


"""simple docstring"""




import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import UperNetForSemanticSegmentation
	from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor
class 					lowerCAmelCase	:




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__						,       lowerCAmelCase__=13						,       lowerCAmelCase__=32						,       lowerCAmelCase__=3						,       lowerCAmelCase__=4						,       lowerCAmelCase__=[10, 20, 30, 40]						,       lowerCAmelCase__=[2, 2, 3, 2]						,       lowerCAmelCase__=True						,       lowerCAmelCase__=True						,       lowerCAmelCase__=37						,       lowerCAmelCase__="gelu"						,       lowerCAmelCase__=10						,       lowerCAmelCase__=0.02						,       lowerCAmelCase__=["stage2", "stage3", "stage4"]						,       lowerCAmelCase__=3						,       lowerCAmelCase__=None						,       )     ->							List[str]:
								SCREAMING_SNAKE_CASE													=					parent
								SCREAMING_SNAKE_CASE													=					batch_size
								SCREAMING_SNAKE_CASE													=					image_size
								SCREAMING_SNAKE_CASE													=					num_channels
								SCREAMING_SNAKE_CASE													=					num_stages
								SCREAMING_SNAKE_CASE													=					hidden_sizes
								SCREAMING_SNAKE_CASE													=					depths
								SCREAMING_SNAKE_CASE													=					is_training
								SCREAMING_SNAKE_CASE													=					use_labels
								SCREAMING_SNAKE_CASE													=					intermediate_size
								SCREAMING_SNAKE_CASE													=					hidden_act
								SCREAMING_SNAKE_CASE													=					type_sequence_label_size
								SCREAMING_SNAKE_CASE													=					initializer_range
								SCREAMING_SNAKE_CASE													=					out_features
								SCREAMING_SNAKE_CASE													=					num_labels
								SCREAMING_SNAKE_CASE													=					scope
								SCREAMING_SNAKE_CASE													=					num_stages







		def 							__A						(	self				)     ->							str:
								SCREAMING_SNAKE_CASE													=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

								SCREAMING_SNAKE_CASE													=					None
								if self.use_labels:
														SCREAMING_SNAKE_CASE													=					ids_tensor([self.batch_size]						,       self.type_sequence_label_size				)

								SCREAMING_SNAKE_CASE													=					self.get_config()

								return config, pixel_values, labels







		def 							__A						(	self				)     ->							Any:
								return ConvNextConfig(
								    num_channels=self.num_channels						,       num_stages=self.num_stages						,       hidden_sizes=self.hidden_sizes						,       depths=self.depths						,       is_training=self.is_training						,       intermediate_size=self.intermediate_size						,       hidden_act=self.hidden_act						,       out_features=self.out_features						,       )







		def 							__A						(	self				)     ->							int:
								return UperNetConfig(
								    backbone_config=self.get_backbone_config()						,       hidden_size=512						,       pool_scales=[1, 2, 3, 6]						,       use_auxiliary_head=lowerCAmelCase__						,       auxiliary_loss_weight=0.4						,       auxiliary_in_channels=40						,       auxiliary_channels=256						,       auxiliary_num_convs=1						,       auxiliary_concat_input=lowerCAmelCase__						,       loss_ignore_index=255						,       num_labels=self.num_labels						,       )







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					UperNetForSemanticSegmentation(config=lowerCAmelCase__				)
								model.to(lowerCAmelCase__				)
								model.eval()
								SCREAMING_SNAKE_CASE													=					model(lowerCAmelCase__				)
								self.parent.assertEqual(
								    result.logits.shape						,       (self.batch_size, self.num_labels, self.image_size, self.image_size)				)







		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					self.prepare_config_and_inputs()
								(
								    (
								    SCREAMING_SNAKE_CASE
								)							,				(
								    SCREAMING_SNAKE_CASE
								)							,				(
								    SCREAMING_SNAKE_CASE
								)							,				
								)													=					config_and_inputs
								SCREAMING_SNAKE_CASE													=					{'pixel_values': pixel_values}
								return config, inputs_dict





