





"""simple docstring"""





import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 ConditionalDetrImageProcessor
class 		SCREAMING_SNAKE_CASE_							(    unittest.TestCase   ):
				"""simple docstring"""




				def __init__(     self  :Dict						,    __lowercase  :List[str]						,    __lowercase  :Dict=7						,    __lowercase  :List[Any]=3						,    __lowercase  :Any=30						,    __lowercase  :Any=400						,    __lowercase  :Any=True						,    __lowercase  :Tuple=None						,    __lowercase  :Union[str, Any]=True						,    __lowercase  :Optional[Any]=[0.5, 0.5, 0.5]						,    __lowercase  :List[str]=[0.5, 0.5, 0.5]						,    __lowercase  :Optional[int]=True						,    __lowercase  :Tuple=1 / 255						,    __lowercase  :Union[str, Any]=True						,    ):
											# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
											__lowerCamelCase      :      Dict         =size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
											__lowerCamelCase      :      int         =parent
											__lowerCamelCase      :      Dict         =batch_size
											__lowerCamelCase      :      Union[str, Any]         =num_channels
											__lowerCamelCase      :      Optional[Any]         =min_resolution
											__lowerCamelCase      :      List[str]         =max_resolution
											__lowerCamelCase      :      Union[str, Any]         =do_resize
											__lowerCamelCase      :      Optional[int]         =size
											__lowerCamelCase      :      Optional[Any]         =do_normalize
											__lowerCamelCase      :      Optional[int]         =image_mean
											__lowerCamelCase      :      List[str]         =image_std
											__lowerCamelCase      :      Dict         =do_rescale
											__lowerCamelCase      :      List[str]         =rescale_factor
											__lowerCamelCase      :      Optional[Any]         =do_pad




				def 					__lowercase  (     self  :Any			):
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_rescale": self.do_rescale,
											    "rescale_factor": self.rescale_factor,
											    "do_pad": self.do_pad,
											}




				def 					__lowercase  (     self  :List[str]						,    __lowercase  :List[Any]						,    __lowercase  :List[str]=False			):
											if not batched:
																		__lowerCamelCase      :      Optional[int]         =image_inputs[0]
																		if isinstance(__lowercase						,    Image.Image			):
																									__lowerCamelCase    ,						__lowerCamelCase      :      Optional[int]         =image.size
																		else:
																									__lowerCamelCase    ,						__lowerCamelCase      :      Optional[int]         =image.shape[1], image.shape[2]
																		if w < h:
																									__lowerCamelCase      :      Union[str, Any]         =int(self.size['''shortest_edge'''] * h / w			)
																									__lowerCamelCase      :      int         =self.size['''shortest_edge''']
																		elif w > h:
																									__lowerCamelCase      :      Union[str, Any]         =self.size['''shortest_edge''']
																									__lowerCamelCase      :      int         =int(self.size['''shortest_edge'''] * w / h			)
																		else:
																									__lowerCamelCase      :      Union[str, Any]         =self.size['''shortest_edge''']
																									__lowerCamelCase      :      Optional[int]         =self.size['''shortest_edge''']

											else:
																		__lowerCamelCase      :      Tuple         =[]
																		for image in image_inputs:
																									__lowerCamelCase    ,						__lowerCamelCase      :      Tuple         =self.get_expected_values([image]			)
																									expected_values.append((expected_height, expected_width)			)
																		__lowerCamelCase      :      Union[str, Any]         =max(__lowercase						,    key=lambda __lowercase			: item[0]			)[0]
																		__lowerCamelCase      :      Union[str, Any]         =max(__lowercase						,    key=lambda __lowercase			: item[1]			)[1]

											return expected_height, expected_width

@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE_							(    snake_case__       ,  unittest.TestCase   ):
				"""simple docstring"""
				__snake_case   :       Any	 =		ConditionalDetrImageProcessor if is_vision_available() else None




				def 					__lowercase  (     self  :int			):
											__lowerCamelCase      :      List[Any]         =ConditionalDetrImageProcessingTester(self			)




				@property
				def 					__lowercase  (     self  :Union[str, Any]			):
											return self.image_processor_tester.prepare_image_processor_dict()




				def 					__lowercase  (     self  :Tuple			):
											__lowerCamelCase      :      Union[str, Any]         =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'''			)			)




				def 					__lowercase  (     self  :Optional[Any]			):
											__lowerCamelCase      :      Optional[Any]         =self.image_processing_class.from_dict(self.image_processor_dict			)
											self.assertEqual(image_processor.size						,    {'''shortest_edge''': 18, '''longest_edge''': 1333}			)
											self.assertEqual(image_processor.do_pad						,    __lowercase			)

