



"""simple docstring"""
from pathlib import Path

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





def   __magic_name__					(				lowercase				,      lowercase				,      lowercase				,      lowercase				,      lowercase			):
   SCREAMING_SNAKE_CASE_:			str        =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
   _UpperCAmelCase  =      cva.imread(
       str(Path(__file__).resolve().parent.parent / """image_data""" / """lena.jpg""")
   )
   # turn image in gray scale value
   _UpperCAmelCase  =      cva.cvtColor(image, cva.COLOR_BGR2GRAY)
   # get image shape
   _UpperCAmelCase,			_UpperCAmelCase  =      gray_img.shape

   # set different points to rotate image
   _UpperCAmelCase  =      np.array([[5_0, 5_0], [2_0_0, 5_0], [5_0, 2_0_0]], np.floataa)
   _UpperCAmelCase  =      np.array([[1_0, 1_0_0], [2_0_0, 5_0], [1_0_0, 2_5_0]], np.floataa)
   _UpperCAmelCase  =      np.array([[5_0, 5_0], [1_5_0, 5_0], [1_2_0, 2_0_0]], np.floataa)
   _UpperCAmelCase  =      np.array([[1_0, 1_0_0], [8_0, 5_0], [1_8_0, 2_5_0]], np.floataa)

   # add all rotated images in a list
   _UpperCAmelCase  =      [
       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
   _UpperCAmelCase  =      plt.figure(1)
   _UpperCAmelCase  =      ["""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()







from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class A    (  UpperCamelCase_	):






      def __init__(   self		:	Union[str, Any]     ,  lowercase_		:	NestedDataStructureLike[PathLike]     ,  lowercase_		:	Optional[NamedSplit] = None     ,  lowercase_		:	Optional[Features] = None     ,  lowercase_		:	str = None     ,  lowercase_		:	bool = False     ,  lowercase_		:	bool = False     ,  lowercase_		:	Optional[int] = None     ,  **lowercase_		:	Optional[int]     ,  )   ->		Union[str, Any]:
             """simple docstring"""




             super().__init__(
                 lowercase_     ,  split=lowercase_     ,  features=lowercase_     ,  cache_dir=lowercase_     ,  keep_in_memory=lowercase_     ,  streaming=lowercase_     ,  num_proc=lowercase_     ,  **lowercase_     ,  )
             _lowerCamelCase			:						Optional[int]            =path_or_paths if isinstance(lowercase_     ,  lowercase_					) else {self.split: path_or_paths}
             _lowerCamelCase			:						int            =Text(
                 cache_dir=lowercase_     ,  data_files=lowercase_     ,  features=lowercase_     ,  **lowercase_     ,  )






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Dict:
             """simple docstring"""




             if self.streaming:
                    _lowerCamelCase			:						Optional[Any]            =self.builder.as_streaming_dataset(split=self.split					)
             # Build regular (map-style) dataset
             else:
                    _lowerCamelCase			:						List[str]            =None
                    _lowerCamelCase			:						int            =None
                    _lowerCamelCase			:						Optional[Any]            =None
                    _lowerCamelCase			:						Any            =None

                    self.builder.download_and_prepare(
                        download_config=lowercase_     ,  download_mode=lowercase_     ,  verification_mode=lowercase_     ,  base_path=lowercase_     ,  num_proc=self.num_proc     ,  )
                    _lowerCamelCase			:						Tuple            =self.builder.as_dataset(
                        split=self.split     ,  verification_mode=lowercase_     ,  in_memory=self.keep_in_memory					)
             return dataset


