

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



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__)
