code: 
  
  
  """simple docstring"""
  
  
  
  
  # Copyright 2023 The HuggingFace Team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
      is_vision_available,
  )
  
  
  __magic_name__	:   Dict          =							{
      'configuration_tvlt': ['TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TvltConfig'],
      'feature_extraction_tvlt': ['TvltFeatureExtractor'],
      'processing_tvlt': ['TvltProcessor'],
  }
  
  try:
  					if not is_torch_available():
  										raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  					pass
  else:
  					__magic_name__	:   Tuple          =							[
  					    'TVLT_PRETRAINED_MODEL_ARCHIVE_LIST',
  					    'TvltModel',
  					    'TvltForPreTraining',
  					    'TvltForAudioVisualClassification',
  					    'TvltPreTrainedModel',
  					]
  
  try:
  					if not is_vision_available():
  										raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  					pass
  else:
  					__magic_name__	:   Optional[Any]          =							['TvltImageProcessor']
  
  
  if TYPE_CHECKING:
  					from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
  					from .processing_tvlt import TvltProcessor
  					from .feature_extraction_tvlt import TvltFeatureExtractor
  
  					try:
  										if not is_torch_available():
  															raise OptionalDependencyNotAvailable()
  					except OptionalDependencyNotAvailable:
  										pass
  					else:
  										from .modeling_tvlt import (
  										    TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
  										    TvltForAudioVisualClassification,
  										    TvltForPreTraining,
  										    TvltModel,
  										    TvltPreTrainedModel,
  										)
  
  					try:
  										if not is_vision_available():
  															raise OptionalDependencyNotAvailable()
  					except OptionalDependencyNotAvailable:
  										pass
  					else:
  										from .image_processing_tvlt import TvltImageProcessor
  
  
  else:
  					import sys
  
  					__magic_name__	:   List[str]          =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import numpy as np
  
  
  
  
  
  def 			a_   (  lowercase__   :List[str]    ):
  			__lowerCamelCase        =			np.exp(lowercase__    )
  
  			# Add up the all the exponentials
  			__lowerCamelCase        =			np.sum(lowercase__    )
  
  			# Divide every exponent by the sum of all exponents
  			__lowerCamelCase        =			exponent_vector / sum_of_exponents
  
  			return softmax_vector
  
  
  if __name__ == "__main__":
  					print(softmax((0,)))
  
  
  
  
  