import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
__lowercase											=      0b1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
__lowercase											=      [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]


class 			lowerCamelCase_  :






    '''simple docstring'''






    def __init__(			self) ->			Optional[int]:
        __UpperCamelCase  :List[Any]    						=	WATERMARK_BITS
        __UpperCamelCase  :str    						=	WatermarkEncoder()

        self.encoder.set_watermark('''bits'''				,				self.watermark)







    def     UpperCamelCase__     (			self				,				__lowercase) ->			Union[str, Any]:
        # can't encode images that are smaller than 256
        if images.shape[-1] < 256:
            return images

        __UpperCamelCase  :str    						=	(255 * (images / 2 + 0.5)).cpu().permute(0				,				2				,				3				,				1).float().numpy()

        __UpperCamelCase  :Union[str, Any]    						=	[self.encoder.encode(__lowercase				,				'''dwtDct''') for image in images]

        __UpperCamelCase  :int    						=	torch.from_numpy(np.array(__lowercase)).permute(0				,				3				,				1				,				2)

        __UpperCamelCase  :int    						=	torch.clamp(2 * (images / 255 - 0.5)				,				min=-1.0				,				max=1.0)
        return images






from collections import Counter

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

__lowercase											=      datasets.load_iris()

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

__lowercase				, __lowercase				, __lowercase				, __lowercase											=      train_test_split(X, y)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







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



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=5			):
    '''simple docstring'''







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





