






"""simple docstring"""





from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
UpperCamelCase_						:					Tuple      				=			TypeVar("""T""", bound="""Enum""")


@unique
class   _lowercase			(  lowerCAmelCase    ):
      _a :  List[str]										= 80
      _a :  str										= 70
      _a :  Dict										= 60
      _a :  str										= 50
      _a :  Tuple										= 40
      _a :  List[Any]										= 30
      _a :  Optional[int]										= 20
      _a :  Union[str, Any]										= 10



@unique
class   _lowercase			(  lowerCAmelCase    ):
      _a :  int										= 24
      _a :  int										= 21
      _a :  Tuple										= 18
      _a :  int										= 15
      _a :  Any										= 12
      _a :  Optional[int]										= 9
      _a :  int										= 6
      _a :  List[str]										= 3
      _a :  Optional[Any]										= 2
      _a :  Any										= 1
      _a :  Optional[int]										= -1
      _a :  Optional[Any]										= -2
      _a :  List[Any]										= -3
      _a :  List[Any]										= -6
      _a :  str										= -9
      _a :  Optional[int]										= -12
      _a :  int										= -15
      _a :  int										= -18
      _a :  Union[str, Any]										= -21
      _a :  Optional[Any]										= -24


      @classmethod
      def _UpperCamelCase    (     cls					:						type[T]    ):





            """simple docstring"""







            return {unit.name: unit.value for unit in cls if unit.value > 0}







      @classmethod
      def _UpperCamelCase    (     cls					:						type[T]    ):





            """simple docstring"""







            return {unit.name: unit.value for unit in cls if unit.value < 0}



def      __lowercase       (     a			:					float			)       ->       str:
      __snake_case   :     Dict	      =SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
      for name_prefix, value_prefix in prefixes.items():
            __snake_case   :     Any	      =value / (10**value_prefix)
            if numerical_part > 1:
                  return f'''{numerical_part!s} {name_prefix}'''
      return str(a			)



def      __lowercase       (     a			:					float			)       ->       str:
      for prefix in BinaryUnit:
            __snake_case   :     List[str]	      =value / (2**prefix.value)
            if numerical_part > 1:
                  return f'''{numerical_part!s} {prefix.name}'''
      return str(a			)


if __name__ == "__main__":
    import doctest

    doctest.testmod()










"""simple docstring"""





import numpy as np



def      __lowercase       (     a			:					Union[str, Any]							,		a			:					Optional[Any]							,		a			:					List[str]			)       ->       Dict:
      __snake_case   :     Dict	      =n_draws * class_counts / class_counts.sum()
      # floored means we don't overshoot n_samples, but probably undershoot
      __snake_case   :     int	      =np.floor(a			)
      # we add samples according to how much "left over" probability
      # they had, until we arrive at n_samples
      __snake_case   :     Dict	      =int(n_draws - floored.sum()			)
      if need_to_add > 0:
            __snake_case   :     List[str]	      =continuous - floored
            __snake_case   :     str	      =np.sort(np.unique(a			)			)[::-1]
            # add according to remainder, but break ties
            # randomly to avoid biases
            for value in values:
                  ((__snake_case)      ,					)   :     Tuple	      =np.where(remainder == value			)
                  # if we need_to_add less than what's in inds
                  # we draw randomly from them.
                  # if we need to add more, we add them all and
                  # go to the next value
                  __snake_case   :     int	      =min(len(a			)							,		a			)
                  __snake_case   :     Any	      =rng.choice(a							,		size=a							,		replace=a			)
                  floored[inds] += 1
                  need_to_add -= add_now
                  if need_to_add == 0:
                        break
      return floored.astype(np.intaa			)



def      __lowercase       (     a			:					Any							,		a			:					str							,		a			:					Dict							,		a			:					Union[str, Any]							,		a			:					Union[str, Any]=10			)       ->       Any:
      __snake_case      ,					__snake_case   :     int	      =np.unique(a							,		return_inverse=a			)
      __snake_case   :     Dict	      =classes.shape[0]
      __snake_case   :     int	      =np.bincount(a			)
      if np.min(a			) < 2:
            raise ValueError('''Minimum class count error'''			)
      if n_train < n_classes:
            raise ValueError(
                '''The train_size = %d should be greater or ''' '''equal to the number of classes = %d''' % (n_train, n_classes)			)
      if n_test < n_classes:
            raise ValueError(
                '''The test_size = %d should be greater or ''' '''equal to the number of classes = %d''' % (n_test, n_classes)			)
      __snake_case   :     str	      =np.split(np.argsort(a							,		kind='''mergesort'''			)							,		np.cumsum(a			)[:-1]			)
      for _ in range(a			):
            __snake_case   :     str	      =approximate_mode(a							,		a							,		a			)
            __snake_case   :     List[str]	      =class_counts - n_i
            __snake_case   :     Union[str, Any]	      =approximate_mode(a							,		a							,		a			)

            __snake_case   :     int	      =[]
            __snake_case   :     Union[str, Any]	      =[]

            for i in range(a			):
                  __snake_case   :     Union[str, Any]	      =rng.permutation(class_counts[i]			)
                  __snake_case   :     Dict	      =class_indices[i].take(a							,		mode='''clip'''			)
                  train.extend(perm_indices_class_i[: n_i[i]]			)
                  test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]			)
            __snake_case   :     Any	      =rng.permutation(a			)
            __snake_case   :     int	      =rng.permutation(a			)

            yield train, test


