
'''simple docstring'''
from pathlib import Path

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






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_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							,    __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 ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowerCAmelCase												=	logging.get_logger(__name__)

__lowerCAmelCase												=	{
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}


class       _lowerCAmelCase   (   __snake_case					,							__snake_case				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						"nat"

							lowerCAmelCase_ 						=						{
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							}

							def __init__(self  ,  UpperCAmelCase=4  ,  UpperCAmelCase=3  ,  UpperCAmelCase=64  ,  UpperCAmelCase=[3, 4, 6, 5]  ,  UpperCAmelCase=[2, 4, 8, 16]  ,  UpperCAmelCase=7  ,  UpperCAmelCase=3.0  ,  UpperCAmelCase=True  ,  UpperCAmelCase=0.0  ,  UpperCAmelCase=0.0  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase="gelu"  ,  UpperCAmelCase=0.02  ,  UpperCAmelCase=1e-5  ,  UpperCAmelCase=0.0  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  **UpperCAmelCase  ,  )					->       str:
														super().__init__(**UpperCAmelCase  )

														_snake_case       =  patch_size
														_snake_case       =  num_channels
														_snake_case       =  embed_dim
														_snake_case       =  depths
														_snake_case       =  len(UpperCAmelCase  )
														_snake_case       =  num_heads
														_snake_case       =  kernel_size
														_snake_case       =  mlp_ratio
														_snake_case       =  qkv_bias
														_snake_case       =  hidden_dropout_prob
														_snake_case       =  attention_probs_dropout_prob
														_snake_case       =  drop_path_rate
														_snake_case       =  hidden_act
														_snake_case       =  layer_norm_eps
														_snake_case       =  initializer_range
														# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
														# this indicates the channel dimension after the last stage of the model
														_snake_case       =  int(embed_dim * 2 ** (len(UpperCAmelCase  ) - 1)  )
														_snake_case       =  layer_scale_init_value
														_snake_case       =  ["""stem"""] + [f"""stage{idx}""" for idx in range(1  ,  len(UpperCAmelCase  ) + 1  )]
														_snake_case,							_snake_case       =  get_aligned_output_features_output_indices(
														    out_features=UpperCAmelCase  ,  out_indices=UpperCAmelCase  ,  stage_names=self.stage_names  )