
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      __lowerCAmelCase						(							UpperCAmelCase__				:      dict       )  ->    tuple:
							return (data["data"], data["target"])



def      __lowerCAmelCase						(							UpperCAmelCase__				:      np.ndarray			,				UpperCAmelCase__				:      np.ndarray       )  ->    XGBClassifier:
							lowerCamelCase_							  =					XGBClassifier()
							classifier.fit(UpperCAmelCase__			,				UpperCAmelCase__       )
							return classifier



def      __lowerCAmelCase						(							)  ->    None:
							lowerCamelCase_							  =					load_iris()
							lowerCamelCase_  ,   lowerCamelCase_							  =					data_handling(UpperCAmelCase__       )
							lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					train_test_split(
							    UpperCAmelCase__			,				UpperCAmelCase__			,				test_size=0.2_5       )

							lowerCamelCase_							  =					iris["""target_names"""]

							# Create an XGBoost Classifier from the training data
							lowerCamelCase_							  =					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()







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
		import torch

		from transformers.generation import DisjunctiveConstraint





@require_torch
class       __A(				unittest.TestCase       ):

					def    lowercase__      (	self					:     Optional[Any]   ):
												# For consistency across different places the DisjunctiveConstraint is called,
												# dc.token_ids is a list of integers. It is also initialized only by integers.

												lowerCamelCase_							  =					[[1, 2, 4], [1, 2, 3, 4]]
												lowerCamelCase_							  =					DisjunctiveConstraint(__UpperCamelCase   )
												self.assertTrue(isinstance(dc.token_ids   ,					__UpperCamelCase   )   )

												with self.assertRaises(__UpperCamelCase   ):
																			DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]   )   )

												with self.assertRaises(__UpperCamelCase   ):
																			DisjunctiveConstraint([torch.LongTensor([1, 2, 4]   ), torch.LongTensor([1, 2, 3, 4, 5]   )]   )

					def    lowercase__      (	self					:     List[Any]   ):
												# We can't have constraints that are complete subsets of another. This leads to a preverse
												# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
												# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
												# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
												# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
												lowerCamelCase_							  =					[[1, 2], [1, 2, 3, 4]]

												with self.assertRaises(__UpperCamelCase   ):
																			DisjunctiveConstraint(__UpperCamelCase   )  # fails here

					def    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					[[1, 2, 3], [1, 2, 4]]

												lowerCamelCase_							  =					DisjunctiveConstraint(__UpperCamelCase   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(1   )
												lowerCamelCase_							  =					stepped is True and completed is False and reset is False
												self.assertTrue(__UpperCamelCase   )
												self.assertTrue(not dc.completed   )
												self.assertTrue(dc.current_seq == [1]   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(2   )
												lowerCamelCase_							  =					stepped is True and completed is False and reset is False
												self.assertTrue(__UpperCamelCase   )
												self.assertTrue(not dc.completed   )
												self.assertTrue(dc.current_seq == [1, 2]   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(3   )
												lowerCamelCase_							  =					stepped is True and completed is True and reset is False
												self.assertTrue(__UpperCamelCase   )
												self.assertTrue(dc.completed   )  # Completed!
												self.assertTrue(dc.current_seq == [1, 2, 3]   )



					def    lowercase__      (	self					:     Tuple   ):
												lowerCamelCase_							  =					[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

												lowerCamelCase_							  =					DisjunctiveConstraint(__UpperCamelCase   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(1   )
												self.assertTrue(not dc.completed   )
												self.assertTrue(dc.current_seq == [1]   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(2   )
												self.assertTrue(not dc.completed   )
												self.assertTrue(dc.current_seq == [1, 2]   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(4   )
												self.assertTrue(not dc.completed   )
												self.assertTrue(dc.current_seq == [1, 2, 4]   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(5   )
												self.assertTrue(dc.completed   )  # Completed!
												self.assertTrue(dc.current_seq == [1, 2, 4, 5]   )

												dc.reset()

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(1   )
												self.assertTrue(not dc.completed   )
												self.assertTrue(dc.remaining() == 3   )
												self.assertTrue(dc.current_seq == [1]   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(2   )
												self.assertTrue(not dc.completed   )
												self.assertTrue(dc.remaining() == 2   )
												self.assertTrue(dc.current_seq == [1, 2]   )

												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					dc.update(5   )
												self.assertTrue(dc.completed   )  # Completed!
												self.assertTrue(dc.remaining() == 0   )
												self.assertTrue(dc.current_seq == [1, 2, 5]   )





