
import numpy as np




def 							a		(				snake_case__:   np.array   ):


				'''simple docstring'''
				return 1 / (1 + np.exp(-vector   ))


if __name__ == "__main__":
						import doctest

						doctest.testmod()









"""simple docstring"""







from typing import TYPE_CHECKING

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


UpperCamelCase     =							{
    """configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PerceiverConfig""", """PerceiverOnnxConfig"""],
    """tokenization_perceiver""": ["""PerceiverTokenizer"""],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase     =							["""PerceiverFeatureExtractor"""]
			UpperCamelCase     =							["""PerceiverImageProcessor"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase     =							[
			    """PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """PerceiverForImageClassificationConvProcessing""",
			    """PerceiverForImageClassificationFourier""",
			    """PerceiverForImageClassificationLearned""",
			    """PerceiverForMaskedLM""",
			    """PerceiverForMultimodalAutoencoding""",
			    """PerceiverForOpticalFlow""",
			    """PerceiverForSequenceClassification""",
			    """PerceiverLayer""",
			    """PerceiverModel""",
			    """PerceiverPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
			from .tokenization_perceiver import PerceiverTokenizer

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_perceiver import PerceiverFeatureExtractor
						from .image_processing_perceiver import PerceiverImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_perceiver import (
						    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    PerceiverForImageClassificationConvProcessing,
						    PerceiverForImageClassificationFourier,
						    PerceiverForImageClassificationLearned,
						    PerceiverForMaskedLM,
						    PerceiverForMultimodalAutoencoding,
						    PerceiverForOpticalFlow,
						    PerceiverForSequenceClassification,
						    PerceiverLayer,
						    PerceiverModel,
						    PerceiverPreTrainedModel,
						)

else:
			import sys

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




