

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
  from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


UpperCAmelCase__			:  List[Any]					     =     logging.get_logger(__name__)

UpperCAmelCase__			:  Tuple					     =     {
    "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/config.json",
    "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/config.json",
    "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json",
    "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json",
    "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json",
    "microsoft/deberta-xlarge-mnli": "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json",
}







class 							a__							( _a							):







  """simple docstring"""




  UpperCAmelCase__   :			Tuple      							="""deberta"""


  def __init__(      self     :			Optional[Any]					,					UpperCAmelCase__     :			Optional[Any]=5_0_2_6_5					,					UpperCAmelCase__     :			Union[str, Any]=7_6_8					,					UpperCAmelCase__     :			str=1_2					,					UpperCAmelCase__     :			List[str]=1_2					,					UpperCAmelCase__     :			Tuple=3_0_7_2					,					UpperCAmelCase__     :			Optional[int]="gelu"					,					UpperCAmelCase__     :			Union[str, Any]=0.1					,					UpperCAmelCase__     :			int=0.1					,					UpperCAmelCase__     :			Any=5_1_2					,					UpperCAmelCase__     :			Tuple=0					,					UpperCAmelCase__     :			Union[str, Any]=0.02					,					UpperCAmelCase__     :			Optional[int]=1e-7					,					UpperCAmelCase__     :			Union[str, Any]=False					,					UpperCAmelCase__     :			List[Any]=-1					,					UpperCAmelCase__     :			List[Any]=0					,					UpperCAmelCase__     :			Optional[int]=True					,					UpperCAmelCase__     :			Dict=None					,					UpperCAmelCase__     :			str=0					,					UpperCAmelCase__     :			Dict="gelu"					,					**UpperCAmelCase__     :			Tuple					,					)       ->List[Any]:
        """simple docstring"""







        super().__init__(**_UpperCAmelCase		)

        SCREAMING_SNAKE_CASE			:   List[Any]	  = hidden_size
        SCREAMING_SNAKE_CASE			:   Dict	  = num_hidden_layers
        SCREAMING_SNAKE_CASE			:   Tuple	  = num_attention_heads
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = intermediate_size
        SCREAMING_SNAKE_CASE			:   List[str]	  = hidden_act
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = hidden_dropout_prob
        SCREAMING_SNAKE_CASE			:   List[str]	  = attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = max_position_embeddings
        SCREAMING_SNAKE_CASE			:   Any	  = type_vocab_size
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = initializer_range
        SCREAMING_SNAKE_CASE			:   List[str]	  = relative_attention
        SCREAMING_SNAKE_CASE			:   Any	  = max_relative_positions
        SCREAMING_SNAKE_CASE			:   int	  = pad_token_id
        SCREAMING_SNAKE_CASE			:   List[str]	  = position_biased_input

        # Backwards compatibility
        if type(_UpperCAmelCase		) == str:
              SCREAMING_SNAKE_CASE			:   List[str]	  = [x.strip() for x in pos_att_type.lower().split("""|"""		)]

        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = pos_att_type
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = vocab_size
        SCREAMING_SNAKE_CASE			:   Any	  = layer_norm_eps

        SCREAMING_SNAKE_CASE			:   List[str]	  = kwargs.get("""pooler_hidden_size"""					,					_UpperCAmelCase		)
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = pooler_dropout
        SCREAMING_SNAKE_CASE			:   List[Any]	  = pooler_hidden_act







class 							a__							( _a							):







  """simple docstring"""




  @property
  def 			_lowercase   (      self     :			List[Any]		)       ->str:
        """simple docstring"""







        if self.task == "multiple-choice":
              SCREAMING_SNAKE_CASE			:   Tuple	  = {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
              SCREAMING_SNAKE_CASE			:   Tuple	  = {0: """batch""", 1: """sequence"""}
        if self._config.type_vocab_size > 0:
              return OrderedDict(
                  [("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)]		)
        else:
              return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)]		)


  @property
  def 			_lowercase   (      self     :			Tuple		)       ->Union[str, Any]:
        """simple docstring"""







        return 1_2




  def 			_lowercase   (      self     :			Tuple					,					UpperCAmelCase__     :			str					,					UpperCAmelCase__     :			List[Any] = -1					,					UpperCAmelCase__     :			Dict = -1					,					UpperCAmelCase__     :			Optional[Any] = -1					,					UpperCAmelCase__     :			Any = False					,					UpperCAmelCase__     :			Optional[Any] = None					,					UpperCAmelCase__     :			str = 3					,					UpperCAmelCase__     :			Optional[int] = 4_0					,					UpperCAmelCase__     :			Union[str, Any] = 4_0					,					UpperCAmelCase__     :			int = None					,					)       ->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = super().generate_dummy_inputs(preprocessor=_UpperCAmelCase					,					framework=_UpperCAmelCase		)
        if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
              del dummy_inputs["token_type_ids"]
        return dummy_inputs





"""simple docstring"""


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_:       Union[str, Any]			     =       logging.get_logger(__name__)

lowerCAmelCase_:       Dict			     =       {
    "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  a__     ( _a						):
   snake_case_				=   "vit-hybrid"


   def __init__(  self,					_UpperCAmelCase=None,					_UpperCAmelCase=768,					_UpperCAmelCase=12,					_UpperCAmelCase=12,					_UpperCAmelCase=3072,					_UpperCAmelCase="gelu",					_UpperCAmelCase=0.0,					_UpperCAmelCase=0.0,					_UpperCAmelCase=0.02,					_UpperCAmelCase=1E-12,					_UpperCAmelCase=224,					_UpperCAmelCase=1,					_UpperCAmelCase=3,					_UpperCAmelCase=[1, 1024, 24, 24],					_UpperCAmelCase=True,					**_UpperCAmelCase,					):
        '''simple docstring'''



        super().__init__(**_UpperCAmelCase	)

        if backbone_config is None:
             logger.info("`backbone_config` is `None`. Initializing the config with a `BiT` backbone."	)
             lowercase__								=				{
                 "global_padding": "same",
                 "layer_type": "bottleneck",
                 "depths": [3, 4, 9],
                 "out_features": ["stage3"],
                 "embedding_dynamic_padding": True,
             }

        if isinstance(_UpperCAmelCase,					_UpperCAmelCase	):
             if "model_type" in backbone_config:
                  lowercase__								=				CONFIG_MAPPING[backbone_config["model_type"]]
             else:
                  logger.info(
                      "`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."	)
                  lowercase__								=				BitConfig
             lowercase__								=				backbone_config_class(**_UpperCAmelCase	)

        lowercase__								=				backbone_featmap_shape
        lowercase__								=				backbone_config
        lowercase__								=				hidden_size
        lowercase__								=				num_hidden_layers
        lowercase__								=				num_attention_heads
        lowercase__								=				intermediate_size
        lowercase__								=				hidden_act
        lowercase__								=				hidden_dropout_prob
        lowercase__								=				attention_probs_dropout_prob
        lowercase__								=				initializer_range
        lowercase__								=				layer_norm_eps
        lowercase__								=				image_size
        lowercase__								=				patch_size
        lowercase__								=				num_channels
        lowercase__								=				qkv_bias


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				copy.deepcopy(self.__dict__	)
        lowercase__								=				self.backbone_config.to_dict()
        lowercase__								=				self.__class__.model_type
        return output




