

import numpy as np



def    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase  )							->			List[Any]:



  """simple docstring"""






  _snake_case		=      n_draws * class_counts / class_counts.sum()
  # floored means we don't overshoot n_samples, but probably undershoot
  _snake_case		=      np.floor(_UpperCamelCase  )
  # 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(_UpperCamelCase  )  )[::-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(_UpperCamelCase  )				, _UpperCamelCase  )
      _snake_case		=      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    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase=10  )							->			str:



  """simple docstring"""






  _snake_case,  _snake_case		=      np.unique(_UpperCamelCase				, return_inverse=_UpperCamelCase  )
  _snake_case		=      classes.shape[0]
  _snake_case		=      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)  )
  _snake_case		=      np.split(np.argsort(_UpperCamelCase				, kind='''mergesort'''  )				, np.cumsum(_UpperCamelCase  )[:-1]  )
  for _ in range(_UpperCamelCase  ):
    _snake_case		=      approximate_mode(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase  )
    _snake_case		=      class_counts - n_i
    _snake_case		=      approximate_mode(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase  )

    _snake_case		=      []
    _snake_case		=      []

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

    yield train, test




__A    	=			{
    '''+''': lambda x, y: x + y,
    '''-''': lambda x, y: x - y,
    '''*''': lambda x, y: x * y,
    '''/''': lambda x, y: x / y,
}



def    snake_case_(_UpperCamelCase  )							->			str:



  """simple docstring"""






  return c.isdigit()



def    snake_case_(_UpperCamelCase  )							->			str:



  """simple docstring"""






  _snake_case		=							[]

  # iterate over the string in reverse order
  for c in expression.split()[::-1]:
    # push operand to stack
    if is_operand(_UpperCamelCase  ):
      stack.append(int(_UpperCamelCase  )  )

    else:
      # pop values from stack can calculate the result
      # push the result onto the stack again
      _snake_case		=							stack.pop()
      _snake_case		=							stack.pop()
      stack.append(calc[c](_UpperCamelCase				, _UpperCamelCase  )  )

  return stack.pop()


# Driver code
if __name__ == "__main__":
 __A    	=			'''+ 9 * 2 6'''
 print(evaluate(test_expression))

 __A    	=			'''/ * 10 2 + 4 1 '''
 print(evaluate(test_expression))

