


from typing import TYPE_CHECKING

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


__lowerCAmelCase	: Optional[int]  				={
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase	: List[Any]  				=[
						    '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:
						__lowerCAmelCase	: Union[str, Any]  				=[
						    '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:
						__lowerCAmelCase	: Optional[Any]  				=[
						    '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

						__lowerCAmelCase	: List[str]  				=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__=False      ):

						if isinstance(lowercase__    ,       lowercase__      ) and isinstance(lowercase__    ,       lowercase__      ):
												__SCREAMING_SNAKE_CASE						:     List[str]					=		len(set_a.intersection(lowercase__      )      )

												if alternative_union:
																		__SCREAMING_SNAKE_CASE						:     int					=		len(lowercase__      ) + len(lowercase__      )
												else:
																		__SCREAMING_SNAKE_CASE						:     int					=		len(set_a.union(lowercase__      )      )

												return intersection / union

						if isinstance(lowercase__    ,       (list, tuple)      ) and isinstance(lowercase__    ,       (list, tuple)      ):
												__SCREAMING_SNAKE_CASE						:     Dict					=		[element for element in set_a if element in set_b]

												if alternative_union:
																		__SCREAMING_SNAKE_CASE						:     Optional[int]					=		len(lowercase__      ) + len(lowercase__      )
																		return len(lowercase__      ) / union
												else:
																		__SCREAMING_SNAKE_CASE						:     Tuple					=		set_a + [element for element in set_b if element not in set_a]
																		return len(lowercase__      ) / len(lowercase__      )

												return len(lowercase__      ) / len(lowercase__      )
						return None


if __name__ == "__main__":
						__lowerCAmelCase	: List[Any]  				={'a', 'b', 'c', 'd', 'e'}
						__lowerCAmelCase	: Optional[Any]  				={'c', 'd', 'e', 'f', 'h', 'i'}
						print(jaccard_similarity(set_a, set_b))
