




'''simple docstring'''






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   int:
      snake_case__       :   int	      =    [0] * len(__lowerCAmelCase		)
      snake_case__       :   Optional[int]	      =    []
      snake_case__       :   Optional[Any]	      =    []
      snake_case__       :   Optional[int]	      =    0

      for values in graph.values():
            for i in values:
                  indegree[i] += 1

      for i in range(len(__lowerCAmelCase		)		):
            if indegree[i] == 0:
                  queue.append(__lowerCAmelCase		)

      while queue:
            snake_case__       :   List[str]	      =    queue.pop(0		)
            cnt += 1
            topo.append(__lowerCAmelCase		)
            for x in graph[vertex]:
                  indegree[x] -= 1
                  if indegree[x] == 0:
                        queue.append(__lowerCAmelCase		)

      if cnt != len(__lowerCAmelCase		):
            print('Cycle exists'		)
      else:
            print(__lowerCAmelCase		)


# Adjacency List of Graph
A_			      =		{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}




topological_sort(graph)





import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor

def        __a			(   __lowerCAmelCase   )     ->     tuple:
					return (data["data"], data["target"])

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     np.ndarray:
					SCREAMING_SNAKE_CASE							:     int						=				XGBRegressor(verbosity=0      ,	random_state=42   )
					xgb.fit(__lowerCAmelCase      ,	__lowerCAmelCase   )
					# Predict target for test data
					SCREAMING_SNAKE_CASE							:     List[Any]						=				xgb.predict(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     str						=				predictions.reshape(len(__lowerCAmelCase   )      ,	1   )
					return predictions

def        __a			(   )     ->     None:
					SCREAMING_SNAKE_CASE							:     Dict						=				fetch_california_housing()
					SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Any						=				data_handling(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     str						=				train_test_split(
					    __lowerCAmelCase      ,	__lowerCAmelCase      ,	test_size=0.25      ,	random_state=1   )
					SCREAMING_SNAKE_CASE							:     int						=				xgboost(__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )
					# Error printing
					print(F'''Mean Absolute Error : {mean_absolute_error(__lowerCAmelCase      ,	__lowerCAmelCase   )}'''   )
					print(F'''Mean Square Error  : {mean_squared_error(__lowerCAmelCase      ,	__lowerCAmelCase   )}'''   )


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)
			main()