





from pathlib import Path

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

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			int  						=   cva.getAffineTransform(__lowerCAmelCase	,			__lowerCAmelCase )
				return cva.warpAffine(__lowerCAmelCase	,			__lowerCAmelCase	,			(rows, cols) )


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

		# set different points to rotate image
		lowercase_		:				Union[str, Any]				=						np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
		lowercase_		:				int				=						np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
		lowercase_		:				str				=						np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
		lowercase_		:				str				=						np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

		# add all rotated images in a list
		lowercase_		:				Optional[int]				=						[
		    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_		:				Tuple				=						plt.figure(1)
		lowercase_		:				Optional[int]				=						['''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()













import torch

from transformers import AutoModel


class 			lowercase				(							torch.nn.Module     ):



						"""simple docstring"""

						def __init__(    self			:		Tuple ,				lowerCamelCase_			:		Dict="sayef/fsner-bert-base-uncased"					):





										'''simple docstring'''







										super(lowerCamelCase_ ,				self					).__init__()

										_snake_case :			Optional[Any]  						=   AutoModel.from_pretrained(lowerCamelCase_ ,				return_dict=lowerCamelCase_					)
										_snake_case :			str  						=   torch.nn.CosineSimilarity(3 ,				1e-08					)
										_snake_case :			str  						=   torch.nn.Softmax(dim=1					)







						def 		__UpperCAmelCase       (    self			:		int ,				**lowerCamelCase_			:		List[str]					):





										'''simple docstring'''







										return self.bert(**lowerCamelCase_					).last_hidden_state







						def 		__UpperCAmelCase       (    self			:		List[Any] ,				lowerCamelCase_			:		Union[str, Any]					):





										'''simple docstring'''







										return token_embeddings.sum(2 ,				keepdim=lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Dict ,				lowerCamelCase_			:		Dict ,				lowerCamelCase_			:		Tuple ,				lowerCamelCase_			:		Optional[Any]=1					):





										'''simple docstring'''







										return self.softmax(T * self.cos(lowerCamelCase_ ,				lowerCamelCase_					)					)







						def 		__UpperCAmelCase       (    self			:		Optional[Any] ,				lowerCamelCase_			:		Any ,				lowerCamelCase_			:		Union[str, Any]					):





										'''simple docstring'''







										_snake_case :			int  						=   W_supports['sizes'].tolist()
										_snake_case :			int  						=   W_supports['start_token_id'].item()
										_snake_case :			List[str]  						=   W_supports['end_token_id'].item()

										del W_supports["sizes"]
										del W_supports["start_token_id"]
										del W_supports["end_token_id"]

										_snake_case :			Optional[int]  						=   self.BERT(**lowerCamelCase_					)
										_snake_case :			Optional[Any]  						=   self.BERT(**lowerCamelCase_					)

										_snake_case :			Optional[int]  						=   None
										_snake_case :			Optional[int]  						=   None

										_snake_case :			List[str]  						=   W_supports['input_ids'] == start_token_id
										_snake_case :			Union[str, Any]  						=   W_supports['input_ids'] == end_token_id

										for i, size in enumerate(lowerCamelCase_					):
														if i == 0:
																		_snake_case :			str  						=   0
														else:
																		_snake_case :			Union[str, Any]  						=   support_sizes[i - 1]

														_snake_case :			Tuple  						=   S[s : s + size][start_token_masks[s : s + size]]
														_snake_case :			Optional[int]  						=   S[s : s + size][end_token_masks[s : s + size]]

														_snake_case :			Tuple  						=   torch.matmul(q[i] ,				s_start.T					).sum(1					).softmax(0					)
														_snake_case :			Union[str, Any]  						=   torch.matmul(q[i] ,				s_end.T					).sum(1					).softmax(0					)

														if p_starts is not None:
																		_snake_case :			Optional[Any]  						=   torch.vstack((p_starts, p_start)					)
																		_snake_case :			List[str]  						=   torch.vstack((p_ends, p_end)					)
														else:
																		_snake_case :			Union[str, Any]  						=   p_start
																		_snake_case :			Any  						=   p_end

										return p_starts, p_ends






