



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase  :						str	=       logging.get_logger(__name__)

UpperCAmelCase  :						List[Any]	=       {
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}


class       SCREAMING_SNAKE_CASE__       (							__UpperCAmelCase      ):
    lowercase__          =      "lilt"

    def __init__( self	: Optional[int]       ,     lowerCAmelCase_	: str=3_0_5_2_2       ,     lowerCAmelCase_	: List[str]=7_6_8       ,     lowerCAmelCase_	: Union[str, Any]=1_2       ,     lowerCAmelCase_	: int=1_2       ,     lowerCAmelCase_	: Dict=3_0_7_2       ,     lowerCAmelCase_	: List[Any]="gelu"       ,     lowerCAmelCase_	: Any=0.1       ,     lowerCAmelCase_	: Optional[Any]=0.1       ,     lowerCAmelCase_	: List[Any]=5_1_2       ,     lowerCAmelCase_	: Optional[Any]=2       ,     lowerCAmelCase_	: Union[str, Any]=0.02       ,     lowerCAmelCase_	: Dict=1E-12       ,     lowerCAmelCase_	: Tuple=0       ,     lowerCAmelCase_	: Optional[Any]="absolute"       ,     lowerCAmelCase_	: Union[str, Any]=None       ,     lowerCAmelCase_	: int=4       ,     lowerCAmelCase_	: Tuple=1_0_2_4       ,     **lowerCAmelCase_	: Any       ,     ):







      """simple docstring"""







      super().__init__(pad_token_id=__lowerCAmelCase       ,     **__lowerCAmelCase)

      lowercase_	    =       vocab_size
      lowercase_	    =       hidden_size
      lowercase_	    =       num_hidden_layers
      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_	    =       position_embedding_type
      lowercase_	    =       classifier_dropout
      lowercase_	    =       channel_shrink_ratio
      lowercase_	    =       max_ad_position_embeddings








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_a 						=      {
    "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
    "tokenization_lxmert": ["LxmertTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a 						=      ["LxmertTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a 						=      [
			    "LxmertEncoder",
			    "LxmertForPreTraining",
			    "LxmertForQuestionAnswering",
			    "LxmertModel",
			    "LxmertPreTrainedModel",
			    "LxmertVisualFeatureEncoder",
			    "LxmertXLayer",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a 						=      [
			    "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFLxmertForPreTraining",
			    "TFLxmertMainLayer",
			    "TFLxmertModel",
			    "TFLxmertPreTrainedModel",
			    "TFLxmertVisualFeatureEncoder",
			]


if TYPE_CHECKING:
			from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
			from .tokenization_lxmert import LxmertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_lxmert_fast import LxmertTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_lxmert import (
						    LxmertEncoder,
						    LxmertForPreTraining,
						    LxmertForQuestionAnswering,
						    LxmertModel,
						    LxmertPreTrainedModel,
						    LxmertVisualFeatureEncoder,
						    LxmertXLayer,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_lxmert import (
						    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFLxmertForPreTraining,
						    TFLxmertMainLayer,
						    TFLxmertModel,
						    TFLxmertPreTrainedModel,
						    TFLxmertVisualFeatureEncoder,
						)

else:
			import sys

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