											__lowerCamelCase      :      str         =self.image_processing_class.from_dict(
											    self.image_processor_dict						,    size=42						,    max_size=84						,    pad_and_return_pixel_mask=__lowercase			)
											self.assertEqual(image_processor.size						,    {'''shortest_edge''': 42, '''longest_edge''': 84}			)
											self.assertEqual(image_processor.do_pad						,    __lowercase			)




				def 					__lowercase  (     self  :Optional[int]			):
											pass




				def 					__lowercase  (     self  :List[Any]			):
											# Initialize image_processing
											__lowerCamelCase      :      List[Any]         =self.image_processing_class(**self.image_processor_dict			)
											# create random PIL images
											__lowerCamelCase      :      List[Any]         =prepare_image_inputs(self.image_processor_tester						,    equal_resolution=__lowercase			)
											for image in image_inputs:
																		self.assertIsInstance(__lowercase						,    Image.Image			)

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

											__lowerCamelCase    ,						__lowerCamelCase      :      List[Any]         =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
											__lowerCamelCase    ,						__lowerCamelCase      :      int         =self.image_processor_tester.get_expected_values(__lowercase						,    batched=__lowercase			)

											__lowerCamelCase      :      Any         =image_processing(__lowercase						,    return_tensors='''pt'''			).pixel_values
											self.assertEqual(
											    encoded_images.shape						,    (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )						,    )




				def 					__lowercase  (     self  :List[Any]			):
											# Initialize image_processing
											__lowerCamelCase      :      int         =self.image_processing_class(**self.image_processor_dict			)
											# create random numpy tensors
											__lowerCamelCase      :      int         =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
											__lowerCamelCase      :      List[Any]         =image_processing(image_inputs[0]						,    return_tensors='''pt'''			).pixel_values

											__lowerCamelCase    ,						__lowerCamelCase      :      Optional[int]         =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
											__lowerCamelCase      :      List[str]         =image_processing(__lowercase						,    return_tensors='''pt'''			).pixel_values

											__lowerCamelCase    ,						__lowerCamelCase      :      Optional[int]         =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 					__lowercase  (     self  :Tuple			):
											# Initialize image_processing
											__lowerCamelCase      :      Tuple         =self.image_processing_class(**self.image_processor_dict			)
											# create random PyTorch tensors
											__lowerCamelCase      :      List[str]         =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
											__lowerCamelCase      :      str         =image_processing(image_inputs[0]						,    return_tensors='''pt'''			).pixel_values

											__lowerCamelCase    ,						__lowerCamelCase      :      Union[str, Any]         =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
											__lowerCamelCase      :      int         =image_processing(__lowercase						,    return_tensors='''pt'''			).pixel_values

											__lowerCamelCase    ,						__lowerCamelCase      :      List[str]         =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,
											    )						,    )




				@slow
				def 					__lowercase  (     self  :Any			):
											# prepare image and target
											__lowerCamelCase      :      List[Any]         =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
											with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''						,    '''r'''			) as f:
																		__lowerCamelCase      :      List[str]         =json.loads(f.read()			)

											__lowerCamelCase      :      Optional[int]         ={'''image_id''': 3_9769, '''annotations''': target}

											# encode them
											__lowerCamelCase      :      Optional[int]         =ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50'''			)
											__lowerCamelCase      :      Optional[int]         =image_processing(images=__lowercase						,    annotations=__lowercase						,    return_tensors='''pt'''			)

											# verify pixel values
											__lowerCamelCase      :      List[Any]         =torch.Size([1, 3, 800, 1066]			)
											self.assertEqual(encoding['''pixel_values'''].shape						,    __lowercase			)

											__lowerCamelCase      :      List[Any]         =torch.tensor([0.2796, 0.3138, 0.3481]			)
											self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]						,    __lowercase						,    atol=1e-4			)			)

