

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




from __future__ import annotations

from typing import Generic, TypeVar

__A    	=			TypeVar('''T''')



class 						lowercase_					(						Generic[T]  ):



      def __init__(    self   :       str    ,      A__   :       T	)					->  Union[str, Any]:
        _snake_case		=							data  # Assign data
        _snake_case		=							None  # Initialize next as null
        _snake_case		=							None  # Initialize prev as null



class 						lowercase_					(						Generic[T]  ):



      def __init__(    self   :       List[str]	)					->  None:
        _snake_case		=							None



      def 		UpperCamelCase_	(    self   :       List[str]    ,      A__   :       T	)					->  None:
        if self.head is None:
          _snake_case		=							Node(A__	)
        else:
          _snake_case		=							Node(A__	)
          _snake_case		=							new_node
          _snake_case		=							self.head
          _snake_case		=							None
          _snake_case		=							new_node



      def 		UpperCamelCase_	(    self   :       Tuple	)					->  T | None:
        if self.head is None:
          return None
        else:
          assert self.head is not None
          _snake_case		=							self.head.data
          _snake_case		=							self.head.next
          if self.head is not None:
            _snake_case		=							None
          return temp



      def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  T | None:
        return self.head.data if self.head is not None else None



      def __len__(    self   :       Optional[int]	)					->  int:
        _snake_case		=							self.head
        _snake_case		=							0
        while temp is not None:
          count += 1
          _snake_case		=							temp.next
        return count



      def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  bool:
        return self.head is None







      def 		UpperCamelCase_	(    self   :       Dict	)					->  None:
        print('''stack elements are:'''	)
        _snake_case		=							self.head
        while temp is not None:
          print(temp.data    ,      end='''->'''	)
          _snake_case		=							temp.next


# Code execution starts here
if __name__ == "__main__":
 # Start with the empty stack
 __A    	=			Stack()

 # Insert 4 at the beginning. So stack becomes 4->None
 print('''Stack operations using Doubly LinkedList''')
 stack.push(4)

 # Insert 5 at the beginning. So stack becomes 4->5->None
 stack.push(5)

 # Insert 6 at the beginning. So stack becomes 4->5->6->None
 stack.push(6)

 # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
 stack.push(7)

 # Print the stack
 stack.print_stack()

 # Print the top element
 print('''\nTop element is ''', stack.top())

 # Print the stack size
 print('''Size of the stack is ''', len(stack))

 # pop the top element
 stack.pop()

 # pop the top element
 stack.pop()

 # two elements have now been popped off
 stack.print_stack()

 # Print True if the stack is empty else False
 print('''\nstack is empty:''', stack.is_empty())

