






"""simple docstring"""






from typing import TYPE_CHECKING

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


UpperCAmelCase_  :					Dict       =					{
    """configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
    """tokenization_convbert""": ["""ConvBertTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_  :					List[str]       =					["""ConvBertTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_  :					Union[str, Any]       =					[
      """CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """ConvBertForMaskedLM""",
      """ConvBertForMultipleChoice""",
      """ConvBertForQuestionAnswering""",
      """ConvBertForSequenceClassification""",
      """ConvBertForTokenClassification""",
      """ConvBertLayer""",
      """ConvBertModel""",
      """ConvBertPreTrainedModel""",
      """load_tf_weights_in_convbert""",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_  :					Tuple       =					[
      """TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFConvBertForMaskedLM""",
      """TFConvBertForMultipleChoice""",
      """TFConvBertForQuestionAnswering""",
      """TFConvBertForSequenceClassification""",
      """TFConvBertForTokenClassification""",
      """TFConvBertLayer""",
      """TFConvBertModel""",
      """TFConvBertPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
  from .tokenization_convbert import ConvBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_convbert_fast import ConvBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_convbert import (
        CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        ConvBertForMaskedLM,
        ConvBertForMultipleChoice,
        ConvBertForQuestionAnswering,
        ConvBertForSequenceClassification,
        ConvBertForTokenClassification,
        ConvBertLayer,
        ConvBertModel,
        ConvBertPreTrainedModel,
        load_tf_weights_in_convbert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_convbert import (
        TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFConvBertForMaskedLM,
        TFConvBertForMultipleChoice,
        TFConvBertForQuestionAnswering,
        TFConvBertForSequenceClassification,
        TFConvBertForTokenClassification,
        TFConvBertLayer,
        TFConvBertModel,
        TFConvBertPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase_  :					Tuple       =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     int:




						'''simple docstring'''







						return 1 if input_a == input_a else 0




def     __SCREAMING_SNAKE_CASE							(       )		->     None:




						'''simple docstring'''







						assert xnor_gate(0      ,       0      ) == 1
						assert xnor_gate(0      ,       1      ) == 0
						assert xnor_gate(1      ,       0      ) == 0
						assert xnor_gate(1      ,       1      ) == 1


if __name__ == "__main__":
				print(xnor_gate(0, 0))
				print(xnor_gate(0, 1))
				print(xnor_gate(1, 0))
				print(xnor_gate(1, 1))

