


import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		{
    'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
    # See all DETR models at https://huggingface.co/models?filter=detr
}





class  _a							(    lowercase__):






	"""simple docstring"""




	_a  :  Union[str, Any]				   =							'detr'
	_a  :  Dict				   =							['past_key_values']
	_a  :  Tuple				   =							{
	    'hidden_size': 'd_model',
	    'num_attention_heads': 'encoder_attention_heads',
	}




	def __init__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		int=True		, _SCREAMING_SNAKE_CASE						:		Tuple=None		, _SCREAMING_SNAKE_CASE						:		str=3		, _SCREAMING_SNAKE_CASE						:		Tuple=100		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=6		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=2048		, _SCREAMING_SNAKE_CASE						:		int=8		, _SCREAMING_SNAKE_CASE						:		List[Any]=6		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=2048		, _SCREAMING_SNAKE_CASE						:		int=8		, _SCREAMING_SNAKE_CASE						:		List[Any]=0.0		, _SCREAMING_SNAKE_CASE						:		Optional[int]=0.0		, _SCREAMING_SNAKE_CASE						:		Dict=True		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]="relu"		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=256		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=0.1		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=0.0		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=0.0		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=0.02		, _SCREAMING_SNAKE_CASE						:		Tuple=1.0		, _SCREAMING_SNAKE_CASE						:		List[str]=False		, _SCREAMING_SNAKE_CASE						:		Any="sine"		, _SCREAMING_SNAKE_CASE						:		Tuple="resnet50"		, _SCREAMING_SNAKE_CASE						:		int=True		, _SCREAMING_SNAKE_CASE						:		Any=False		, _SCREAMING_SNAKE_CASE						:		Dict=1		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=5		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=2		, _SCREAMING_SNAKE_CASE						:		List[Any]=1		, _SCREAMING_SNAKE_CASE						:		Optional[int]=1		, _SCREAMING_SNAKE_CASE						:		Dict=5		, _SCREAMING_SNAKE_CASE						:		List[Any]=2		, _SCREAMING_SNAKE_CASE						:		List[Any]=0.1		, **_SCREAMING_SNAKE_CASE						:		Dict		, )->	Optional[Any]:
			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.'''		)
							lowerCAmelCase__       :      Optional[int]   					= CONFIG_MAPPING["""resnet"""](out_features=['''stage4''']		)
					elif isinstance(_UpperCamelCase		, _UpperCamelCase		):
							lowerCAmelCase__       :      Any   					= backbone_config.get('''model_type'''		)
							lowerCAmelCase__       :      Tuple   					= CONFIG_MAPPING[backbone_model_type]
							lowerCAmelCase__       :      List[Any]   					= config_class.from_dict(_UpperCamelCase		)
					# set timm attributes to None
					lowerCAmelCase__       :      List[str]   					= None, None, None

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




	@property
	def 							UpperCAmelCase__( self						:		Union[str, Any]		)->	Tuple:
			return self.encoder_attention_heads




	@property
	def 							UpperCAmelCase__( self						:		int		)->	Dict:
			return self.d_model




	@classmethod
	def 							UpperCAmelCase__( cls						:		int		, _SCREAMING_SNAKE_CASE						:		PretrainedConfig		, **_SCREAMING_SNAKE_CASE						:		Any		)->	Dict:
			return cls(backbone_config=_UpperCamelCase		, **_UpperCamelCase		)




	def 							UpperCAmelCase__( self						:		int		)->	Tuple:
			lowerCAmelCase__       :      Tuple   					= copy.deepcopy(self.__dict__		)
			if output["backbone_config"] is not None:
					lowerCAmelCase__       :      Optional[Any]   					= self.backbone_config.to_dict()
			lowerCAmelCase__       :      Dict   					= self.__class__.model_type
			return output





class  _a							(    lowercase__):






	"""simple docstring"""




	_a  :  Tuple				   =							version.parse('''1.11''')




	@property
	def 							UpperCAmelCase__( self						:		int		)->	List[str]:
			return OrderedDict(
			    [
			        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
			        ('''pixel_mask''', {0: '''batch'''}),
			    ]		)




	@property
	def 							UpperCAmelCase__( self						:		Union[str, Any]		)->	Dict:
			return 1E-5




	@property
	def 							UpperCAmelCase__( self						:		Optional[int]		)->	str:
			return 12








from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[Any]   					= k_size // 2
		lowerCAmelCase__					,     lowerCAmelCase__       :      Union[str, Any]   					= mgrid[0 - center : k_size - center, 0 - center : k_size - center]
		lowerCAmelCase__       :      str   					= 1 / (2 * pi * sigma) * exp(-(square(_a	) + square(_a	)) / (2 * square(_a	))	)
		return g






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__					,     lowerCAmelCase__       :      int   					= image.shape[0], image.shape[1]
		# dst image height and width
		lowerCAmelCase__       :      Any   					= height - k_size + 1
		lowerCAmelCase__       :      Tuple   					= width - k_size + 1

		# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
		lowerCAmelCase__       :      int   					= zeros((dst_height * dst_width, k_size * k_size)	)
		lowerCAmelCase__       :      List[str]   					= 0
		for i, j in product(range(_a	)     ,       range(_a	)	):
				lowerCAmelCase__       :      Union[str, Any]   					= ravel(image[i : i + k_size, j : j + k_size]	)
				lowerCAmelCase__       :      List[Any]   					= window
				row += 1

		#  turn the kernel into shape(k*k, 1)
		lowerCAmelCase__       :      List[Any]   					= gen_gaussian_kernel(_a     ,       _a	)
		lowerCAmelCase__       :      str   					= ravel(_a	)

		# reshape and get the dst image
		lowerCAmelCase__       :      int   					= dot(_a     ,       _a	).reshape(_a     ,       _a	).astype(_a	)

		return dst


if __name__ == "__main__":
		# read original image
		lowerCamelCase						=		imread(R'''../image_data/lena.jpg''')
		# turn image in gray scale value
		lowerCamelCase						=		cvtColor(img, COLOR_BGR2GRAY)

		# get values with two different mask size
		lowerCamelCase						=		gaussian_filter(gray, 3, sigma=1)
		lowerCamelCase						=		gaussian_filter(gray, 5, sigma=0.8)

		# show result images
		imshow('''gaussian filter with 3x3 mask''', gaussianaxa)
		imshow('''gaussian filter with 5x5 mask''', gaussianaxa)
		waitKey()




