
from typing import TYPE_CHECKING

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


__A				={'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiTOnnxConfig''']}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=['''DeiTFeatureExtractor''']
			__A				=['''DeiTImageProcessor''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''DeiTForImageClassification''',
			    '''DeiTForImageClassificationWithTeacher''',
			    '''DeiTForMaskedImageModeling''',
			    '''DeiTModel''',
			    '''DeiTPreTrainedModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFDeiTForImageClassification''',
			    '''TFDeiTForImageClassificationWithTeacher''',
			    '''TFDeiTForMaskedImageModeling''',
			    '''TFDeiTModel''',
			    '''TFDeiTPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_deit import DeiTFeatureExtractor
						from .image_processing_deit import DeiTImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_deit import (
						    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DeiTForImageClassification,
						    DeiTForImageClassificationWithTeacher,
						    DeiTForMaskedImageModeling,
						    DeiTModel,
						    DeiTPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_deit import (
						    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFDeiTForImageClassification,
						    TFDeiTForImageClassificationWithTeacher,
						    TFDeiTForMaskedImageModeling,
						    TFDeiTModel,
						    TFDeiTPreTrainedModel,
						)


else:
			import sys

			__A				=_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()