@require_torch
class 					lowerCAmelCase	(					lowerCamelCase_	,    lowerCamelCase_	,    unittest.TestCase    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     List[Any]								=     (UperNetForSemanticSegmentation,) if is_torch_available() else ()
		SCREAMING_SNAKE_CASE_						:     str								=     {"""image-segmentation""": UperNetForSemanticSegmentation} if is_torch_available() else {}
		SCREAMING_SNAKE_CASE_						:     Dict								=     False
		SCREAMING_SNAKE_CASE_						:     Any								=     False
		SCREAMING_SNAKE_CASE_						:     List[Any]								=     False
		SCREAMING_SNAKE_CASE_						:     Optional[Any]								=     False
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     False
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     False







		def 							__A						(	self				)     ->							Dict:
								SCREAMING_SNAKE_CASE													=					UperNetModelTester(self				)
								SCREAMING_SNAKE_CASE													=					ConfigTester(self						,       config_class=lowerCAmelCase__						,       has_text_modality=lowerCAmelCase__						,       hidden_size=37				)







		def 							__A						(	self				)     ->							Union[str, Any]:
								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()







		def 							__A						(	self				)     ->							Dict:
								return







		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														SCREAMING_SNAKE_CASE													=					model_class(lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					inspect.signature(model.forward				)
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														SCREAMING_SNAKE_CASE													=					[*signature.parameters.keys()]

														SCREAMING_SNAKE_CASE													=					['pixel_values']
														self.assertListEqual(arg_names[:1]						,       lowerCAmelCase__				)







		def 							__A						(	self				)     ->							Dict:
								SCREAMING_SNAKE_CASE													=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase__				)







		@unittest.skip(reason='UperNet does not use inputs_embeds'				)
		def 							__A						(	self				)     ->							Union[str, Any]:
								pass







		@unittest.skip(reason='UperNet does not support input and output embeddings'				)
		def 							__A						(	self				)     ->							Any:
								pass







		@unittest.skip(reason='UperNet does not have a base model'				)
		def 							__A						(	self				)     ->							Union[str, Any]:
								pass







		@unittest.skip(reason='UperNet does not have a base model'				)
		def 							__A						(	self				)     ->							Dict:
								pass







		@require_torch_multi_gpu
		@unittest.skip(reason='UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'				)
		def 							__A						(	self				)     ->							Any:
								pass







		@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'				)
		def 							__A						(	self				)     ->							List[str]:
								pass







		def 							__A						(	self				)     ->							Optional[int]:
								def check_hidden_states_output(lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				):
														SCREAMING_SNAKE_CASE													=					model_class(lowerCAmelCase__				)
														model.to(lowerCAmelCase__				)
														model.eval()

														with torch.no_grad():
																				SCREAMING_SNAKE_CASE													=					model(**self._prepare_for_class(lowerCAmelCase__						,       lowerCAmelCase__				)				)

														SCREAMING_SNAKE_CASE													=					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

														SCREAMING_SNAKE_CASE													=					self.model_tester.num_stages
														self.assertEqual(len(lowerCAmelCase__				)						,       expected_num_stages + 1				)

														# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
														self.assertListEqual(
														    list(hidden_states[0].shape[-2:]				)						,       [self.model_tester.image_size // 4, self.model_tester.image_size // 4]						,       )

								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														SCREAMING_SNAKE_CASE													=					True
														check_hidden_states_output(lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														SCREAMING_SNAKE_CASE													=					True

														check_hidden_states_output(lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)







		def 							__A						(	self				)     ->							List[str]:
								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					self.model_tester.prepare_config_and_inputs_for_common()

								SCREAMING_SNAKE_CASE													=					_config_zero_init(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					_config_zero_init(configs_no_init.backbone_config				)
								for model_class in self.all_model_classes:
														SCREAMING_SNAKE_CASE													=					model_class(config=lowerCAmelCase__				)
														for name, param in model.named_parameters():
																				if param.requires_grad:
																										self.assertIn(
																										    ((param.data.mean() * 1e9).round() / 1e9).item()						,       [0.0, 1.0]						,       msg=F'Parameter {name} of model {model_class} seems not properly initialized'						,       )







		@unittest.skip(reason='UperNet does not have tied weights'				)
		def 							__A						(	self				)     ->							List[str]:
								pass







		@slow
		def 							__A						(	self				)     ->							int:
								for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														SCREAMING_SNAKE_CASE													=					UperNetForSemanticSegmentation.from_pretrained(lowerCAmelCase__				)
														self.assertIsNotNone(lowerCAmelCase__				)





def     lowercase			()   ->						Any:
						SCREAMING_SNAKE_CASE													=					hf_hub_download(
						    repo_id='hf-internal-testing/fixtures_ade20k' ,			repo_type='dataset' ,			filename='ADE_val_00000001.jpg'  )
						SCREAMING_SNAKE_CASE													=					Image.open(SCREAMING_SNAKE_CASE_  ).convert('RGB'  )
						return image


