




'''simple docstring'''





from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE							:   str      		= {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

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

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

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE							:   Tuple      		= [
		    "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

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






import numpy as np






def        a_		(       __lowercase   :	np.array ) ->  np.array:
			return 1 / (1 + np.exp(-vector ))


if __name__ == "__main__":
			import doctest

			doctest.testmod()