



'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

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


__a          =		logging.get_logger(__name__)

__a          =		{
    "andreasmadsen/efficient_mlm_m0.40": (
        "https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"
    ),
}







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		"roberta-prelayernorm"




    def __init__(    self					:     Optional[int] ,     snake_case_					:     List[Any]=50_265 ,     snake_case_					:     Union[str, Any]=768 ,     snake_case_					:     List[str]=12 ,     snake_case_					:     List[str]=12 ,     snake_case_					:     int=3_072 ,     snake_case_					:     int="gelu" ,     snake_case_					:     Optional[int]=0.1 ,     snake_case_					:     str=0.1 ,     snake_case_					:     str=512 ,     snake_case_					:     str=2 ,     snake_case_					:     str=0.02 ,     snake_case_					:     str=1E-1_2 ,     snake_case_					:     Optional[Any]=1 ,     snake_case_					:     Union[str, Any]=0 ,     snake_case_					:     Optional[int]=2 ,     snake_case_					:     List[Any]="absolute" ,     snake_case_					:     Optional[Any]=True ,     snake_case_					:     Union[str, Any]=None ,     **snake_case_					:     List[str] ,     ):
          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__  :    Union[str, Any]    						=      hidden_size
          snake_case__  :    Dict    						=      num_hidden_layers
          snake_case__  :    Tuple    						=      num_attention_heads
          snake_case__  :    Dict    						=      hidden_act
          snake_case__  :    int    						=      intermediate_size
          snake_case__  :    List[str]    						=      hidden_dropout_prob
          snake_case__  :    List[Any]    						=      attention_probs_dropout_prob
          snake_case__  :    Any    						=      max_position_embeddings
          snake_case__  :    Any    						=      type_vocab_size
          snake_case__  :    Optional[Any]    						=      initializer_range
          snake_case__  :    Tuple    						=      layer_norm_eps
          snake_case__  :    Optional[Any]    						=      position_embedding_type
          snake_case__  :    int    						=      use_cache
          snake_case__  :    List[Any]    						=      classifier_dropout







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    @property
    def lowerCamelCase       (    self					:     List[Any]						):
          if self.task == "multiple-choice":
                snake_case__  :    Optional[Any]    						=      {0: """batch""", 1: """choice""", 2: """sequence"""}
          else:
                snake_case__  :    Optional[Any]    						=      {0: """batch""", 1: """sequence"""}
          return OrderedDict(
              [
                  ("""input_ids""", dynamic_axis),
                  ("""attention_mask""", dynamic_axis),
              ]						)






import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


lowerCamelCase__ :   List[str]					=							logging.get_logger(__name__)





class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 def __init__(							self  :		str				,				*_lowerCAmelCase  :		Optional[Any]				,				**_lowerCAmelCase  :		Optional[Any]						):
       warnings.warn(
           'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
           ' Please use ImageGPTImageProcessor instead.'				,				_lowerCAmelCase				,				)
       super().__init__(*_lowerCAmelCase				,				**_lowerCAmelCase						)