






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


__UpperCamelCase   :   Tuple      =    logging.get_logger(__name__)

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


class 							__UpperCamelCase     (  _lowerCAmelCase ):
	__snake_case						:Union[str, Any] =							'lxmert'
	__snake_case						:Union[str, Any] =							{}







	def __init__(	self   :				List[str]							,					_lowerCAmelCase   :				Dict=3_0522							,					_lowerCAmelCase   :				List[str]=768							,					_lowerCAmelCase   :				Union[str, Any]=12							,					_lowerCAmelCase   :				Union[str, Any]=9500							,					_lowerCAmelCase   :				Union[str, Any]=1600							,					_lowerCAmelCase   :				Optional[Any]=400							,					_lowerCAmelCase   :				Tuple=3072							,					_lowerCAmelCase   :				List[Any]="gelu"							,					_lowerCAmelCase   :				int=0.1							,					_lowerCAmelCase   :				Dict=0.1							,					_lowerCAmelCase   :				Tuple=512							,					_lowerCAmelCase   :				Tuple=2							,					_lowerCAmelCase   :				Optional[Any]=0.02							,					_lowerCAmelCase   :				List[str]=1e-12							,					_lowerCAmelCase   :				Any=9							,					_lowerCAmelCase   :				Optional[Any]=5							,					_lowerCAmelCase   :				Any=5							,					_lowerCAmelCase   :				Dict=2048							,					_lowerCAmelCase   :				int=4							,					_lowerCAmelCase   :				Optional[Any]=6.67							,					_lowerCAmelCase   :				Optional[Any]=True							,					_lowerCAmelCase   :				int=True							,					_lowerCAmelCase   :				int=True							,					_lowerCAmelCase   :				str=True							,					_lowerCAmelCase   :				Dict=True							,					_lowerCAmelCase   :				int=True							,					_lowerCAmelCase   :				int=True							,					**_lowerCAmelCase   :				Tuple							,					)		->			Dict:
		"""simple docstring"""







		__lowercase   							=	vocab_size
		__lowercase   							=	hidden_size
		__lowercase   							=	num_attention_heads
		__lowercase   							=	hidden_act
		__lowercase   							=	intermediate_size
		__lowercase   							=	hidden_dropout_prob
		__lowercase   							=	attention_probs_dropout_prob
		__lowercase   							=	max_position_embeddings
		__lowercase   							=	type_vocab_size
		__lowercase   							=	initializer_range
		__lowercase   							=	layer_norm_eps
		__lowercase   							=	num_qa_labels
		__lowercase   							=	num_object_labels
		__lowercase   							=	num_attr_labels
		__lowercase   							=	l_layers
		__lowercase   							=	x_layers
		__lowercase   							=	r_layers
		__lowercase   							=	visual_feat_dim
		__lowercase   							=	visual_pos_dim
		__lowercase   							=	visual_loss_normalizer
		__lowercase   							=	task_matched
		__lowercase   							=	task_mask_lm
		__lowercase   							=	task_obj_predict
		__lowercase   							=	task_qa
		__lowercase   							=	visual_obj_loss
		__lowercase   							=	visual_attr_loss
		__lowercase   							=	visual_feat_loss
		__lowercase   							=	{"""vision""": r_layers, """cross_encoder""": x_layers, """language""": l_layers}
		super().__init__(**_lowerCAmelCase      )



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A  =	{"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A  =	[
			    "SEW_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "SEWForCTC",
			    "SEWForSequenceClassification",
			    "SEWModel",
			    "SEWPreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_sew import (
						    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SEWForCTC,
						    SEWForSequenceClassification,
						    SEWModel,
						    SEWPreTrainedModel,
						)


else:
			import sys

			_A  =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



