
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase=3     ):
				A_       	=  int(mask / 2     )
				# copy image size
				A_       	=  zeros_like(__UpperCamelCase     )
				for i in range(__UpperCamelCase ,						gray_img.shape[0] - bd     ):
								for j in range(__UpperCamelCase ,						gray_img.shape[1] - bd     ):
												# get mask according with mask
												A_       	=  ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]     )
												# calculate mask median
												A_       	=  sort(__UpperCamelCase     )[inta(divide((multiply(__UpperCamelCase ,						__UpperCamelCase     )) ,						2     ) + 1     )]
												A_       	=  median
				return median_img


if __name__ == "__main__":
						# read original image
						SCREAMING_SNAKE_CASE   :		str					 =       imread("../image_data/lena.jpg")
						# turn image in gray scale value
						SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       cvtColor(img, COLOR_BGR2GRAY)

						# get values with two different mask size
						SCREAMING_SNAKE_CASE   :		Tuple					 =       median_filter(gray, 3)
						SCREAMING_SNAKE_CASE   :		Union[str, Any]					 =       median_filter(gray, 5)

						# show result images
						imshow("median filter with 3x3 mask", medianaxa)
						imshow("median filter with 5x5 mask", medianaxa)
						waitKey(0)

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available






@dataclass
class 					__lowercase		(					A       ):
					__magic_name__						:							int     	=	{
					    '''sld_warmup_steps''': 15,
					    '''sld_guidance_scale''': 20,
					    '''sld_threshold''': 0.0,
					    '''sld_momentum_scale''': 0.0,
					    '''sld_mom_beta''': 0.0,
					}
					__magic_name__						:							Optional[int]     	=	{
					    '''sld_warmup_steps''': 10,
					    '''sld_guidance_scale''': 10_00,
					    '''sld_threshold''': 0.01,
					    '''sld_momentum_scale''': 0.3,
					    '''sld_mom_beta''': 0.4,
					}
					__magic_name__						:							str     	=	{
					    '''sld_warmup_steps''': 7,
					    '''sld_guidance_scale''': 20_00,
					    '''sld_threshold''': 0.0_25,
					    '''sld_momentum_scale''': 0.5,
					    '''sld_mom_beta''': 0.7,
					}
					__magic_name__						:							List[Any]     	=	{
					    '''sld_warmup_steps''': 0,
					    '''sld_guidance_scale''': 50_00,
					    '''sld_threshold''': 1.0,
					    '''sld_momentum_scale''': 0.5,
					    '''sld_mom_beta''': 0.7,
					}






@dataclass
class 					__lowercase		(					A       ):
					__magic_name__						:							Union[List[PIL.Image.Image], np.ndarray]
					__magic_name__						:							Optional[List[bool]]
					__magic_name__						:							Optional[Union[List[PIL.Image.Image], np.ndarray]]
					__magic_name__						:							Optional[str]


if is_transformers_available() and is_torch_available():
						from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
						from .safety_checker import SafeStableDiffusionSafetyChecker