


from __future__ import annotations






def 							__lowercase   ( __lowerCAmelCase							:				list[int | str]   ):
							create_state_space_tree(_UpperCAmelCase					,    []					,    0					,    [0 for i in range(len(_UpperCAmelCase   )   )]   )






def 							__lowercase   ( __lowerCAmelCase							:				list[int | str]					,    __lowerCAmelCase							:				list[int | str]					,    __lowerCAmelCase							:				int					,    __lowerCAmelCase							:				list[int]					,    ):







							if index == len(_UpperCAmelCase   ):
														print(_UpperCAmelCase   )
														return

							for i in range(len(_UpperCAmelCase   )   ):
														if not index_used[i]:
																					current_sequence.append(sequence[i]   )
																					a__             =     True
																					create_state_space_tree(_UpperCAmelCase					,    _UpperCAmelCase					,    index + 1					,    _UpperCAmelCase   )
																					current_sequence.pop()
																					a__             =     False
snake_case							:       list[int | str]     			=      [3, 1, 2, 4]
generate_all_permutations(sequence)

snake_case							:       list[int | str]     			=      ["A", "B", "C"]






generate_all_permutations(sequence_a)





'''simple docstring'''




import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier

def 	UpperCamelCase_							(					_UpperCAmelCase  :      dict    )    ->    tuple:


       """simple docstring"""







       return (data["data"], data["target"])

def 	UpperCamelCase_							(					_UpperCAmelCase  :      np.ndarray			,	_UpperCAmelCase  :      np.ndarray    )    ->    XGBClassifier:


       """simple docstring"""







       _UpperCAmelCase     :  Any							      =    XGBClassifier()
       classifier.fit(_UpperCAmelCase			,	_UpperCAmelCase    )
       return classifier

def 	UpperCamelCase_							(					)    ->    None:


       """simple docstring"""







       _UpperCAmelCase     :  List[str]							      =    load_iris()
       _UpperCAmelCase      ,  _UpperCAmelCase     :  Dict							      =    data_handling(_UpperCAmelCase    )
       _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase     :  str							      =    train_test_split(
           _UpperCAmelCase			,	_UpperCAmelCase			,	test_size=0.2_5    )

       _UpperCAmelCase     :  Optional[Any]							      =    iris["target_names"]

       # Create an XGBoost Classifier from the training data
       _UpperCAmelCase     :  Tuple							      =    xgboost(_UpperCAmelCase			,	_UpperCAmelCase    )

       # Display the confusion matrix of the classifier with both training and test sets
       ConfusionMatrixDisplay.from_estimator(
           _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	display_labels=_UpperCAmelCase			,	cmap="Blues"			,	normalize="true"			,	)
       plt.title("Normalized Confusion Matrix - IRIS Dataset"    )
       plt.show()


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)
  main()