											# verify area
											__lowerCamelCase      :      Dict         =torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]			)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']						,    __lowercase			)			)
											# verify boxes
											__lowerCamelCase      :      Any         =torch.Size([6, 4]			)
											self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape						,    __lowercase			)
											__lowerCamelCase      :      List[str]         =torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]			)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]						,    __lowercase						,    atol=1e-3			)			)
											# verify image_id
											__lowerCamelCase      :      int         =torch.tensor([3_9769]			)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']						,    __lowercase			)			)
											# verify is_crowd
											__lowerCamelCase      :      Dict         =torch.tensor([0, 0, 0, 0, 0, 0]			)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']						,    __lowercase			)			)
											# verify class_labels
											__lowerCamelCase      :      Optional[int]         =torch.tensor([75, 75, 63, 65, 17, 17]			)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']						,    __lowercase			)			)
											# verify orig_size
											__lowerCamelCase      :      Optional[Any]         =torch.tensor([480, 640]			)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']						,    __lowercase			)			)
											# verify size
											__lowerCamelCase      :      Any         =torch.tensor([800, 1066]			)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']						,    __lowercase			)			)




				@slow
				def 					__lowercase  (     self  :Tuple			):
											# prepare image, target and masks_path
											__lowerCamelCase      :      Tuple         =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
											with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''						,    '''r'''			) as f:
																		__lowerCamelCase      :      Tuple         =json.loads(f.read()			)

											__lowerCamelCase      :      Union[str, Any]         ={'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

											__lowerCamelCase      :      Union[str, Any]         =pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''			)

											# encode them
											__lowerCamelCase      :      Tuple         =ConditionalDetrImageProcessor(format='''coco_panoptic'''			)
											__lowerCamelCase      :      Optional[Any]         =image_processing(images=__lowercase						,    annotations=__lowercase						,    masks_path=__lowercase						,    return_tensors='''pt'''			)

											# verify pixel values
											__lowerCamelCase      :      Optional[Any]         =torch.Size([1, 3, 800, 1066]			)
											self.assertEqual(encoding['''pixel_values'''].shape						,    __lowercase			)

											__lowerCamelCase      :      Tuple         =torch.tensor([0.2796, 0.3138, 0.3481]			)
											self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]						,    __lowercase						,    atol=1e-4			)			)

											# verify area
											__lowerCamelCase      :      List[Any]         =torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]			)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']						,    __lowercase			)			)
											# verify boxes
											__lowerCamelCase      :      List[str]         =torch.Size([6, 4]			)
											self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape						,    __lowercase			)
											__lowerCamelCase      :      int         =torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]			)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]						,    __lowercase						,    atol=1e-3			)			)
											# verify image_id
											__lowerCamelCase      :      Any         =torch.tensor([3_9769]			)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']						,    __lowercase			)			)
											# verify is_crowd
											__lowerCamelCase      :      Tuple         =torch.tensor([0, 0, 0, 0, 0, 0]			)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']						,    __lowercase			)			)
											# verify class_labels
											__lowerCamelCase      :      str         =torch.tensor([17, 17, 63, 75, 75, 93]			)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']						,    __lowercase			)			)
											# verify masks
											__lowerCamelCase      :      Dict         =82_2873
											self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()						,    __lowercase			)
											# verify orig_size
											__lowerCamelCase      :      Union[str, Any]         =torch.tensor([480, 640]			)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']						,    __lowercase			)			)
											# verify size
											__lowerCamelCase      :      Any         =torch.tensor([800, 1066]			)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']						,    __lowercase			)			)











"""simple docstring"""





from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload
class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""




				def __init__(     self  :Optional[Any]						,    __lowercase  :Collection[float] | None = None			):
											if components is None:
																		__lowerCamelCase      :      str         =[]
											__lowerCamelCase      :      int         =list(__lowercase			)




				def __len__(     self  :Union[str, Any]			):
											return len(self.__components			)




				def __str__(     self  :List[Any]			):
											return "(" + ",".join(map(__lowercase						,    self.__components			)			) + ")"




				def __add__(     self  :Union[str, Any]						,    __lowercase  :Vector			):
											__lowerCamelCase      :      Tuple         =len(self			)
											if size == len(__lowercase			):
																		__lowerCamelCase      :      int         =[self.__components[i] + other.component(__lowercase			) for i in range(__lowercase			)]
																		return Vector(__lowercase			)
											else:
																		raise Exception('''must have the same size'''			)




				def __sub__(     self  :int						,    __lowercase  :Vector			):
											__lowerCamelCase      :      int         =len(self			)
											if size == len(__lowercase			):
																		__lowerCamelCase      :      Union[str, Any]         =[self.__components[i] - other.component(__lowercase			) for i in range(__lowercase			)]
																		return Vector(__lowercase			)
											else:  # error case
																		raise Exception('''must have the same size'''			)




				@overload
				def __mul__(     self  :List[str]						,    __lowercase  :float			):
											...




