






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


snake_case__   :						str													=   logging.get_logger(__name__)

snake_case__   :						Optional[int]													=   {
    "facebook/data2vec-vision-base-ft": (
        "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
    ),
}


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = """data2vec-vision"""




  def __init__(  self		,		_UpperCAmelCase=768		,		_UpperCAmelCase=12		,		_UpperCAmelCase=12		,		_UpperCAmelCase=3072		,		_UpperCAmelCase="gelu"		,		_UpperCAmelCase=0.0		,		_UpperCAmelCase=0.0		,		_UpperCAmelCase=0.0_2		,		_UpperCAmelCase=1e-12		,		_UpperCAmelCase=224		,		_UpperCAmelCase=16		,		_UpperCAmelCase=3		,		_UpperCAmelCase=False		,		_UpperCAmelCase=False		,		_UpperCAmelCase=False		,		_UpperCAmelCase=False		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=True		,		_UpperCAmelCase=[3, 5, 7, 11]		,		_UpperCAmelCase=[1, 2, 3, 6]		,		_UpperCAmelCase=True		,		_UpperCAmelCase=0.4		,		_UpperCAmelCase=256		,		_UpperCAmelCase=1		,		_UpperCAmelCase=False		,		_UpperCAmelCase=255		,		**_UpperCAmelCase		,		)      ->			List[str]:
     super().__init__(**a_			)

     UpperCamelCase_						=  hidden_size
     UpperCamelCase_						=  num_hidden_layers
     UpperCamelCase_						=  num_attention_heads
     UpperCamelCase_						=  intermediate_size
     UpperCamelCase_						=  hidden_act
     UpperCamelCase_						=  hidden_dropout_prob
     UpperCamelCase_						=  attention_probs_dropout_prob
     UpperCamelCase_						=  initializer_range
     UpperCamelCase_						=  layer_norm_eps

     UpperCamelCase_						=  image_size
     UpperCamelCase_						=  patch_size
     UpperCamelCase_						=  num_channels
     UpperCamelCase_						=  use_mask_token
     UpperCamelCase_						=  use_absolute_position_embeddings
     UpperCamelCase_						=  use_relative_position_bias
     UpperCamelCase_						=  use_shared_relative_position_bias
     UpperCamelCase_						=  layer_scale_init_value
     UpperCamelCase_						=  drop_path_rate
     UpperCamelCase_						=  use_mean_pooling
     # decode head attributes (semantic segmentation)
     UpperCamelCase_						=  out_indices
     UpperCamelCase_						=  pool_scales
     # auxiliary head attributes (semantic segmentation)
     UpperCamelCase_						=  use_auxiliary_head
     UpperCamelCase_						=  auxiliary_loss_weight
     UpperCamelCase_						=  auxiliary_channels
     UpperCamelCase_						=  auxiliary_num_convs
     UpperCamelCase_						=  auxiliary_concat_input
     UpperCamelCase_						=  semantic_loss_ignore_index




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = version.parse("""1.11"""     )




  @property
  def  _UpperCAmelCase  (  self			)      ->			Optional[Any]:
     return OrderedDict(
         [
             ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
         ]			)




  @property
  def  _UpperCAmelCase  (  self			)      ->			Optional[Any]:
     return 1e-4










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_		(					snake_case__    ,			snake_case__					)	->     Dict:



				"""simple docstring"""





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







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     str:



				"""simple docstring"""





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

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

				#  turn the kernel into shape(k*k, 1)
				lowerCAmelCase__		  =  gen_gaussian_kernel(snake_case__    ,			snake_case__					)
				lowerCAmelCase__		  =  ravel(snake_case__					)

				# reshape and get the dst image
				lowerCAmelCase__		  =  dot(snake_case__    ,			snake_case__					).reshape(snake_case__    ,			snake_case__					).astype(snake_case__					)

				return dst


if __name__ == "__main__":
	# read original image
	_lowerCAmelCase	:   List[str]    =   imread(R"../image_data/lena.jpg")
	# turn image in gray scale value
	_lowerCAmelCase	:   Optional[Any]    =   cvtColor(img, COLOR_BGR2GRAY)

	# get values with two different mask size
	_lowerCAmelCase	:   Optional[int]    =   gaussian_filter(gray, 3, sigma=1)
	_lowerCAmelCase	:   Tuple    =   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()
