




from typing import TYPE_CHECKING

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


lowercase__							:	Union[str, Any]											=		{
    "configuration_longformer": [
        "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "LongformerConfig",
        "LongformerOnnxConfig",
    ],
    "tokenization_longformer": ["LongformerTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__							:	str											=		["LongformerTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__							:	int											=		[
		    "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:
		lowercase__							:	Optional[int]											=		[
		    "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

		lowercase__							:	Union[str, Any]											=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






import numpy as np


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	return (2 / (1 + np.exp(-2 * vector				))) - 1


if __name__ == "__main__":
		import doctest

		doctest.testmod()
