





'''simple docstring'''






from ....configuration_utils import PretrainedConfig
from ....utils import logging


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

__lowerCamelCase						: str		=   {
    'Visual-Attention-Network/van-base': (
        'https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'
    ),
}







class 		UpperCAmelCase   (				__snake_case):


  """simple docstring"""







  lowerCAmelCase_					=				"""van"""


  def __init__(				self    :					Union[str, Any]							,	UpperCamelCase__    :					Tuple=224							,	UpperCamelCase__    :					Any=3							,	UpperCamelCase__    :					Dict=[7, 3, 3, 3]							,	UpperCamelCase__    :					int=[4, 2, 2, 2]							,	UpperCamelCase__    :					Optional[Any]=[64, 128, 320, 512]							,	UpperCamelCase__    :					str=[3, 3, 12, 3]							,	UpperCamelCase__    :					Any=[8, 8, 4, 4]							,	UpperCamelCase__    :					Union[str, Any]="gelu"							,	UpperCamelCase__    :					List[str]=0.02							,	UpperCamelCase__    :					List[str]=1E-6							,	UpperCamelCase__    :					Optional[Any]=1E-2							,	UpperCamelCase__    :					str=0.0							,	UpperCamelCase__    :					Any=0.0							,	**UpperCamelCase__    :					Any							,	)     ->						List[Any]:
    super().__init__(**_lowercase  )
    _UpperCamelCase       			=image_size
    _UpperCamelCase       			=num_channels
    _UpperCamelCase       			=patch_sizes
    _UpperCamelCase       			=strides
    _UpperCamelCase       			=hidden_sizes
    _UpperCamelCase       			=depths
    _UpperCamelCase       			=mlp_ratios
    _UpperCamelCase       			=hidden_act
    _UpperCamelCase       			=initializer_range
    _UpperCamelCase       			=layer_norm_eps
    _UpperCamelCase       			=layer_scale_init_value
    _UpperCamelCase       			=drop_path_rate
    _UpperCamelCase       			=dropout_rate













'''simple docstring'''






from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=cva.getAffineTransform(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
  return cva.warpAffine(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      (rows, cols)   )


if __name__ == "__main__":
      # read original image
      __lowerCamelCase						: List[Any]		=   cva.imread(
          str(Path(__file__).resolve().parent.parent / 'image_data' / 'lena.jpg')
      )
      # turn image in gray scale value
      __lowerCamelCase						: List[Any]		=   cva.cvtColor(image, cva.COLOR_BGR2GRAY)
      # get image shape
      __lowerCamelCase		, __lowerCamelCase						: Any		=   gray_img.shape

      # set different points to rotate image
      __lowerCamelCase						: Optional[Any]		=   np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
      __lowerCamelCase						: Any		=   np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
      __lowerCamelCase						: int		=   np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
      __lowerCamelCase						: List[Any]		=   np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

      # add all rotated images in a list
      __lowerCamelCase						: int		=   [
          gray_img,
          get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
          get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
          get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      ]

      # plot different image rotations
      __lowerCamelCase						: Optional[Any]		=   plt.figure(1)
      __lowerCamelCase						: Tuple		=   ['Original', 'Rotation 1', 'Rotation 2', 'Rotation 3']
      for i, image in enumerate(images):
            plt.subplot(2, 2, i + 1), plt.imshow(image, 'gray')
            plt.title(titles[i])
            plt.axis('off')
            plt.subplots_adjust(left=0.0, bottom=0.0_5, right=1.0, top=0.9_5)
      plt.show()






