =			vocab_size
											A__						:				Any         =			max_position_embeddings
											A__						:				List[str]         =			d_model
											A__						:				Union[str, Any]         =			encoder_ffn_dim
											A__						:				Tuple         =			encoder_layers
											A__						:				List[Any]         =			encoder_attention_heads
											A__						:				str         =			decoder_ffn_dim
											A__						:				Union[str, Any]         =			decoder_layers
											A__						:				Tuple         =			decoder_attention_heads
											A__						:				List[str]         =			dropout
											A__						:				Optional[Any]         =			attention_dropout
											A__						:				List[Any]         =			activation_dropout
											A__						:				str         =			activation_function
											A__						:				Tuple         =			init_std
											A__						:				Any         =			encoder_layerdrop
											A__						:				Union[str, Any]         =			decoder_layerdrop
											A__						:				str         =			use_cache
											A__						:				Optional[Any]         =			encoder_layers
											A__						:				Optional[Any]         =			scale_embedding  # scale factor will be sqrt(d_model) if True

											A__						:				Optional[Any]         =			num_global_tokens
											A__						:				Union[str, Any]         =			block_size
											A__						:				Union[str, Any]         =			stagger_local_blocks

											super().__init__(
											    pad_token_id=_A      ,		eos_token_id=_A      ,		is_encoder_decoder=_A      ,		decoder_start_token_id=_A      ,		forced_eos_token_id=_A      ,		**_A      ,		)







					@property
					def 			_lowercase      (							self   :  str):
											return self.encoder_attention_heads







					@property
					def 			_lowercase      (							self   :  Any):
											return self.d_model




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


snake_case     :    Any						=    logging.get_logger(__name__)

snake_case     :    List[str]						=    {
    'MCG-NJU/videomae-base': 'https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json',
}




class 				lowerCAmelCase__	(						UpperCamelCase    ):
					__A  :     str     =    'videomae'







					def __init__(							self   :  Optional[Any]      ,		_A   :  str=224      ,		_A   :  Union[str, Any]=16      ,		_A   :  int=3      ,		_A   :  Tuple=16      ,		_A   :  int=2      ,		_A   :  List[Any]=768      ,		_A   :  List[Any]=12      ,		_A   :  Tuple=12      ,		_A   :  List[Any]=3072      ,		_A   :  Union[str, Any]="gelu"      ,		_A   :  Any=0.0      ,		_A   :  str=0.0      ,		_A   :  Tuple=0.02      ,		_A   :  Optional[Any]=1e-12      ,		_A   :  Tuple=True      ,		_A   :  Dict=True      ,		_A   :  str=6      ,		_A   :  Optional[int]=384      ,		_A   :  Any=4      ,		_A   :  Any=1536      ,		_A   :  Optional[int]=True      ,		**_A   :  Tuple      ,		):
											super().__init__(**_A)

											A__						:				List[str]         =			image_size
											A__						:				Union[str, Any]         =			patch_size
											A__						:				Optional[int]         =			num_channels
											A__						:				int         =			num_frames
											A__						:				List[Any]         =			tubelet_size

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

											A__						:				int         =			decoder_num_attention_heads
											A__						:				Tuple         =			decoder_hidden_size
											A__						:				int         =			decoder_num_hidden_layers
											A__						:				int         =			decoder_intermediate_size
											A__						:				Any         =			norm_pix_loss