



from __future__ import annotations

from typing import Any


def       UpperCamelCase(  *__UpperCamelCase							:		list[list[int]]			):
		if all(_check_not_integer(__UpperCamelCase			) for m in matrix_s			):
				for i in matrix_s[1:]:
						_verify_matrix_sizes(matrix_s[0]					,__UpperCamelCase			)
				return [[sum(__UpperCamelCase			) for t in zip(*__UpperCamelCase			)] for m in zip(*__UpperCamelCase			)]
		raise TypeError('''Expected a matrix, got int/list instead'''			)


def       UpperCamelCase(  __UpperCamelCase							:		list[list[int]]					,__UpperCamelCase							:		list[list[int]]			):
		if (
		    _check_not_integer(__UpperCamelCase			)
		    and _check_not_integer(__UpperCamelCase			)
		    and _verify_matrix_sizes(__UpperCamelCase					,__UpperCamelCase			)
		):
				return [[i - j for i, j in zip(*__UpperCamelCase			)] for m in zip(__UpperCamelCase					,__UpperCamelCase			)]
		raise TypeError('''Expected a matrix, got int/list instead'''			)


def       UpperCamelCase(  __UpperCamelCase							:		list[list[int]]					,__UpperCamelCase							:		int | float			):
		return [[x * n for x in row] for row in matrix]


def       UpperCamelCase(  __UpperCamelCase							:		list[list[int]]					,__UpperCamelCase							:		list[list[int]]			):
		if _check_not_integer(__UpperCamelCase			) and _check_not_integer(__UpperCamelCase			):
				lowerCAmelCase_ ,  lowerCAmelCase_      :					Tuple     =			_verify_matrix_sizes(__UpperCamelCase					,__UpperCamelCase			)

		if cols[0] != rows[1]:
				lowerCAmelCase_      :					Tuple     =			(
				    '''Cannot multiply matrix of dimensions '''
				    f"""({rows[0]},{cols[0]}) and ({rows[1]},{cols[1]})"""
				)
				raise ValueError(__UpperCamelCase			)
		return [
		    [sum(m * n for m, n in zip(__UpperCamelCase					,__UpperCamelCase			)			) for j in zip(*__UpperCamelCase			)] for i in matrix_a
		]


def       UpperCamelCase(  __UpperCamelCase							:		int			):
		lowerCAmelCase_      :					Optional[int]     =			int(__UpperCamelCase			)
		return [[int(row == column			) for column in range(__UpperCamelCase			)] for row in range(__UpperCamelCase			)]


def       UpperCamelCase(  __UpperCamelCase							:		list[list[int]]					,__UpperCamelCase							:		bool = True			):
		if _check_not_integer(__UpperCamelCase			):
				if return_map:
						return map(__UpperCamelCase					,zip(*__UpperCamelCase			)			)
				else:
						return list(map(__UpperCamelCase					,zip(*__UpperCamelCase			)			)			)
		raise TypeError('''Expected a matrix, got int/list instead'''			)


def       UpperCamelCase(  __UpperCamelCase							:		list[list[int]]					,__UpperCamelCase							:		int					,__UpperCamelCase							:		int			):
		lowerCAmelCase_      :					Union[str, Any]     =			matrix[:row] + matrix[row + 1 :]
		return [row[:column] + row[column + 1 :] for row in minor]


def       UpperCamelCase(  __UpperCamelCase							:		list[list[int]]			):
		if len(__UpperCamelCase			) == 1:
				return matrix[0][0]

		return sum(
		    x * determinant(minor(__UpperCamelCase					,0					,__UpperCamelCase			)			) * (-1) ** i
		    for i, x in enumerate(matrix[0]			)			)


def       UpperCamelCase(  __UpperCamelCase							:		list[list[int]]			):
		lowerCAmelCase_      :					Optional[int]     =			determinant(__UpperCamelCase			)
		if det == 0:
				return None

		lowerCAmelCase_      :					Union[str, Any]     =			[
		    [determinant(minor(__UpperCamelCase					,__UpperCamelCase					,__UpperCamelCase			)			) for j in range(len(__UpperCamelCase			)			)]
		    for i in range(len(__UpperCamelCase			)			)
		]

