

'''simple docstring'''

from __future__ import annotations

def  __UpperCamelCase							(		lowercase__  :     float,    lowercase__  :     float,    lowercase__  :     float       ):






     '''simple docstring'''





     if days_between_payments <= 0:
          raise ValueError('days_between_payments must be > 0'       )
     if daily_interest_rate < 0:
          raise ValueError('daily_interest_rate must be >= 0'       )
     if principal <= 0:
          raise ValueError('principal must be > 0'       )
     return principal * daily_interest_rate * days_between_payments

def  __UpperCamelCase							(		lowercase__  :     float,    lowercase__  :     float,    lowercase__  :     float,    ):






     '''simple docstring'''





     if number_of_compounding_periods <= 0:
          raise ValueError('number_of_compounding_periods must be > 0'       )
     if nominal_annual_interest_rate_percentage < 0:
          raise ValueError('nominal_annual_interest_rate_percentage must be >= 0'       )
     if principal <= 0:
          raise ValueError('principal must be > 0'       )

     return principal * (
         (1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
         - 1
     )

def  __UpperCamelCase							(		lowercase__  :     float,    lowercase__  :     float,    lowercase__  :     float,    ):






     '''simple docstring'''





     if number_of_years <= 0:
          raise ValueError('number_of_years must be > 0'       )
     if nominal_annual_percentage_rate < 0:
          raise ValueError('nominal_annual_percentage_rate must be >= 0'       )
     if principal <= 0:
          raise ValueError('principal must be > 0'       )

     return compound_interest(
         __snake_case,    nominal_annual_percentage_rate / 3_65,    number_of_years * 3_65       )


if __name__ == "__main__":
       import doctest

       doctest.testmod()










"""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 torch





class 					_UpperCAmelCase       ( TensorFormatter[Mapping, "torch.Tensor", Mapping]							):




					def __init__(				self     :						Any    ,						A     :						int=None    ,						**A     :						str   )  ->  Union[str, Any]:
							super().__init__(features=A   )
							lowercase_ : Union[str, Any]							       =   torch_tensor_kwargs
							import torch  # noqa import torch at initialization




					def   A  (				self     :						Dict    ,						A     :						int   )  ->  List[Any]:
							import torch

							if isinstance(A    ,						A   ) and column:
									if all(
									    isinstance(A    ,						torch.Tensor   ) and x.shape == column[0].shape and x.dtype == column[0].dtype
									    for x in column   ):
											return torch.stack(A   )
							return column




					def   A  (				self     :						int    ,						A     :						Any   )  ->  Optional[Any]:
							import torch

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

							lowercase_ : Any							       =   {}

							if isinstance(A    ,						(np.number, np.ndarray)   ) and np.issubdtype(value.dtype    ,						np.integer   ):
									lowercase_ : Any							       =   {'''dtype''': torch.intaa}
							elif isinstance(A    ,						(np.number, np.ndarray)   ) and np.issubdtype(value.dtype    ,						np.floating   ):
									lowercase_ : Dict							       =   {'''dtype''': torch.floataa}
							elif config.PIL_AVAILABLE and "PIL" in sys.modules:
									import PIL.Image

									if isinstance(A    ,						PIL.Image.Image   ):
											lowercase_ : Dict							       =   np.asarray(A   )
							return torch.tensor(A    ,						**{**default_dtype, **self.torch_tensor_kwargs}   )




					def   A  (				self     :						Union[str, Any]    ,						A     :						Optional[int]   )  ->  str:
							import torch

							# support for torch, tf, jax etc.
							if hasattr(A    ,						'''__array__'''   ) and not isinstance(A    ,						torch.Tensor   ):
									lowercase_ : Optional[int]							       =   data_struct.__array__()
							# support for nested types like struct of list of struct
							if isinstance(A    ,						np.ndarray   ):
									if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
											return self._consolidate([self.recursive_tensorize(A   ) for substruct in data_struct]   )
							elif isinstance(A    ,						(list, tuple)   ):
									return self._consolidate([self.recursive_tensorize(A   ) for substruct in data_struct]   )
							return self._tensorize(A   )




					def   A  (				self     :						Dict    ,						A     :						dict   )  ->  Tuple:
							return map_nested(self._recursive_tensorize    ,						A    ,						map_list=A   )




					def   A  (				self     :						str    ,						A     :						pa.Table   )  ->  Mapping:
							lowercase_ : Optional[Any]							       =   self.numpy_arrow_extractor().extract_row(A   )
							lowercase_ : str							       =   self.python_features_decoder.decode_row(A   )
							return self.recursive_tensorize(A   )




					def   A  (				self     :						List[Any]    ,						A     :						pa.Table   )  ->  "torch.Tensor":
							lowercase_ : List[str]							       =   self.numpy_arrow_extractor().extract_column(A   )
							lowercase_ : str							       =   self.python_features_decoder.decode_column(A    ,						pa_table.column_names[0]   )
							lowercase_ : Optional[int]							       =   self.recursive_tensorize(A   )
							lowercase_ : Any							       =   self._consolidate(A   )
							return column






					def   A  (				self     :						List[str]    ,						A     :						pa.Table   )  ->  Mapping:
							lowercase_ : Optional[int]							       =   self.numpy_arrow_extractor().extract_batch(A   )
							lowercase_ : int							       =   self.python_features_decoder.decode_batch(A   )
							lowercase_ : Dict							       =   self.recursive_tensorize(A   )
							for column_name in batch:
									lowercase_ : Optional[Any]							       =   self._consolidate(batch[column_name]   )
							return batch



