





from __future__ import annotations





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    )       ->   list[float | int]:




    """simple docstring"""




    if not all((nth_term, start_term_a, common_ratio_r)							):
        return []
    _A    =       []
    _A    =       1
    _A    =       common_ratio_r
    for _ in range(int(_SCREAMING_SNAKE_CASE							)							):
        if not series:
            series.append(_SCREAMING_SNAKE_CASE							)
        else:
            power += 1
            series.append(float(start_term_a * multiple							)							)
            _A    =       pow(float(_SCREAMING_SNAKE_CASE							)       ,    _SCREAMING_SNAKE_CASE							)
    return series


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 __A       : Union[str, Any]						    = float(input("Enter the last number (n term) of the Geometric Series"))
 __A       : List[str]						    = float(input("Enter the starting term (a) of the Geometric Series"))
 __A       : Union[str, Any]						    = 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_tf_available, is_torch_available


__A       : Dict						    = {"configuration_hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       : List[str]						    = [
     "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "HubertForCTC",
     "HubertForSequenceClassification",
     "HubertModel",
     "HubertPreTrainedModel",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       : Tuple						    = [
     "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFHubertForCTC",
     "TFHubertModel",
     "TFHubertPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_hubert import (
      HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      HubertForCTC,
      HubertForSequenceClassification,
      HubertModel,
      HubertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_hubert import (
      TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFHubertForCTC,
      TFHubertModel,
      TFHubertPreTrainedModel,
  )


else:
 import sys

 __A       : Dict						    = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





