
"""simple docstring"""
import unittest

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 ViltImageProcessor




class  _snake_case	(    unittest.TestCase							):






							def __init__(       self    :      Any    ,			snake_case__    :      Union[str, Any]    ,			snake_case__    :      int=7    ,			snake_case__    :      Tuple=3    ,			snake_case__    :      Optional[int]=1_8    ,			snake_case__    :      str=3_0    ,			snake_case__    :      Tuple=4_0_0    ,			snake_case__    :      Any=True    ,			snake_case__    :      Union[str, Any]=None    ,			snake_case__    :      Dict=2    ,			snake_case__    :      Union[str, Any]=True    ,			snake_case__    :      List[Any]=[0.5, 0.5, 0.5]    ,			snake_case__    :      Optional[Any]=[0.5, 0.5, 0.5]    ,			):






												"""simple docstring"""



												__a:    str		     =       size if size is not None else {"shortest_edge": 3_0}
												__a:    int		     =       parent
												__a:    List[str]		     =       batch_size
												__a:    Dict		     =       num_channels
												__a:    str		     =       image_size
												__a:    Any		     =       min_resolution
												__a:    Optional[int]		     =       max_resolution
												__a:    str		     =       do_resize
												__a:    List[Any]		     =       size
												__a:    Optional[Any]		     =       size_divisor
												__a:    str		     =       do_normalize
												__a:    Union[str, Any]		     =       image_mean
												__a:    Optional[Any]		     =       image_std






							def 				__lowercase	(       self    :      List[Any]     ):






												"""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 				__lowercase	(       self    :      int    ,			snake_case__    :      Optional[Any]    ,			snake_case__    :      Optional[int]=False     ):






												"""simple docstring"""



												if not batched:
																	__a:    List[str]		     =       self.size["shortest_edge"]
																	__a:    Dict		     =       image_inputs[0]
																	if isinstance(_a    ,			Image.Image     ):
																						__a:    Dict		     =       image.size
																	else:
																						__a:    List[str]		     =       image.shape[1], image.shape[2]
																	__a:    List[str]		     =       size / min(_a    ,			_a     )
																	if h < w:
																						__a:    List[Any]		     =       size, scale * w
																	else:
																						__a:    Any		     =       scale * h, size

																	__a:    Any		     =       int((1_3_3_3 / 8_0_0) * size     )
																	if max(_a    ,			_a     ) > max_size:
																						__a:    Optional[int]		     =       max_size / max(_a    ,			_a     )
																						__a:    List[str]		     =       newh * scale
																						__a:    Optional[int]		     =       neww * scale

																	__a:    Optional[Any]		     =       int(newh + 0.5     ), int(neww + 0.5     )
																	__a:    int		     =       (
																	    newh // self.size_divisor * self.size_divisor,
																	    neww // self.size_divisor * self.size_divisor,
																	)

												else:
																	__a:    int		     =       []
																	for image in image_inputs:
																						__a:    Any		     =       self.get_expected_values([image]     )
																						expected_values.append((expected_height, expected_width)     )
																	__a:    Any		     =       max(_a    ,			key=lambda snake_case__     : item[0]     )[0]
																	__a:    List[str]		     =       max(_a    ,			key=lambda snake_case__     : item[1]     )[1]

												return expected_height, expected_width






@require_torch
@require_vision
class  _snake_case	(    a_			,					unittest.TestCase							):
							SCREAMING_SNAKE_CASE		  =    ViltImageProcessor if is_vision_available() else None






							def 				__lowercase	(       self    :      List[str]     ):






												"""simple docstring"""



												__a:    Union[str, Any]		     =       ViltImageProcessingTester(self     )






							@property
							def 				__lowercase	(       self    :      int     ):






												"""simple docstring"""



												return self.image_processor_tester.prepare_image_processor_dict()






							def 				__lowercase	(       self    :      int     ):






												"""simple docstring"""



