



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 LevitImageProcessor


class 	__snake_case   ( unittest.TestCase ):
		def __init__(							self    ,a_    ,a_=7    ,a_=3    ,a_=18    ,a_=30    ,a_=400    ,a_=True    ,a_=None    ,a_=True    ,a_=None    ,a_=True    ,a_=[0.5, 0.5, 0.5]    ,a_=[0.5, 0.5, 0.5]    ,):







						"""simple docstring"""
						lowerCAmelCase__		  =  size if size is not None else {'shortest_edge': 18}
						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
						lowerCAmelCase__		  =  do_normalize
						lowerCAmelCase__		  =  image_mean
						lowerCAmelCase__		  =  image_std



		def 						SCREAMING_SNAKE_CASE_					(							self						):







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



@require_torch
@require_vision
class 	__snake_case   ( SCREAMING_SNAKE_CASE							, unittest.TestCase ):
		SCREAMING_SNAKE_CASE__								=	LevitImageProcessor if is_vision_available() else None
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  LevitImageProcessingTester(self						)
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.image_processor_tester.prepare_image_processor_dict()
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.image_processing_class(**self.image_processor_dict						)
						self.assertTrue(hasattr(a_    ,'image_mean'						)						)
						self.assertTrue(hasattr(a_    ,'image_std'						)						)
						self.assertTrue(hasattr(a_    ,'do_normalize'						)						)
						self.assertTrue(hasattr(a_    ,'do_resize'						)						)
						self.assertTrue(hasattr(a_    ,'do_center_crop'						)						)
						self.assertTrue(hasattr(a_    ,'size'						)						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.image_processing_class.from_dict(self.image_processor_dict						)
						self.assertEqual(image_processor.size    ,{'shortest_edge': 18}						)
						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 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						pass
		def 						SCREAMING_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=a_						)
						for image in image_inputs:
										self.assertIsInstance(a_    ,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(a_    ,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 						SCREAMING_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=a_    ,numpify=a_						)
						for image in image_inputs:
										self.assertIsInstance(a_    ,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(a_    ,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 						SCREAMING_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=a_    ,torchify=a_						)
						for image in image_inputs:
										self.assertIsInstance(a_    ,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(a_    ,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'],
						    )    ,)





from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_lowerCAmelCase	:   int    =   datasets.load_iris()

_lowerCAmelCase	:   Tuple    =   np.array(data["data"])
_lowerCAmelCase	:   Any    =   np.array(data["target"])
_lowerCAmelCase	:   Union[str, Any]    =   data["target_names"]

_lowerCAmelCase,		_lowerCAmelCase,		_lowerCAmelCase,		_lowerCAmelCase	:   Optional[Any]    =   train_test_split(X, y)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Tuple:



				"""simple docstring"""





				return np.linalg.norm(np.array(snake_case__					) - np.array(snake_case__					)					)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__=5					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  zip(snake_case__    ,			snake_case__					)
				# List of distances of all points from the point to be classified
				lowerCAmelCase__		  =  []
				for data_point in data:
								lowerCAmelCase__		  =  euclidean_distance(data_point[0]    ,			snake_case__					)
								distances.append((distance, data_point[1])					)
				# Choosing 'k' points with the least distances.
				lowerCAmelCase__		  =  [i[1] for i in sorted(snake_case__					)[:k]]
				# Most commonly occurring class among them
				# is the class into which the point is classified
				lowerCAmelCase__		  =  Counter(snake_case__					).most_common(1					)[0][0]
				return classes[result]


if __name__ == "__main__":
	print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
