


'''simple docstring'''


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


_UpperCamelCase 							=  logging.get_logger(__name__)

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







class 							__magic_name__	(       lowerCAmelCase ):

     """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=0.02			,     lowerCamelCase=1E-12			,     lowerCamelCase=0			,     lowerCamelCase="absolute"			,     lowerCamelCase=None			,     lowerCamelCase=4			,     lowerCamelCase=1024			,     **lowerCamelCase			,     ):


          '''simple docstring'''





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

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





'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase 							=  {
    """configuration_upernet""": ["""UperNetConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCamelCase 							=  [
          """UperNetForSemanticSegmentation""",
          """UperNetPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_upernet import UperNetConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
      import sys

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

