





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      _UpperCamelCase  (a__ :Tuple ,      a__ :Dict							):






							"""simple docstring"""





							UpperCamelCase__					  =      k_size // 2
							UpperCamelCase__  ,  UpperCamelCase__					  =      mgrid[0 - center : k_size - center, 0 - center : k_size - center]
							UpperCamelCase__					  =      1 / (2 * pi * sigma) * exp(-(square(a__							) + square(a__							)) / (2 * square(a__							))							)
							return g





def      _UpperCamelCase  (a__ :Dict ,      a__ :Optional[int] ,      a__ :Dict							):






							"""simple docstring"""





							UpperCamelCase__  ,  UpperCamelCase__					  =      image.shape[0], image.shape[1]
							# dst image height and width
							UpperCamelCase__					  =      height - k_size + 1
							UpperCamelCase__					  =      width - k_size + 1

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

							#  turn the kernel into shape(k*k, 1)
							UpperCamelCase__					  =      gen_gaussian_kernel(a__ ,      a__							)
							UpperCamelCase__					  =      ravel(a__							)

							# reshape and get the dst image
							UpperCamelCase__					  =      dot(a__ ,      a__							).reshape(a__ ,      a__							).astype(a__							)

							return dst


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

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











from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
					import timm


if is_torch_available():
					from torch import Tensor

class 							__SCREAMING_SNAKE_CASE       (  _a ,		_a							):
							snake_case  :   int      =							"""pixel_values"""
							snake_case  :   List[Any]      =							False
							snake_case  :   str      =							TimmBackboneConfig






							def __init__(    self	,			__lowerCAmelCase	,			**__lowerCAmelCase	):
														requires_backends(self	,			"""timm"""	)
														super().__init__(__lowerCAmelCase	)
														UpperCamelCase__					  =      config

														if config.backbone is None:
																					raise ValueError("""backbone is not set in the config. Please set it to a timm model name."""	)

														if config.backbone not in timm.list_models():
																					raise ValueError(f"""backbone {config.backbone} is not supported by timm."""	)

														if hasattr(__lowerCAmelCase	,			"""out_features"""	) and config.out_features is not None:
																					raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead."""	)

														UpperCamelCase__					  =      getattr(__lowerCAmelCase	,			"""use_pretrained_backbone"""	,			__lowerCAmelCase	)
														if pretrained is None:
																					raise ValueError("""use_pretrained_backbone is not set in the config. Please set it to True or False."""	)

														# We just take the final layer by default. This matches the default for the transformers models.
														UpperCamelCase__					  =      config.out_indices if getattr(__lowerCAmelCase	,			"""out_indices"""	,			__lowerCAmelCase	) is not None else (-1,)

														UpperCamelCase__					  =      timm.create_model(
														    config.backbone	,			pretrained=__lowerCAmelCase	,			features_only=config.features_only	,			in_chans=config.num_channels	,			out_indices=__lowerCAmelCase	,			**__lowerCAmelCase	,			)
														# These are used to control the output of the model when called. If output_hidden_states is True, then
														# return_layers is modified to include all layers.
														UpperCamelCase__					  =      self._backbone.return_layers
														UpperCamelCase__					  =      {layer["""module"""]: str(__lowerCAmelCase	) for i, layer in enumerate(self._backbone.feature_info.info	)}
														super()._init_backbone(__lowerCAmelCase	)






							@classmethod
							def 					_lowerCamelCase		(    cls	,			__lowerCAmelCase	,			*__lowerCAmelCase	,			**__lowerCAmelCase	):
														requires_backends(cls	,			["""vision""", """timm"""]	)
														from ...models.timm_backbone import TimmBackboneConfig

														UpperCamelCase__					  =      kwargs.pop("""config"""	,			TimmBackboneConfig()	)

														UpperCamelCase__					  =      kwargs.pop("""use_timm_backbone"""	,			__lowerCAmelCase	)
														if not use_timm:
																					raise ValueError("""use_timm_backbone must be True for timm backbones"""	)

														UpperCamelCase__					  =      kwargs.pop("""num_channels"""	,			config.num_channels	)
														UpperCamelCase__					  =      kwargs.pop("""features_only"""	,			config.features_only	)
														UpperCamelCase__					  =      kwargs.pop("""use_pretrained_backbone"""	,			config.use_pretrained_backbone	)
														UpperCamelCase__					  =      kwargs.pop("""out_indices"""	,			config.out_indices	)
														UpperCamelCase__					  =      TimmBackboneConfig(
														    backbone=__lowerCAmelCase	,			num_channels=__lowerCAmelCase	,			features_only=__lowerCAmelCase	,			use_pretrained_backbone=__lowerCAmelCase	,			out_indices=__lowerCAmelCase	,			)
														return super()._from_config(__lowerCAmelCase	,			**__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														pass







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase=None	,			__lowerCAmelCase=None	,			__lowerCAmelCase=None	,			**__lowerCAmelCase	):
														UpperCamelCase__					  =      return_dict if return_dict is not None else self.config.use_return_dict
														UpperCamelCase__					  =      (
														    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
														)
														UpperCamelCase__					  =      output_attentions if output_attentions is not None else self.config.output_attentions

														if output_attentions:
																					raise ValueError("""Cannot output attentions for timm backbones at the moment"""	)

														if output_hidden_states:
																					# We modify the return layers to include all the stages of the backbone
																					UpperCamelCase__					  =      self._all_layers
																					UpperCamelCase__					  =      self._backbone(__lowerCAmelCase	,			**__lowerCAmelCase	)
																					UpperCamelCase__					  =      self._return_layers
																					UpperCamelCase__					  =      tuple(hidden_states[i] for i in self.out_indices	)
														else:
																					UpperCamelCase__					  =      self._backbone(__lowerCAmelCase	,			**__lowerCAmelCase	)
																					UpperCamelCase__					  =      None

														UpperCamelCase__					  =      tuple(__lowerCAmelCase	)
														UpperCamelCase__					  =      tuple(__lowerCAmelCase	) if hidden_states is not None else None

														if not return_dict:
																					UpperCamelCase__					  =      (feature_maps,)
																					if output_hidden_states:
																												UpperCamelCase__					  =      output + (hidden_states,)
																					return output

														return BackboneOutput(feature_maps=__lowerCAmelCase	,			hidden_states=__lowerCAmelCase	,			attentions=__lowerCAmelCase	)