				@overload
				def __mul__(     self  :Union[str, Any]						,    __lowercase  :Vector			):
											...




				def __mul__(     self  :List[Any]						,    __lowercase  :float | Vector			):
											if isinstance(__lowercase						,    (float, int)			):
																		__lowerCamelCase      :      Optional[int]         =[c * other for c in self.__components]
																		return Vector(__lowercase			)
											elif isinstance(__lowercase						,    __lowercase			) and len(self			) == len(__lowercase			):
																		__lowerCamelCase      :      Dict         =len(self			)
																		__lowerCamelCase      :      Union[str, Any]         =[self.__components[i] * other.component(__lowercase			) for i in range(__lowercase			)]
																		return sum(__lowercase			)
											else:  # error case
																		raise Exception('''invalid operand!'''			)




				def 					__lowercase  (     self  :Optional[Any]			):
											return Vector(self.__components			)




				def 					__lowercase  (     self  :Union[str, Any]						,    __lowercase  :int			):
											if isinstance(__lowercase						,    __lowercase			) and -len(self.__components			) <= i < len(self.__components			):
																		return self.__components[i]
											else:
																		raise Exception('''index out of range'''			)




				def 					__lowercase  (     self  :int						,    __lowercase  :int						,    __lowercase  :float			):
											assert -len(self.__components			) <= pos < len(self.__components			)
											__lowerCamelCase      :      int         =value




				def 					__lowercase  (     self  :Union[str, Any]			):
											if len(self.__components			) == 0:
																		raise Exception('''Vector is empty'''			)
											__lowerCamelCase      :      Tuple         =[c**2 for c in self.__components]
											return math.sqrt(sum(__lowercase			)			)




				def 					__lowercase  (     self  :Any						,    __lowercase  :Vector						,    __lowercase  :bool = False			):
											__lowerCamelCase      :      Tuple         =self * other
											__lowerCamelCase      :      str         =self.euclidean_length() * other.euclidean_length()
											if deg:
																		return math.degrees(math.acos(num / den			)			)
											else:
																		return math.acos(num / den			)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							assert isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							return Vector([0] * dimension   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							assert isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ) and (isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ))
							__lowerCamelCase      :      List[Any]         =[0] * dimension
							__lowerCamelCase      :      Union[str, Any]         =1
							return Vector(SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							float    ,						SCREAMING_SNAKE_CASE       :							Vector    ,						SCREAMING_SNAKE_CASE       :							Vector   ):


							'''simple docstring'''







							assert (
							    isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							    and isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							    and (isinstance(SCREAMING_SNAKE_CASE    ,						(int, float)   ))
							)
							return x * scalar + y



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							random.seed(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Optional[int]         =[random.randint(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ) for _ in range(SCREAMING_SNAKE_CASE   )]
							return Vector(SCREAMING_SNAKE_CASE   )
class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""




				def __init__(     self  :Tuple						,    __lowercase  :list[list[float]]						,    __lowercase  :int						,    __lowercase  :int			):
											__lowerCamelCase      :      Optional[Any]         =matrix
											__lowerCamelCase      :      int         =w
											__lowerCamelCase      :      str         =h




				def __str__(     self  :Optional[Any]			):
											__lowerCamelCase      :      Optional[int]         =''''''
											for i in range(self.__height			):
																		ans += "|"
																		for j in range(self.__width			):
																									if j < self.__width - 1:
																																ans += str(self.__matrix[i][j]			) + ","
																									else:
																																ans += str(self.__matrix[i][j]			) + "|\n"
											return ans




				def __add__(     self  :Any						,    __lowercase  :Matrix			):
											if self.__width == other.width() and self.__height == other.height():
																		__lowerCamelCase      :      Optional[int]         =[]
																		for i in range(self.__height			):
																									__lowerCamelCase      :      Optional[int]         =[
																									    self.__matrix[i][j] + other.component(__lowercase						,    __lowercase			)
																									    for j in range(self.__width			)
																									]
																									matrix.append(__lowercase			)
																		return Matrix(__lowercase						,    self.__width						,    self.__height			)
											else:
																		raise Exception('''matrix must have the same dimension!'''			)




				def __sub__(     self  :Tuple						,    __lowercase  :Matrix			):
											if self.__width == other.width() and self.__height == other.height():
																		__lowerCamelCase      :      List[str]         =[]
																		for i in range(self.__height			):
																									__lowerCamelCase      :      Dict         =[
																									    self.__matrix[i][j] - other.component(__lowercase						,    __lowercase			)
																									    for j in range(self.__width			)
																									]
																									matrix.append(__lowercase			)
																		return Matrix(__lowercase						,    self.__width						,    self.__height			)
											else:
																		raise Exception('''matrices must have the same dimension!'''			)




				@overload
				def __mul__(     self  :List[Any]						,    __lowercase  :float			):
											...




