



'''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 MobileViTImageProcessor





class _UpperCamelCase		(  unittest.TestCase		):





				'''simple docstring'''





				def __init__(				self	:	Dict	,							_lowerCAmelCase	:	Optional[int]	,							_lowerCAmelCase	:	List[Any]=7	,							_lowerCAmelCase	:	List[str]=3	,							_lowerCAmelCase	:	str=1_8	,							_lowerCAmelCase	:	Union[str, Any]=3_0	,							_lowerCAmelCase	:	Optional[Any]=4_0_0	,							_lowerCAmelCase	:	Optional[int]=True	,							_lowerCAmelCase	:	Union[str, Any]=None	,							_lowerCAmelCase	:	Tuple=True	,							_lowerCAmelCase	:	int=None	,							_lowerCAmelCase	:	Optional[int]=True	,							):







											'''simple docstring'''

											__lowercase													=size if size is not None else {'shortest_edge': 2_0}
											__lowercase													=crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
											__lowercase													=parent
											__lowercase													=batch_size
											__lowercase													=num_channels
											__lowercase													=image_size
											__lowercase													=min_resolution
											__lowercase													=max_resolution
											__lowercase													=do_resize
											__lowercase													=size
											__lowercase													=do_center_crop
											__lowercase													=crop_size
											__lowercase													=do_flip_channel_order

				def 					__lowerCamelCase  (				self	:	List[Any]):







											'''simple docstring'''

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





@require_torch
@require_vision
class _UpperCamelCase		(  SCREAMING_SNAKE_CASE_   ,							unittest.TestCase		):





				'''simple docstring'''





				lowerCAmelCase__            =     MobileViTImageProcessor if is_vision_available() else None

				def 					__lowerCamelCase  (				self	:	Any):







											'''simple docstring'''

											__lowercase													=MobileViTImageProcessingTester(self)

				@property
				def 					__lowerCamelCase  (				self	:	Optional[int]):







											'''simple docstring'''

											return self.image_processor_tester.prepare_image_processor_dict()

				def 					__lowerCamelCase  (				self	:	Union[str, Any]):







											'''simple docstring'''

											__lowercase													=self.image_processing_class(**self.image_processor_dict)
											self.assertTrue(hasattr(lowercase_	,							'do_resize'))
											self.assertTrue(hasattr(lowercase_	,							'size'))
											self.assertTrue(hasattr(lowercase_	,							'do_center_crop'))
											self.assertTrue(hasattr(lowercase_	,							'center_crop'))
											self.assertTrue(hasattr(lowercase_	,							'do_flip_channel_order'))

				def 					__lowerCamelCase  (				self	:	List[str]):







											'''simple docstring'''

											__lowercase													=self.image_processing_class.from_dict(self.image_processor_dict)
											self.assertEqual(image_processor.size	,							{'shortest_edge': 2_0})
											self.assertEqual(image_processor.crop_size	,							{'height': 1_8, 'width': 1_8})

											__lowercase													=self.image_processing_class.from_dict(self.image_processor_dict	,							size=4_2	,							crop_size=8_4)
											self.assertEqual(image_processor.size	,							{'shortest_edge': 4_2})
											self.assertEqual(image_processor.crop_size	,							{'height': 8_4, 'width': 8_4})

				def 					__lowerCamelCase  (				self	:	List[str]):







											'''simple docstring'''

											pass

				def 					__lowerCamelCase  (				self	:	List[str]):







											'''simple docstring'''

											__lowercase													=self.image_processing_class(**self.image_processor_dict)
											# create random PIL images
											__lowercase													=prepare_image_inputs(self.image_processor_tester	,							equal_resolution=lowercase_)
											for image in image_inputs:
																		self.assertIsInstance(lowercase_	,							Image.Image)

											# Test not batched input
											__lowercase													=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
											__lowercase													=image_processing(lowercase_	,							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 					__lowerCamelCase  (				self	:	Dict):







											'''simple docstring'''

											__lowercase													=self.image_processing_class(**self.image_processor_dict)
											# create random numpy tensors
											__lowercase													=prepare_image_inputs(self.image_processor_tester	,							equal_resolution=lowercase_	,							numpify=lowercase_)
											for image in image_inputs:
																		self.assertIsInstance(lowercase_	,							np.ndarray)

											# Test not batched input
											__lowercase													=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
											__lowercase													=image_processing(lowercase_	,							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 					__lowerCamelCase  (				self	:	Dict):







											'''simple docstring'''

											__lowercase													=self.image_processing_class(**self.image_processor_dict)
											# create random PyTorch tensors
											__lowercase													=prepare_image_inputs(self.image_processor_tester	,							equal_resolution=lowercase_	,							torchify=lowercase_)
											for image in image_inputs:
																		self.assertIsInstance(lowercase_	,							torch.Tensor)

											# Test not batched input
											__lowercase													=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
											__lowercase													=image_processing(lowercase_	,							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"""

from collections import Counter

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

SCREAMING_SNAKE_CASE_            =    datasets.load_iris()

SCREAMING_SNAKE_CASE_            =    np.array(data['''data'''])
SCREAMING_SNAKE_CASE_            =    np.array(data['''target'''])
SCREAMING_SNAKE_CASE_            =    data['''target_names''']

SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_            =    train_test_split(X, y)




def      lowercase__    (       lowerCAmelCase					:       Optional[int]			,					lowerCAmelCase					:       Any			)			->   Optional[Any]:






    """simple docstring"""




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




def      lowercase__    (       lowerCAmelCase					:       str			,					lowerCAmelCase					:       Any			,					lowerCAmelCase					:       List[Any]			,					lowerCAmelCase					:       Tuple			,					lowerCAmelCase					:       Dict=5			)			->   str:






    """simple docstring"""




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



