



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_lowercase     							=						{
    '''configuration_groupvit''': [
        '''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''GroupViTConfig''',
        '''GroupViTOnnxConfig''',
        '''GroupViTTextConfig''',
        '''GroupViTVisionConfig''',
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase     							=						[
				    '''GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''GroupViTModel''',
				    '''GroupViTPreTrainedModel''',
				    '''GroupViTTextModel''',
				    '''GroupViTVisionModel''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase     							=						[
				    '''TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFGroupViTModel''',
				    '''TFGroupViTPreTrainedModel''',
				    '''TFGroupViTTextModel''',
				    '''TFGroupViTVisionModel''',
				]

if TYPE_CHECKING:
				from .configuration_groupvit import (
				    GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    GroupViTConfig,
				    GroupViTOnnxConfig,
				    GroupViTTextConfig,
				    GroupViTVisionConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_groupvit import (
								    GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    GroupViTModel,
								    GroupViTPreTrainedModel,
								    GroupViTTextModel,
								    GroupViTVisionModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_groupvit import (
								    TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFGroupViTModel,
								    TFGroupViTPreTrainedModel,
								    TFGroupViTTextModel,
								    TFGroupViTVisionModel,
								)

else:
				import sys

				_lowercase     							=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




from pathlib import Path

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






def      _A				(UpperCamelCase		:      np.ndarray      ,			UpperCamelCase		:      np.ndarray      ,			UpperCamelCase		:      np.ndarray      ,			UpperCamelCase		:      int      ,			UpperCamelCase		:      int )  ->np.ndarray:



			'''simple docstring'''







			lowerCamelCase__ :      Union[str, Any]								=		cva.getAffineTransform(UpperCamelCase      ,			UpperCamelCase )
			return cva.warpAffine(UpperCamelCase      ,			UpperCamelCase      ,			(rows, cols) )


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

				# set different points to rotate image
				_lowercase     							=						np.array([[50, 50], [2_00, 50], [50, 2_00]], np.floataa)
				_lowercase     							=						np.array([[10, 1_00], [2_00, 50], [1_00, 2_50]], np.floataa)
				_lowercase     							=						np.array([[50, 50], [1_50, 50], [1_20, 2_00]], np.floataa)
				_lowercase     							=						np.array([[10, 1_00], [80, 50], [1_80, 2_50]], np.floataa)

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



