



"""simple docstring"""
from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE  :						List[Any]    							=      {
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						str    							=      [
			    '''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

			__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





"""simple docstring"""
import numpy as np



def        lowerCAmelCase_(   lowercase_     :							np.array							)	->       np.array:
				return 1 / (1 + np.exp(-vector							))



def        lowerCAmelCase_(   lowercase_     :							np.array							)	->       np.array:
				return vector * sigmoid(1.7_0_2 * vector							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
