
import numpy as np
def        __lowercase(							__snake_case				:		Tuple    ,__snake_case				:		Optional[int]    ,__snake_case				:		List[Any]					)       ->   Tuple:
       __snake_case						       =   n_draws * class_counts / class_counts.sum()
       # floored means we don't overshoot n_samples, but probably undershoot
       __snake_case						       =   np.floor(__snake_case					)
       # we add samples according to how much "left over" probability
       # they had, until we arrive at n_samples
       __snake_case						       =   int(n_draws - floored.sum()					)
       if need_to_add > 0:
              __snake_case						       =   continuous - floored
              __snake_case						       =   np.sort(np.unique(__snake_case					)					)[::-1]
              # add according to remainder, but break ties
              # randomly to avoid biases
              for value in values:
                     ((__snake_case		)       ,       		)						       =   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						       =   min(len(__snake_case					)    ,__snake_case					)
                     __snake_case						       =   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        __lowercase(							__snake_case				:		str    ,__snake_case				:		Optional[Any]    ,__snake_case				:		Tuple    ,__snake_case				:		Dict    ,__snake_case				:		List[str]=10					)       ->   int:
       __snake_case       ,       __snake_case						       =   np.unique(__snake_case    ,return_inverse=__snake_case					)
       __snake_case						       =   classes.shape[0]
       __snake_case						       =   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)					)
       __snake_case						       =   np.split(np.argsort(__snake_case    ,kind='mergesort'					)    ,np.cumsum(__snake_case					)[:-1]					)
       for _ in range(__snake_case					):
              __snake_case						       =   approximate_mode(__snake_case    ,__snake_case    ,__snake_case					)
              __snake_case						       =   class_counts - n_i
              __snake_case						       =   approximate_mode(__snake_case    ,__snake_case    ,__snake_case					)

              __snake_case						       =   []
              __snake_case						       =   []

              for i in range(__snake_case					):
                     __snake_case						       =   rng.permutation(class_counts[i]					)
                     __snake_case						       =   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]]					)
              __snake_case						       =   rng.permutation(__snake_case					)
              __snake_case						       =   rng.permutation(__snake_case					)

              yield train, test







class 		_lowerCAmelCase:

  """simple docstring"""







  def __init__(   self      )->   None:
     # numbermap keys are the number and its values are lists of indexes sorted
     # in ascending order
     __A    =  {}
     # indexmap keys are an index and it's values are the number at that index
     __A    =  {}

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase      )->   list[int]:
     if isinstance(UpperCAmelCase    ,							(range, str)      ):
        __A    =  list(UpperCAmelCase      )
     elif not isinstance(UpperCAmelCase    ,							UpperCAmelCase      ):
        raise TypeError(
            '''binary_search_delete() only accepts either a list, range or str'''      )

     __A    =  0
     __A    =  len(UpperCAmelCase      ) - 1

     while low <= high:
        __A    =  (low + high) // 2
        if array[mid] == item:
           array.pop(UpperCAmelCase      )
           return array
        elif array[mid] < item:
           __A    =  mid + 1
        else:
           __A    =  mid - 1
     raise ValueError(
         '''Either the item is not in the array or the array was unsorted'''      )

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase      )->   list[int]:
     if isinstance(UpperCAmelCase    ,							(range, str)      ):
        __A    =  list(UpperCAmelCase      )
     elif not isinstance(UpperCAmelCase    ,							UpperCAmelCase      ):
        raise TypeError(
            '''binary_search_insert() only accepts either a list, range or str'''      )

     __A    =  0
     __A    =  len(UpperCAmelCase      ) - 1

     while low <= high:
        __A    =  (low + high) // 2
        if array[mid] == index:
           # If the item already exists in the array,
           # insert it after the existing item
           array.insert(mid + 1    ,							UpperCAmelCase      )
           return array
        elif array[mid] < index:
           __A    =  mid + 1
        else:
           __A    =  mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
     array.insert(UpperCAmelCase    ,							UpperCAmelCase      )
     return array

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase      )->   None:
     # Remove previous index
     if index in self.indexmap:
        __A    =  self.indexmap[index]
        if len(self.numbermap[n]      ) == 1:
           del self.numbermap[n]
        else:
           __A    =  self.binary_search_delete(self.numbermap[n]    ,							UpperCAmelCase      )

        # Set new index
     __A    =  number

     # Number not seen before or empty so insert number value
     if number not in self.numbermap:
        __A    =  [index]

     # Here we need to perform a binary search insertion in order to insert
     # The item in the correct place
     else:
        __A    =  self.binary_search_insert(
            self.numbermap[number]    ,							UpperCAmelCase      )





  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   int:
     return self.numbermap.get(UpperCAmelCase    ,							[-1]      )[0]


if __name__ == "__main__":
    import doctest

    doctest.testmod()

