






"""simple docstring"""
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_ (      __UpperCAmelCase    ,		__UpperCAmelCase					):







      '''simple docstring'''

      def __init__(self   ):

       '''simple docstring'''



       super().__init__()

       self.register_buffer('watermark_image',	torch.zeros((6_2, 6_2, 4)   )   )
       lowerCamelCase__	:     str								=		None





      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_=None   ):

       '''simple docstring'''



       lowerCamelCase__	:     List[str]								=		images[0].height
       lowerCamelCase__	:     Optional[int]								=		images[0].width

       lowerCamelCase__	:     Any								=		sample_size or h

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

       lowerCamelCase__							,	lowerCamelCase__	:     Optional[int]								=		1_0_2_4**2, img_w * img_h
       lowerCamelCase__	:     int								=		(Sa / Sa) ** 0.5
       lowerCamelCase__							,	lowerCamelCase__							,	lowerCamelCase__	:     Union[str, Any]								=		int(K * 6_2   ), img_w - int(1_4 * K   ), img_h - int(1_4 * K   )

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

       lowerCamelCase__	:     Any								=		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


"""simple docstring"""






from sklearn.neural_network import MLPClassifier

lowerCAmelCase__    :    Any			=      [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
lowerCAmelCase__    :    Optional[int]			=      [0, 1, 0, 0]


lowerCAmelCase__    :    Dict			=      MLPClassifier(
    solver='lbfgs', alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


lowerCAmelCase__    :    Optional[int]			=      [[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
lowerCAmelCase__    :    Union[str, Any]			=      clf.predict(test)



def     a_		(   lowerCamelCase			):
				return list(lowerCamelCase			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()



