
from __future__ import annotations

from typing import Any




def 						A_			(				*snake_case				: list[list[int]]			)   ->     list[list[int]]:



						'''simple docstring'''

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




def 						A_			(				snake_case				: list[list[int]]			,   snake_case				: list[list[int]]			)   ->     list[list[int]]:



						'''simple docstring'''

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




def 						A_			(				snake_case				: list[list[int]]			,   snake_case				: int | float			)   ->     list[list[float]]:



						'''simple docstring'''

						return [[x * n for x in row] for row in matrix]




def 						A_			(				snake_case				: list[list[int]]			,   snake_case				: list[list[int]]			)   ->     list[list[int]]:



						'''simple docstring'''

						if _check_not_integer(snake_case			) and _check_not_integer(snake_case			):
												__UpperCamelCase	,				__UpperCamelCase  	=      _verify_matrix_sizes(snake_case			,   snake_case			)

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




def 						A_			(				snake_case				: int			)   ->     list[list[int]]:



						'''simple docstring'''

						__UpperCamelCase  	=      int(snake_case			)
						return [[int(row == column			) for column in range(snake_case			)] for row in range(snake_case			)]




def 						A_			(				snake_case				: list[list[int]]			,   snake_case				: bool = True			)   ->     list[list[int]] | map[list[int]]:



						'''simple docstring'''

						if _check_not_integer(snake_case			):
												if return_map:
																		return map(snake_case			,   zip(*snake_case			)			)
												else:
																		return list(map(snake_case			,   zip(*snake_case			)			)			)
						raise TypeError('''Expected a matrix, got int/list instead'''			)




def 						A_			(				snake_case				: list[list[int]]			,   snake_case				: int			,   snake_case				: int			)   ->     list[list[int]]:



						'''simple docstring'''

						__UpperCamelCase  	=      matrix[:row] + matrix[row + 1 :]
						return [row[:column] + row[column + 1 :] for row in minor]




def 						A_			(				snake_case				: list[list[int]]			)   ->     Any:



						'''simple docstring'''

						if len(snake_case			) == 1:
												return matrix[0][0]

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




def 						A_			(				snake_case				: list[list[int]]			)   ->     list[list[float]] | None:



						'''simple docstring'''

						__UpperCamelCase  	=      determinant(snake_case			)
						if det == 0:
												return None

						__UpperCamelCase  	=      [
						    [determinant(minor(snake_case			,   snake_case			,   snake_case			)			) for j in range(len(snake_case			)			)]
						    for i in range(len(snake_case			)			)
						]

						__UpperCamelCase  	=      [
						    [x * (-1) ** (row + col) for col, x in enumerate(matrix_minor[row]			)]
						    for row in range(len(snake_case			)			)
						]
						__UpperCamelCase  	=      list(transpose(snake_case			)			)
						return scalar_multiply(snake_case			,   1 / det			)




def 						A_			(				snake_case				: list[list[int]]			)   ->     bool:



						'''simple docstring'''

						return not isinstance(snake_case			,   snake_case			) and not isinstance(matrix[0]			,   snake_case			)




def 						A_			(				snake_case				: list[list[int]]			)   ->     tuple[int, int]:



						'''simple docstring'''

						return len(snake_case			), len(matrix[0]			)




def 						A_			(				snake_case				: list[list[int]]			,   snake_case				: list[list[int]]			)   ->     tuple[tuple[int, int], tuple[int, int]]:



						'''simple docstring'''

						__UpperCamelCase  	=      _shape(snake_case			) + _shape(snake_case			)
						if shape[0] != shape[3] or shape[1] != shape[2]:
												__UpperCamelCase  	=      (
												    '''operands could not be broadcast together with shape '''
												    f"({shape[0], shape[1]}), ({shape[2], shape[3]})"
												)
												raise ValueError(snake_case			)
						return (shape[0], shape[2]), (shape[1], shape[3])




def 						A_			(				)   ->     None:



						'''simple docstring'''

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


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import DeiTImageProcessor






class 							SCREAMING_SNAKE_CASE__     (				unittest.TestCase     ):

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=7  ,						SCREAMING_SNAKE_CASE_=3  ,						SCREAMING_SNAKE_CASE_=18  ,						SCREAMING_SNAKE_CASE_=30  ,						SCREAMING_SNAKE_CASE_=400  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=None  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=None  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]  ,						SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]  ,						)-> List[str]:





