




'''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







'''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)