		lowerCAmelCase_      :					Optional[Any]     =			[
		    [x * (-1) ** (row + col) for col, x in enumerate(matrix_minor[row]			)]
		    for row in range(len(__UpperCamelCase			)			)
		]
		lowerCAmelCase_      :					Union[str, Any]     =			list(transpose(__UpperCamelCase			)			)
		return scalar_multiply(__UpperCamelCase					,1 / det			)


def       UpperCamelCase(  __UpperCamelCase							:		list[list[int]]			):
		return not isinstance(__UpperCamelCase					,__UpperCamelCase			) and not isinstance(matrix[0]					,__UpperCamelCase			)


def       UpperCamelCase(  __UpperCamelCase							:		list[list[int]]			):
		return len(__UpperCamelCase			), len(matrix[0]			)


def       UpperCamelCase(  __UpperCamelCase							:		list[list[int]]					,__UpperCamelCase							:		list[list[int]]			):
		lowerCAmelCase_      :					List[Any]     =			_shape(__UpperCamelCase			) + _shape(__UpperCamelCase			)
		if shape[0] != shape[3] or shape[1] != shape[2]:
				lowerCAmelCase_      :					Dict     =			(
				    '''operands could not be broadcast together with shape '''
				    f"""({shape[0], shape[1]}), ({shape[2], shape[3]})"""
				)
				raise ValueError(__UpperCamelCase			)
		return (shape[0], shape[2]), (shape[1], shape[3])


def       UpperCamelCase(  ):
		lowerCAmelCase_      :					List[Any]     =			[[12, 10], [3, 9]]
		lowerCAmelCase_      :					str     =			[[3, 4], [7, 4]]
		lowerCAmelCase_      :					int     =			[[11, 12, 13, 14], [21, 22, 23, 24], [31, 32, 33, 34], [41, 42, 43, 44]]
		lowerCAmelCase_      :					Union[str, Any]     =			[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
		print(f"""Add Operation, {add(__UpperCamelCase					,__UpperCamelCase			) = } \n"""			)
		print(
		    f"""Multiply Operation, {multiply(__UpperCamelCase					,__UpperCamelCase			) = } \n"""					,)
		print(f"""Identity: {identity(5			)}\n"""			)
		print(f"""Minor of {matrix_c} = {minor(__UpperCamelCase					,1					,2			)} \n"""			)
		print(f"""Determinant of {matrix_b} = {determinant(__UpperCamelCase			)} \n"""			)
		print(f"""Inverse of {matrix_d} = {inverse(__UpperCamelCase			)}\n"""			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()










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_video_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import VideoMAEImageProcessor



class     __snake_case						(					unittest.TestCase						):




							def __init__(	self					:	str    ,    A_					:	str    ,    A_					:	int=7    ,    A_					:	List[Any]=3    ,    A_					:	Dict=1_0    ,    A_					:	List[str]=1_8    ,    A_					:	List[Any]=3_0    ,    A_					:	str=4_0_0    ,    A_					:	str=True    ,    A_					:	Tuple=None    ,    A_					:	Optional[Any]=True    ,    A_					:	Tuple=[0.5, 0.5, 0.5]    ,    A_					:	Any=[0.5, 0.5, 0.5]    ,    A_					:	List[str]=None    ,    ):
									lowerCAmelCase_      :					Optional[Any]     =			size if size is not None else {'''shortest_edge''': 1_8}
									lowerCAmelCase_      :					Optional[int]     =			crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}

									lowerCAmelCase_      :					Any     =			parent
									lowerCAmelCase_      :					Optional[Any]     =			batch_size
									lowerCAmelCase_      :					List[Any]     =			num_channels
									lowerCAmelCase_      :					Optional[int]     =			num_frames
									lowerCAmelCase_      :					Optional[int]     =			image_size
									lowerCAmelCase_      :					List[Any]     =			min_resolution
									lowerCAmelCase_      :					str     =			max_resolution
									lowerCAmelCase_      :					List[str]     =			do_resize
									lowerCAmelCase_      :					int     =			size
									lowerCAmelCase_      :					Union[str, Any]     =			do_normalize
									lowerCAmelCase_      :					Union[str, Any]     =			image_mean
									lowerCAmelCase_      :					List[str]     =			image_std
									lowerCAmelCase_      :					Any     =			crop_size







							def        UpperCAmelCase__      (	self					:	Any):
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "crop_size": self.crop_size,
									}





