






from typing import TYPE_CHECKING

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


lowerCAmelCase_     =			{
    'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_     =			[
					    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'NystromformerForMaskedLM',
					    'NystromformerForMultipleChoice',
					    'NystromformerForQuestionAnswering',
					    'NystromformerForSequenceClassification',
					    'NystromformerForTokenClassification',
					    'NystromformerLayer',
					    'NystromformerModel',
					    'NystromformerPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_nystromformer import (
										    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    NystromformerForMaskedLM,
										    NystromformerForMultipleChoice,
										    NystromformerForQuestionAnswering,
										    NystromformerForSequenceClassification,
										    NystromformerForTokenClassification,
										    NystromformerLayer,
										    NystromformerModel,
										    NystromformerPreTrainedModel,
										)


else:
					import sys

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













import numpy as np







def   snake_case		(				UpperCAmelCase						:							Any ):
				A									=   np.exp(UpperCAmelCase )

				# Add up the all the exponentials
				A									=   np.sum(UpperCAmelCase )

				# Divide every exponent by the sum of all exponents
				A									=   exponent_vector / sum_of_exponents

				return softmax_vector


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





