


'''simple docstring'''





from typing import TYPE_CHECKING

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


UpperCAmelCase__  			=				{
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__  			=				[
					    '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''XLMForMultipleChoice''',
					    '''XLMForQuestionAnswering''',
					    '''XLMForQuestionAnsweringSimple''',
					    '''XLMForSequenceClassification''',
					    '''XLMForTokenClassification''',
					    '''XLMModel''',
					    '''XLMPreTrainedModel''',
					    '''XLMWithLMHeadModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__  			=				[
					    '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFXLMForMultipleChoice''',
					    '''TFXLMForQuestionAnsweringSimple''',
					    '''TFXLMForSequenceClassification''',
					    '''TFXLMForTokenClassification''',
					    '''TFXLMMainLayer''',
					    '''TFXLMModel''',
					    '''TFXLMPreTrainedModel''',
					    '''TFXLMWithLMHeadModel''',
					]


if TYPE_CHECKING:
					from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
					from .tokenization_xlm import XLMTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm import (
										    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMForMultipleChoice,
										    XLMForQuestionAnswering,
										    XLMForQuestionAnsweringSimple,
										    XLMForSequenceClassification,
										    XLMForTokenClassification,
										    XLMModel,
										    XLMPreTrainedModel,
										    XLMWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xlm import (
										    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXLMForMultipleChoice,
										    TFXLMForQuestionAnsweringSimple,
										    TFXLMForSequenceClassification,
										    TFXLMForTokenClassification,
										    TFXLMMainLayer,
										    TFXLMModel,
										    TFXLMPreTrainedModel,
										    TFXLMWithLMHeadModel,
										)

else:
					import sys

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







'''simple docstring'''





def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: int ):






			"""simple docstring"""




			return int((input_a, input_a).count(1 ) != 0 )






def 						UpperCAmelCase__(				):






			"""simple docstring"""




			assert or_gate(0,0 ) == 0
			assert or_gate(0,1 ) == 1
			assert or_gate(1,0 ) == 1
			assert or_gate(1,1 ) == 1


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



