


import numpy as np
def 							_UpperCAmelCase       (UpperCamelCase__  :						List[str]		):
       _A   : Any       					=				np.exp(UpperCamelCase__		)

       # Add up the all the exponentials
       _A   : int       					=				np.sum(UpperCamelCase__		)

       # Divide every exponent by the sum of all exponents
       _A   : Any       					=				exponent_vector / sum_of_exponents

       return softmax_vector


if __name__ == "__main__":
    print(softmax((0,)))




from typing import TYPE_CHECKING

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


lowerCAmelCase__   	=				{
    'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__   	=				['SegformerFeatureExtractor']
    lowerCAmelCase__   	=				['SegformerImageProcessor']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__   	=				[
        'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'SegformerDecodeHead',
        'SegformerForImageClassification',
        'SegformerForSemanticSegmentation',
        'SegformerLayer',
        'SegformerModel',
        'SegformerPreTrainedModel',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__   	=				[
        'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFSegformerDecodeHead',
        'TFSegformerForImageClassification',
        'TFSegformerForSemanticSegmentation',
        'TFSegformerModel',
        'TFSegformerPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_segformer import SegformerFeatureExtractor
        from .image_processing_segformer import SegformerImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_segformer import (
            SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            SegformerDecodeHead,
            SegformerForImageClassification,
            SegformerForSemanticSegmentation,
            SegformerLayer,
            SegformerModel,
            SegformerPreTrainedModel,
        )
    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_segformer import (
            TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFSegformerDecodeHead,
            TFSegformerForImageClassification,
            TFSegformerForSemanticSegmentation,
            TFSegformerModel,
            TFSegformerPreTrainedModel,
        )

else:
    import sys

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