

"""simple docstring"""




from pathlib import Path

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

def 	A       (     snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      cva.getAffineTransform(snake_case__		,				snake_case__    )
			return cva.warpAffine(snake_case__		,				snake_case__		,				(rows, cols)    )


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

						# set different points to rotate image
						A_							:			Optional[int]											= 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_							:			Any											= np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

						# add all rotated images in a list
						A_							:			Dict											= [
						    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_							:			int											= ["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()





"""simple docstring"""




from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments


