





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


__lowerCamelCase	:					str			  =					logging.get_logger(__name__)

__lowerCamelCase	:					Optional[int]			  =					{
    """unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
}



class  A__       (	_UpperCAmelCase      ):
      _UpperCAmelCase     :Dict						   =       "lxmert"
      _UpperCAmelCase     :List[str]						   =       {}





      def __init__(			self	,							A_=3_0522	,							A_=768	,							A_=12	,							A_=9500	,							A_=1600	,							A_=400	,							A_=3072	,							A_="gelu"	,							A_=0.1	,							A_=0.1	,							A_=512	,							A_=2	,							A_=0.02	,							A_=1e-12	,							A_=9	,							A_=5	,							A_=5	,							A_=2048	,							A_=4	,							A_=6.67	,							A_=True	,							A_=True	,							A_=True	,							A_=True	,							A_=True	,							A_=True	,							A_=True	,							**A_	,							):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       vocab_size
       UpperCamelCase     : List[Any]	     =       hidden_size
       UpperCamelCase     : Any	     =       num_attention_heads
       UpperCamelCase     : Tuple	     =       hidden_act
       UpperCamelCase     : Any	     =       intermediate_size
       UpperCamelCase     : Optional[Any]	     =       hidden_dropout_prob
       UpperCamelCase     : Dict	     =       attention_probs_dropout_prob
       UpperCamelCase     : int	     =       max_position_embeddings
       UpperCamelCase     : Tuple	     =       type_vocab_size
       UpperCamelCase     : Tuple	     =       initializer_range
       UpperCamelCase     : str	     =       layer_norm_eps
       UpperCamelCase     : Tuple	     =       num_qa_labels
       UpperCamelCase     : Dict	     =       num_object_labels
       UpperCamelCase     : Any	     =       num_attr_labels
       UpperCamelCase     : Any	     =       l_layers
       UpperCamelCase     : Union[str, Any]	     =       x_layers
       UpperCamelCase     : Optional[Any]	     =       r_layers
       UpperCamelCase     : List[Any]	     =       visual_feat_dim
       UpperCamelCase     : str	     =       visual_pos_dim
       UpperCamelCase     : Dict	     =       visual_loss_normalizer
       UpperCamelCase     : str	     =       task_matched
       UpperCamelCase     : Optional[int]	     =       task_mask_lm
       UpperCamelCase     : Tuple	     =       task_obj_predict
       UpperCamelCase     : Any	     =       task_qa
       UpperCamelCase     : int	     =       visual_obj_loss
       UpperCamelCase     : Tuple	     =       visual_attr_loss
       UpperCamelCase     : Tuple	     =       visual_feat_loss
       UpperCamelCase     : Any	     =       {'''vision''': r_layers, '''cross_encoder''': x_layers, '''language''': l_layers}
       super().__init__(**_UpperCAmelCase       )







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


__lowerCamelCase	:					Tuple			  =					logging.get_logger(__name__)



class  A__       (	__snake_case      ):
      _UpperCAmelCase     :List[Any]						   =       'timm_backbone'





      def __init__(			self	,							A_=None	,							A_=3	,							A_=True	,							A_=True	,							A_=None	,							**A_	,							):
       '''simple docstring'''




       super().__init__(**A_       )
       UpperCamelCase     : Tuple	     =       backbone
       UpperCamelCase     : Dict	     =       num_channels
       UpperCamelCase     : Tuple	     =       features_only
       UpperCamelCase     : Optional[int]	     =       use_pretrained_backbone
       UpperCamelCase     : Dict	     =       True
       UpperCamelCase     : List[str]	     =       out_indices if out_indices is not None else (-1,)
