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


A_ :				int						 =    logging.get_logger(__name__)

A_ :				Optional[Any]						 =    {
    'facebook/vit-mae-base': 'https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					UpperCAmelCase__:     List[Any]													= '''vit_mae'''






					def __init__(				self					, A__=768					, A__=12					, A__=12					, A__=3072					, A__="gelu"					, A__=0.0					, A__=0.0					, A__=0.0_2					, A__=1e-12					, A__=224					, A__=16					, A__=3					, A__=True					, A__=16					, A__=512					, A__=8					, A__=2048					, A__=0.7_5					, A__=False					, **A__					, ):
						super().__init__(**A__    )

						A__							:					Dict       =	hidden_size
						A__							:					Optional[Any]       =	num_hidden_layers
						A__							:					List[str]       =	num_attention_heads
						A__							:					Any       =	intermediate_size
						A__							:					Union[str, Any]       =	hidden_act
						A__							:					List[str]       =	hidden_dropout_prob
						A__							:					Union[str, Any]       =	attention_probs_dropout_prob
						A__							:					Optional[int]       =	initializer_range
						A__							:					Optional[Any]       =	layer_norm_eps
						A__							:					Tuple       =	image_size
						A__							:					Dict       =	patch_size
						A__							:					Optional[int]       =	num_channels
						A__							:					List[str]       =	qkv_bias
						A__							:					Tuple       =	decoder_num_attention_heads
						A__							:					Any       =	decoder_hidden_size
						A__							:					Optional[int]       =	decoder_num_hidden_layers
						A__							:					Tuple       =	decoder_intermediate_size
						A__							:					int       =	mask_ratio
						A__							:					Union[str, Any]       =	norm_pix_loss






from pathlib import Path

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

def 		UpperCamelCase      (lowercase_:				np.ndarray					,   lowercase_:				np.ndarray					,   lowercase_:				np.ndarray					,   lowercase_:				int					,   lowercase_:				int      )  ->   np.ndarray:
	A__							:					Any       =	cva.getAffineTransform(lowercase_					,   lowercase_      )
	return cva.warpAffine(lowercase_					,   lowercase_					,   (rows, cols)      )


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

	# set different points to rotate image
	A_ :				str						 =    np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
	A_ :				Dict						 =    np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
	A_ :				Optional[int]						 =    np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
	A_ :				Optional[int]						 =    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_ :				Union[str, Any]						 =    plt.figure(1)
	A_ :				Union[str, Any]						 =    ['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()



