



'''simple docstring'''

import math
import sys

import cva
import numpy as np



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



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



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_    )     -> np.ndarray:
		# Creates a gaussian kernel of given dimension.
		_a	: str								=				np.zeros((kernel_size, kernel_size)    )
		for i in range(0		,     lowerCAmelCase_    ):
				for j in range(0		,     lowerCAmelCase_    ):
						_a	: Union[str, Any]								=				math.sqrt(
						    abs(i - kernel_size // 2    ) ** 2 + abs(j - kernel_size // 2    ) ** 2    )
		return vec_gaussian(lowerCAmelCase_		,     lowerCAmelCase_    )



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



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


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

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









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available


__lowerCAmelCase		     =  {
    '''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		     =  [
							    '''ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''ErnieForCausalLM''',
							    '''ErnieForMaskedLM''',
							    '''ErnieForMultipleChoice''',
							    '''ErnieForNextSentencePrediction''',
							    '''ErnieForPreTraining''',
							    '''ErnieForQuestionAnswering''',
							    '''ErnieForSequenceClassification''',
							    '''ErnieForTokenClassification''',
							    '''ErnieModel''',
							    '''ErniePreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_ernie import (
														    ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ErnieForCausalLM,
														    ErnieForMaskedLM,
														    ErnieForMultipleChoice,
														    ErnieForNextSentencePrediction,
														    ErnieForPreTraining,
														    ErnieForQuestionAnswering,
														    ErnieForSequenceClassification,
														    ErnieForTokenClassification,
														    ErnieModel,
														    ErniePreTrainedModel,
														)

else:
							import sys

							__lowerCAmelCase		     =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




