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


__A						:							Union[str, Any]							=  logging.get_logger(__name__)

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




class 			UpperCAmelCase_      (   __SCREAMING_SNAKE_CASE       ):




							'''simple docstring'''



							a__						  =     'lxmert'
							a__						  =     {}





							def __init__(					self       :	List[Any]       ,     a       :	Dict=30_522       ,     a       :	Optional[Any]=768       ,     a       :	List[Any]=12       ,     a       :	List[Any]=9_500       ,     a       :	List[Any]=1_600       ,     a       :	int=400       ,     a       :	List[str]=3_072       ,     a       :	Optional[Any]="gelu"       ,     a       :	List[Any]=0.1       ,     a       :	List[str]=0.1       ,     a       :	Optional[int]=512       ,     a       :	int=2       ,     a       :	Dict=0.02       ,     a       :	Any=1E-1_2       ,     a       :	Union[str, Any]=9       ,     a       :	int=5       ,     a       :	Any=5       ,     a       :	Union[str, Any]=2_048       ,     a       :	List[str]=4       ,     a       :	List[str]=6.67       ,     a       :	Tuple=True       ,     a       :	Dict=True       ,     a       :	List[Any]=True       ,     a       :	List[str]=True       ,     a       :	Optional[Any]=True       ,     a       :	str=True       ,     a       :	Dict=True       ,     **a       :	Optional[int]       ,     )			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					vocab_size
										SCREAMING_SNAKE_CASE           =					hidden_size
										SCREAMING_SNAKE_CASE           =					num_attention_heads
										SCREAMING_SNAKE_CASE           =					hidden_act
										SCREAMING_SNAKE_CASE           =					intermediate_size
										SCREAMING_SNAKE_CASE           =					hidden_dropout_prob
										SCREAMING_SNAKE_CASE           =					attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE           =					max_position_embeddings
										SCREAMING_SNAKE_CASE           =					type_vocab_size
										SCREAMING_SNAKE_CASE           =					initializer_range
										SCREAMING_SNAKE_CASE           =					layer_norm_eps
										SCREAMING_SNAKE_CASE           =					num_qa_labels
										SCREAMING_SNAKE_CASE           =					num_object_labels
										SCREAMING_SNAKE_CASE           =					num_attr_labels
										SCREAMING_SNAKE_CASE           =					l_layers
										SCREAMING_SNAKE_CASE           =					x_layers
										SCREAMING_SNAKE_CASE           =					r_layers
										SCREAMING_SNAKE_CASE           =					visual_feat_dim
										SCREAMING_SNAKE_CASE           =					visual_pos_dim
										SCREAMING_SNAKE_CASE           =					visual_loss_normalizer
										SCREAMING_SNAKE_CASE           =					task_matched
										SCREAMING_SNAKE_CASE           =					task_mask_lm
										SCREAMING_SNAKE_CASE           =					task_obj_predict
										SCREAMING_SNAKE_CASE           =					task_qa
										SCREAMING_SNAKE_CASE           =					visual_obj_loss
										SCREAMING_SNAKE_CASE           =					visual_attr_loss
										SCREAMING_SNAKE_CASE           =					visual_feat_loss
										SCREAMING_SNAKE_CASE           =					{"""vision""": r_layers, """cross_encoder""": x_layers, """language""": l_layers}
										super().__init__(**_a			)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A						:							Dict							=  {
    """configuration_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A						:							List[Any]							=  [
		    """NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """NllbMoeForConditionalGeneration""",
		    """NllbMoeModel""",
		    """NllbMoePreTrainedModel""",
		    """NllbMoeTop2Router""",
		    """NllbMoeSparseMLP""",
		]


if TYPE_CHECKING:
		from .configuration_nllb_moe import (
		    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    NllbMoeConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_nllb_moe import (
				    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    NllbMoeForConditionalGeneration,
				    NllbMoeModel,
				    NllbMoePreTrainedModel,
				    NllbMoeSparseMLP,
				    NllbMoeTopaRouter,
				)


else:
		import sys

		__A						:							Optional[Any]							=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






