
from pathlib import Path

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


def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__						,    a__  )       ->  np.ndarray:
				__a										=    cva.getAffineTransform(a__						,    a__  )
				return cva.warpAffine(a__						,    a__						,    (rows, cols)  )


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

					# set different points to rotate image
					A     :	str    = np.array([[5_0, 5_0], [2_0_0, 5_0], [5_0, 2_0_0]], np.floataa)
					A     :	Tuple    = np.array([[1_0, 1_0_0], [2_0_0, 5_0], [1_0_0, 2_5_0]], np.floataa)
					A     :	Union[str, Any]    = np.array([[5_0, 5_0], [1_5_0, 5_0], [1_2_0, 2_0_0]], np.floataa)
					A     :	Any    = 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
					A     :	Optional[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
					A     :	Optional[int]    = plt.figure(1)
					A     :	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=0.05, right=1.0, top=0.95)
					plt.show()

from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate







@dataclass(frozen=a			)
class 			__A( a			):
				snake_case_						   =    field(default='''language-modeling'''  ,   metadata={'''include_in_asdict_even_if_is_default''': True}			)

				snake_case_						   =    Features({'''text''': Value('''string'''			)}			)
				snake_case_						   =    Features({}			)
				snake_case_						   =    "text"




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict[str, str]:


								'''simple docstring'''




								return {self.text_column: "text"}