



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase     							=						logging.get_logger(__name__)

_lowercase     							=						{
    '''microsoft/swinv2-tiny-patch4-window8-256''': (
        '''https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'''
    ),
}
class 				__A					( _a      ):
			UpperCamelCase					:List[str]     =			'''swinv2'''

			UpperCamelCase					:Union[str, Any]     =			{
			    '''num_attention_heads''': '''num_heads''',
			    '''num_hidden_layers''': '''num_layers''',
			}





			def __init__(self							,    __magic_name__=224							,    __magic_name__=4							,    __magic_name__=3							,    __magic_name__=96							,    __magic_name__=[2, 2, 6, 2]							,    __magic_name__=[3, 6, 12, 24]							,    __magic_name__=7							,    __magic_name__=4.0							,    __magic_name__=True							,    __magic_name__=0.0							,    __magic_name__=0.0							,    __magic_name__=0.1							,    __magic_name__="gelu"							,    __magic_name__=False							,    __magic_name__=0.02							,    __magic_name__=1E-5							,    __magic_name__=32							,    **__magic_name__							,    ):
						super().__init__(**_A				)

						lowerCamelCase__ :      Union[str, Any]								=		image_size
						lowerCamelCase__ :      Optional[int]								=		patch_size
						lowerCamelCase__ :      str								=		num_channels
						lowerCamelCase__ :      Union[str, Any]								=		embed_dim
						lowerCamelCase__ :      List[Any]								=		depths
						lowerCamelCase__ :      Any								=		len(_A				)
						lowerCamelCase__ :      List[str]								=		num_heads
						lowerCamelCase__ :      Optional[int]								=		window_size
						lowerCamelCase__ :      Any								=		mlp_ratio
						lowerCamelCase__ :      Optional[Any]								=		qkv_bias
						lowerCamelCase__ :      Optional[Any]								=		hidden_dropout_prob
						lowerCamelCase__ :      Optional[int]								=		attention_probs_dropout_prob
						lowerCamelCase__ :      Tuple								=		drop_path_rate
						lowerCamelCase__ :      int								=		hidden_act
						lowerCamelCase__ :      Any								=		use_absolute_embeddings
						lowerCamelCase__ :      List[str]								=		layer_norm_eps
						lowerCamelCase__ :      Optional[int]								=		initializer_range
						lowerCamelCase__ :      str								=		encoder_stride
						# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
						# this indicates the channel dimension after the last stage of the model
						lowerCamelCase__ :      Optional[int]								=		int(embed_dim * 2 ** (len(_A				) - 1)				)
						lowerCamelCase__ :      Dict								=		(0, 0, 0, 0)




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()



