





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   (				__a  :dict       )						->							tuple:






		"""simple docstring"""

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







def   __lowerCamelCase   (				__a  :np.ndarray       ,	__a  :np.ndarray       )						->							XGBClassifier:






		"""simple docstring"""

		A__     =  XGBClassifier()
		classifier.fit(__a       ,	__a       )
		return classifier







def   __lowerCamelCase   (				)						->							None:






		"""simple docstring"""

		A__     =  load_iris()
		A__ ,     A__     =  data_handling(__a       )
		A__ ,     A__ ,     A__ ,     A__     =  train_test_split(
		    __a       ,	__a       ,	test_size=0.25       )

		A__     =  iris["""target_names"""]

		# Create an XGBoost Classifier from the training data
		A__     =  xgboost(__a       ,	__a       )

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


if __name__ == "__main__":
					import doctest

					doctest.testmod(verbose=True)
					main()










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A       :   Optional[int]					= {
    '''configuration_pix2struct''': [
        '''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Pix2StructConfig''',
        '''Pix2StructTextConfig''',
        '''Pix2StructVisionConfig''',
    ],
    '''processing_pix2struct''': ['''Pix2StructProcessor'''],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   Optional[int]					= ['''Pix2StructImageProcessor''']


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   Dict					= [
					    '''PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''Pix2StructPreTrainedModel''',
					    '''Pix2StructForConditionalGeneration''',
					    '''Pix2StructVisionModel''',
					    '''Pix2StructTextModel''',
					]

if TYPE_CHECKING:
					from .configuration_pixastruct import (
					    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    PixaStructConfig,
					    PixaStructTextConfig,
					    PixaStructVisionConfig,
					)
					from .processing_pixastruct import PixaStructProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_pixastruct import PixaStructImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_pixastruct import (
										    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    PixaStructForConditionalGeneration,
										    PixaStructPreTrainedModel,
										    PixaStructTextModel,
										    PixaStructVisionModel,
										)

else:
					import sys

					A       :   Any					= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



