
from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


_A     =  logging.get_logger(__name__)





class 			_lowerCAmelCase      (			__lowercase ):
				_lowercase    	=['pixel_values']




				def __init__(      self       ,       _UpperCamelCase = True       ,       _UpperCamelCase = 1 / 255       ,       _UpperCamelCase = True       ,       _UpperCamelCase = 8       ,       **_UpperCamelCase       ,       )       ->   None:
							super().__init__(**__A  )

							lowerCAmelCase_      			=			do_rescale
							lowerCAmelCase_      			=			rescale_factor
							lowerCAmelCase_      			=			do_pad
							lowerCAmelCase_      			=			pad_size




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase = None       ,       **_UpperCamelCase  )       ->   np.ndarray:
							return rescale(__A       ,       scale=__A       ,       data_format=__A       ,       **__A  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase = None  )       ->   int:
							lowerCAmelCase_			,						lowerCAmelCase_      			=			get_image_size(__A  )
							lowerCAmelCase_      			=			(old_height // size + 1) * size - old_height
							lowerCAmelCase_      			=			(old_width // size + 1) * size - old_width

							return pad(__A       ,       ((0, pad_height), (0, pad_width))       ,       mode="symmetric"       ,       data_format=__A  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = None       ,       _UpperCamelCase = None       ,       _UpperCamelCase = None       ,       _UpperCamelCase = None       ,       _UpperCamelCase = None       ,       _UpperCamelCase = ChannelDimension.FIRST       ,       **_UpperCamelCase       ,       )       ->   Dict:
							lowerCAmelCase_      			=			do_rescale if do_rescale is not None else self.do_rescale
							lowerCAmelCase_      			=			rescale_factor if rescale_factor is not None else self.rescale_factor
							lowerCAmelCase_      			=			do_pad if do_pad is not None else self.do_pad
							lowerCAmelCase_      			=			pad_size if pad_size is not None else self.pad_size

							lowerCAmelCase_      			=			make_list_of_images(__A  )

							if not valid_images(__A  ):
										raise ValueError(
										    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
										    "torch.Tensor, tf.Tensor or jax.ndarray."  )

							if do_rescale and rescale_factor is None:
										raise ValueError("Rescale factor must be specified if do_rescale is True."  )

							# All transformations expect numpy arrays.
							lowerCAmelCase_      			=			[to_numpy_array(__A  ) for image in images]

							if do_rescale:
										lowerCAmelCase_      			=			[self.rescale(image=__A       ,       scale=__A  ) for image in images]

							if do_pad:
										lowerCAmelCase_      			=			[self.pad(__A       ,       size=__A  ) for image in images]

							lowerCAmelCase_      			=			[to_channel_dimension_format(__A       ,       __A  ) for image in images]

							lowerCAmelCase_      			=			{"pixel_values": images}
							return BatchFeature(data=__A       ,       tensor_type=__A  )





from collections import Counter

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

_A     =  datasets.load_iris()

_A     =  np.array(data["data"])
_A     =  np.array(data["target"])
_A     =  data["target_names"]

_A,   _A,   _A,   _A     =  train_test_split(X, y)






def      lowerCamelCase__       (       __lowerCAmelCase						:						Union[str, Any]					,							__lowerCAmelCase						:						Union[str, Any]       ):




			"""simple docstring"""


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






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[Any]					,							__lowerCAmelCase						:						str					,							__lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						Any=5       ):




			"""simple docstring"""


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



