

"""simple docstring"""
import math
import sys

import cva
import numpy as np




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




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )	-> np.ndarray:
      UpperCAmelCase__			:							Optional[int]							=    kernel_size // 2
      return img[x - half : x + half + 1, y - half : y + half + 1]




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> np.ndarray:
      # Creates a gaussian kernel of given dimension.
      UpperCAmelCase__			:							int							=    np.zeros((kernel_size, kernel_size)    )
      for i in range(0			,	lowerCAmelCase    ):
            for j in range(0			,	lowerCAmelCase    ):
                  UpperCAmelCase__			:							Optional[Any]							=    math.sqrt(
                      abs(i - kernel_size // 2    ) ** 2 + abs(j - kernel_size // 2    ) ** 2    )
      return vec_gaussian(lowerCAmelCase			,	lowerCAmelCase    )




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	)	-> np.ndarray:
      UpperCAmelCase__			:							Dict							=    np.zeros(img.shape    )
      UpperCAmelCase__			:							int							=    get_gauss_kernel(lowerCAmelCase			,	lowerCAmelCase    )
      UpperCAmelCase__					,		UpperCAmelCase__			:							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    ):
                  UpperCAmelCase__			:							Tuple							=    get_slice(lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )
                  UpperCAmelCase__			:							Dict							=    img_s - img_s[kernel_size // 2, kernel_size // 2]
                  UpperCAmelCase__			:							List[Any]							=    vec_gaussian(lowerCAmelCase			,	lowerCAmelCase    )
                  UpperCAmelCase__			:							List[Any]							=    np.multiply(lowerCAmelCase			,	lowerCAmelCase    )
                  UpperCAmelCase__			:							List[str]							=    np.multiply(lowerCAmelCase			,	lowerCAmelCase    )
                  UpperCAmelCase__			:							int							=    np.sum(lowerCAmelCase    ) / np.sum(lowerCAmelCase    )
                  UpperCAmelCase__			:							Optional[int]							=    val
      return imga




def 					a__						(						lowerCAmelCase    )	-> tuple:
      UpperCAmelCase__			:							List[Any]							=    args[1] if args[1:] else """../image_data/lena.jpg"""
      UpperCAmelCase__			:							Tuple							=    float(args[2]    ) if args[2:] else 1.0
      UpperCAmelCase__			:							Dict							=    float(args[3]    ) if args[3:] else 1.0
      if args[4:]:
            UpperCAmelCase__			:							Tuple							=    int(args[4]    )
            UpperCAmelCase__			:							List[Any]							=    kernel_size + abs(kernel_size % 2 - 1    )
      else:
            UpperCAmelCase__			:							List[str]							=    5
      return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
    _A					,     _A					,     _A					,     _A							=  parse_args(sys.argv)
    _A							=  cva.imread(filename, 0)
    cva.imshow("""input image""", img)

    _A							=  img / 2_55
    _A							=  out.astype("""float32""")
    _A							=  bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
    _A							=  out * 2_55
    _A							=  np.uinta(out)
    cva.imshow("""output image""", out)
    cva.waitKey(0)
    cva.destroyAllWindows()




"""simple docstring"""
# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    """pipelines_utils""",
    """0.22.0""",
    """Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
    standard_warn=False,
    stacklevel=3,
)

