
'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A__ :		Any					={
    'configuration_sam': [
        'SAM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SamConfig',
        'SamMaskDecoderConfig',
        'SamPromptEncoderConfig',
        'SamVisionConfig',
    ],
    'processing_sam': ['SamProcessor'],
}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		List[str]					=[
         'SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
         'SamModel',
         'SamPreTrainedModel',
     ]
try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		List[str]					=[
         'TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFSamModel',
         'TFSamPreTrainedModel',
     ]
try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		List[Any]					=['SamImageProcessor']


if TYPE_CHECKING:
     from .configuration_sam import (
         SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
         SamConfig,
         SamMaskDecoderConfig,
         SamPromptEncoderConfig,
         SamVisionConfig,
     )
     from .processing_sam import SamProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .image_processing_sam import SamImageProcessor

else:
     import sys

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





'''simple docstring'''
from __future__ import annotations







def 							A_  (    __SCREAMING_SNAKE_CASE    :	float | int      ,  __SCREAMING_SNAKE_CASE    :	float | int      ,  __SCREAMING_SNAKE_CASE    :	float | int      ,  )					->							list[float | int]:





  """simple docstring"""

  if not all((nth_term, start_term_a, common_ratio_r)			):
    return []
  __A		:  list[float | int]       				=		[]
  __A		:  Union[str, Any]       				=		1
  __A		:  List[str]       				=		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		:  List[str]       				=		pow(float(__SCREAMING_SNAKE_CASE			)      ,  __SCREAMING_SNAKE_CASE			)
  return series


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     A__ :		Optional[int]					=float(input('Enter the last number (n term) of the Geometric Series'))
     A__ :		List[Any]					=float(input('Enter the starting term (a) of the Geometric Series'))
     A__ :		List[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))



