


'''simple docstring'''






import numpy as np






def 						_A					(lowerCAmelCase__	:Union[str, Any]					,    lowerCAmelCase__	:Optional[Any]					,    lowerCAmelCase__	:List[str]		) ->	Dict:




     '''simple docstring'''



     _a         =				n_draws * class_counts / class_counts.sum()
     # floored means we don't overshoot n_samples, but probably undershoot
     _a         =				np.floor(lowerCAmelCase__		)
     # we add samples according to how much "left over" probability
     # they had, until we arrive at n_samples
     _a         =				int(n_draws - floored.sum()		)
     if need_to_add > 0:
          _a         =				continuous - floored
          _a         =				np.sort(np.unique(lowerCAmelCase__		)		)[::-1]
          # add according to remainder, but break ties
          # randomly to avoid biases
          for value in values:
               (_a					)         =				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
               _a         =				min(len(lowerCAmelCase__		)					,    lowerCAmelCase__		)
               _a         =				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 						_A					(lowerCAmelCase__	:Any					,    lowerCAmelCase__	:str					,    lowerCAmelCase__	:Dict					,    lowerCAmelCase__	:Union[str, Any]					,    lowerCAmelCase__	:Union[str, Any]=10		) ->	Any:




     '''simple docstring'''



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

          _a         =				[]
          _a         =				[]

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

          yield train, test









"""simple docstring"""





from __future__ import annotations


class   _lowercase			:


      def __init__(     self					:						int				, a					:						int = 0    ):





            """simple docstring"""







            __snake_case   :     Optional[Any]	      =value
            __snake_case   :     Node | None	      =None
            __snake_case   :     Node | None	      =None



def      __lowercase       (     a			:					Node | None							,		a			:					Node | None			)       ->       Node | None:
      if treea is None:
            return treea
      if treea is None:
            return treea

      __snake_case   :     Dict	      =treea.value + treea.value
      __snake_case   :     int	      =merge_two_binary_trees(treea.left							,		treea.left			)
      __snake_case   :     List[str]	      =merge_two_binary_trees(treea.right							,		treea.right			)
      return treea



def      __lowercase       (     a			:					Node | None			)       ->       None:
      if root:
            print(root.value			)
            print_preorder(root.left			)
            print_preorder(root.right			)


if __name__ == "__main__":
    UpperCamelCase_						:					List[str]      				=			Node(1)
    UpperCamelCase_						:					List[Any]      				=			Node(2)
    UpperCamelCase_						:					Optional[Any]      				=			Node(3)
    UpperCamelCase_						:					int      				=			Node(4)

    UpperCamelCase_						:					int      				=			Node(2)
    UpperCamelCase_						:					List[str]      				=			Node(4)
    UpperCamelCase_						:					Any      				=			Node(6)
    UpperCamelCase_						:					Union[str, Any]      				=			Node(9)
    UpperCamelCase_						:					Dict      				=			Node(5)

    print("""Tree1 is: """)
    print_preorder(treea)
    print("""Tree2 is: """)
    print_preorder(treea)
    UpperCamelCase_						:					Optional[Any]      				=			merge_two_binary_trees(treea, treea)
    print("""Merged Tree is: """)
    print_preorder(merged_tree)


