





from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		k_size // 2
      lowerCamelCase			,							lowerCamelCase								=		mgrid[0 - center : k_size - center, 0 - center : k_size - center]
      lowerCamelCase								=		1 / (2 * pi * sigma) * exp(-(square(UpperCAmelCase__ ) + square(UpperCAmelCase__ )) / (2 * square(UpperCAmelCase__ )) )
      return g

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase			,							lowerCamelCase								=		image.shape[0], image.shape[1]
      # dst image height and width
      lowerCamelCase								=		height - k_size + 1
      lowerCamelCase								=		width - k_size + 1

      # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
      lowerCamelCase								=		zeros((dst_height * dst_width, k_size * k_size) )
      lowerCamelCase								=		0
      for i, j in product(range(UpperCAmelCase__ )   ,   range(UpperCAmelCase__ ) ):
            lowerCamelCase								=		ravel(image[i : i + k_size, j : j + k_size] )
            lowerCamelCase								=		window
            row += 1

      #  turn the kernel into shape(k*k, 1)
      lowerCamelCase								=		gen_gaussian_kernel(UpperCAmelCase__   ,   UpperCAmelCase__ )
      lowerCamelCase								=		ravel(UpperCAmelCase__ )

      # reshape and get the dst image
      lowerCamelCase								=		dot(UpperCAmelCase__   ,   UpperCAmelCase__ ).reshape(UpperCAmelCase__   ,   UpperCAmelCase__ ).astype(UpperCAmelCase__ )

      return dst


if __name__ == "__main__":
     # read original image
     a_						:     int	     =		imread(R'../image_data/lena.jpg')
     # turn image in gray scale value
     a_						:     Dict	     =		cvtColor(img, COLOR_BGR2GRAY)

     # get values with two different mask size
     a_						:     str	     =		gaussian_filter(gray, 3, sigma=1)
     a_						:     str	     =		gaussian_filter(gray, 5, sigma=0.8)

     # show result images
     imshow('gaussian filter with 3x3 mask', gaussianaxa)
     imshow('gaussian filter with 5x5 mask', gaussianaxa)
     waitKey()




import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel

from ...utils import logging


a_						:     Tuple	     =		logging.get_logger(__name__)





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     _A       =			CLIPConfig

     _A       =			['CLIPEncoderLayer']







     def __init__(self				,			__a    ):






           '''simple docstring'''



           super().__init__(__a    )

           lowerCamelCase								=		CLIPVisionModelWithProjection(config.vision_config    )

           lowerCamelCase								=		nn.Linear(config.vision_config.projection_dim				,			1    )
           lowerCamelCase								=		nn.Linear(config.vision_config.projection_dim				,			1    )







     @torch.no_grad()
     def 						_a      (self				,			__a				,			__a				,			__a=0.5				,			__a=0.5    ):






           '''simple docstring'''



           lowerCamelCase								=		self.vision_model(__a    )[0]

           lowerCamelCase								=		self.p_head(__a    )
           lowerCamelCase								=		nsfw_detected.flatten()
           lowerCamelCase								=		nsfw_detected > p_threshold
           lowerCamelCase								=		nsfw_detected.tolist()

           if any(__a    ):
                 logger.warning(
                     "Potential NSFW content was detected in one or more images. A black image will be returned instead."
                     " Try again with a different prompt and/or seed."    )

           for idx, nsfw_detected_ in enumerate(__a    ):
                 if nsfw_detected_:
                       lowerCamelCase								=		np.zeros(images[idx].shape    )

           lowerCamelCase								=		self.w_head(__a    )
           lowerCamelCase								=		watermark_detected.flatten()
           lowerCamelCase								=		watermark_detected > w_threshold
           lowerCamelCase								=		watermark_detected.tolist()

           if any(__a    ):
                 logger.warning(
                     "Potential watermarked content was detected in one or more images. A black image will be returned instead."
                     " Try again with a different prompt and/or seed."    )

           for idx, watermark_detected_ in enumerate(__a    ):
                 if watermark_detected_:
                       lowerCamelCase								=		np.zeros(images[idx].shape    )

           return images, nsfw_detected, watermark_detected