@require_torch
@require_vision
@slow
class 					lowerCAmelCase	(					unittest.TestCase    ):




		'''simple docstring'''







		def 							__A						(	self				)     ->							List[str]:
								SCREAMING_SNAKE_CASE													=					AutoImageProcessor.from_pretrained('openmmlab/upernet-swin-tiny'				)
								SCREAMING_SNAKE_CASE													=					UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-swin-tiny'				).to(lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					prepare_img()
								SCREAMING_SNAKE_CASE													=					processor(images=lowerCAmelCase__						,       return_tensors='pt'				).to(lowerCAmelCase__				)

								with torch.no_grad():
														SCREAMING_SNAKE_CASE													=					model(**lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					torch.Size((1, model.config.num_labels, 512, 512)				)
								self.assertEqual(outputs.logits.shape						,       lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					torch.tensor(
								    [[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]]				).to(lowerCAmelCase__				)
								self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]						,       lowerCAmelCase__						,       atol=1e-4				)				)







		def 							__A						(	self				)     ->							Optional[int]:
								SCREAMING_SNAKE_CASE													=					AutoImageProcessor.from_pretrained('openmmlab/upernet-convnext-tiny'				)
								SCREAMING_SNAKE_CASE													=					UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-convnext-tiny'				).to(lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					prepare_img()
								SCREAMING_SNAKE_CASE													=					processor(images=lowerCAmelCase__						,       return_tensors='pt'				).to(lowerCAmelCase__				)

								with torch.no_grad():
														SCREAMING_SNAKE_CASE													=					model(**lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					torch.Size((1, model.config.num_labels, 512, 512)				)
								self.assertEqual(outputs.logits.shape						,       lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					torch.tensor(
								    [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]				).to(lowerCAmelCase__				)
								self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]						,       lowerCAmelCase__						,       atol=1e-4				)				)






"""simple docstring"""




from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload
class 					lowerCAmelCase	:




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__ = None				)     ->							None:
								if components is None:
														SCREAMING_SNAKE_CASE													=					[]
								SCREAMING_SNAKE_CASE													=					list(lowerCAmelCase__				)







		def __len__(	self				)     ->							int:
								return len(self.__components				)







		def __str__(	self				)     ->							str:
								return "(" + ",".join(map(lowerCAmelCase__						,       self.__components				)				) + ")"







		def __add__(	self						,       lowerCAmelCase__				)     ->							Vector:
								SCREAMING_SNAKE_CASE													=					len(self				)
								if size == len(lowerCAmelCase__				):
														SCREAMING_SNAKE_CASE													=					[self.__components[i] + other.component(lowerCAmelCase__				) for i in range(lowerCAmelCase__				)]
														return Vector(lowerCAmelCase__				)
								else:
														raise Exception('must have the same size'				)







		def __sub__(	self						,       lowerCAmelCase__				)     ->							Vector:
								SCREAMING_SNAKE_CASE													=					len(self				)
								if size == len(lowerCAmelCase__				):
														SCREAMING_SNAKE_CASE													=					[self.__components[i] - other.component(lowerCAmelCase__				) for i in range(lowerCAmelCase__				)]
														return Vector(lowerCAmelCase__				)
								else:  # error case
														raise Exception('must have the same size'				)







		@overload
		def __mul__(	self						,       lowerCAmelCase__				)     ->							Vector:
								...







		@overload
		def __mul__(	self						,       lowerCAmelCase__				)     ->							float:
								...







		def __mul__(	self						,       lowerCAmelCase__				)     ->							float | Vector:
								if isinstance(lowerCAmelCase__						,       (float, int)				):
														SCREAMING_SNAKE_CASE													=					[c * other for c in self.__components]
														return Vector(lowerCAmelCase__				)
								elif isinstance(lowerCAmelCase__						,       lowerCAmelCase__				) and len(self				) == len(lowerCAmelCase__				):
														SCREAMING_SNAKE_CASE													=					len(self				)
														SCREAMING_SNAKE_CASE													=					[self.__components[i] * other.component(lowerCAmelCase__				) for i in range(lowerCAmelCase__				)]
														return sum(lowerCAmelCase__				)
								else:  # error case
														raise Exception('invalid operand!'				)







		def 							__A						(	self				)     ->							Vector:
								return Vector(self.__components				)







