



from typing import TYPE_CHECKING

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


A__								=						{
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

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

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A__								=						[
				    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''RoFormerForCausalLM''',
				    '''RoFormerForMaskedLM''',
				    '''RoFormerForMultipleChoice''',
				    '''RoFormerForQuestionAnswering''',
				    '''RoFormerForSequenceClassification''',
				    '''RoFormerForTokenClassification''',
				    '''RoFormerLayer''',
				    '''RoFormerModel''',
				    '''RoFormerPreTrainedModel''',
				    '''load_tf_weights_in_roformer''',
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A__								=						[
				    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFRoFormerForCausalLM''',
				    '''TFRoFormerForMaskedLM''',
				    '''TFRoFormerForMultipleChoice''',
				    '''TFRoFormerForQuestionAnswering''',
				    '''TFRoFormerForSequenceClassification''',
				    '''TFRoFormerForTokenClassification''',
				    '''TFRoFormerLayer''',
				    '''TFRoFormerModel''',
				    '''TFRoFormerPreTrainedModel''',
				]


try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A__								=						[
				    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''FlaxRoFormerForMaskedLM''',
				    '''FlaxRoFormerForMultipleChoice''',
				    '''FlaxRoFormerForQuestionAnswering''',
				    '''FlaxRoFormerForSequenceClassification''',
				    '''FlaxRoFormerForTokenClassification''',
				    '''FlaxRoFormerModel''',
				    '''FlaxRoFormerPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
				from .tokenization_roformer import RoFormerTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_roformer_fast import RoFormerTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_roformer import (
								    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RoFormerForCausalLM,
								    RoFormerForMaskedLM,
								    RoFormerForMultipleChoice,
								    RoFormerForQuestionAnswering,
								    RoFormerForSequenceClassification,
								    RoFormerForTokenClassification,
								    RoFormerLayer,
								    RoFormerModel,
								    RoFormerPreTrainedModel,
								    load_tf_weights_in_roformer,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_roformer import (
								    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRoFormerForCausalLM,
								    TFRoFormerForMaskedLM,
								    TFRoFormerForMultipleChoice,
								    TFRoFormerForQuestionAnswering,
								    TFRoFormerForSequenceClassification,
								    TFRoFormerForTokenClassification,
								    TFRoFormerLayer,
								    TFRoFormerModel,
								    TFRoFormerPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_roformer import (
								    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    FlaxRoFormerForMaskedLM,
								    FlaxRoFormerForMultipleChoice,
								    FlaxRoFormerForQuestionAnswering,
								    FlaxRoFormerForSequenceClassification,
								    FlaxRoFormerForTokenClassification,
								    FlaxRoFormerModel,
								    FlaxRoFormerPreTrainedModel,
								)


else:
				import sys

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






def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					int:


				"""simple docstring"""

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



def 			_lowerCAmelCase       (					)   ->					None:


				"""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))

