





import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
				import sqlitea

				import sqlalchemy


__lowerCAmelCase 	=      datasets.utils.logging.get_logger(__name__)




@dataclass
class 	_UpperCAmelCase						( datasets.BuilderConfig       ):
					__A        = None
					__A        = None
					__A        = None
					__A        = True
					__A        = None
					__A        = None
					__A        = None
					__A        = 1_00_00
					__A        = None




					def 						snake_case_		(    self							)    ->	Any:
							if self.sql is None:
									raise ValueError("""sql must be specified"""							)
							if self.con is None:
									raise ValueError("""con must be specified"""							)




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase = None     ,   )    ->	str:
							UpperCamelCase__								=						config_kwargs.copy()
							# We need to stringify the Selectable object to make its hash deterministic

							# The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
							UpperCamelCase__								=						config_kwargs["""sql"""]
							if not isinstance(__lowerCAmelCase     ,   __lowerCAmelCase							):
									if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
											import sqlalchemy

											if isinstance(__lowerCAmelCase     ,   sqlalchemy.sql.Selectable							):
													UpperCamelCase__								=						sqlalchemy.create_engine(config_kwargs["""con"""].split("""://"""							)[0] + """://"""							)
													UpperCamelCase__								=						str(sql.compile(dialect=engine.dialect							)							)
													UpperCamelCase__								=						sql_str
											else:
													raise TypeError(
													    F"""Supported types for 'sql' are string and sqlalchemy.sql.Selectable but got {type(__lowerCAmelCase							)}: {sql}"""							)
									else:
											raise TypeError(
											    F"""Supported types for 'sql' are string and sqlalchemy.sql.Selectable but got {type(__lowerCAmelCase							)}: {sql}"""							)
							UpperCamelCase__								=						config_kwargs["""con"""]
							if not isinstance(__lowerCAmelCase     ,   __lowerCAmelCase							):
									UpperCamelCase__								=						id(__lowerCAmelCase							)
									logger.info(
									    F"""SQL connection 'con' of type {type(__lowerCAmelCase							)} couldn't be hashed properly. To enable hashing, specify 'con' as URI string instead."""							)

							return super().create_config_id(__lowerCAmelCase     ,   custom_features=__lowerCAmelCase							)




					@property
					def 						snake_case_		(    self							)    ->	int:
							UpperCamelCase__								=						{
							    """index_col""": self.index_col,
							    """columns""": self.columns,
							    """params""": self.params,
							    """coerce_float""": self.coerce_float,
							    """parse_dates""": self.parse_dates,
							}
							return pd_read_sql_kwargs




class 	_UpperCAmelCase						( datasets.ArrowBasedBuilder       ):
					__A        = SqlConfig




					def 						snake_case_		(    self							)    ->	Any:
							return datasets.DatasetInfo(features=self.config.features							)




					def 						snake_case_		(    self     ,   __lowerCAmelCase							)    ->	Dict:
							return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,   gen_kwargs={}							)]




					def 						snake_case_		(    self     ,   __lowerCAmelCase							)    ->	pa.Table:
							if self.config.features is not None:
									UpperCamelCase__								=						self.config.features.arrow_schema
									if all(not require_storage_cast(__lowerCAmelCase							) for feature in self.config.features.values()							):
											# cheaper cast
											UpperCamelCase__								=						pa.Table.from_arrays([pa_table[field.name] for field in schema]     ,   schema=__lowerCAmelCase							)
									else:
											# more expensive cast; allows str <-> int/float or str to Audio for example
											UpperCamelCase__								=						table_cast(__lowerCAmelCase     ,   __lowerCAmelCase							)
							return pa_table




					def 						snake_case_		(    self							)    ->	str:
							UpperCamelCase__								=						self.config.chunksize
							UpperCamelCase__								=						pd.read_sql(
							    self.config.sql     ,   self.config.con     ,   chunksize=__lowerCAmelCase     ,   **self.config.pd_read_sql_kwargs							)
							UpperCamelCase__								=						[sql_reader] if chunksize is None else sql_reader
							for chunk_idx, df in enumerate(__lowerCAmelCase							):
									UpperCamelCase__								=						pa.Table.from_pandas(__lowerCAmelCase							)
									yield chunk_idx, self._cast_table(__lowerCAmelCase							)












from __future__ import annotations

def      a    (   _lowercase :  float | int			,    _lowercase :  float | int			,    _lowercase :  float | int			,    )  ->list[float | int]:
		if not all((nth_term, start_term_a, common_ratio_r)							):
				return []
		UpperCamelCase__								=						[]
		UpperCamelCase__								=						1
		UpperCamelCase__								=						common_ratio_r
		for _ in range(int(_lowercase							)							):
				if not series:
						series.append(_lowercase							)
				else:
						power += 1
						series.append(float(start_term_a * multiple							)							)
						UpperCamelCase__								=						pow(float(_lowercase							)			,    _lowercase							)
		return series


if __name__ == "__main__":
				import doctest

				doctest.testmod()

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





