


import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
			import torch

			from transformers import AutoModelForImageClassification

if is_vision_available():
			from transformers import AutoImageProcessor

@require_torch
@require_vision
class     a		(unittest.TestCase				):




							"""simple docstring"""


							@slow
							def    __snake_case (							self	:  List[Any]    )     ->   str:
														__snake_case		:      Optional[Any]										=       AutoImageProcessor.from_pretrained("microsoft/dit-base-finetuned-rvlcdip"    )
														__snake_case		:      int										=       AutoModelForImageClassification.from_pretrained("microsoft/dit-base-finetuned-rvlcdip"    )
														model.to(lowerCamelCase    )

														from datasets import load_dataset

														__snake_case		:      Optional[int]										=       load_dataset("nielsr/rvlcdip-demo"    )

														__snake_case		:      List[str]										=       dataset["train"][0]["image"].convert("RGB"    )

														__snake_case		:      int										=       image_processor(lowerCamelCase   ,     return_tensors="pt"    ).to(lowerCamelCase    )

														# forward pass
														with torch.no_grad():
																					__snake_case		:      Union[str, Any]										=       model(**lowerCamelCase    )
																					__snake_case		:      List[Any]										=       outputs.logits

														__snake_case		:      Tuple										=       torch.Size((1, 16)    )
														self.assertEqual(logits.shape   ,     lowerCamelCase    )

														__snake_case		:      List[Any]										=       torch.tensor(
														    [-0.41_58, -0.40_92, -0.43_47]   ,     device=lowerCamelCase   ,     dtype=torch.float   ,     )
														self.assertTrue(torch.allclose(logits[0, :3]   ,     lowerCamelCase   ,     atol=1E-4    )    )





from collections import Counter

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

_snake_case					:		Union[str, Any]      =    datasets.load_iris()

_snake_case					:		Tuple      =    np.array(data["data"])
_snake_case					:		int      =    np.array(data["target"])
_snake_case					:		int      =    data["target_names"]

_snake_case ,				_snake_case ,				_snake_case ,				_snake_case					:		Any      =    train_test_split(X, y)







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return np.linalg.norm(np.array(__lowerCamelCase     ) - np.array(__lowerCamelCase     )     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase=5     ):
							__snake_case		:      Optional[Any]										=       zip(__lowerCamelCase							,     __lowerCamelCase     )
							# List of distances of all points from the point to be classified
							__snake_case		:      Optional[int]										=       []
							for data_point in data:
														__snake_case		:      Union[str, Any]										=       euclidean_distance(data_point[0]							,     __lowerCamelCase     )
														distances.append((distance, data_point[1])     )
							# Choosing 'k' points with the least distances.
							__snake_case		:      Dict										=       [i[1] for i in sorted(__lowerCamelCase     )[:k]]
							# Most commonly occurring class among them
							# is the class into which the point is classified
							__snake_case		:      Any										=       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]))