				@overload
				def __mul__(     self  :List[Any]						,    __lowercase  :Vector			):
											...




				def __mul__(     self  :Union[str, Any]						,    __lowercase  :float | Vector			):
											if isinstance(__lowercase						,    __lowercase			):  # matrix-vector
																		if len(__lowercase			) == self.__width:
																									__lowerCamelCase      :      int         =zero_vector(self.__height			)
																									for i in range(self.__height			):
																																__lowerCamelCase      :      Union[str, Any]         =[
																																    self.__matrix[i][j] * other.component(__lowercase			)
																																    for j in range(self.__width			)
																																]
																																ans.change_component(__lowercase						,    sum(__lowercase			)			)
																									return ans
																		else:
																									raise Exception(
																									    '''vector must have the same size as the '''
																									    '''number of columns of the matrix!'''			)
											elif isinstance(__lowercase						,    (int, float)			):  # matrix-scalar
																		__lowerCamelCase      :      Optional[Any]         =[
																		    [self.__matrix[i][j] * other for j in range(self.__width			)]
																		    for i in range(self.__height			)
																		]
																		return Matrix(__lowercase						,    self.__width						,    self.__height			)
											return None




				def 					__lowercase  (     self  :Union[str, Any]			):
											return self.__height




				def 					__lowercase  (     self  :Tuple			):
											return self.__width




				def 					__lowercase  (     self  :Any						,    __lowercase  :int						,    __lowercase  :int			):
											if 0 <= x < self.__height and 0 <= y < self.__width:
																		return self.__matrix[x][y]
											else:
																		raise Exception('''change_component: indices out of bounds'''			)




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :int						,    __lowercase  :int						,    __lowercase  :float			):
											if 0 <= x < self.__height and 0 <= y < self.__width:
																		__lowerCamelCase      :      int         =value
											else:
																		raise Exception('''change_component: indices out of bounds'''			)




				def 					__lowercase  (     self  :List[Any]						,    __lowercase  :int						,    __lowercase  :int			):
											if self.__height != self.__width:
																		raise Exception('''Matrix is not square'''			)
											__lowerCamelCase      :      Union[str, Any]         =self.__matrix[:x] + self.__matrix[x + 1 :]
											for i in range(len(__lowercase			)			):
																		__lowerCamelCase      :      Optional[int]         =minor[i][:y] + minor[i][y + 1 :]
											return Matrix(__lowercase						,    self.__width - 1						,    self.__height - 1			).determinant()




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :int						,    __lowercase  :int			):
											if self.__height != self.__width:
																		raise Exception('''Matrix is not square'''			)
											if 0 <= x < self.__height and 0 <= y < self.__width:
																		return (-1) ** (x + y) * self.minor(__lowercase						,    __lowercase			)
											else:
																		raise Exception('''Indices out of bounds'''			)




				def 					__lowercase  (     self  :Optional[Any]			):
											if self.__height != self.__width:
																		raise Exception('''Matrix is not square'''			)
											if self.__height < 1:
																		raise Exception('''Matrix has no element'''			)
											elif self.__height == 1:
																		return self.__matrix[0][0]
											elif self.__height == 2:
																		return (
																		    self.__matrix[0][0] * self.__matrix[1][1]
																		    - self.__matrix[0][1] * self.__matrix[1][0]
																		)
											else:
																		__lowerCamelCase      :      int         =[
																		    self.__matrix[0][y] * self.cofactor(0						,    __lowercase			) for y in range(self.__width			)
																		]
																		return sum(__lowercase			)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							__lowerCamelCase      :      list[list[float]]         =[[0] * n for _ in range(SCREAMING_SNAKE_CASE   )]
							return Matrix(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							random.seed(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      list[list[float]]         =[
							    [random.randint(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ) for _ in range(SCREAMING_SNAKE_CASE   )] for _ in range(SCREAMING_SNAKE_CASE   )
							]
							return Matrix(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )




