


"""simple docstring"""


from typing import TYPE_CHECKING

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


a_       							=    {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    ["FNetTokenizer"]

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    ["FNetTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "FNetForMaskedLM",
		    "FNetForMultipleChoice",
		    "FNetForNextSentencePrediction",
		    "FNetForPreTraining",
		    "FNetForQuestionAnswering",
		    "FNetForSequenceClassification",
		    "FNetForTokenClassification",
		    "FNetLayer",
		    "FNetModel",
		    "FNetPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_fnet import FNetTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_fnet_fast import FNetTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_fnet import (
				    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    FNetForMaskedLM,
				    FNetForMultipleChoice,
				    FNetForNextSentencePrediction,
				    FNetForPreTraining,
				    FNetForQuestionAnswering,
				    FNetForSequenceClassification,
				    FNetForTokenClassification,
				    FNetLayer,
				    FNetModel,
				    FNetPreTrainedModel,
				)


else:
		import sys

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



"""simple docstring"""


import numpy as np






def      a__ (				__lowercase     ,			__lowercase   )	->	np.ndarray:
				return np.where(vector > 0     ,			__lowercase     ,			(alpha * (np.exp(__lowercase   ) - 1))   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()