@require_torch
@require_vision
class     __snake_case						(					UpperCamelCase_						,unittest.TestCase						):
							_a      				=			VideoMAEImageProcessor if is_vision_available() else None




							def        UpperCAmelCase__      (	self					:	Tuple):
									lowerCAmelCase_      :					int     =			VideoMAEImageProcessingTester(self)




							@property
							def        UpperCAmelCase__      (	self					:	Tuple):
									return self.image_processor_tester.prepare_image_processor_dict()




							def        UpperCAmelCase__      (	self					:	Optional[int]):
									lowerCAmelCase_      :					int     =			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_    ,    '''do_center_crop'''))
									self.assertTrue(hasattr(A_    ,    '''size'''))




							def        UpperCAmelCase__      (	self					:	Dict):
									lowerCAmelCase_      :					int     =			self.image_processing_class.from_dict(self.image_processor_dict)
									self.assertEqual(image_processor.size    ,    {'''shortest_edge''': 1_8})
									self.assertEqual(image_processor.crop_size    ,    {'''height''': 1_8, '''width''': 1_8})

									lowerCAmelCase_      :					str     =			self.image_processing_class.from_dict(self.image_processor_dict    ,    size=4_2    ,    crop_size=8_4)
									self.assertEqual(image_processor.size    ,    {'''shortest_edge''': 4_2})
									self.assertEqual(image_processor.crop_size    ,    {'''height''': 8_4, '''width''': 8_4})




							def        UpperCAmelCase__      (	self					:	Dict):
									pass




							def        UpperCAmelCase__      (	self					:	str):
									# Initialize image_processing
									lowerCAmelCase_      :					int     =			self.image_processing_class(**self.image_processor_dict)
									# create random PIL videos
									lowerCAmelCase_      :					Optional[int]     =			prepare_video_inputs(self.image_processor_tester    ,    equal_resolution=A_)
									for video in video_inputs:
											self.assertIsInstance(A_    ,    A_)
											self.assertIsInstance(video[0]    ,    Image.Image)

									# Test not batched input
									lowerCAmelCase_      :					int     =			image_processing(video_inputs[0]    ,    return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,    (
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,    )

									# Test batched
									lowerCAmelCase_      :					Tuple     =			image_processing(A_    ,    return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,    (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,    )




							def        UpperCAmelCase__      (	self					:	int):
									# Initialize image_processing
									lowerCAmelCase_      :					Dict     =			self.image_processing_class(**self.image_processor_dict)
									# create random numpy tensors
									lowerCAmelCase_      :					str     =			prepare_video_inputs(self.image_processor_tester    ,    equal_resolution=A_    ,    numpify=A_)
									for video in video_inputs:
											self.assertIsInstance(A_    ,    A_)
											self.assertIsInstance(video[0]    ,    np.ndarray)

									# Test not batched input
									lowerCAmelCase_      :					Tuple     =			image_processing(video_inputs[0]    ,    return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,    (
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,    )

									# Test batched
									lowerCAmelCase_      :					Union[str, Any]     =			image_processing(A_    ,    return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,    (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,    )







							def        UpperCAmelCase__      (	self					:	List[str]):
									# Initialize image_processing
									lowerCAmelCase_      :					Tuple     =			self.image_processing_class(**self.image_processor_dict)
									# create random PyTorch tensors
									lowerCAmelCase_      :					Tuple     =			prepare_video_inputs(self.image_processor_tester    ,    equal_resolution=A_    ,    torchify=A_)
									for video in video_inputs:
											self.assertIsInstance(A_    ,    A_)
											self.assertIsInstance(video[0]    ,    torch.Tensor)

									# Test not batched input
									lowerCAmelCase_      :					List[str]     =			image_processing(video_inputs[0]    ,    return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,    (
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,    )

									# Test batched
									lowerCAmelCase_      :					List[Any]     =			image_processing(A_    ,    return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_videos.shape    ,    (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )    ,    )





