

import unittest

from .lib import (
    Matrix,
    Vector,
    axpy,
    square_zero_matrix,
    unit_basis_vector,
    zero_vector,
)


class 			A						(							unittest.TestCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	Optional[Any]		)->   None:





        '''simple docstring'''







        A__   				=       Vector([1, 2, 3]		)
        self.assertEqual(x.component(0		),1		)
        self.assertEqual(x.component(2		),3		)
        A__   				=       Vector()


    def 						snake_case__					(				self			:	List[Any]		)->   None:





        '''simple docstring'''







        A__   				=       Vector([0, 0, 0, 0, 0, 1]		)
        self.assertEqual(str(lowercase_		),'(0,0,0,0,0,1)'		)


    def 						snake_case__					(				self			:	Optional[int]		)->   None:





        '''simple docstring'''







        A__   				=       Vector([1, 2, 3, 4]		)
        self.assertEqual(len(lowercase_		),4		)


    def 						snake_case__					(				self			:	Tuple		)->   None:





        '''simple docstring'''







        A__   				=       Vector([1, 2]		)
        A__   				=       Vector([1, 2, 3, 4, 5]		)
        A__   				=       Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]		)
        A__   				=       Vector([1, -1, 1, -1, 2, -3, 4, -5]		)
        self.assertAlmostEqual(x.euclidean_length(),2.236,3		)
        self.assertAlmostEqual(y.euclidean_length(),7.416,3		)
        self.assertEqual(z.euclidean_length(),0		)
        self.assertAlmostEqual(w.euclidean_length(),7.616,3		)


    def 						snake_case__					(				self			:	List[Any]		)->   None:





        '''simple docstring'''







        A__   				=       Vector([1, 2, 3]		)
        A__   				=       Vector([1, 1, 1]		)
        self.assertEqual((x + y).component(0		),2		)
        self.assertEqual((x + y).component(1		),3		)
        self.assertEqual((x + y).component(2		),4		)


    def 						snake_case__					(				self			:	Any		)->   None:





        '''simple docstring'''







        A__   				=       Vector([1, 2, 3]		)
        A__   				=       Vector([1, 1, 1]		)
        self.assertEqual((x - y).component(0		),0		)
        self.assertEqual((x - y).component(1		),1		)
        self.assertEqual((x - y).component(2		),2		)


    def 						snake_case__					(				self			:	List[str]		)->   None:





        '''simple docstring'''







        A__   				=       Vector([1, 2, 3]		)
        A__   				=       Vector([2, -1, 4]		)  # for test of dot product
        A__   				=       Vector([1, -2, -1]		)
        self.assertEqual(str(x * 3.0		),'(3.0,6.0,9.0)'		)
        self.assertEqual((a * b),0		)


    def 						snake_case__					(				self			:	int		)->   None:





        '''simple docstring'''







        self.assertEqual(str(zero_vector(1_0		)		).count('0'		),1_0		)


    def 						snake_case__					(				self			:	Optional[Any]		)->   None:





        '''simple docstring'''







        self.assertEqual(str(unit_basis_vector(3,1		)		),'(0,1,0)'		)


    def 						snake_case__					(				self			:	int		)->   None:





        '''simple docstring'''







        A__   				=       Vector([1, 2, 3]		)
        A__   				=       Vector([1, 0, 1]		)
        self.assertEqual(str(axpy(2,lowercase_,lowercase_		)		),'(3,4,7)'		)


    def 						snake_case__					(				self			:	Any		)->   None:





        '''simple docstring'''







        A__   				=       Vector([1, 0, 0, 0, 0, 0]		)
        A__   				=       x.copy()
        self.assertEqual(str(lowercase_		),str(lowercase_		)		)


    def 						snake_case__					(				self			:	Tuple		)->   None:





        '''simple docstring'''







        A__   				=       Vector([1, 0, 0]		)
        x.change_component(0,0		)
        x.change_component(1,1		)
        self.assertEqual(str(lowercase_		),'(0,1,0)'		)


    def 						snake_case__					(				self			:	Union[str, Any]		)->   None:





        '''simple docstring'''







        A__   				=       Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]],3,3		)
        self.assertEqual('|1,2,3|\n|2,4,5|\n|6,7,8|\n',str(lowercase_		)		)


    def 						snake_case__					(				self			:	Any		)->   None:





        '''simple docstring'''







