
"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
    import tensorflow as tf






class   _UpperCAmelCase					(       TensorFormatter[Mapping, """tf.Tensor""", Mapping]):
    def __init__(     self    ,     snake_case_=None    ,     **snake_case_  ):
      super().__init__(features=snake_case_  )
      _snake_case  :    List[str]            = tf_tensor_kwargs
      import tensorflow as tf  # noqa: import tf at initialization
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      import tensorflow as tf

      if isinstance(snake_case_    ,     snake_case_  ) and column:
        if all(
            isinstance(snake_case_    ,     tf.Tensor  ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column  ):
          return tf.stack(snake_case_  )
        elif all(
            isinstance(snake_case_    ,     (tf.Tensor, tf.RaggedTensor)  ) and x.ndim == 1 and x.dtype == column[0].dtype
            for x in column  ):
          # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
          return tf.ragged.stack(snake_case_  )

      return column
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      import tensorflow as tf

      if value is None:
        return value

      _snake_case  :    List[Any]            = {}

      if isinstance(snake_case_    ,     (np.number, np.ndarray)  ) and np.issubdtype(value.dtype    ,     np.integer  ):
        _snake_case  :    Optional[int]            = {"dtype": tf.intaa}
      elif isinstance(snake_case_    ,     (np.number, np.ndarray)  ) and np.issubdtype(value.dtype    ,     np.floating  ):
        _snake_case  :    str            = {"dtype": tf.floataa}
      elif config.PIL_AVAILABLE and "PIL" in sys.modules:
        import PIL.Image

        if isinstance(snake_case_    ,     PIL.Image.Image  ):
          _snake_case  :    Dict            = np.asarray(snake_case_  )

      return tf.convert_to_tensor(snake_case_    ,     **{**default_dtype, **self.tf_tensor_kwargs}  )
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      import tensorflow as tf

      # support for torch, tf, jax etc.
      if config.TORCH_AVAILABLE and "torch" in sys.modules:
        import torch

        if isinstance(snake_case_    ,     torch.Tensor  ):
          return self._tensorize(data_struct.detach().cpu().numpy()[()]  )
      if hasattr(snake_case_    ,     "__array__"  ) and not isinstance(snake_case_    ,     tf.Tensor  ):
        _snake_case  :    Any            = data_struct.__array__()
      # support for nested types like struct of list of struct
      if isinstance(snake_case_    ,     np.ndarray  ):
        if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
          return self._consolidate([self.recursive_tensorize(snake_case_  ) for substruct in data_struct]  )
      elif isinstance(snake_case_    ,     (list, tuple)  ):
        return self._consolidate([self.recursive_tensorize(snake_case_  ) for substruct in data_struct]  )
      return self._tensorize(snake_case_  )
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      return map_nested(self._recursive_tensorize    ,     snake_case_    ,     map_list=snake_case_  )
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      _snake_case  :    Optional[Any]            = self.numpy_arrow_extractor().extract_row(snake_case_  )
      _snake_case  :    List[str]            = self.python_features_decoder.decode_row(snake_case_  )
      return self.recursive_tensorize(snake_case_  )
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      _snake_case  :    List[Any]            = self.numpy_arrow_extractor().extract_column(snake_case_  )
      _snake_case  :    str            = self.python_features_decoder.decode_column(snake_case_    ,     pa_table.column_names[0]  )
      _snake_case  :    List[Any]            = self.recursive_tensorize(snake_case_  )
      _snake_case  :    Dict            = self._consolidate(snake_case_  )
      return column


    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      _snake_case  :    str            = self.numpy_arrow_extractor().extract_batch(snake_case_  )
      _snake_case  :    Any            = self.python_features_decoder.decode_batch(snake_case_  )
      _snake_case  :    Optional[Any]            = self.recursive_tensorize(snake_case_  )
      for column_name in batch:
        _snake_case  :    Any            = self._consolidate(batch[column_name]  )
      return batch





'''simple docstring'''

from __future__ import annotations






def __UpperCamelCase (      __lowerCamelCase		:			float | int   ,	__lowerCamelCase		:			float | int   ,	__lowerCamelCase		:			float | int   ,	)   ->  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(__lowerCamelCase					)					):
          if not series:
               series.append(__lowerCamelCase					)
          else:
               power += 1
               series.append(float(start_term_a * multiple					)					)
               _a            =		pow(float(__lowerCamelCase					)   ,	__lowerCamelCase					)
     return series


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   lowercase__					  =		float(input("Enter the last number (n term) of the Geometric Series"))
   lowercase__					  =		float(input("Enter the starting term (a) of the Geometric Series"))
   lowercase__					  =		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))

