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 ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE__		:       List[str]       						=       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:       List[Any]       						=       {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}

class    snake_case       (   UpperCamelCase_ ,    UpperCamelCase_    ):
  lowercase_								=  'swin'

  lowercase_								=  {
      'num_attention_heads': 'num_heads',
      'num_hidden_layers': 'num_layers',
  }




  def __init__(   self  :						str  ,			a_  :						Dict=224  ,			a_  :						Optional[int]=4  ,			a_  :						Optional[Any]=3  ,			a_  :						Optional[int]=96  ,			a_  :						List[str]=[2, 2, 6, 2]  ,			a_  :						Union[str, Any]=[3, 6, 12, 24]  ,			a_  :						int=7  ,			a_  :						List[Any]=4.0  ,			a_  :						Any=True  ,			a_  :						str=0.0  ,			a_  :						str=0.0  ,			a_  :						List[str]=0.1  ,			a_  :						Union[str, Any]="gelu"  ,			a_  :						List[str]=False  ,			a_  :						List[str]=0.02  ,			a_  :						Union[str, Any]=1e-5  ,			a_  :						int=32  ,			a_  :						Optional[Any]=None  ,			a_  :						List[Any]=None  ,			**a_  :						Dict  ,			)->							Any:




       """simple docstring"""
       super().__init__(**a_					)

       SCREAMING_SNAKE_CASE__					:					Dict      =		image_size
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		patch_size
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		num_channels
       SCREAMING_SNAKE_CASE__					:					Dict      =		embed_dim
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		depths
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		len(a_					)
       SCREAMING_SNAKE_CASE__					:					str      =		num_heads
       SCREAMING_SNAKE_CASE__					:					Any      =		window_size
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		mlp_ratio
       SCREAMING_SNAKE_CASE__					:					Any      =		qkv_bias
       SCREAMING_SNAKE_CASE__					:					Tuple      =		hidden_dropout_prob
       SCREAMING_SNAKE_CASE__					:					int      =		attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__					:					Dict      =		drop_path_rate
       SCREAMING_SNAKE_CASE__					:					Dict      =		hidden_act
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		use_absolute_embeddings
       SCREAMING_SNAKE_CASE__					:					Dict      =		layer_norm_eps
       SCREAMING_SNAKE_CASE__					:					Tuple      =		initializer_range
       SCREAMING_SNAKE_CASE__					:					List[str]      =		encoder_stride
       # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		int(embed_dim * 2 ** (len(a_					) - 1)					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		['stem'] + [F'''stage{idx}''' for idx in range(1  ,			len(a_					) + 1					)]
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					List[str]      =		get_aligned_output_features_output_indices(
           out_features=a_  ,			out_indices=a_  ,			stage_names=self.stage_names					)




class    snake_case       (   UpperCamelCase_    ):
  lowercase_								=  version.parse('1.11'    )




  @property
  def 						__lowercase(   self  :						Optional[Any]					)->							Mapping[str, Mapping[int, str]]:




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




  @property
  def 						__lowercase(   self  :						List[str]					)->							float:




       """simple docstring"""
       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)




