


"""simple docstring"""




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


lowercase_			     =  logging.get_logger(__name__)

lowercase_			     =  {
    "microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
    "microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json",
}
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			int										=				'markuplm'

		def __init__(  self							, _a=30_522							, _a=768							, _a=12							, _a=12							, _a=3_072							, _a="gelu"							, _a=0.1							, _a=0.1							, _a=512							, _a=2							, _a=0.02							, _a=1E-12							, _a=0							, _a=0							, _a=2							, _a=256							, _a=1_024							, _a=216							, _a=1_001							, _a=32							, _a=50							, _a="absolute"							, _a=True							, _a=None							, **_a							, ):
				super().__init__(
				    pad_token_id=_a							, bos_token_id=_a							, eos_token_id=_a							, **_a							, )
				__a           =			vocab_size
				__a           =			hidden_size
				__a           =			num_hidden_layers
				__a           =			num_attention_heads
				__a           =			hidden_act
				__a           =			intermediate_size
				__a           =			hidden_dropout_prob
				__a           =			attention_probs_dropout_prob
				__a           =			max_position_embeddings
				__a           =			type_vocab_size
				__a           =			initializer_range
				__a           =			layer_norm_eps
				__a           =			position_embedding_type
				__a           =			use_cache
				__a           =			classifier_dropout
				# additional properties
				__a           =			max_depth
				__a           =			max_xpath_tag_unit_embeddings
				__a           =			max_xpath_subs_unit_embeddings
				__a           =			tag_pad_id
				__a           =			subs_pad_id
				__a           =			xpath_unit_hidden_size













from pathlib import Path

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



def _a				(	lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   int					,				lowerCamelCase:   int )	->						np.ndarray:







      '''simple docstring'''






      __A          =				cva.getAffineTransform(lowerCamelCase					,				lowerCamelCase )
      return cva.warpAffine(lowerCamelCase					,				lowerCamelCase					,				(rows, cols) )


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

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

       # add all rotated images in a list
       snake_case__					:							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
       snake_case__					:							Optional[Any]													=     plt.figure(1)
       snake_case__					:							Dict													=     ['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=0.05, right=1.0, top=0.95)
       plt.show()





