


'''simple docstring'''




from typing import TYPE_CHECKING

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


_lowerCAmelCase  :		Tuple		     =	{
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase  :		Dict		     =	['ConditionalDetrFeatureExtractor']
				_lowerCAmelCase  :		Dict		     =	['ConditionalDetrImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase  :		List[str]		     =	[
				    'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'ConditionalDetrForObjectDetection',
				    'ConditionalDetrForSegmentation',
				    'ConditionalDetrModel',
				    'ConditionalDetrPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_conditional_detr import (
				    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    ConditionalDetrConfig,
				    ConditionalDetrOnnxConfig,
				)

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
								from .image_processing_conditional_detr import ConditionalDetrImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_conditional_detr import (
								    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ConditionalDetrForObjectDetection,
								    ConditionalDetrForSegmentation,
								    ConditionalDetrModel,
								    ConditionalDetrPreTrainedModel,
								)

else:
				import sys

				_lowerCAmelCase  :		List[Any]		     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




'''simple docstring'''




import math
import sys

import cva
import numpy as np

def 			__UpperCamelCase    (    _A    :				np.ndarray					,   _A    :				float				)    ->      np.ndarray:




	"""simple docstring"""





	lowerCAmelCase					:    Union[str, Any]      							= math.sqrt(_A				)
	lowerCAmelCase					:    Union[str, Any]      							= 1 / (sigma * math.sqrt(2 * math.pi				))
	return cons * np.exp(-((img / sigma) ** 2) * 0.5				)

def 			__UpperCamelCase    (    _A    :				np.ndarray					,   _A    :				int					,   _A    :				int					,   _A    :				int				)    ->      np.ndarray:




	"""simple docstring"""





	lowerCAmelCase					:    int      							= kernel_size // 2
	return img[x - half : x + half + 1, y - half : y + half + 1]

def 			__UpperCamelCase    (    _A    :				int					,   _A    :				float				)    ->      np.ndarray:




	"""simple docstring"""





	lowerCAmelCase					:    Dict      							= np.zeros((kernel_size, kernel_size)				)
	for i in range(0					,   _A				):
		for j in range(0					,   _A				):
			lowerCAmelCase					:    Optional[int]      							= math.sqrt(
			    abs(i - kernel_size // 2				) ** 2 + abs(j - kernel_size // 2				) ** 2				)
	return vec_gaussian(_A					,   _A				)

def 			__UpperCamelCase    (    _A    :				np.ndarray					,   _A    :				float					,   _A    :				float					,   _A    :				int					,   )    ->      np.ndarray:




	"""simple docstring"""





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

def 			__UpperCamelCase    (    _A    :				list				)    ->      tuple:




	"""simple docstring"""





	lowerCAmelCase					:    List[Any]      							= args[1] if args[1:] else '../image_data/lena.jpg'
	lowerCAmelCase					:    Any      							= float(args[2]				) if args[2:] else 1.0
	lowerCAmelCase					:    Union[str, Any]      							= float(args[3]				) if args[3:] else 1.0
	if args[4:]:
		lowerCAmelCase					:    int      							= int(args[4]				)
		lowerCAmelCase					:    Optional[Any]      							= kernel_size + abs(kernel_size % 2 - 1				)
	else:
		lowerCAmelCase					:    Optional[int]      							= 5
	return filename, spatial_variance, intensity_variance, kernel_size


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

				_lowerCAmelCase  :		Union[str, Any]		     =	img / 255
				_lowerCAmelCase  :		List[str]		     =	out.astype('float32')
				_lowerCAmelCase  :		Optional[int]		     =	bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
				_lowerCAmelCase  :		Union[str, Any]		     =	out * 255
				_lowerCAmelCase  :		Optional[Any]		     =	np.uinta(out)
				cva.imshow('output image', out)
				cva.waitKey(0)
				cva.destroyAllWindows()
