



"""simple docstring"""

import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowercase__			   =  logging.get_logger(__name__)

lowercase__			   =  {
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}




class SCREAMING_SNAKE_CASE__					(		__A	):
						_lowerCAmelCase							=					"conditional_detr"
						_lowerCAmelCase							=					["past_key_values"]
						_lowerCAmelCase							=					{
						    "hidden_size": "d_model",
						    "num_attention_heads": "encoder_attention_heads",
						}



						def __init__(self						,     _lowercase=True						,     _lowercase=None						,     _lowercase=3						,     _lowercase=300						,     _lowercase=6						,     _lowercase=2048						,     _lowercase=8						,     _lowercase=6						,     _lowercase=2048						,     _lowercase=8						,     _lowercase=0.0						,     _lowercase=0.0						,     _lowercase=True						,     _lowercase="relu"						,     _lowercase=256						,     _lowercase=0.1						,     _lowercase=0.0						,     _lowercase=0.0						,     _lowercase=0.02						,     _lowercase=1.0						,     _lowercase=False						,     _lowercase="sine"						,     _lowercase="resnet50"						,     _lowercase=True						,     _lowercase=False						,     _lowercase=2						,     _lowercase=5						,     _lowercase=2						,     _lowercase=1						,     _lowercase=1						,     _lowercase=2						,     _lowercase=5						,     _lowercase=2						,     _lowercase=0.25						,     **_lowercase						,     ):





													'''simple docstring'''

													if backbone_config is not None and use_timm_backbone:
																				raise ValueError("""You can\'t specify both `backbone_config` and `use_timm_backbone`."""							)

													if not use_timm_backbone:
																				if backbone_config is None:
																											logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""							)
																											__a		:		Union[str, Any]									=       CONFIG_MAPPING['resnet'](out_features=["""stage4"""]							)
																				elif isinstance(_lowercase						,     _lowercase							):
																											__a		:		int									=       backbone_config.get("""model_type"""							)
																											__a		:		Any									=       CONFIG_MAPPING[backbone_model_type]
																											__a		:		Tuple									=       config_class.from_dict(_lowercase							)

													__a		:		Optional[int]									=       use_timm_backbone
													__a		:		Dict									=       backbone_config
													__a		:		List[str]									=       num_channels
													__a		:		Dict									=       num_queries
													__a		:		Dict									=       d_model
													__a		:		str									=       encoder_ffn_dim
													__a		:		Optional[Any]									=       encoder_layers
													__a		:		str									=       encoder_attention_heads
													__a		:		Optional[int]									=       decoder_ffn_dim
													__a		:		Optional[int]									=       decoder_layers
													__a		:		List[Any]									=       decoder_attention_heads
													__a		:		Dict									=       dropout
													__a		:		Any									=       attention_dropout
													__a		:		Optional[int]									=       activation_dropout
													__a		:		Dict									=       activation_function
													__a		:		Optional[int]									=       init_std
													__a		:		Dict									=       init_xavier_std
													__a		:		Any									=       encoder_layerdrop
													__a		:		str									=       decoder_layerdrop
													__a		:		int									=       encoder_layers
													__a		:		str									=       auxiliary_loss
													__a		:		Optional[int]									=       position_embedding_type
													__a		:		List[str]									=       backbone
													__a		:		int									=       use_pretrained_backbone
													__a		:		Dict									=       dilation
													# Hungarian matcher
													__a		:		List[str]									=       class_cost
													__a		:		str									=       bbox_cost
													__a		:		Optional[Any]									=       giou_cost
													# Loss coefficients
													__a		:		Tuple									=       mask_loss_coefficient
													__a		:		Optional[int]									=       dice_loss_coefficient
													__a		:		List[str]									=       cls_loss_coefficient
													__a		:		Union[str, Any]									=       bbox_loss_coefficient
													__a		:		Union[str, Any]									=       giou_loss_coefficient
													__a		:		Optional[Any]									=       focal_alpha
													super().__init__(is_encoder_decoder=_lowercase						,     **_lowercase							)



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self.encoder_attention_heads



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self.d_model





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Union[str, Any]									=       copy.deepcopy(self.__dict__							)
													if self.backbone_config is not None:
																				__a		:		List[Any]									=       self.backbone_config.to_dict()
													__a		:		Union[str, Any]									=       self.__class__.model_type
													return output







class SCREAMING_SNAKE_CASE__					(		__A	):
						_lowerCAmelCase							=					version.parse("1.11"	)



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return OrderedDict(
													    [
													        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
													        ("""pixel_mask""", {0: """batch"""}),
													    ]							)



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return 1e-5





						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return 12






"""simple docstring"""

import math
import sys

import cva
import numpy as np





def       __magic_name__    (   _lowerCamelCase				: np.ndarray  ,       _lowerCamelCase				: float					):
							# For applying gaussian function for each element in matrix.
							__a		:		int									=       math.sqrt(_lowerCamelCase					)
							__a		:		Any									=       1 / (sigma * math.sqrt(2 * math.pi					))
							return cons * np.exp(-((img / sigma) ** 2) * 0.5					)





def       __magic_name__    (   _lowerCamelCase				: np.ndarray  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int					):
							__a		:		Any									=       kernel_size // 2
							return img[x - half : x + half + 1, y - half : y + half + 1]





def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: float					):
							# Creates a gaussian kernel of given dimension.
							__a		:		int									=       np.zeros((kernel_size, kernel_size)					)
							for i in range(0  ,       _lowerCamelCase					):
														for j in range(0  ,       _lowerCamelCase					):
																					__a		:		Any									=       math.sqrt(
																					    abs(i - kernel_size // 2					) ** 2 + abs(j - kernel_size // 2					) ** 2					)
							return vec_gaussian(_lowerCamelCase  ,       _lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: np.ndarray  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: int  ,       ):
							__a		:		Tuple									=       np.zeros(img.shape					)
							__a		:		Optional[int]									=       get_gauss_kernel(_lowerCamelCase  ,       _lowerCamelCase					)
							__a							,     __a		:		int									=       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		:		List[str]									=       get_slice(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Any									=       img_s - img_s[kernel_size // 2, kernel_size // 2]
																					__a		:		Optional[Any]									=       vec_gaussian(_lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Optional[Any]									=       np.multiply(_lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Any									=       np.multiply(_lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Tuple									=       np.sum(_lowerCamelCase					) / np.sum(_lowerCamelCase					)
																					__a		:		Optional[Any]									=       val
							return imga





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


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

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

