"""simple docstring"""
from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__      						=  {
    "configuration_squeezebert": [
        "SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SqueezeBertConfig",
        "SqueezeBertOnnxConfig",
    ],
    "tokenization_squeezebert": ["SqueezeBertTokenizer"],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__      						=  ["SqueezeBertTokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__      						=  [
					    "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

					SCREAMING_SNAKE_CASE__      						=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





"""simple docstring"""
def    lowerCAmelCase__    (			_UpperCamelCase       :				int ,			_UpperCamelCase       :				int					)					->				int:





						"""simple docstring"""






						return int((input_a, input_a).count(0					) == 0					)





def    lowerCAmelCase__    (			)					->				None:





						"""simple docstring"""






						assert and_gate(0 ,			0					) == 0
						assert and_gate(0 ,			1					) == 0
						assert and_gate(1 ,			0					) == 0
						assert and_gate(1 ,			1					) == 1


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