													'''simple docstring'''






													__UpperCamelCase  	=      size if size is not None else {'''height''': 20, '''width''': 20}
													__UpperCamelCase  	=      crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

													__UpperCamelCase  	=      parent
													__UpperCamelCase  	=      batch_size
													__UpperCamelCase  	=      num_channels
													__UpperCamelCase  	=      image_size
													__UpperCamelCase  	=      min_resolution
													__UpperCamelCase  	=      max_resolution
													__UpperCamelCase  	=      do_resize
													__UpperCamelCase  	=      size
													__UpperCamelCase  	=      do_center_crop
													__UpperCamelCase  	=      crop_size
													__UpperCamelCase  	=      do_normalize
													__UpperCamelCase  	=      image_mean
													__UpperCamelCase  	=      image_std



							def      A__		(	self      )-> int:





													'''simple docstring'''






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






@require_torch
@require_vision
class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_ ,		unittest.TestCase     ):

							"""simple docstring"""






							_snake_case  		=	DeiTImageProcessor if is_vision_available() else None
							_snake_case  		=	True



							def      A__		(	self      )-> int:





													'''simple docstring'''






													__UpperCamelCase  	=      DeiTImageProcessingTester(self      )



							@property
							def      A__		(	self      )-> Any:





													'''simple docstring'''






													return self.image_processor_tester.prepare_image_processor_dict()



							def      A__		(	self      )-> Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.image_processing_class(**self.image_processor_dict      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''do_resize'''      )      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''size'''      )      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''do_center_crop'''      )      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''center_crop'''      )      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''do_normalize'''      )      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''image_mean'''      )      )
													self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_  ,						'''image_std'''      )      )



							def      A__		(	self      )-> int:





													'''simple docstring'''






													__UpperCamelCase  	=      self.image_processing_class.from_dict(self.image_processor_dict      )
													self.assertEqual(image_processor.size  ,						{'''height''': 20, '''width''': 20}      )
													self.assertEqual(image_processor.crop_size  ,						{'''height''': 18, '''width''': 18}      )

													__UpperCamelCase  	=      self.image_processing_class.from_dict(self.image_processor_dict  ,						size=42  ,						crop_size=84      )
													self.assertEqual(image_processor.size  ,						{'''height''': 42, '''width''': 42}      )
													self.assertEqual(image_processor.crop_size  ,						{'''height''': 84, '''width''': 84}      )



							def      A__		(	self      )-> Union[str, Any]:





													'''simple docstring'''






													pass



							def      A__		(	self      )-> str:





													'''simple docstring'''






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

													# Test not batched input
													__UpperCamelCase  	=      image_processing(image_inputs[0]  ,						return_tensors='''pt'''      ).pixel_values
													self.assertEqual(
													    encoded_images.shape  ,						(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )  ,						)

													# Test batched
													__UpperCamelCase  	=      image_processing(SCREAMING_SNAKE_CASE_  ,						return_tensors='''pt'''      ).pixel_values
													self.assertEqual(
													    encoded_images.shape  ,						(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )  ,						)



							def      A__		(	self      )-> List[str]:





													'''simple docstring'''






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

													# Test not batched input
													__UpperCamelCase  	=      image_processing(image_inputs[0]  ,						return_tensors='''pt'''      ).pixel_values
													self.assertEqual(
													    encoded_images.shape  ,						(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )  ,						)

													# Test batched
													__UpperCamelCase  	=      image_processing(SCREAMING_SNAKE_CASE_  ,						return_tensors='''pt'''      ).pixel_values
													self.assertEqual(
													    encoded_images.shape  ,						(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )  ,						)



							def      A__		(	self      )-> str:





													'''simple docstring'''






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

													# Test not batched input
													__UpperCamelCase  	=      image_processing(image_inputs[0]  ,						return_tensors='''pt'''      ).pixel_values
													self.assertEqual(
													    encoded_images.shape  ,						(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )  ,						)

													# Test batched
													__UpperCamelCase  	=      image_processing(SCREAMING_SNAKE_CASE_  ,						return_tensors='''pt'''      ).pixel_values
													self.assertEqual(
													    encoded_images.shape  ,						(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )  ,						)




