
'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A			      =      {
    'configuration_squeezebert': [
        'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SqueezeBertConfig',
        'SqueezeBertOnnxConfig',
    ],
    'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}

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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A			      =      [
					    'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'SqueezeBertForMaskedLM',
					    'SqueezeBertForMultipleChoice',
					    'SqueezeBertForQuestionAnswering',
					    'SqueezeBertForSequenceClassification',
					    'SqueezeBertForTokenClassification',
					    'SqueezeBertModel',
					    'SqueezeBertModule',
					    'SqueezeBertPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_squeezebert import (
					    SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    SqueezeBertConfig,
					    SqueezeBertOnnxConfig,
					)
					from .tokenization_squeezebert import SqueezeBertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_squeezebert import (
										    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SqueezeBertForMaskedLM,
										    SqueezeBertForMultipleChoice,
										    SqueezeBertForQuestionAnswering,
										    SqueezeBertForSequenceClassification,
										    SqueezeBertForTokenClassification,
										    SqueezeBertModel,
										    SqueezeBertModule,
										    SqueezeBertPreTrainedModel,
										)

else:
					import sys

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





'''simple docstring'''






import numpy as np





def    UpperCAmelCase							(  UpperCAmelCase__							:							np.array):
				return 1 / (1 + np.exp(-vector))


if __name__ == "__main__":
					import doctest

					doctest.testmod()
