

"""simple docstring"""


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

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





@dataclass(frozen=__snake_case  )
class    snake_case     (    __snake_case  ):
				# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
				SCREAMING_SNAKE_CASE_       :				str       				=  field(default="""text-classification""",							metadata={"""include_in_asdict_even_if_is_default""": True}  )
				SCREAMING_SNAKE_CASE_       :				ClassVar[Features]       				=  Features({"""text""": Value("""string"""  )}  )
				SCREAMING_SNAKE_CASE_       :				ClassVar[Features]       				=  Features({"""labels""": ClassLabel}  )
				SCREAMING_SNAKE_CASE_       :				str       				=  "text"
				SCREAMING_SNAKE_CASE_       :				str       				=  "labels"

				def 							lowercase_					(     self							:    Union[str, Any]					,  UpperCamelCase__							:    List[Any])->       Tuple:



						'''simple docstring'''


						if self.label_column not in features:
								raise ValueError(f"Column {self.label_column} is not present in features.")
						if not isinstance(features[self.label_column]					,  UpperCamelCase__):
								raise ValueError(f"Column {self.label_column} is not a ClassLabel.")
						__lowerCAmelCase:	List[str]   				=      copy.deepcopy(self)
						__lowerCAmelCase:	Tuple   				=      self.label_schema.copy()
						__lowerCAmelCase:	Optional[Any]   				=      features[self.label_column]
						__lowerCAmelCase:	List[str]   				=      label_schema
						return task_template




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



						'''simple docstring'''


						return {
						    self.text_column: "text",
						    self.label_column: "labels",
						}





"""simple docstring"""


from pathlib import Path

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






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     np.ndarray:
		__lowerCAmelCase:	List[Any]   				=      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
			__A						       =      cva.imread(
			    str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
			)
			# turn image in gray scale value
			__A						       =      cva.cvtColor(image, cva.COLOR_BGR2GRAY)
			# get image shape
			__A       ,    __A						       =      gray_img.shape

			# set different points to rotate image
			__A						       =      np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
			__A						       =      np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
			__A						       =      np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
			__A						       =      np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

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