												__a:    Tuple		     =       self.image_processing_class(**self.image_processor_dict     )
												self.assertTrue(hasattr(_a    ,			"""image_mean"""     )     )
												self.assertTrue(hasattr(_a    ,			"""image_std"""     )     )
												self.assertTrue(hasattr(_a    ,			"""do_normalize"""     )     )
												self.assertTrue(hasattr(_a    ,			"""do_resize"""     )     )
												self.assertTrue(hasattr(_a    ,			"""size"""     )     )
												self.assertTrue(hasattr(_a    ,			"""size_divisor"""     )     )






							def 				__lowercase	(       self    :      List[Any]     ):






												"""simple docstring"""



												__a:    Dict		     =       self.image_processing_class.from_dict(self.image_processor_dict     )
												self.assertEqual(image_processor.size    ,			{"""shortest_edge""": 3_0}     )

												__a:    Optional[int]		     =       self.image_processing_class.from_dict(self.image_processor_dict    ,			size=4_2     )
												self.assertEqual(image_processor.size    ,			{"""shortest_edge""": 4_2}     )






							def 				__lowercase	(       self    :      Any     ):






												"""simple docstring"""



												pass






							def 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												__a:    Optional[int]		     =       self.image_processing_class(**self.image_processor_dict     )
												# create random PIL images
												__a:    Union[str, Any]		     =       prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=_a     )
												for image in image_inputs:
																	self.assertIsInstance(_a    ,			Image.Image     )

												# Test not batched input
												__a:    int		     =       image_processing(image_inputs[0]    ,			return_tensors="""pt"""     ).pixel_values

												__a:    List[str]		     =       self.image_processor_tester.get_expected_values(_a     )
												self.assertEqual(
												    encoded_images.shape    ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,			)

												# Test batched
												__a:    Optional[int]		     =       image_processing(_a    ,			return_tensors="""pt"""     ).pixel_values

												__a:    Any		     =       self.image_processor_tester.get_expected_values(_a    ,			batched=_a     )
												self.assertEqual(
												    encoded_images.shape    ,			(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )    ,			)






							def 				__lowercase	(       self    :      Optional[Any]     ):






												"""simple docstring"""



												__a:    Any		     =       self.image_processing_class(**self.image_processor_dict     )
												# create random numpy tensors
												__a:    List[Any]		     =       prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=_a    ,			numpify=_a     )
												for image in image_inputs:
																	self.assertIsInstance(_a    ,			np.ndarray     )

												# Test not batched input
												__a:    Optional[int]		     =       image_processing(image_inputs[0]    ,			return_tensors="""pt"""     ).pixel_values

												__a:    str		     =       self.image_processor_tester.get_expected_values(_a     )
												self.assertEqual(
												    encoded_images.shape    ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,			)

												# Test batched
												__a:    Any		     =       image_processing(_a    ,			return_tensors="""pt"""     ).pixel_values

												__a:    Dict		     =       self.image_processor_tester.get_expected_values(_a    ,			batched=_a     )
												self.assertEqual(
												    encoded_images.shape    ,			(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )    ,			)






							def 				__lowercase	(       self    :      Dict     ):






												"""simple docstring"""



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

												# Test not batched input
												__a:    List[str]		     =       image_processing(image_inputs[0]    ,			return_tensors="""pt"""     ).pixel_values

												__a:    Optional[Any]		     =       self.image_processor_tester.get_expected_values(_a     )
												self.assertEqual(
												    encoded_images.shape    ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,			)

												# Test batched
												__a:    Union[str, Any]		     =       image_processing(_a    ,			return_tensors="""pt"""     ).pixel_values

												__a:    Any		     =       self.image_processor_tester.get_expected_values(_a    ,			batched=_a     )
												self.assertEqual(
												    encoded_images.shape    ,			(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )    ,			)




import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

_snake_case				=			[[1_2, 1_0], [3, 9]]
_snake_case				=			[[3, 4], [7, 4]]
_snake_case				=			[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
_snake_case				=			[[3, 0, -2], [2, 0, 2], [0, 1, 1]]
_snake_case				=			[[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
_snake_case				=			[1]
_snake_case				=			[2]

