'''simple docstring'''

from typing import TYPE_CHECKING

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


A_     :						List[str]    							={
    '''configuration_data2vec_audio''': ['''DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Data2VecAudioConfig'''],
    '''configuration_data2vec_text''': [
        '''DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecTextConfig''',
        '''Data2VecTextOnnxConfig''',
    ],
    '''configuration_data2vec_vision''': [
        '''DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecVisionConfig''',
        '''Data2VecVisionOnnxConfig''',
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 A_     :						List[Any]    							=[
     '''DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''Data2VecAudioForAudioFrameClassification''',
     '''Data2VecAudioForCTC''',
     '''Data2VecAudioForSequenceClassification''',
     '''Data2VecAudioForXVector''',
     '''Data2VecAudioModel''',
     '''Data2VecAudioPreTrainedModel''',
 ]
 A_     :						Union[str, Any]    							=[
     '''DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''Data2VecTextForCausalLM''',
     '''Data2VecTextForMaskedLM''',
     '''Data2VecTextForMultipleChoice''',
     '''Data2VecTextForQuestionAnswering''',
     '''Data2VecTextForSequenceClassification''',
     '''Data2VecTextForTokenClassification''',
     '''Data2VecTextModel''',
     '''Data2VecTextPreTrainedModel''',
 ]
 A_     :						int    							=[
     '''DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''Data2VecVisionForImageClassification''',
     '''Data2VecVisionForMaskedImageModeling''',
     '''Data2VecVisionForSemanticSegmentation''',
     '''Data2VecVisionModel''',
     '''Data2VecVisionPreTrainedModel''',
 ]

if is_tf_available():
 A_     :						Union[str, Any]    							=[
     '''TFData2VecVisionForImageClassification''',
     '''TFData2VecVisionForSemanticSegmentation''',
     '''TFData2VecVisionModel''',
     '''TFData2VecVisionPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
 from .configuration_dataavec_text import (
     DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DataaVecTextConfig,
     DataaVecTextOnnxConfig,
 )
 from .configuration_dataavec_vision import (
     DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DataaVecVisionConfig,
     DataaVecVisionOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_dataavec_audio import (
      DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecAudioForAudioFrameClassification,
      DataaVecAudioForCTC,
      DataaVecAudioForSequenceClassification,
      DataaVecAudioForXVector,
      DataaVecAudioModel,
      DataaVecAudioPreTrainedModel,
  )
  from .modeling_dataavec_text import (
      DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecTextForCausalLM,
      DataaVecTextForMaskedLM,
      DataaVecTextForMultipleChoice,
      DataaVecTextForQuestionAnswering,
      DataaVecTextForSequenceClassification,
      DataaVecTextForTokenClassification,
      DataaVecTextModel,
      DataaVecTextPreTrainedModel,
  )
  from .modeling_dataavec_vision import (
      DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecVisionForImageClassification,
      DataaVecVisionForMaskedImageModeling,
      DataaVecVisionForSemanticSegmentation,
      DataaVecVisionModel,
      DataaVecVisionPreTrainedModel,
  )
 if is_tf_available():
  from .modeling_tf_dataavec_vision import (
      TFDataaVecVisionForImageClassification,
      TFDataaVecVisionForSemanticSegmentation,
      TFDataaVecVisionModel,
      TFDataaVecVisionPreTrainedModel,
  )

else:
 import sys

 A_     :						str    							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






'''simple docstring'''

def snake_case_      (					__snake_case  :							int	,   __snake_case  :							int	,   __snake_case  :							int)			->					float:
   lowerCAmelCase_			=      (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
   # formula for sum of series
   return total



def snake_case_      (					)			->					str:
   print(sum_of_series(1	,   1	,   10))


if __name__ == "__main__":
 import doctest

 doctest.testmod()





