


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


__A :  Dict    							=				logging.get_logger(__name__)

__A :  Optional[Any]    							=				{
    "microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase       			= 'cvt'

    def __init__(       self   :  Optional[Any]   ,						lowerCamelCase   :  Any=3   ,						lowerCamelCase   :  str=[7, 3, 3]   ,						lowerCamelCase   :  Optional[Any]=[4, 2, 2]   ,						lowerCamelCase   :  List[str]=[2, 1, 1]   ,						lowerCamelCase   :  List[str]=[64, 1_92, 3_84]   ,						lowerCamelCase   :  Optional[int]=[1, 3, 6]   ,						lowerCamelCase   :  Optional[int]=[1, 2, 10]   ,						lowerCamelCase   :  Any=[4.0, 4.0, 4.0]   ,						lowerCamelCase   :  int=[0.0, 0.0, 0.0]   ,						lowerCamelCase   :  Optional[Any]=[0.0, 0.0, 0.0]   ,						lowerCamelCase   :  Dict=[0.0, 0.0, 0.1]   ,						lowerCamelCase   :  Tuple=[True, True, True]   ,						lowerCamelCase   :  int=[False, False, True]   ,						lowerCamelCase   :  Dict=["dw_bn", "dw_bn", "dw_bn"]   ,						lowerCamelCase   :  Dict=[3, 3, 3]   ,						lowerCamelCase   :  Optional[int]=[1, 1, 1]   ,						lowerCamelCase   :  Optional[int]=[2, 2, 2]   ,						lowerCamelCase   :  Tuple=[1, 1, 1]   ,						lowerCamelCase   :  List[Any]=[1, 1, 1]   ,						lowerCamelCase   :  int=0.02   ,						lowerCamelCase   :  Tuple=1E-12   ,						**lowerCamelCase   :  Optional[int]   ,						)       ->  List[str]:
      super().__init__(**lowerCamelCase		)
      lowerCAmelCase_      :				Optional[int]      =       num_channels
      lowerCAmelCase_      :				str      =       patch_sizes
      lowerCAmelCase_      :				Union[str, Any]      =       patch_stride
      lowerCAmelCase_      :				Any      =       patch_padding
      lowerCAmelCase_      :				str      =       embed_dim
      lowerCAmelCase_      :				Tuple      =       num_heads
      lowerCAmelCase_      :				int      =       depth
      lowerCAmelCase_      :				Tuple      =       mlp_ratio
      lowerCAmelCase_      :				List[str]      =       attention_drop_rate
      lowerCAmelCase_      :				Optional[int]      =       drop_rate
      lowerCAmelCase_      :				Optional[int]      =       drop_path_rate
      lowerCAmelCase_      :				Any      =       qkv_bias
      lowerCAmelCase_      :				int      =       cls_token
      lowerCAmelCase_      :				List[Any]      =       qkv_projection_method
      lowerCAmelCase_      :				int      =       kernel_qkv
      lowerCAmelCase_      :				Tuple      =       padding_kv
      lowerCAmelCase_      :				Optional[int]      =       stride_kv
      lowerCAmelCase_      :				Optional[Any]      =       padding_q
      lowerCAmelCase_      :				int      =       stride_q
      lowerCAmelCase_      :				List[str]      =       initializer_range
      lowerCAmelCase_      :				Any      =       layer_norm_eps







'''simple docstring'''






from pathlib import Path

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







def     UpperCamelCase_      (   A__ :       np.ndarray							,   A__ :       np.ndarray							,   A__ :       np.ndarray							,   A__ :       int							,   A__ :       int ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[Any]      =       cva.getAffineTransform(A__							,   A__ )
  return cva.warpAffine(A__							,   A__							,   (rows, cols) )


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

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

  # add all rotated images in a list
  __A :  Optional[Any]    							=				[
      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
  __A :  Dict    							=				plt.figure(1)
  __A :  Optional[Any]    							=				["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()



