"""simple docstring"""



import numpy as np
def    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase       ):
							A__					:    Optional[Any]														= n_draws * class_counts / class_counts.sum()
							# floored means we don't overshoot n_samples, but probably undershoot
							A__					:    Tuple														= np.floor(_UpperCAmelCase       )
							# we add samples according to how much "left over" probability
							# they had, until we arrive at n_samples
							A__					:    Any														= int(n_draws - floored.sum()       )
							if need_to_add > 0:
														A__					:    Union[str, Any]														= continuous - floored
														A__					:    Dict														= np.sort(np.unique(_UpperCAmelCase       )       )[::-1]
														# add according to remainder, but break ties
														# randomly to avoid biases
														for value in values:
																					(A__  )					:    Any														= 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
																					A__					:    Union[str, Any]														= min(len(_UpperCAmelCase       ) ,     _UpperCAmelCase       )
																					A__					:    Optional[Any]														= rng.choice(_UpperCAmelCase ,     size=_UpperCAmelCase ,     replace=_UpperCAmelCase       )
																					floored[inds] += 1
																					need_to_add -= add_now
																					if need_to_add == 0:
																												break
							return floored.astype(np.intaa       )



def    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase=10       ):
							A__					:    Tuple														= np.unique(_UpperCAmelCase ,     return_inverse=_UpperCAmelCase       )
							A__					:    Dict														= classes.shape[0]
							A__					:    Union[str, Any]														= np.bincount(_UpperCAmelCase       )
							if np.min(_UpperCAmelCase       ) < 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)       )
							A__					:    Any														= np.split(np.argsort(_UpperCAmelCase ,     kind="""mergesort"""       ) ,     np.cumsum(_UpperCAmelCase       )[:-1]       )
							for _ in range(_UpperCAmelCase       ):
														A__					:    Tuple														= approximate_mode(_UpperCAmelCase ,     _UpperCAmelCase ,     _UpperCAmelCase       )
														A__					:    Optional[Any]														= class_counts - n_i
														A__					:    Any														= approximate_mode(_UpperCAmelCase ,     _UpperCAmelCase ,     _UpperCAmelCase       )

														A__					:    Optional[int]														= []
														A__					:    Union[str, Any]														= []

														for i in range(_UpperCAmelCase       ):
																					A__					:    List[str]														= rng.permutation(class_counts[i]       )
																					A__					:    Union[str, Any]														= class_indices[i].take(_UpperCAmelCase ,     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]]       )
														A__					:    Optional[Any]														= rng.permutation(_UpperCAmelCase       )
														A__					:    Union[str, Any]														= rng.permutation(_UpperCAmelCase       )

														yield train, test






"""simple docstring"""



import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


_UpperCamelCase         =		set()
_UpperCamelCase         =		get_logger(__name__)

def    _A(		lowerCAmelCase = None       ):

							def decorator(lowerCAmelCase       ):
														global _emitted_deprecation_warnings

														if inspect.isclass(lowerCAmelCase       ):
																					A__					:    List[Any]														= deprecated_class_or_function.__init__
																					A__					:    Any														= deprecated_class_or_function.__name__
														else:
																					A__					:    Dict														= deprecated_class_or_function
																					A__					:    Optional[int]														= deprecated_function.__name__
																					# Support deprecating __init__ class method: class name instead
																					A__					:    List[str]														= name if name != """__init__""" else deprecated_function.__qualname__.split("""."""       )[-2]

														A__					:    Union[str, Any]														= (
														    F'''{name} is deprecated and will be removed in the next major version of datasets.''' + F''' {help_message}'''
														    if help_message
														    else """"""
														)

														@wraps(lowerCAmelCase       )
														def wrapper(*lowerCAmelCase ,     **lowerCAmelCase       ):
																					A__					:    Tuple														= hash(lowerCAmelCase       )
																					if func_hash not in _emitted_deprecation_warnings:
																												warnings.warn(lowerCAmelCase ,     category=lowerCAmelCase ,     stacklevel=2       )
																												_emitted_deprecation_warnings.add(lowerCAmelCase       )
																					return deprecated_function(*lowerCAmelCase ,     **lowerCAmelCase       )

														A__					:    int														= """deprecated"""

														if inspect.isclass(lowerCAmelCase       ):
																					A__					:    Union[str, Any]														= wrapper
																					return deprecated_class_or_function
														else:
																					return wrapper

							return decorator
class 	__UpperCAmelCase						(enum.EnumMeta    ):







			'''simple docstring'''





			def __getattribute__( cls      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Union[str, Any]														= super().__getattribute__(snake_case_       )
										if isinstance(snake_case_      ,			enum.Enum       ) and obj._on_access:
																	obj._on_access()
										return obj





			def __getitem__( cls      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Tuple														= super().__getitem__(snake_case_       )
										if member._on_access:
																	member._on_access()
										return member





			def __call__( cls      ,			snake_case_      ,			snake_case_=None      ,			*, snake_case_=None      ,			snake_case_=None      ,			snake_case_=None      ,			snake_case_=1       ):







										'''simple docstring'''


										A__					:    List[Any]														= super().__call__(snake_case_      ,			snake_case_      ,			module=snake_case_      ,			qualname=snake_case_      ,			type=snake_case_      ,			start=snake_case_       )
										if isinstance(snake_case_      ,			enum.Enum       ) and obj._on_access:
																	obj._on_access()
										return obj





class 	__UpperCAmelCase						(enum.Enum       ,      metaclass=__A    ):







			'''simple docstring'''





			def __new__( cls      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Tuple														= object.__new__(cls       )
										A__					:    int														= value
										A__					:    Optional[int]														= member.deprecate
										return member





			@property
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										return ""





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Tuple														= F''' {self.help_message}''' if self.help_message else """"""
										warnings.warn(
										    F'''\'{self.__objclass__.__name__}\' is deprecated and will be removed in the next major version of datasets.'''
										    + help_message      ,			snake_case_      ,			stacklevel=3      ,			)





