


'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import MobileNetVaImageProcessor





class   snake_case					(					unittest.TestCase    ):





		"""simple docstring"""




		def __init__(					self    ,						UpperCamelCase    ,						UpperCamelCase=7    ,						UpperCamelCase=3    ,						UpperCamelCase=18    ,						UpperCamelCase=30    ,						UpperCamelCase=400    ,						UpperCamelCase=True    ,						UpperCamelCase=None    ,						UpperCamelCase=True    ,						UpperCamelCase=None    ,						):







			"""simple docstring"""
			lowerCamelCase_  				=						size if size is not None else {"shortest_edge": 20}
			lowerCamelCase_  				=						crop_size if crop_size is not None else {"height": 18, "width": 18}
			lowerCamelCase_  				=						parent
			lowerCamelCase_  				=						batch_size
			lowerCamelCase_  				=						num_channels
			lowerCamelCase_  				=						image_size
			lowerCamelCase_  				=						min_resolution
			lowerCamelCase_  				=						max_resolution
			lowerCamelCase_  				=						do_resize
			lowerCamelCase_  				=						size
			lowerCamelCase_  				=						do_center_crop
			lowerCamelCase_  				=						crop_size







		def 						snake_case	(					self  ):







			"""simple docstring"""
			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_center_crop": self.do_center_crop,
			    "crop_size": self.crop_size,
			}





@require_torch
@require_vision
class   snake_case					(					lowercase    ,		unittest.TestCase    ):





		"""simple docstring"""




		_lowerCamelCase						=						MobileNetVaImageProcessor if is_vision_available() else None






		def 						snake_case	(					self  ):







			"""simple docstring"""
			lowerCamelCase_  				=						MobileNetVaImageProcessingTester(self  )






		@property
		def 						snake_case	(					self  ):







			"""simple docstring"""
			return self.image_processor_tester.prepare_image_processor_dict()






		def 						snake_case	(					self  ):







			"""simple docstring"""
			lowerCamelCase_  				=						self.image_processing_class(**self.image_processor_dict  )
			self.assertTrue(hasattr(UpperCamelCase    ,						"do_resize"  )  )
			self.assertTrue(hasattr(UpperCamelCase    ,						"size"  )  )
			self.assertTrue(hasattr(UpperCamelCase    ,						"do_center_crop"  )  )
			self.assertTrue(hasattr(UpperCamelCase    ,						"crop_size"  )  )






		def 						snake_case	(					self  ):







			"""simple docstring"""
			lowerCamelCase_  				=						self.image_processing_class.from_dict(self.image_processor_dict  )
			self.assertEqual(image_processor.size    ,						{"shortest_edge": 20}  )
			self.assertEqual(image_processor.crop_size    ,						{"height": 18, "width": 18}  )

			lowerCamelCase_  				=						self.image_processing_class.from_dict(self.image_processor_dict    ,						size=42    ,						crop_size=84  )
			self.assertEqual(image_processor.size    ,						{"shortest_edge": 42}  )
			self.assertEqual(image_processor.crop_size    ,						{"height": 84, "width": 84}  )






		def 						snake_case	(					self  ):







			"""simple docstring"""
			pass






		def 						snake_case	(					self  ):







			"""simple docstring"""
			# Initialize image_processing
			lowerCamelCase_  				=						self.image_processing_class(**self.image_processor_dict  )
			# create random PIL images
			lowerCamelCase_  				=						prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase  )
			for image in image_inputs:
				self.assertIsInstance(UpperCamelCase    ,						Image.Image  )

			# Test not batched input
			lowerCamelCase_  				=						image_processing(image_inputs[0]    ,						return_tensors="pt"  ).pixel_values
			self.assertEqual(
			    encoded_images.shape    ,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size["height"],
			        self.image_processor_tester.crop_size["width"],
			    )    ,						)

			# Test batched
			lowerCamelCase_  				=						image_processing(UpperCamelCase    ,						return_tensors="pt"  ).pixel_values
			self.assertEqual(
			    encoded_images.shape    ,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size["height"],
			        self.image_processor_tester.crop_size["width"],
			    )    ,						)






		def 						snake_case	(					self  ):







			"""simple docstring"""
			# Initialize image_processing
			lowerCamelCase_  				=						self.image_processing_class(**self.image_processor_dict  )
			# create random numpy tensors
			lowerCamelCase_  				=						prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase    ,						numpify=UpperCamelCase  )
			for image in image_inputs:
				self.assertIsInstance(UpperCamelCase    ,						np.ndarray  )

			# Test not batched input
			lowerCamelCase_  				=						image_processing(image_inputs[0]    ,						return_tensors="pt"  ).pixel_values
			self.assertEqual(
			    encoded_images.shape    ,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size["height"],
			        self.image_processor_tester.crop_size["width"],
			    )    ,						)

			# Test batched
			lowerCamelCase_  				=						image_processing(UpperCamelCase    ,						return_tensors="pt"  ).pixel_values
			self.assertEqual(
			    encoded_images.shape    ,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size["height"],
			        self.image_processor_tester.crop_size["width"],
			    )    ,						)







		def 						snake_case	(					self  ):







			"""simple docstring"""
			# Initialize image_processing
			lowerCamelCase_  				=						self.image_processing_class(**self.image_processor_dict  )
			# create random PyTorch tensors
			lowerCamelCase_  				=						prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase    ,						torchify=UpperCamelCase  )
			for image in image_inputs:
				self.assertIsInstance(UpperCamelCase    ,						torch.Tensor  )

			# Test not batched input
			lowerCamelCase_  				=						image_processing(image_inputs[0]    ,						return_tensors="pt"  ).pixel_values
			self.assertEqual(
			    encoded_images.shape    ,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size["height"],
			        self.image_processor_tester.crop_size["width"],
			    )    ,						)

			# Test batched
			lowerCamelCase_  				=						image_processing(UpperCamelCase    ,						return_tensors="pt"  ).pixel_values
			self.assertEqual(
			    encoded_images.shape    ,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size["height"],
			        self.image_processor_tester.crop_size["width"],
			    )    ,						)







'''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 __snake_case      (   UpperCAmelCase_		:		dict	):
 return (data["data"], data["target"])





def __snake_case      (   UpperCAmelCase_		:		np.ndarray      ,       UpperCAmelCase_		:		np.ndarray	):
 lowerCamelCase_  				=						XGBClassifier()
 classifier.fit(UpperCAmelCase_      ,       UpperCAmelCase_	)
 return classifier





def __snake_case      (   ):
 lowerCamelCase_  				=						load_iris()
 lowerCamelCase_				,lowerCamelCase_  				=						data_handling(UpperCAmelCase_	)
 lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_  				=						train_test_split(
     UpperCAmelCase_      ,       UpperCAmelCase_      ,       test_size=0.25	)

 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()



