





from pathlib import Path

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






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				np.ndarray       , UpperCamelCase__:				np.ndarray       , UpperCamelCase__:				np.ndarray       , UpperCamelCase__:				int       , UpperCamelCase__:				int     ):
		SCREAMING_SNAKE_CASE__								=   cva.getAffineTransform(_UpperCAmelCase       , _UpperCAmelCase     )
		return cva.warpAffine(_UpperCAmelCase       , _UpperCAmelCase       , (rows, cols)     )


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

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

	# add all rotated images in a list
	_lowerCamelCase 					=      [
	    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
	_lowerCamelCase 					=      plt.figure(1)
	_lowerCamelCase 					=      ['''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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__    :   List[Any]           =					{
    '''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
    '''tokenization_luke''': ['''LukeTokenizer'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__    :   Union[str, Any]           =					[
        '''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''LukeForEntityClassification''',
        '''LukeForEntityPairClassification''',
        '''LukeForEntitySpanClassification''',
        '''LukeForMultipleChoice''',
        '''LukeForQuestionAnswering''',
        '''LukeForSequenceClassification''',
        '''LukeForTokenClassification''',
        '''LukeForMaskedLM''',
        '''LukeModel''',
        '''LukePreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
    from .tokenization_luke import LukeTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_luke import (
            LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
            LukeForEntityClassification,
            LukeForEntityPairClassification,
            LukeForEntitySpanClassification,
            LukeForMaskedLM,
            LukeForMultipleChoice,
            LukeForQuestionAnswering,
            LukeForSequenceClassification,
            LukeForTokenClassification,
            LukeModel,
            LukePreTrainedModel,
        )

else:
    import sys

    A__    :   List[str]           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



