"""simple docstring"""




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


__lowerCAmelCase     :				Optional[int]    				=    logging.get_logger(__name__)

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

class    a_   (  __UpperCamelCase      ):
					UpperCamelCase_			:						List[Any]      =     "data2vec-vision"




					def __init__(						self    :  Union[str, Any]	,    snake_case__    :  Tuple=768	,    snake_case__    :  Tuple=12	,    snake_case__    :  Any=12	,    snake_case__    :  Tuple=3072	,    snake_case__    :  Tuple="gelu"	,    snake_case__    :  Dict=0.0	,    snake_case__    :  List[Any]=0.0	,    snake_case__    :  str=0.02	,    snake_case__    :  List[Any]=1E-12	,    snake_case__    :  List[str]=224	,    snake_case__    :  List[str]=16	,    snake_case__    :  str=3	,    snake_case__    :  Any=False	,    snake_case__    :  Optional[Any]=False	,    snake_case__    :  Any=False	,    snake_case__    :  Any=False	,    snake_case__    :  List[Any]=0.1	,    snake_case__    :  Any=0.1	,    snake_case__    :  List[Any]=True	,    snake_case__    :  Any=[3, 5, 7, 11]	,    snake_case__    :  List[Any]=[1, 2, 3, 6]	,    snake_case__    :  List[Any]=True	,    snake_case__    :  Optional[int]=0.4	,    snake_case__    :  Tuple=256	,    snake_case__    :  str=1	,    snake_case__    :  Tuple=False	,    snake_case__    :  Tuple=255	,    **snake_case__    :  Optional[Any]	,    ):
							super().__init__(**snake_case__  )

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

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

class    a_   (  __UpperCamelCase      ):
					UpperCamelCase_			:						Optional[Any]      =     version.parse("1.11"      )




					@property
					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							return OrderedDict(
							    [
							        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
							    ]  )




					@property
					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							return 1E-4





"""simple docstring"""




import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		if (ksize % 2) == 0:
				lowerCAmelCase__   = ksize + 1
		lowerCAmelCase__   = np.zeros((ksize, ksize)      ,	dtype=np.floataa  )

		# each value
		for y in range(lowerCamelCase__  ):
				for x in range(lowerCamelCase__  ):
						# distance from center
						lowerCAmelCase__   = x - ksize // 2
						lowerCAmelCase__   = y - ksize // 2

						# degree to radiant
						lowerCAmelCase__   = theta / 180 * np.pi
						lowerCAmelCase__   = np.cos(_theta  )
						lowerCAmelCase__   = np.sin(_theta  )

						# get kernel x
						lowerCAmelCase__   = cos_theta * px + sin_theta * py

						# get kernel y
						lowerCAmelCase__   = -sin_theta * px + cos_theta * py

						# fill kernel
						lowerCAmelCase__   = np.exp(
						    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)  ) * np.cos(2 * np.pi * _x / lambd + psi  )

		return gabor


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			# read original image
			__lowerCAmelCase     :				Tuple    				=    imread("../image_data/lena.jpg")
			# turn image in gray scale value
			__lowerCAmelCase     :				List[str]    				=    cvtColor(img, COLOR_BGR2GRAY)

			# Apply multiple Kernel to detect edges
			__lowerCAmelCase     :				Union[str, Any]    				=    np.zeros(gray.shape[:2])
			for theta in [0, 30, 60, 90, 1_20, 1_50]:
						__lowerCAmelCase     :				Union[str, Any]    				=    gabor_filter_kernel(10, 8, theta, 10, 0, 0)
						out += filteraD(gray, CV_8UC3, kernel_aa)
			__lowerCAmelCase     :				Optional[int]    				=    out / out.max() * 2_55
			__lowerCAmelCase     :				Tuple    				=    out.astype(np.uinta)

			imshow("Original", gray)
			imshow("Gabor filter with 20x20 mask and 6 directions", out)

			waitKey(0)




