'''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


A_     :						Dict    							=logging.get_logger(__name__)

A_     :						Tuple    							={
    '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json''',
    '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json''',
    '''microsoft/deberta-v2-xlarge-mnli''': (
        '''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'''
    ),
    '''microsoft/deberta-v2-xxlarge-mnli''': (
        '''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'''
    ),
}


class     __UpperCAmelCase       ( __a		):
  __A :				str											= 'deberta-v2'





  def __init__(	self    ,     _lowerCamelCase=12_8100    ,     _lowerCamelCase=1536    ,     _lowerCamelCase=24    ,     _lowerCamelCase=24    ,     _lowerCamelCase=6144    ,     _lowerCamelCase="gelu"    ,     _lowerCamelCase=0.1    ,     _lowerCamelCase=0.1    ,     _lowerCamelCase=512    ,     _lowerCamelCase=0    ,     _lowerCamelCase=0.02    ,     _lowerCamelCase=1E-7    ,     _lowerCamelCase=False    ,     _lowerCamelCase=-1    ,     _lowerCamelCase=0    ,     _lowerCamelCase=True    ,     _lowerCamelCase=None    ,     _lowerCamelCase=0    ,     _lowerCamelCase="gelu"    ,     **_lowerCamelCase    ,     ):
     super().__init__(**_lowerCamelCase )

     lowerCAmelCase_			=      hidden_size
     lowerCAmelCase_			=      num_hidden_layers
     lowerCAmelCase_			=      num_attention_heads
     lowerCAmelCase_			=      intermediate_size
     lowerCAmelCase_			=      hidden_act
     lowerCAmelCase_			=      hidden_dropout_prob
     lowerCAmelCase_			=      attention_probs_dropout_prob
     lowerCAmelCase_			=      max_position_embeddings
     lowerCAmelCase_			=      type_vocab_size
     lowerCAmelCase_			=      initializer_range
     lowerCAmelCase_			=      relative_attention
     lowerCAmelCase_			=      max_relative_positions
     lowerCAmelCase_			=      pad_token_id
     lowerCAmelCase_			=      position_biased_input

     # Backwards compatibility
     if type(_lowerCamelCase ) == str:
        lowerCAmelCase_			=      [x.strip() for x in pos_att_type.lower().split('''|''' )]

     lowerCAmelCase_			=      pos_att_type
     lowerCAmelCase_			=      vocab_size
     lowerCAmelCase_			=      layer_norm_eps

     lowerCAmelCase_			=      kwargs.get('''pooler_hidden_size'''    ,     _lowerCamelCase )
     lowerCAmelCase_			=      pooler_dropout
     lowerCAmelCase_			=      pooler_hidden_act






class     __UpperCAmelCase       ( __a		):





  @property
  def 						UpperCAmelCase_	(	self ):
     if self.task == "multiple-choice":
        lowerCAmelCase_			=      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
        lowerCAmelCase_			=      {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 						UpperCAmelCase_	(	self ):
     return 12







  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase = -1    ,     _lowerCamelCase = -1    ,     _lowerCamelCase = -1    ,     _lowerCamelCase = False    ,     _lowerCamelCase = None    ,     _lowerCamelCase = 3    ,     _lowerCamelCase = 40    ,     _lowerCamelCase = 40    ,     _lowerCamelCase = None    ,     ):
     lowerCAmelCase_			=      super().generate_dummy_inputs(preprocessor=_lowerCamelCase    ,     framework=_lowerCamelCase )
     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'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A_     :						Optional[Any]    							={
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 A_     :						Dict    							=['''SqueezeBertTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 A_     :						Optional[int]    							=[
     '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''SqueezeBertForMaskedLM''',
     '''SqueezeBertForMultipleChoice''',
     '''SqueezeBertForQuestionAnswering''',
     '''SqueezeBertForSequenceClassification''',
     '''SqueezeBertForTokenClassification''',
     '''SqueezeBertModel''',
     '''SqueezeBertModule''',
     '''SqueezeBertPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_squeezebert import (
     SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     SqueezeBertConfig,
     SqueezeBertOnnxConfig,
 )
 from .tokenization_squeezebert import SqueezeBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_squeezebert import (
      SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SqueezeBertForMaskedLM,
      SqueezeBertForMultipleChoice,
      SqueezeBertForQuestionAnswering,
      SqueezeBertForSequenceClassification,
      SqueezeBertForTokenClassification,
      SqueezeBertModel,
      SqueezeBertModule,
      SqueezeBertPreTrainedModel,
  )

else:
 import sys

 A_     :						List[str]    							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





