



import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

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



class       _lowercase				(  TensorFormatter[Mapping, np.ndarray, Mapping]      ):





	"""simple docstring"""




	def __init__(self						,			lowerCamelCase_=None						,			**lowerCamelCase_						):
						"""simple docstring"""

						super().__init__(features=__lowerCAmelCase						)
						a   	=  np_array_kwargs




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						if isinstance(__lowerCAmelCase						,			__lowerCAmelCase						):
											if column and all(
											    isinstance(__lowerCAmelCase						,			np.ndarray						) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column						):
																return np.stack(__lowerCAmelCase						)
											else:
																# don't use np.array(column, dtype=object)
																# since it fails in certain cases
																# see https://stackoverflow.com/q/51005699
																a   	=  np.empty(len(__lowerCAmelCase						)						,			dtype=__lowerCAmelCase						)
																a   	=  column
																return out
						return column




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						if isinstance(__lowerCAmelCase						,			(str, bytes, type(__lowerCAmelCase						))						):
											return value
						elif isinstance(__lowerCAmelCase						,			(np.character, np.ndarray)						) and np.issubdtype(value.dtype						,			np.character						):
											return value
						elif isinstance(__lowerCAmelCase						,			np.number						):
											return value

						a   	=  {}

						if isinstance(__lowerCAmelCase						,			np.ndarray						) and np.issubdtype(value.dtype						,			np.integer						):
											a   	=  {"dtype": np.intaa}
						elif isinstance(__lowerCAmelCase						,			np.ndarray						) and np.issubdtype(value.dtype						,			np.floating						):
											a   	=  {"dtype": np.floataa}
						elif config.PIL_AVAILABLE and "PIL" in sys.modules:
											import PIL.Image

											if isinstance(__lowerCAmelCase						,			PIL.Image.Image						):
																return np.asarray(__lowerCAmelCase						,			**self.np_array_kwargs						)

						return np.asarray(__lowerCAmelCase						,			**{**default_dtype, **self.np_array_kwargs}						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						if config.TORCH_AVAILABLE and "torch" in sys.modules:
											import torch

											if isinstance(__lowerCAmelCase						,			torch.Tensor						):
																return self._tensorize(data_struct.detach().cpu().numpy()[()]						)
						if hasattr(__lowerCAmelCase						,			"__array__"						) and not isinstance(__lowerCAmelCase						,			(np.ndarray, np.character, np.number)						):
											a   	=  data_struct.__array__()
						# support for nested types like struct of list of struct
						if isinstance(__lowerCAmelCase						,			np.ndarray						):
											if data_struct.dtype == object:
																return self._consolidate([self.recursive_tensorize(__lowerCAmelCase						) for substruct in data_struct]						)
						if isinstance(__lowerCAmelCase						,			(list, tuple)						):
											return self._consolidate([self.recursive_tensorize(__lowerCAmelCase						) for substruct in data_struct]						)
						return self._tensorize(__lowerCAmelCase						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						return map_nested(self._recursive_tensorize						,			__lowerCAmelCase						,			map_list=__lowerCAmelCase						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						a   	=  self.numpy_arrow_extractor().extract_row(__lowerCAmelCase						)
						a   	=  self.python_features_decoder.decode_row(__lowerCAmelCase						)
						return self.recursive_tensorize(__lowerCAmelCase						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						a   	=  self.numpy_arrow_extractor().extract_column(__lowerCAmelCase						)
						a   	=  self.python_features_decoder.decode_column(__lowerCAmelCase						,			pa_table.column_names[0]						)
						a   	=  self.recursive_tensorize(__lowerCAmelCase						)
						a   	=  self._consolidate(__lowerCAmelCase						)
						return column




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						a   	=  self.numpy_arrow_extractor().extract_batch(__lowerCAmelCase						)
						a   	=  self.python_features_decoder.decode_batch(__lowerCAmelCase						)
						a   	=  self.recursive_tensorize(__lowerCAmelCase						)
						for column_name in batch:
											a   	=  self._consolidate(batch[column_name]						)
						return batch





from __future__ import annotations


def 						UpperCAmelCase__   (   UpperCAmelCase__			:float | int			,       UpperCAmelCase__			:float | int			,       UpperCAmelCase__			: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(UpperCAmelCase__       )       ):
  if not series:
   series.append(UpperCAmelCase__       )
  else:
   power += 1
   series.append(float(start_term_a * multiple       )       )
   a							=		pow(float(UpperCAmelCase__       )			,       UpperCAmelCase__       )
 return series


if __name__ == "__main__":
     import doctest

     doctest.testmod()

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

