


'''simple docstring'''






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


__A :  List[Any]    							=				logging.get_logger(__name__)

__A :  List[Any]    							=				{
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase       			= 'lilt'

    def __init__(       self   :  str   ,						lowerCamelCase   :  str=3_05_22   ,						lowerCamelCase   :  List[Any]=7_68   ,						lowerCamelCase   :  str=12   ,						lowerCamelCase   :  Any=12   ,						lowerCamelCase   :  Tuple=30_72   ,						lowerCamelCase   :  str="gelu"   ,						lowerCamelCase   :  Tuple=0.1   ,						lowerCamelCase   :  int=0.1   ,						lowerCamelCase   :  Dict=5_12   ,						lowerCamelCase   :  List[Any]=2   ,						lowerCamelCase   :  List[Any]=0.02   ,						lowerCamelCase   :  Any=1E-12   ,						lowerCamelCase   :  List[str]=0   ,						lowerCamelCase   :  Tuple="absolute"   ,						lowerCamelCase   :  List[Any]=None   ,						lowerCamelCase   :  Union[str, Any]=4   ,						lowerCamelCase   :  Tuple=10_24   ,						**lowerCamelCase   :  str   ,						)       ->  Union[str, Any]:
      super().__init__(pad_token_id=lowerCamelCase   ,						**lowerCamelCase		)

      lowerCAmelCase_      :				Union[str, Any]      =       vocab_size
      lowerCAmelCase_      :				Optional[int]      =       hidden_size
      lowerCAmelCase_      :				Optional[Any]      =       num_hidden_layers
      lowerCAmelCase_      :				int      =       num_attention_heads
      lowerCAmelCase_      :				Optional[Any]      =       hidden_act
      lowerCAmelCase_      :				Any      =       intermediate_size
      lowerCAmelCase_      :				int      =       hidden_dropout_prob
      lowerCAmelCase_      :				int      =       attention_probs_dropout_prob
      lowerCAmelCase_      :				Tuple      =       max_position_embeddings
      lowerCAmelCase_      :				Any      =       type_vocab_size
      lowerCAmelCase_      :				Dict      =       initializer_range
      lowerCAmelCase_      :				List[str]      =       layer_norm_eps
      lowerCAmelCase_      :				Optional[int]      =       position_embedding_type
      lowerCAmelCase_      :				Optional[int]      =       classifier_dropout
      lowerCAmelCase_      :				Dict      =       channel_shrink_ratio
      lowerCAmelCase_      :				Tuple      =       max_ad_position_embeddings







'''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()