_snake_case				=			logging.getLogger()
_snake_case				=			logging.DEBUG
_snake_case				=			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")		,		[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]			)
def 	A (		A__		,		A__			):
     if (np.array(A__			)).shape < (2, 2) or (np.array(A__			)).shape < (2, 2):
          with pytest.raises(A__			):
               logger.info(f'\n\t{test_addition.__name__} returned integer'			)
               matop.add(A__		,		A__			)
     elif (np.array(A__			)).shape == (np.array(A__			)).shape:
          logger.info(f'\n\t{test_addition.__name__} with same matrix dims'			)
          UpperCAmelCase    :					Tuple			=      (np.array(A__			) + np.array(A__			)).tolist()
          UpperCAmelCase    :					Optional[Any]			=      matop.add(A__		,		A__			)
          assert theo == act
     else:
          with pytest.raises(A__			):
               logger.info(f'\n\t{test_addition.__name__} with different matrix dims'			)
               matop.add(A__		,		A__			)







@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")		,		[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]			)
def 	A (		A__		,		A__			):
     if (np.array(A__			)).shape < (2, 2) or (np.array(A__			)).shape < (2, 2):
          with pytest.raises(A__			):
               logger.info(f'\n\t{test_subtraction.__name__} returned integer'			)
               matop.subtract(A__		,		A__			)
     elif (np.array(A__			)).shape == (np.array(A__			)).shape:
          logger.info(f'\n\t{test_subtraction.__name__} with same matrix dims'			)
          UpperCAmelCase    :					Tuple			=      (np.array(A__			) - np.array(A__			)).tolist()
          UpperCAmelCase    :					Optional[int]			=      matop.subtract(A__		,		A__			)
          assert theo == act
     else:
          with pytest.raises(A__			):
               logger.info(f'\n\t{test_subtraction.__name__} with different matrix dims'			)
               assert matop.subtract(A__		,		A__			)







@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")		,		[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]			)
def 	A (		A__		,		A__			):
     if (np.array(A__			)).shape < (2, 2) or (np.array(A__			)).shape < (2, 2):
          logger.info(f'\n\t{test_multiplication.__name__} returned integer'			)
          with pytest.raises(A__			):
               matop.add(A__		,		A__			)
     elif (np.array(A__			)).shape == (np.array(A__			)).shape:
          logger.info(f'\n\t{test_multiplication.__name__} meets dim requirements'			)
          UpperCAmelCase    :					List[str]			=      (np.matmul(A__		,		A__			)).tolist()
          UpperCAmelCase    :					int			=      matop.multiply(A__		,		A__			)
          assert theo == act
     else:
          with pytest.raises(A__			):
               logger.info(
                   f'\n\t{test_multiplication.__name__} does not meet dim requirements'			)
               assert matop.subtract(A__		,		A__			)







@pytest.mark.mat_ops
def 	A (		):
     UpperCAmelCase    :					int			=      (3.5 * np.array(A__			)).tolist()
     UpperCAmelCase    :					Optional[int]			=      matop.scalar_multiply(A__		,		3.5			)
     assert theo == act







@pytest.mark.mat_ops
def 	A (		):
     UpperCAmelCase    :					List[Any]			=      (np.identity(5			)).tolist()
     UpperCAmelCase    :					Optional[int]			=      matop.identity(5			)
     assert theo == act







@pytest.mark.mat_ops
@pytest.mark.parametrize("mat"		,		[mat_a, mat_b, mat_c, mat_d, mat_e, mat_f]			)
def 	A (		A__			):
     if (np.array(A__			)).shape < (2, 2):
          with pytest.raises(A__			):
               logger.info(f'\n\t{test_transpose.__name__} returned integer'			)
               matop.transpose(A__			)
     else:
          UpperCAmelCase    :					Dict			=      (np.transpose(A__			)).tolist()
          UpperCAmelCase    :					List[str]			=      matop.transpose(A__		,		return_map=A__			)
          assert theo == act



