





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


a_			=      logging.get_logger(__name__)

a_			=      {
    'SCUT-DLVCLab/lilt-roberta-en-base': (
        'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
    ),
}






class 		__snake_case (		UpperCamelCase__					):





 """simple docstring"""


 _lowerCamelCase				=					"""lilt"""

 def __init__(   self ,						__lowerCamelCase=3_0522 ,						__lowerCamelCase=768 ,						__lowerCamelCase=12 ,						__lowerCamelCase=12 ,						__lowerCamelCase=3072 ,						__lowerCamelCase="gelu" ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=512 ,						__lowerCamelCase=2 ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=1e-1_2 ,						__lowerCamelCase=0 ,						__lowerCamelCase="absolute" ,						__lowerCamelCase=None ,						__lowerCamelCase=4 ,						__lowerCamelCase=1024 ,						**__lowerCamelCase ,						):



     '''simple docstring'''




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

     __A   :						Tuple									=					vocab_size
     __A   :						List[Any]									=					hidden_size
     __A   :						str									=					num_hidden_layers
     __A   :						Optional[int]									=					num_attention_heads
     __A   :						List[Any]									=					hidden_act
     __A   :						int									=					intermediate_size
     __A   :						List[Any]									=					hidden_dropout_prob
     __A   :						Union[str, Any]									=					attention_probs_dropout_prob
     __A   :						List[Any]									=					max_position_embeddings
     __A   :						Union[str, Any]									=					type_vocab_size
     __A   :						Union[str, Any]									=					initializer_range
     __A   :						List[str]									=					layer_norm_eps
     __A   :						Dict									=					position_embedding_type
     __A   :						Optional[int]									=					classifier_dropout
     __A   :						Dict									=					channel_shrink_ratio
     __A   :						List[str]									=					max_ad_position_embeddings











"""simple docstring"""



from typing import TYPE_CHECKING

from ..utils import _LazyModule


a_			=      {
    """config""": [
        """EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
        """OnnxConfig""",
        """OnnxConfigWithPast""",
        """OnnxSeq2SeqConfigWithPast""",
        """PatchingSpec""",
    ],
    """convert""": ["""export""", """validate_model_outputs"""],
    """features""": ["""FeaturesManager"""],
    """utils""": ["""ParameterFormat""", """compute_serialized_parameters_size"""],
}


if TYPE_CHECKING:
       from .config import (
           EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
           OnnxConfig,
           OnnxConfigWithPast,
           OnnxSeqaSeqConfigWithPast,
           PatchingSpec,
       )
       from .convert import export, validate_model_outputs
       from .features import FeaturesManager
       from .utils import ParameterFormat, compute_serialized_parameters_size

else:
       import sys

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




