
from typing import TYPE_CHECKING

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


lowercase        =  {
    '''configuration_bridgetower''': [
        '''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BridgeTowerConfig''',
        '''BridgeTowerTextConfig''',
        '''BridgeTowerVisionConfig''',
    ],
    '''processing_bridgetower''': ['''BridgeTowerProcessor'''],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  ['''BridgeTowerImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  [
		    '''BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BridgeTowerForContrastiveLearning''',
		    '''BridgeTowerForImageAndTextRetrieval''',
		    '''BridgeTowerForMaskedLM''',
		    '''BridgeTowerModel''',
		    '''BridgeTowerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_bridgetower import (
		    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BridgeTowerConfig,
		    BridgeTowerTextConfig,
		    BridgeTowerVisionConfig,
		)
		from .processing_bridgetower import BridgeTowerProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_bridgetower import BridgeTowerImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bridgetower import (
				    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BridgeTowerForContrastiveLearning,
				    BridgeTowerForImageAndTextRetrieval,
				    BridgeTowerForMaskedLM,
				    BridgeTowerModel,
				    BridgeTowerPreTrainedModel,
				)


else:
		import sys

		lowercase        =  _LazyModule(__name__, globals()['''__file__'''], _import_structure)







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()





