
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

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


_a   :      str       =				logging.get_logger(__name__)

_a   :      Dict       =				{
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			int	=       """rembert"""
    def __init__(     self    ,     snake_case_=25_03_00    ,     snake_case_=11_52    ,     snake_case_=32    ,     snake_case_=18    ,     snake_case_=2_56    ,     snake_case_=16_64    ,     snake_case_=46_08    ,     snake_case_="gelu"    ,     snake_case_=0.0    ,     snake_case_=0.0    ,     snake_case_=0.1    ,     snake_case_=5_12    ,     snake_case_=2    ,     snake_case_=0.02    ,     snake_case_=1E-12    ,     snake_case_=True    ,     snake_case_=0    ,     snake_case_=3_12    ,     snake_case_=3_13    ,     **snake_case_    ,     ):
      super().__init__(pad_token_id=snake_case_    ,     bos_token_id=snake_case_    ,     eos_token_id=snake_case_    ,     **snake_case_  )

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






class   _UpperCAmelCase					(       _snake_case):
    @property
    def        lowerCamelCase__						(     self  ):
      if self.task == "multiple-choice":
        _snake_case  :    Optional[int]            = {0: "batch", 1: "choice", 2: "sequence"}
      else:
        _snake_case  :    Any            = {0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("input_ids", dynamic_axis),
              ("attention_mask", dynamic_axis),
              ("token_type_ids", dynamic_axis),
          ]  )


    @property
    def        lowerCamelCase__						(     self  ):
      return 1E-4







'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_levit import LevitImageProcessor


a__         =     logging.get_logger(__name__)





class __magic_name__(						__lowerCAmelCase      ):






						def __init__(   self						:						Optional[Any]					,   *__UpperCamelCase						:						Union[str, Any]					,   **__UpperCamelCase						:						Dict       ):



									'''simple docstring'''






									warnings.warn(
									    """The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
									    """ use LevitImageProcessor instead."""					,   __UpperCamelCase					,   )
									super().__init__(*__UpperCamelCase					,   **__UpperCamelCase       )