





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase_    =    {
    '''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
    '''tokenization_luke''': ['''LukeTokenizer'''],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_    =    [
	    '''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''LukeForEntityClassification''',
	    '''LukeForEntityPairClassification''',
	    '''LukeForEntitySpanClassification''',
	    '''LukeForMultipleChoice''',
	    '''LukeForQuestionAnswering''',
	    '''LukeForSequenceClassification''',
	    '''LukeForTokenClassification''',
	    '''LukeForMaskedLM''',
	    '''LukeModel''',
	    '''LukePreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
	from .tokenization_luke import LukeTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_luke import (
		    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    LukeForEntityClassification,
		    LukeForEntityPairClassification,
		    LukeForEntitySpanClassification,
		    LukeForMaskedLM,
		    LukeForMultipleChoice,
		    LukeForQuestionAnswering,
		    LukeForSequenceClassification,
		    LukeForTokenClassification,
		    LukeModel,
		    LukePreTrainedModel,
		)

else:
	import sys

	lowerCAmelCase_    =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







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			)		->						tuple:






				"""simple docstring"""


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






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						XGBClassifier:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  XGBClassifier()
				classifier.fit(_UpperCamelCase			,    _UpperCamelCase			)
				return classifier






def 					lowerCamelCase_						(    )		->						None:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  load_iris()
				snake_case_      ,							snake_case_						:   str           =  data_handling(_UpperCamelCase			)
				snake_case_      ,							snake_case_      ,							snake_case_      ,							snake_case_						:   Dict           =  train_test_split(
				    _UpperCamelCase			,    _UpperCamelCase			,    test_size=0.25			)

				snake_case_						:   List[str]           =  iris['''target_names''']

				# Create an XGBoost Classifier from the training data
				snake_case_						:   int           =  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()
