


'''simple docstring'''






def 	_A				(lowerCAmelCase__  :int				,						lowerCAmelCase__  :int		)   ->  int:




     '''simple docstring'''



     return 1 if input_a == input_a else 0






def 	_A				()   ->  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))





'''simple docstring'''






from typing import TYPE_CHECKING

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


a_					:    str          =    {
    "configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
    "configuration_data2vec_text": [
        "DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecTextConfig",
        "Data2VecTextOnnxConfig",
    ],
    "configuration_data2vec_vision": [
        "DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecVisionConfig",
        "Data2VecVisionOnnxConfig",
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    List[str]          =    [
     "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Data2VecAudioForAudioFrameClassification",
     "Data2VecAudioForCTC",
     "Data2VecAudioForSequenceClassification",
     "Data2VecAudioForXVector",
     "Data2VecAudioModel",
     "Data2VecAudioPreTrainedModel",
 ]
 a_					:    str          =    [
     "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Data2VecTextForCausalLM",
     "Data2VecTextForMaskedLM",
     "Data2VecTextForMultipleChoice",
     "Data2VecTextForQuestionAnswering",
     "Data2VecTextForSequenceClassification",
     "Data2VecTextForTokenClassification",
     "Data2VecTextModel",
     "Data2VecTextPreTrainedModel",
 ]
 a_					:    Tuple          =    [
     "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Data2VecVisionForImageClassification",
     "Data2VecVisionForMaskedImageModeling",
     "Data2VecVisionForSemanticSegmentation",
     "Data2VecVisionModel",
     "Data2VecVisionPreTrainedModel",
 ]

if is_tf_available():
 a_					:    Dict          =    [
     "TFData2VecVisionForImageClassification",
     "TFData2VecVisionForSemanticSegmentation",
     "TFData2VecVisionModel",
     "TFData2VecVisionPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
 from .configuration_dataavec_text import (
     DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DataaVecTextConfig,
     DataaVecTextOnnxConfig,
 )
 from .configuration_dataavec_vision import (
     DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DataaVecVisionConfig,
     DataaVecVisionOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_dataavec_audio import (
      DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecAudioForAudioFrameClassification,
      DataaVecAudioForCTC,
      DataaVecAudioForSequenceClassification,
      DataaVecAudioForXVector,
      DataaVecAudioModel,
      DataaVecAudioPreTrainedModel,
  )
  from .modeling_dataavec_text import (
      DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecTextForCausalLM,
      DataaVecTextForMaskedLM,
      DataaVecTextForMultipleChoice,
      DataaVecTextForQuestionAnswering,
      DataaVecTextForSequenceClassification,
      DataaVecTextForTokenClassification,
      DataaVecTextModel,
      DataaVecTextPreTrainedModel,
  )
  from .modeling_dataavec_vision import (
      DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecVisionForImageClassification,
      DataaVecVisionForMaskedImageModeling,
      DataaVecVisionForSemanticSegmentation,
      DataaVecVisionModel,
      DataaVecVisionPreTrainedModel,
  )
 if is_tf_available():
  from .modeling_tf_dataavec_vision import (
      TFDataaVecVisionForImageClassification,
      TFDataaVecVisionForSemanticSegmentation,
      TFDataaVecVisionModel,
      TFDataaVecVisionPreTrainedModel,
  )

else:
 import sys

 a_					:    List[Any]          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

