


import math
import sys

import cva
import numpy as np



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		np.ndarray:
    # For applying gaussian function for each element in matrix.
    _lowercase :							Tuple   	=	math.sqrt(lowerCamelCase_				)
    _lowercase :							Optional[Any]   	=	1 / (sigma * math.sqrt(2 * math.pi				))
    return cons * np.exp(-((img / sigma) ** 2) * 0.5				)



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		np.ndarray:
    _lowercase :							List[Any]   	=	kernel_size // 2
    return img[x - half : x + half + 1, y - half : y + half + 1]



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		np.ndarray:
    # Creates a gaussian kernel of given dimension.
    _lowercase :							Any   	=	np.zeros((kernel_size, kernel_size)				)
    for i in range(0							,				lowerCamelCase_				):
        for j in range(0							,				lowerCamelCase_				):
            _lowercase :							Tuple   	=	math.sqrt(
                abs(i - kernel_size // 2				) ** 2 + abs(j - kernel_size // 2				) ** 2				)
    return vec_gaussian(lowerCamelCase_							,				lowerCamelCase_				)



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				)					->		np.ndarray:
    _lowercase :							Union[str, Any]   	=	np.zeros(img.shape				)
    _lowercase :							Any   	=	get_gauss_kernel(lowerCamelCase_							,				lowerCamelCase_				)
    _lowercase	,       _lowercase :							Union[str, Any]   	=	img.shape
    for i in range(kernel_size // 2							,				size_x - kernel_size // 2				):
        for j in range(kernel_size // 2							,				size_y - kernel_size // 2				):
            _lowercase :							Tuple   	=	get_slice(lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)
            _lowercase :							Dict   	=	img_s - img_s[kernel_size // 2, kernel_size // 2]
            _lowercase :							Tuple   	=	vec_gaussian(lowerCamelCase_							,				lowerCamelCase_				)
            _lowercase :							Optional[int]   	=	np.multiply(lowerCamelCase_							,				lowerCamelCase_				)
            _lowercase :							Optional[Any]   	=	np.multiply(lowerCamelCase_							,				lowerCamelCase_				)
            _lowercase :							Dict   	=	np.sum(lowerCamelCase_				) / np.sum(lowerCamelCase_				)
            _lowercase :							Union[str, Any]   	=	val
    return imga



def        UpperCamelCase_(   lowerCamelCase_				)					->		tuple:
    _lowercase :							Dict   	=	args[1] if args[1:] else '../image_data/lena.jpg'
    _lowercase :							Tuple   	=	float(args[2]				) if args[2:] else 1.0
    _lowercase :							Dict   	=	float(args[3]				) if args[3:] else 1.0
    if args[4:]:
        _lowercase :							Tuple   	=	int(args[4]				)
        _lowercase :							Dict   	=	kernel_size + abs(kernel_size % 2 - 1				)
    else:
        _lowercase :							Optional[Any]   	=	5
    return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE :   Optional[int]							      =				parse_args(sys.argv)
 SCREAMING_SNAKE_CASE :   Optional[int]							      =				cva.imread(filename, 0)
 cva.imshow("input image", img)

 SCREAMING_SNAKE_CASE :   Tuple							      =				img / 255
 SCREAMING_SNAKE_CASE :   Tuple							      =				out.astype("float32")
 SCREAMING_SNAKE_CASE :   Optional[Any]							      =				bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
 SCREAMING_SNAKE_CASE :   Tuple							      =				out * 255
 SCREAMING_SNAKE_CASE :   Optional[Any]							      =				np.uinta(out)
 cva.imshow("output image", out)
 cva.waitKey(0)
 cva.destroyAllWindows()






from typing import Dict

from .base import GenericTensor, Pipeline






class _lowerCamelCase(		_a     ):
       def     UpperCamelCase  (      self,   lowerCamelCase=None,   lowerCamelCase=None,   lowerCamelCase=None,   **lowerCamelCase) ->     int:




           """simple docstring"""

           if tokenize_kwargs is None:
               _lowercase :							Any   	=	{}

           if truncation is not None:
               if "truncation" in tokenize_kwargs:
                   raise ValueError(
                       'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)')
               _lowercase :							List[Any]   	=	truncation

           _lowercase :							Optional[Any]   	=	tokenize_kwargs

           _lowercase :							Tuple   	=	{}
           if return_tensors is not None:
               _lowercase :							Union[str, Any]   	=	return_tensors

           return preprocess_params, {}, postprocess_params
       def     UpperCamelCase  (      self,   lowerCamelCase,   **lowerCamelCase) ->     Dict[str, GenericTensor]:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	self.framework
           _lowercase :							Dict   	=	self.tokenizer(lowerCamelCase,   return_tensors=lowerCamelCase,   **lowerCamelCase)
           return model_inputs
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							str   	=	self.model(**lowerCamelCase)
           return model_outputs
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase=False) ->     Dict:




           """simple docstring"""

           if return_tensors:
               return model_outputs[0]
           if self.framework == "pt":
               return model_outputs[0].tolist()
           elif self.framework == "tf":
               return model_outputs[0].numpy().tolist()




       def __call__(      self,   *lowerCamelCase,   **lowerCamelCase) ->     Union[str, Any]:




           """simple docstring"""

           return super().__call__(*lowerCamelCase,   **lowerCamelCase)


