code: 
  
  import copy
  from typing import Dict
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  from ..auto.configuration_auto import CONFIG_MAPPING
  from ..bit import BitConfig
  
  
  lowerCamelCase         =      logging.get_logger(__name__)
  
  lowerCamelCase         =      {
      '''google/vit-hybrid-base-bit-384''': '''https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json''',
      # See all ViT hybrid models at https://huggingface.co/models?filter=vit
  }
  
  
  
  
  
  
  
  class 		__magic_name__    (							lowerCamelCase__							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			Tuple												=     'vit-hybrid'
  
  
  
     def __init__(       self,					lowercase_=None,					lowercase_=768,					lowercase_=12,					lowercase_=12,					lowercase_=3072,					lowercase_="gelu",					lowercase_=0.0,					lowercase_=0.0,					lowercase_=0.02,					lowercase_=1E-12,					lowercase_=224,					lowercase_=1,					lowercase_=3,					lowercase_=[1, 1024, 24, 24],					lowercase_=True,					**lowercase_,					)							->					Tuple:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       super().__init__(**lowercase_ )
  
       if backbone_config is None:
         logger.info('''`backbone_config` is `None`. Initializing the config with a `BiT` backbone.''' )
         a__						={
             '''global_padding''': '''same''',
             '''layer_type''': '''bottleneck''',
             '''depths''': [3, 4, 9],
             '''out_features''': ['''stage3'''],
             '''embedding_dynamic_padding''': True,
         }
  
       if isinstance(lowercase_,					lowercase_ ):
         if "model_type" in backbone_config:
           a__						=CONFIG_MAPPING[backbone_config['''model_type''']]
         else:
           logger.info(
               '''`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.''' )
           a__						=BitConfig
         a__						=backbone_config_class(**lowercase_ )
  
       a__						=backbone_featmap_shape
       a__						=backbone_config
       a__						=hidden_size
       a__						=num_hidden_layers
       a__						=num_attention_heads
       a__						=intermediate_size
       a__						=hidden_act
       a__						=hidden_dropout_prob
       a__						=attention_probs_dropout_prob
       a__						=initializer_range
       a__						=layer_norm_eps
       a__						=image_size
       a__						=patch_size
       a__						=num_channels
       a__						=qkv_bias
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Dict[str, any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=copy.deepcopy(self.__dict__ )
       a__						=self.backbone_config.to_dict()
       a__						=self.__class__.model_type
       return output
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  lowerCamelCase     =      {
      '''configuration_deberta_v2''': ['''DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaV2Config''', '''DebertaV2OnnxConfig'''],
      '''tokenization_deberta_v2''': ['''DebertaV2Tokenizer'''],
  }
  
  try:
         if not is_tokenizers_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowerCamelCase     =      ['''DebertaV2TokenizerFast''']
  
  try:
         if not is_tf_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowerCamelCase     =      [
             '''TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''TFDebertaV2ForMaskedLM''',
             '''TFDebertaV2ForQuestionAnswering''',
             '''TFDebertaV2ForSequenceClassification''',
             '''TFDebertaV2ForTokenClassification''',
             '''TFDebertaV2Model''',
             '''TFDebertaV2PreTrainedModel''',
         ]
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowerCamelCase     =      [
             '''DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''DebertaV2ForMaskedLM''',
             '''DebertaV2ForMultipleChoice''',
             '''DebertaV2ForQuestionAnswering''',
             '''DebertaV2ForSequenceClassification''',
             '''DebertaV2ForTokenClassification''',
             '''DebertaV2Model''',
             '''DebertaV2PreTrainedModel''',
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_deberta_va import (
             DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
             DebertaVaConfig,
             DebertaVaOnnxConfig,
         )
         from .tokenization_deberta_va import DebertaVaTokenizer
  
         try:
                if not is_tokenizers_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .tokenization_deberta_va_fast import DebertaVaTokenizerFast
  
         try:
                if not is_tf_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_tf_deberta_va import (
                    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                    TFDebertaVaForMaskedLM,
                    TFDebertaVaForQuestionAnswering,
                    TFDebertaVaForSequenceClassification,
                    TFDebertaVaForTokenClassification,
                    TFDebertaVaModel,
                    TFDebertaVaPreTrainedModel,
                )
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_deberta_va import (
                    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                    DebertaVaForMaskedLM,
                    DebertaVaForMultipleChoice,
                    DebertaVaForQuestionAnswering,
                    DebertaVaForSequenceClassification,
                    DebertaVaForTokenClassification,
                    DebertaVaModel,
                    DebertaVaPreTrainedModel,
                )
  
  else:
         import sys
  
         lowerCamelCase     =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  