





from __future__ import annotations



def 							lowerCAmelCase_				(    A_  ,A_  ,A_  ,):
    if not all((nth_term, start_term_a, common_ratio_r)):
        return []
    UpperCamelCase__:					list[float | int]												=      []
    UpperCamelCase__:					List[str]												=      1
    UpperCamelCase__:					Dict												=      common_ratio_r
    for _ in range(int(A_)):
        if not series:
            series.append(A_)
        else:
            power += 1
            series.append(float(start_term_a * multiple))
            UpperCamelCase__:					List[Any]												=      pow(float(A_)  ,A_)
    return series


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   A__:  Any       					=					float(input('''Enter the last number (n term) of the Geometric Series'''))
   A__:  Tuple       					=					float(input('''Enter the starting term (a) of the Geometric Series'''))
   A__:  int       					=					float(
       input('''Enter the common ratio between two terms (r) of the Geometric Series''')
   )
   print('''Formula of Geometric Series => a + ar + ar^2 ... +ar^n''')
   print(geometric_series(nth_term, start_term_a, common_ratio_r))








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__:  List[str]       					=					{
    '''configuration_wav2vec2_conformer''': [
        '''WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Wav2Vec2ConformerConfig''',
    ],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:  int       					=					[
       '''WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''Wav2Vec2ConformerForAudioFrameClassification''',
       '''Wav2Vec2ConformerForCTC''',
       '''Wav2Vec2ConformerForPreTraining''',
       '''Wav2Vec2ConformerForSequenceClassification''',
       '''Wav2Vec2ConformerForXVector''',
       '''Wav2Vec2ConformerModel''',
       '''Wav2Vec2ConformerPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_wavaveca_conformer import (
       WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       WavaVecaConformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_wavaveca_conformer import (
          WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          WavaVecaConformerForAudioFrameClassification,
          WavaVecaConformerForCTC,
          WavaVecaConformerForPreTraining,
          WavaVecaConformerForSequenceClassification,
          WavaVecaConformerForXVector,
          WavaVecaConformerModel,
          WavaVecaConformerPreTrainedModel,
      )

else:
   import sys

   A__:  Dict       					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