        A__   				=       Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]],3,3		)
        A__   				=       [[-3, -1_4, -1_0], [-5, -1_0, -5], [-2, -1, 0]]
        for x in range(a.height()		):
            for y in range(a.width()		):
                self.assertEqual(minors[x][y],a.minor(lowercase_,lowercase_		)		)


    def 						snake_case__					(				self			:	Any		)->   None:





        '''simple docstring'''







        A__   				=       Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]],3,3		)
        A__   				=       [[-3, 1_4, -1_0], [5, -1_0, 5], [-2, 1, 0]]
        for x in range(a.height()		):
            for y in range(a.width()		):
                self.assertEqual(cofactors[x][y],a.cofactor(lowercase_,lowercase_		)		)


    def 						snake_case__					(				self			:	List[str]		)->   None:





        '''simple docstring'''







        A__   				=       Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]],3,3		)
        self.assertEqual(-5,a.determinant()		)


    def 						snake_case__					(				self			:	Dict		)->   None:





        '''simple docstring'''







        A__   				=       Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]],3,3		)
        A__   				=       Vector([1, 2, 3]		)
        self.assertEqual('(14,32,50)',str(a * x		)		)
        self.assertEqual('|2,4,6|\n|8,10,12|\n|14,16,18|\n',str(a * 2		)		)


    def 						snake_case__					(				self			:	Tuple		)->   None:





        '''simple docstring'''







        A__   				=       Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]],3,3		)
        a.change_component(0,2,5		)
        self.assertEqual('|1,2,5|\n|2,4,5|\n|6,7,8|\n',str(lowercase_		)		)


    def 						snake_case__					(				self			:	List[Any]		)->   None:





        '''simple docstring'''







        A__   				=       Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]],3,3		)
        self.assertEqual(7,a.component(2,1		),0.01		)


    def 						snake_case__					(				self			:	str		)->   None:





        '''simple docstring'''







        A__   				=       Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]],3,3		)
        A__   				=       Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 1_0]],3,3		)
        self.assertEqual('|2,4,10|\n|4,8,10|\n|12,14,18|\n',str(a + b		)		)


    def 						snake_case__					(				self			:	List[Any]		)->   None:





        '''simple docstring'''







        A__   				=       Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]],3,3		)
        A__   				=       Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 1_0]],3,3		)
        self.assertEqual('|0,0,-4|\n|0,0,0|\n|0,0,-2|\n',str(a - b		)		)


    def 						snake_case__					(				self			:	Optional[Any]		)->   None:





        '''simple docstring'''







        self.assertEqual(
            '|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n',str(square_zero_matrix(5		)		),)


if __name__ == "__main__":
     unittest.main()




import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import BridgeTowerImageProcessor


class 			A						(							unittest.TestCase			):







    """simple docstring"""





    def __init__(				self			:	List[str],lowercase_			:	List[str],lowercase_			:	bool = True,lowercase_			:	Dict[str, int] = None,lowercase_			:	int = 3_2,lowercase_			:	bool = True,lowercase_			:	Union[int, float] = 1 / 2_5_5,lowercase_			:	bool = True,lowercase_			:	bool = True,lowercase_			:	Optional[Union[float, List[float]]] = [0.48_145_466, 0.4_578_275, 0.40_821_073],lowercase_			:	Optional[Union[float, List[float]]] = [0.26_862_954, 0.26_130_258, 0.27_577_711],lowercase_			:	bool = True,lowercase_			:	Tuple=7,lowercase_			:	str=3_0,lowercase_			:	Union[str, Any]=4_0_0,lowercase_			:	Dict=3,)->   List[Any]:





        '''simple docstring'''







        A__   				=       parent
        A__   				=       do_resize
        A__   				=       size if size is not None else {'shortest_edge': 2_8_8}
        A__   				=       size_divisor
        A__   				=       do_rescale
        A__   				=       rescale_factor
        A__   				=       do_normalize
        A__   				=       do_center_crop
        A__   				=       image_mean
        A__   				=       image_std
        A__   				=       do_pad
        A__   				=       batch_size
        A__   				=       num_channels
        A__   				=       min_resolution
        A__   				=       max_resolution


    def 						snake_case__					(				self			:	Optional[Any]		)->   Optional[int]:





        '''simple docstring'''







        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "size_divisor": self.size_divisor,
        }


    def 						snake_case__					(				self			:	int,lowercase_			:	Optional[int],lowercase_			:	List[str]=False		)->   Any:





