

from typing import TYPE_CHECKING

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


__A						       =      {
    '''configuration_longformer''': [
        '''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LongformerConfig''',
        '''LongformerOnnxConfig''',
    ],
    '''tokenization_longformer''': ['''LongformerTokenizer'''],
}

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A						       =      [
			    '''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''LongformerForMaskedLM''',
			    '''LongformerForMultipleChoice''',
			    '''LongformerForQuestionAnswering''',
			    '''LongformerForSequenceClassification''',
			    '''LongformerForTokenClassification''',
			    '''LongformerModel''',
			    '''LongformerPreTrainedModel''',
			    '''LongformerSelfAttention''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A						       =      [
			    '''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFLongformerForMaskedLM''',
			    '''TFLongformerForMultipleChoice''',
			    '''TFLongformerForQuestionAnswering''',
			    '''TFLongformerForSequenceClassification''',
			    '''TFLongformerForTokenClassification''',
			    '''TFLongformerModel''',
			    '''TFLongformerPreTrainedModel''',
			    '''TFLongformerSelfAttention''',
			]


if TYPE_CHECKING:
			from .configuration_longformer import (
			    LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    LongformerConfig,
			    LongformerOnnxConfig,
			)
			from .tokenization_longformer import LongformerTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_longformer_fast import LongformerTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_longformer import (
						    LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LongformerForMaskedLM,
						    LongformerForMultipleChoice,
						    LongformerForQuestionAnswering,
						    LongformerForSequenceClassification,
						    LongformerForTokenClassification,
						    LongformerModel,
						    LongformerPreTrainedModel,
						    LongformerSelfAttention,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_longformer import (
						    TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFLongformerForMaskedLM,
						    TFLongformerForMultipleChoice,
						    TFLongformerForQuestionAnswering,
						    TFLongformerForSequenceClassification,
						    TFLongformerForTokenClassification,
						    TFLongformerModel,
						    TFLongformerPreTrainedModel,
						    TFLongformerSelfAttention,
						)

else:
			import sys

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





"""simple docstring"""


def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     int:
		return int((input_a, input_a).count(0			) == 0			)






def    a__   (   )     ->     None:
		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))


