

"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase       :				int         =					logging.get_logger(__name__)

lowerCAmelCase       :				Any         =					{
    'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/config.json',
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}
class 	_A						(					_a):
  SCREAMING_SNAKE_CASE      : Dict    =					"""rembert"""

  def __init__(			self	,   _SCREAMING_SNAKE_CASE=25_0300	,   _SCREAMING_SNAKE_CASE=1152	,   _SCREAMING_SNAKE_CASE=32	,   _SCREAMING_SNAKE_CASE=18	,   _SCREAMING_SNAKE_CASE=256	,   _SCREAMING_SNAKE_CASE=1664	,   _SCREAMING_SNAKE_CASE=4608	,   _SCREAMING_SNAKE_CASE="gelu"	,   _SCREAMING_SNAKE_CASE=0.0	,   _SCREAMING_SNAKE_CASE=0.0	,   _SCREAMING_SNAKE_CASE=0.1	,   _SCREAMING_SNAKE_CASE=512	,   _SCREAMING_SNAKE_CASE=2	,   _SCREAMING_SNAKE_CASE=0.02	,   _SCREAMING_SNAKE_CASE=1e-12	,   _SCREAMING_SNAKE_CASE=True	,   _SCREAMING_SNAKE_CASE=0	,   _SCREAMING_SNAKE_CASE=312	,   _SCREAMING_SNAKE_CASE=313	,   **_SCREAMING_SNAKE_CASE	,   ):





        """simple docstring"""







        super().__init__(pad_token_id=snake_case_	,   bos_token_id=snake_case_	,   eos_token_id=snake_case_	,   **snake_case_   )

        SCREAMING_SNAKE_CASE_			:					Tuple          =       vocab_size
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       input_embedding_size
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       output_embedding_size
        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       max_position_embeddings
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       hidden_size
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       num_hidden_layers
        SCREAMING_SNAKE_CASE_			:					Tuple          =       num_attention_heads
        SCREAMING_SNAKE_CASE_			:					int          =       intermediate_size
        SCREAMING_SNAKE_CASE_			:					int          =       hidden_act
        SCREAMING_SNAKE_CASE_			:					str          =       hidden_dropout_prob
        SCREAMING_SNAKE_CASE_			:					int          =       attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE_			:					int          =       classifier_dropout_prob
        SCREAMING_SNAKE_CASE_			:					Dict          =       initializer_range
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       type_vocab_size
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       layer_norm_eps
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       use_cache
        SCREAMING_SNAKE_CASE_			:					Dict          =       False

class 	_A						(					_a):

  @property
  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        if self.task == "multiple-choice":
              SCREAMING_SNAKE_CASE_			:					Optional[int]          =       {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
              SCREAMING_SNAKE_CASE_			:					List[Any]          =       {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
                ('token_type_ids', dynamic_axis),
            ]   )



  @property
  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        return 1e-4









from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCAmelCase       :				Tuple         =					{'tokenization_phobert': ['PhobertTokenizer']}


if TYPE_CHECKING:
    from .tokenization_phobert import PhobertTokenizer

else:
    import sys

    lowerCAmelCase       :				Optional[int]         =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






