code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  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
  
  
  lowercase__			=	logging.get_logger(__name__)
  
  lowercase__			=	{
      '''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 						lowercase     (   UpperCAmelCase_):
        __lowercase 		=  '''deberta'''
  
  
  
  
  
  
        def __init__(self ,  a__=5_0_2_6_5 ,  a__=7_6_8 ,  a__=1_2 ,  a__=1_2 ,  a__=3_0_7_2 ,  a__="gelu" ,  a__=0.1 ,  a__=0.1 ,  a__=5_1_2 ,  a__=0 ,  a__=0.02 ,  a__=1E-7 ,  a__=False ,  a__=-1 ,  a__=0 ,  a__=True ,  a__=None ,  a__=0 ,  a__="gelu" ,  **a__ ,  )       ->   Optional[Any]:
           super().__init__(**a__					)
  
           SCREAMING_SNAKE_CASE__			:				Dict       						= hidden_size
           SCREAMING_SNAKE_CASE__			:				Tuple       						= num_hidden_layers
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= num_attention_heads
           SCREAMING_SNAKE_CASE__			:				Dict       						= intermediate_size
           SCREAMING_SNAKE_CASE__			:				List[str]       						= hidden_act
           SCREAMING_SNAKE_CASE__			:				int       						= hidden_dropout_prob
           SCREAMING_SNAKE_CASE__			:				str       						= attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= max_position_embeddings
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= type_vocab_size
           SCREAMING_SNAKE_CASE__			:				int       						= initializer_range
           SCREAMING_SNAKE_CASE__			:				Dict       						= relative_attention
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= max_relative_positions
           SCREAMING_SNAKE_CASE__			:				Dict       						= pad_token_id
           SCREAMING_SNAKE_CASE__			:				int       						= position_biased_input
  
           # Backwards compatibility
           if type(a__					) == str:
              SCREAMING_SNAKE_CASE__			:				List[str]       						= [x.strip() for x in pos_att_type.lower().split('''|'''					)]
  
           SCREAMING_SNAKE_CASE__			:				List[str]       						= pos_att_type
           SCREAMING_SNAKE_CASE__			:				Dict       						= vocab_size
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= layer_norm_eps
  
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= kwargs.get('''pooler_hidden_size''' ,  a__					)
           SCREAMING_SNAKE_CASE__			:				Any       						= pooler_dropout
           SCREAMING_SNAKE_CASE__			:				Tuple       						= pooler_hidden_act
  
  
  
  
  
  
  class 						lowercase     (   UpperCAmelCase_):
  
  
  
  
  
  
        @property
        def    A__(self					)       ->   Mapping[str, Mapping[int, str]]:
           if self.task == "multiple-choice":
              SCREAMING_SNAKE_CASE__			:				Any       						= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
           else:
              SCREAMING_SNAKE_CASE__			:				Optional[int]       						= {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    A__(self					)       ->   int:
           return 1_2
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ = -1 ,  a__ = -1 ,  a__ = -1 ,  a__ = False ,  a__ = None ,  a__ = 3 ,  a__ = 4_0 ,  a__ = 4_0 ,  a__ = None ,  )       ->   Mapping[str, Any]:
           SCREAMING_SNAKE_CASE__			:				Any       						= super().generate_dummy_inputs(preprocessor=a__ ,  framework=a__					)
           if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
              del dummy_inputs["token_type_ids"]
           return dummy_inputs
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  # Copyright 2023 The HuggingFace Team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  from typing import TYPE_CHECKING
  
  # rely on isort to merge the imports
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
  )
  
  
  lowercase__			=	{
      '''configuration_convnextv2''': [
          '''CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
          '''ConvNextV2Config''',
      ]
  }
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowercase__			=	[
             '''CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''ConvNextV2ForImageClassification''',
             '''ConvNextV2Model''',
             '''ConvNextV2PreTrainedModel''',
             '''ConvNextV2Backbone''',
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_convnextva import (
             CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
             ConvNextVaConfig,
         )
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_convnextva import (
                    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                    ConvNextVaBackbone,
                    ConvNextVaForImageClassification,
                    ConvNextVaModel,
                    ConvNextVaPreTrainedModel,
                )
  
  else:
         import sys
  
         lowercase__			=	_LazyModule(__name__, globals()['''__file__'''], _import_structure)
  
  