        '''simple docstring'''







        if not batched:
            A__   				=       self.size['shortest_edge']
            A__   				=       image_inputs[0]
            if isinstance(lowercase_,Image.Image		):
                A__				,						A__   				=       image.size
            else:
                A__				,						A__   				=       image.shape[1], image.shape[2]
            A__   				=       size / min(lowercase_,lowercase_		)
            if h < w:
                A__				,						A__   				=       size, scale * w
            else:
                A__				,						A__   				=       scale * h, size

            A__   				=       int((1_3_3_3 / 8_0_0) * size		)
            if max(lowercase_,lowercase_		) > max_size:
                A__   				=       max_size / max(lowercase_,lowercase_		)
                A__   				=       newh * scale
                A__   				=       neww * scale

            A__				,						A__   				=       int(newh + 0.5		), int(neww + 0.5		)
            A__				,						A__   				=       (
                newh // self.size_divisor * self.size_divisor,
                neww // self.size_divisor * self.size_divisor,
            )

        else:
            A__   				=       []
            for image in image_inputs:
                A__				,						A__   				=       self.get_expected_values([image]		)
                expected_values.append((expected_height, expected_width)		)
            A__   				=       max(lowercase_,key=lambda lowercase_		: item[0]		)[0]
            A__   				=       max(lowercase_,key=lambda lowercase_		: item[1]		)[1]

        return expected_height, expected_width




@require_torch
@require_vision
class 			A						(							_UpperCAmelCase   ,							unittest.TestCase			):







    """simple docstring"""





    lowerCamelCase		     =							BridgeTowerImageProcessor if is_vision_available() else None


    def 						snake_case__					(				self			:	str		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       BridgeTowerImageProcessingTester(self		)


    @property
    def 						snake_case__					(				self			:	Dict		)->   List[str]:





        '''simple docstring'''







        return self.image_processor_tester.prepare_image_processor_dict()


    def 						snake_case__					(				self			:	Optional[Any]		)->   Tuple:





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        self.assertTrue(hasattr(lowercase_,'image_mean'		)		)
        self.assertTrue(hasattr(lowercase_,'image_std'		)		)
        self.assertTrue(hasattr(lowercase_,'do_normalize'		)		)
        self.assertTrue(hasattr(lowercase_,'do_resize'		)		)
        self.assertTrue(hasattr(lowercase_,'size'		)		)
        self.assertTrue(hasattr(lowercase_,'size_divisor'		)		)


    def 						snake_case__					(				self			:	Any		)->   List[str]:





        '''simple docstring'''







        pass


    def 						snake_case__					(				self			:	int		)->   Any:





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        # create random PIL images
        A__   				=       prepare_image_inputs(self.image_processor_tester,equal_resolution=lowercase_		)
        for image in image_inputs:
            self.assertIsInstance(lowercase_,Image.Image		)

        # Test not batched input
        A__   				=       image_processing(image_inputs[0],return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_		)
        self.assertEqual(
            encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

        # Test batched
        A__   				=       image_processing(lowercase_,return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_,batched=lowercase_		)
        self.assertEqual(
            encoded_images.shape,(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),)


    def 						snake_case__					(				self			:	List[str]		)->   Tuple:





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        A__   				=       prepare_image_inputs(self.image_processor_tester,equal_resolution=lowercase_,numpify=lowercase_		)
        for image in image_inputs:
            self.assertIsInstance(lowercase_,np.ndarray		)

        # Test not batched input
        A__   				=       image_processing(image_inputs[0],return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_		)
        self.assertEqual(
            encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

        # Test batched
        A__   				=       image_processing(lowercase_,return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_,batched=lowercase_		)
        self.assertEqual(
            encoded_images.shape,(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),)


    def 						snake_case__					(				self			:	Optional[Any]		)->   List[Any]:





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        A__   				=       prepare_image_inputs(self.image_processor_tester,equal_resolution=lowercase_,torchify=lowercase_		)
        for image in image_inputs:
            self.assertIsInstance(lowercase_,torch.Tensor		)

        # Test not batched input
        A__   				=       image_processing(image_inputs[0],return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_		)
        self.assertEqual(
            encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

        # Test batched
        A__   				=       image_processing(lowercase_,return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_,batched=lowercase_		)
        self.assertEqual(
            encoded_images.shape,(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),)

