



import math
import sys

import cva
import numpy as np





def 							A     (					_UpperCAmelCase    :       Union[str, Any]							,       _UpperCAmelCase    :       Optional[int]   )    ->			np.ndarray:







			'''simple docstring'''
			_UpperCAmelCase			=       math.sqrt(__lowerCAmelCase   )
			_UpperCAmelCase			=       1 / (sigma * math.sqrt(2 * math.pi   ))
			return cons * np.exp(-((img / sigma) ** 2) * 0.5   )





def 							A     (					_UpperCAmelCase    :       Optional[Any]							,       _UpperCAmelCase    :       Any							,       _UpperCAmelCase    :       str							,       _UpperCAmelCase    :       int   )    ->			np.ndarray:







			'''simple docstring'''
			_UpperCAmelCase			=       kernel_size // 2
			return img[x - half : x + half + 1, y - half : y + half + 1]





def 							A     (					_UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       Optional[int]   )    ->			np.ndarray:







			'''simple docstring'''
			_UpperCAmelCase			=       np.zeros((kernel_size, kernel_size)   )
			for i in range(0							,       __lowerCAmelCase   ):
						for j in range(0							,       __lowerCAmelCase   ):
									_UpperCAmelCase			=       math.sqrt(
									    abs(i - kernel_size // 2   ) ** 2 + abs(j - kernel_size // 2   ) ** 2   )
			return vec_gaussian(__lowerCAmelCase							,       __lowerCAmelCase   )





def 							A     (					_UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       Tuple							,       _UpperCAmelCase    :       List[Any]							,       _UpperCAmelCase    :       Optional[Any]							,       )    ->			np.ndarray:







			'''simple docstring'''
			_UpperCAmelCase			=       np.zeros(img.shape   )
			_UpperCAmelCase			=       get_gauss_kernel(__lowerCAmelCase							,       __lowerCAmelCase   )
			_UpperCAmelCase			=       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			=       get_slice(__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase   )
									_UpperCAmelCase			=       img_s - img_s[kernel_size // 2, kernel_size // 2]
									_UpperCAmelCase			=       vec_gaussian(__lowerCAmelCase							,       __lowerCAmelCase   )
									_UpperCAmelCase			=       np.multiply(__lowerCAmelCase							,       __lowerCAmelCase   )
									_UpperCAmelCase			=       np.multiply(__lowerCAmelCase							,       __lowerCAmelCase   )
									_UpperCAmelCase			=       np.sum(__lowerCAmelCase   ) / np.sum(__lowerCAmelCase   )
									_UpperCAmelCase			=       val
			return imga





def 							A     (					_UpperCAmelCase    :       List[Any]   )    ->			tuple:







			'''simple docstring'''
			_UpperCAmelCase			=       args[1] if args[1:] else '''../image_data/lena.jpg'''
			_UpperCAmelCase			=       float(args[2]   ) if args[2:] else 1.0
			_UpperCAmelCase			=       float(args[3]   ) if args[3:] else 1.0
			if args[4:]:
						_UpperCAmelCase			=       int(args[4]   )
						_UpperCAmelCase			=       kernel_size + abs(kernel_size % 2 - 1   )
			else:
						_UpperCAmelCase			=       5
			return filename, spatial_variance, intensity_variance, kernel_size


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

							UpperCAmelCase__       =	img / 255
							UpperCAmelCase__       =	out.astype("float32")
							UpperCAmelCase__       =	bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
							UpperCAmelCase__       =	out * 255
							UpperCAmelCase__       =	np.uinta(out)
							cva.imshow("output image", out)
							cva.waitKey(0)
							cva.destroyAllWindows()






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase__       =	{
    "configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "MEGA_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "MegaForCausalLM",
							    "MegaForMaskedLM",
							    "MegaForMultipleChoice",
							    "MegaForQuestionAnswering",
							    "MegaForSequenceClassification",
							    "MegaForTokenClassification",
							    "MegaModel",
							    "MegaPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mega import (
														    MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MegaForCausalLM,
														    MegaForMaskedLM,
														    MegaForMultipleChoice,
														    MegaForQuestionAnswering,
														    MegaForSequenceClassification,
														    MegaForTokenClassification,
														    MegaModel,
														    MegaPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
