






"""simple docstring"""
from .stack import Stack


def        A	(							__snake_case:   str     )						->		bool:

       """simple docstring"""





       __magic_name__										=						Stack()
       __magic_name__										=						{'(': ')', '[': ']', '{': '}'}
       for bracket in parentheses:
              if bracket in bracket_pairs:
                     stack.push(__snake_case     )
              elif bracket in (")", "]", "}"):
                     if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                            return False
       return stack.is_empty()


if __name__ == "__main__":
 from doctest import testmod

 testmod()

 snake_case   :   Optional[Any]    		=		["""((()))""", """((())""", """(()))"""]
 print("""Balanced parentheses demonstration:\n""")
 for example in examples:
  snake_case   :   Optional[Any]    		=		"""""" if balanced_parentheses(example) else """not """
  print(f"""{example} is {not_str}balanced""")







"""simple docstring"""
import numpy as np


def        A	(							__snake_case:   Any   ,    __snake_case:   List[str]   ,    __snake_case:   Tuple     )						->		Dict:

       """simple docstring"""





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






def        A	(							__snake_case:   Any   ,    __snake_case:   str   ,    __snake_case:   int   ,    __snake_case:   Optional[Any]   ,    __snake_case:   Any=1_0     )						->		Any:

       """simple docstring"""





       __magic_name__							,     __magic_name__										=						np.unique(__snake_case   ,    return_inverse=__snake_case     )
       __magic_name__										=						classes.shape[0]
       __magic_name__										=						np.bincount(__snake_case     )
       if np.min(__snake_case     ) < 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)     )
       __magic_name__										=						np.split(np.argsort(__snake_case   ,    kind='mergesort'     )   ,    np.cumsum(__snake_case     )[:-1]     )
       for _ in range(__snake_case     ):
              __magic_name__										=						approximate_mode(__snake_case   ,    __snake_case   ,    __snake_case     )
              __magic_name__										=						class_counts - n_i
              __magic_name__										=						approximate_mode(__snake_case   ,    __snake_case   ,    __snake_case     )

              __magic_name__										=						[]
              __magic_name__										=						[]

              for i in range(__snake_case     ):
                     __magic_name__										=						rng.permutation(class_counts[i]     )
                     __magic_name__										=						class_indices[i].take(__snake_case   ,    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]]     )
              __magic_name__										=						rng.permutation(__snake_case     )
              __magic_name__										=						rng.permutation(__snake_case     )

              yield train, test