from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase     						=			logging.get_logger(__name__)

UpperCAmelCase     						=			{
    '''squeezebert/squeezebert-uncased''': (
        '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'''
    ),
    '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json''',
    '''squeezebert/squeezebert-mnli-headless''': (
        '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'''
    ),
}




class 				__lowerCAmelCase					(    a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		Union[str, Any]			      =	SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
 _lowerCamelCase  :		Dict			      =	'squeezebert'

 def __init__(     self    :						str    ,     lowerCamelCase    :						str=3_0_5_2_2    ,     lowerCamelCase    :						int=7_6_8    ,     lowerCamelCase    :						List[str]=1_2    ,     lowerCamelCase    :						Optional[int]=1_2    ,     lowerCamelCase    :						int=3_0_7_2    ,     lowerCamelCase    :						str="gelu"    ,     lowerCamelCase    :						str=0.1    ,     lowerCamelCase    :						str=0.1    ,     lowerCamelCase    :						Any=5_1_2    ,     lowerCamelCase    :						Any=2    ,     lowerCamelCase    :						Optional[Any]=0.0_2    ,     lowerCamelCase    :						Optional[int]=1e-12    ,     lowerCamelCase    :						Any=0    ,     lowerCamelCase    :						Dict=7_6_8    ,     lowerCamelCase    :						List[Any]=4    ,     lowerCamelCase    :						Tuple=4    ,     lowerCamelCase    :						Union[str, Any]=4    ,     lowerCamelCase    :						Any=1    ,     lowerCamelCase    :						Dict=4    ,     lowerCamelCase    :						int=4    ,     **lowerCamelCase    :						int    ,     )	->							Tuple:
        super().__init__(pad_token_id=lowerCamelCase    ,     **lowerCamelCase					)

        _UpperCamelCase       :    str					  =					vocab_size
        _UpperCamelCase       :    Any					  =					hidden_size
        _UpperCamelCase       :    Any					  =					num_hidden_layers
        _UpperCamelCase       :    Tuple					  =					num_attention_heads
        _UpperCamelCase       :    str					  =					hidden_act
        _UpperCamelCase       :    List[Any]					  =					intermediate_size
        _UpperCamelCase       :    Any					  =					hidden_dropout_prob
        _UpperCamelCase       :    Union[str, Any]					  =					attention_probs_dropout_prob
        _UpperCamelCase       :    Optional[Any]					  =					max_position_embeddings
        _UpperCamelCase       :    List[str]					  =					type_vocab_size
        _UpperCamelCase       :    Union[str, Any]					  =					initializer_range
        _UpperCamelCase       :    List[str]					  =					layer_norm_eps
        _UpperCamelCase       :    Any					  =					embedding_size
        _UpperCamelCase       :    Optional[int]					  =					q_groups
        _UpperCamelCase       :    Optional[int]					  =					k_groups
        _UpperCamelCase       :    List[Any]					  =					v_groups
        _UpperCamelCase       :    Optional[Any]					  =					post_attention_groups
        _UpperCamelCase       :    Any					  =					intermediate_groups
        _UpperCamelCase       :    Optional[Any]					  =					output_groups







class 				__lowerCAmelCase					(    a_  ):



 '''simple docstring'''

 @property
 def    __snake_case   (     self    :						int					)	->							Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
               _UpperCamelCase       :    Optional[int]					  =					{0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
               _UpperCamelCase       :    Optional[int]					  =					{0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
                ('token_type_ids', dynamic_axis),
            ]					)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase     						=			{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			['''MPNetTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			[
     '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MPNetForMaskedLM''',
     '''MPNetForMultipleChoice''',
     '''MPNetForQuestionAnswering''',
     '''MPNetForSequenceClassification''',
     '''MPNetForTokenClassification''',
     '''MPNetLayer''',
     '''MPNetModel''',
     '''MPNetPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			[
     '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFMPNetEmbeddings''',
     '''TFMPNetForMaskedLM''',
     '''TFMPNetForMultipleChoice''',
     '''TFMPNetForQuestionAnswering''',
     '''TFMPNetForSequenceClassification''',
     '''TFMPNetForTokenClassification''',
     '''TFMPNetMainLayer''',
     '''TFMPNetModel''',
     '''TFMPNetPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
 from .tokenization_mpnet import MPNetTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_mpnet_fast import MPNetTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mpnet import (
      MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      MPNetForMaskedLM,
      MPNetForMultipleChoice,
      MPNetForQuestionAnswering,
      MPNetForSequenceClassification,
      MPNetForTokenClassification,
      MPNetLayer,
      MPNetModel,
      MPNetPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mpnet import (
      TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFMPNetEmbeddings,
      TFMPNetForMaskedLM,
      TFMPNetForMultipleChoice,
      TFMPNetForQuestionAnswering,
      TFMPNetForSequenceClassification,
      TFMPNetForTokenClassification,
      TFMPNetMainLayer,
      TFMPNetModel,
      TFMPNetPreTrainedModel,
  )

else:
 import sys

 UpperCAmelCase     						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