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							float:
								if isinstance(lowerCAmelCase__						,       lowerCAmelCase__				) and -len(self.__components				) <= i < len(self.__components				):
														return self.__components[i]
								else:
														raise Exception('index out of range'				)







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							None:
								assert -len(self.__components				) <= pos < len(self.__components				)
								SCREAMING_SNAKE_CASE													=					value







		def 							__A						(	self				)     ->							float:
								if len(self.__components				) == 0:
														raise Exception('Vector is empty'				)
								SCREAMING_SNAKE_CASE													=					[c**2 for c in self.__components]
								return math.sqrt(sum(lowerCAmelCase__				)				)







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__ = False				)     ->							float:
								SCREAMING_SNAKE_CASE													=					self * other
								SCREAMING_SNAKE_CASE													=					self.euclidean_length() * other.euclidean_length()
								if deg:
														return math.degrees(math.acos(num / den				)				)
								else:
														return math.acos(num / den				)





def     lowercase			(SCREAMING_SNAKE_CASE_		:				int  )   ->						Vector:
						assert isinstance(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
						return Vector([0] * dimension  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				int  )   ->						Vector:
						assert isinstance(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ) and (isinstance(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ))
						SCREAMING_SNAKE_CASE													=					[0] * dimension
						SCREAMING_SNAKE_CASE													=					1
						return Vector(SCREAMING_SNAKE_CASE_  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				float ,			SCREAMING_SNAKE_CASE_		:				Vector ,			SCREAMING_SNAKE_CASE_		:				Vector  )   ->						Vector:
						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     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				int  )   ->						Vector:
						random.seed(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					[random.randint(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ) for _ in range(SCREAMING_SNAKE_CASE_  )]
						return Vector(SCREAMING_SNAKE_CASE_  )
class 					lowerCAmelCase	:




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							None:
								SCREAMING_SNAKE_CASE													=					matrix
								SCREAMING_SNAKE_CASE													=					w
								SCREAMING_SNAKE_CASE													=					h







		def __str__(	self				)     ->							str:
								SCREAMING_SNAKE_CASE													=					''
								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						,       lowerCAmelCase__				)     ->							Matrix:
								if self.__width == other.width() and self.__height == other.height():
														SCREAMING_SNAKE_CASE													=					[]
														for i in range(self.__height				):
																				SCREAMING_SNAKE_CASE													=					[
																				    self.__matrix[i][j] + other.component(lowerCAmelCase__						,       lowerCAmelCase__				)
																				    for j in range(self.__width				)
																				]
																				matrix.append(lowerCAmelCase__				)
														return Matrix(lowerCAmelCase__						,       self.__width						,       self.__height				)
								else:
														raise Exception('matrix must have the same dimension!'				)







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







		@overload
		def __mul__(	self						,       lowerCAmelCase__				)     ->							Matrix:
								...







		@overload
		def __mul__(	self						,       lowerCAmelCase__				)     ->							Vector:
								...







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







		def 							__A						(	self				)     ->							int:
								return self.__height







		def 							__A						(	self				)     ->							int:
								return self.__width







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							float:
								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 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							None:
								if 0 <= x < self.__height and 0 <= y < self.__width:
														SCREAMING_SNAKE_CASE													=					value
								else:
														raise Exception('change_component: indices out of bounds'				)







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							float:
								if self.__height != self.__width:
														raise Exception('Matrix is not square'				)
								SCREAMING_SNAKE_CASE													=					self.__matrix[:x] + self.__matrix[x + 1 :]
								for i in range(len(lowerCAmelCase__				)				):
														SCREAMING_SNAKE_CASE													=					minor[i][:y] + minor[i][y + 1 :]
								return Matrix(lowerCAmelCase__						,       self.__width - 1						,       self.__height - 1				).determinant()







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							float:
								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(lowerCAmelCase__						,       lowerCAmelCase__				)
								else:
														raise Exception('Indices out of bounds'				)







		def 							__A						(	self				)     ->							float:
								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:
														SCREAMING_SNAKE_CASE													=					[
														    self.__matrix[0][y] * self.cofactor(0						,       lowerCAmelCase__				) for y in range(self.__width				)
														]
														return sum(lowerCAmelCase__				)





def     lowercase			(SCREAMING_SNAKE_CASE_		:				int  )   ->						Matrix:
						SCREAMING_SNAKE_CASE													=					[[0] * n for _ in range(SCREAMING_SNAKE_CASE_  )]
						return Matrix(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				int  )   ->						Matrix:
						random.seed(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					[
						    [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_  )


