






from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION




class 							__A	(     a ,	a					):





				"""simple docstring"""







				def __init__(					self       ):




									"""simple docstring"""

									super().__init__()

									self.register_buffer('watermark_image'				,    torch.zeros((62, 62, 4)       )       )
									__UpperCamelCase :   Tuple									=None







				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__=None       ):




									"""simple docstring"""

									__UpperCamelCase :   Optional[Any]									=images[0].height
									__UpperCamelCase :   Optional[int]									=images[0].width

									__UpperCamelCase :   List[Any]									=sample_size or h

									__UpperCamelCase :   List[str]									=min(h / sample_size				,    w / sample_size       )
									__UpperCamelCase						,							__UpperCamelCase :   Any									=(int(h / coef       ), int(w / coef       )) if coef < 1 else (h, w)

									__UpperCamelCase						,							__UpperCamelCase :   Union[str, Any]									=1024**2, img_w * img_h
									__UpperCamelCase :   List[str]									=(Sa / Sa) ** 0.5
									__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase :   Union[str, Any]									=int(K * 62       ), img_w - int(14 * K       ), img_h - int(14 * K       )

									if self.watermark_image_as_pil is None:
														__UpperCamelCase :   Dict									=self.watermark_image.to(torch.uinta       ).cpu().numpy()
														__UpperCamelCase :   int									=Image.fromarray(lowerCamelCase__				,    mode='RGBA'       )
														__UpperCamelCase :   Optional[Any]									=watermark_image

									__UpperCamelCase :   List[str]									=self.watermark_image_as_pil.resize(
									    (wm_size, wm_size)				,    PIL_INTERPOLATION['bicubic']				,    reducing_gap=lowerCamelCase__       )

									for pil_img in images:
														pil_img.paste(lowerCamelCase__				,    box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)				,    mask=wm_img.split()[-1]       )

									return images









from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
A_			:Union[str, Any]	    =		load_iris()
iris.keys()


print(f"Target names: \n {iris.target_names} ")
print(f"\n Features: \n {iris.feature_names}")

# Train set e Test set
A_    ,A_    ,A_    ,A_			:Tuple	    =		train_test_split(
    iris['''data'''], iris['''target'''], random_state=4
)

# KNN

A_			:Any	    =		KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
A_			:Optional[Any]	    =		[[1, 2, 1, 4], [2, 3, 4, 5]]

A_			:Union[str, Any]	    =		knn.predict(X_new)

print(
    f"\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"
    f" {iris['target_names'][prediction]}"
)

