






"""simple docstring"""





class 					a						:






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






    def        UpperCamelCase	(	self					:							Any	,						__SCREAMING_SNAKE_CASE					:							list | str | range	,						__SCREAMING_SNAKE_CASE					:							int )					->		list[int]:
      if isinstance(__SCREAMING_SNAKE_CASE	,						(range, str) ):
        lowerCamelCase_   					=						list(__SCREAMING_SNAKE_CASE )
      elif not isinstance(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE ):
        raise TypeError(
            'binary_search_delete() only accepts either a list, range or str' )

      lowerCamelCase_   					=						0
      lowerCamelCase_   					=						len(__SCREAMING_SNAKE_CASE ) - 1

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






    def        UpperCamelCase	(	self					:							Tuple	,						__SCREAMING_SNAKE_CASE					:							list | str | range	,						__SCREAMING_SNAKE_CASE					:							int )					->		list[int]:
      if isinstance(__SCREAMING_SNAKE_CASE	,						(range, str) ):
        lowerCamelCase_   					=						list(__SCREAMING_SNAKE_CASE )
      elif not isinstance(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE ):
        raise TypeError(
            'binary_search_insert() only accepts either a list, range or str' )

      lowerCamelCase_   					=						0
      lowerCamelCase_   					=						len(__SCREAMING_SNAKE_CASE ) - 1

      while low <= high:
        lowerCamelCase_   					=						(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	,						__SCREAMING_SNAKE_CASE )
          return array
        elif array[mid] < index:
          lowerCamelCase_   					=						mid + 1
        else:
          lowerCamelCase_   					=						mid - 1

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






    def        UpperCamelCase	(	self					:							str	,						__SCREAMING_SNAKE_CASE					:							int	,						__SCREAMING_SNAKE_CASE					:							int )					->		None:
      # Remove previous index
      if index in self.indexmap:
        lowerCamelCase_   					=						self.indexmap[index]
        if len(self.numbermap[n] ) == 1:
          del self.numbermap[n]
        else:
          lowerCamelCase_   					=						self.binary_search_delete(self.numbermap[n]	,						__SCREAMING_SNAKE_CASE )

        # Set new index
      lowerCamelCase_   					=						number

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

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






    def        UpperCamelCase	(	self					:							List[str]	,						__SCREAMING_SNAKE_CASE					:							int )					->		int:
      return self.numbermap.get(__SCREAMING_SNAKE_CASE	,						[-1] )[0]


if __name__ == "__main__":
       import doctest

       doctest.testmod()












"""simple docstring"""





import numpy as np







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







def 					lowerCamelCase__	(							_lowerCamelCase	:		Any		,    _lowerCamelCase	:		int		,    _lowerCamelCase	:		Optional[Any]		,    _lowerCamelCase	:		List[str]		,    _lowerCamelCase	:		Optional[int]=10			)				->    Any:
  lowerCamelCase_					,     lowerCamelCase_   					=						np.unique(_lowerCamelCase		,    return_inverse=_lowerCamelCase			)
  lowerCamelCase_   					=						classes.shape[0]
  lowerCamelCase_   					=						np.bincount(_lowerCamelCase			)
  if np.min(_lowerCamelCase			) < 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)			)
  lowerCamelCase_   					=						np.split(np.argsort(_lowerCamelCase		,    kind='mergesort'			)		,    np.cumsum(_lowerCamelCase			)[:-1]			)
  for _ in range(_lowerCamelCase			):
    lowerCamelCase_   					=						approximate_mode(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)
    lowerCamelCase_   					=						class_counts - n_i
    lowerCamelCase_   					=						approximate_mode(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)

    lowerCamelCase_   					=						[]
    lowerCamelCase_   					=						[]

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

    yield train, test




