



"""simple docstring"""





import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
  import torch

  from transformers import (
      MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
      MODEL_MAPPING,
      LevitForImageClassification,
      LevitForImageClassificationWithTeacher,
      LevitModel,
  )
  from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import LevitImageProcessor
class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


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


             '''simple docstring'''




             UpperCAmelCase_    				=							self.config_class(**self.inputs_dict					)
             self.parent.assertTrue(hasattr(_UpperCAmelCase	,       "hidden_sizes"					)					)
             self.parent.assertTrue(hasattr(_UpperCAmelCase	,       "num_attention_heads"					)					)



class    lowercase__     :







       '''simple docstring'''


       def __init__(							self    :						Union[str, Any]	,       _UpperCAmelCase    :						int	,       _UpperCAmelCase    :						List[Any]=13	,       _UpperCAmelCase    :						Tuple=64	,       _UpperCAmelCase    :						Union[str, Any]=3	,       _UpperCAmelCase    :						Optional[Any]=3	,       _UpperCAmelCase    :						str=2	,       _UpperCAmelCase    :						Tuple=1	,       _UpperCAmelCase    :						Union[str, Any]=16	,       _UpperCAmelCase    :						Any=[128, 256, 384]	,       _UpperCAmelCase    :						Dict=[4, 6, 8]	,       _UpperCAmelCase    :						Any=[2, 3, 4]	,       _UpperCAmelCase    :						Optional[int]=[16, 16, 16]	,       _UpperCAmelCase    :						str=0	,       _UpperCAmelCase    :						Optional[int]=[2, 2, 2]	,       _UpperCAmelCase    :						Optional[int]=[2, 2, 2]	,       _UpperCAmelCase    :						Optional[int]=0.02	,       _UpperCAmelCase    :						Optional[Any]=True	,       _UpperCAmelCase    :						Union[str, Any]=True	,       _UpperCAmelCase    :						Optional[int]=2	,       )				->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							parent
             UpperCAmelCase_    				=							batch_size
             UpperCAmelCase_    				=							image_size
             UpperCAmelCase_    				=							num_channels
             UpperCAmelCase_    				=							kernel_size
             UpperCAmelCase_    				=							stride
             UpperCAmelCase_    				=							padding
             UpperCAmelCase_    				=							hidden_sizes
             UpperCAmelCase_    				=							num_attention_heads
             UpperCAmelCase_    				=							depths
             UpperCAmelCase_    				=							key_dim
             UpperCAmelCase_    				=							drop_path_rate
             UpperCAmelCase_    				=							patch_size
             UpperCAmelCase_    				=							attention_ratio
             UpperCAmelCase_    				=							mlp_ratio
             UpperCAmelCase_    				=							initializer_range
             UpperCAmelCase_    				=							[
                 ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
                 ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
             ]
             UpperCAmelCase_    				=							is_training
             UpperCAmelCase_    				=							use_labels
             UpperCAmelCase_    				=							num_labels
             UpperCAmelCase_    				=							initializer_range



       def   lowercase__       (							self    :						int					)				->		Any:


             '''simple docstring'''




             UpperCAmelCase_    				=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

             UpperCAmelCase_    				=							None
             if self.use_labels:
                   UpperCAmelCase_    				=							ids_tensor([self.batch_size]	,       self.num_labels					)

             UpperCAmelCase_    				=							self.get_config()
             return config, pixel_values, labels



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


             '''simple docstring'''




             return LevitConfig(
                 image_size=self.image_size	,       num_channels=self.num_channels	,       kernel_size=self.kernel_size	,       stride=self.stride	,       padding=self.padding	,       patch_size=self.patch_size	,       hidden_sizes=self.hidden_sizes	,       num_attention_heads=self.num_attention_heads	,       depths=self.depths	,       key_dim=self.key_dim	,       drop_path_rate=self.drop_path_rate	,       mlp_ratio=self.mlp_ratio	,       attention_ratio=self.attention_ratio	,       initializer_range=self.initializer_range	,       down_ops=self.down_ops	,       )



       def   lowercase__       (							self    :						List[Any]	,       _UpperCAmelCase    :						Optional[Any]	,       _UpperCAmelCase    :						List[str]	,       _UpperCAmelCase    :						Dict					)				->		int:


             '''simple docstring'''




             UpperCAmelCase_    				=							LevitModel(config=_UpperCAmelCase					)
             model.to(_UpperCAmelCase					)
             model.eval()
             UpperCAmelCase_    				=							model(_UpperCAmelCase					)
             UpperCAmelCase_    				=							(self.image_size, self.image_size)
             UpperCAmelCase_				,					UpperCAmelCase_    				=							image_size[0], image_size[1]
             for _ in range(4					):
                   UpperCAmelCase_    				=							floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1					)
                   UpperCAmelCase_    				=							floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1					)
             self.parent.assertEqual(
                 result.last_hidden_state.shape	,       (self.batch_size, ceil(height / 4					) * ceil(width / 4					), self.hidden_sizes[-1])	,       )



       def   lowercase__       (							self    :						Tuple	,       _UpperCAmelCase    :						Any	,       _UpperCAmelCase    :						Union[str, Any]	,       _UpperCAmelCase    :						int					)				->		Dict:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.num_labels
             UpperCAmelCase_    				=							LevitForImageClassification(_UpperCAmelCase					)
             model.to(_UpperCAmelCase					)
             model.eval()
             UpperCAmelCase_    				=							model(_UpperCAmelCase	,       labels=_UpperCAmelCase					)
             self.parent.assertEqual(result.logits.shape	,       (self.batch_size, self.num_labels)					)







       def   lowercase__       (							self    :						Tuple					)				->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.prepare_config_and_inputs()
             UpperCAmelCase_				,					UpperCAmelCase_				,					UpperCAmelCase_    				=							config_and_inputs
             UpperCAmelCase_    				=							{"pixel_values": pixel_values}
             return config, inputs_dict



@require_torch
class    lowercase__     (					SCREAMING_SNAKE_CASE       ,      SCREAMING_SNAKE_CASE       ,      unittest.TestCase   ):







       '''simple docstring'''


       UpperCamelCase		=  (
           (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
           if is_torch_available()
           else ()
       )
       UpperCamelCase		=  (
           {
               '''feature-extraction''': LevitModel,
               '''image-classification''': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
           }
           if is_torch_available()
           else {}
       )

       UpperCamelCase		=  False
       UpperCamelCase		=  False
       UpperCamelCase		=  False
       UpperCamelCase		=  False
       UpperCamelCase		=  False



       def   lowercase__       (							self    :						int					)				->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_    				=							LevitModelTester(self					)
             UpperCAmelCase_    				=							ConfigTester(self	,       config_class=_UpperCAmelCase	,       has_text_modality=_UpperCAmelCase	,       hidden_size=37					)



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


             '''simple docstring'''




             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   lowercase__       (							self    :						str					)				->		Any:


             '''simple docstring'''




             return



       @unittest.skip(reason="Levit does not use inputs_embeds"					)
       def   lowercase__       (							self    :						str					)				->		Optional[Any]:


             '''simple docstring'''




             pass



       @unittest.skip(reason="Levit does not support input and output embeddings"					)
       def   lowercase__       (							self    :						List[str]					)				->		str:


             '''simple docstring'''




             pass



       @unittest.skip(reason="Levit does not output attentions"					)
       def   lowercase__       (							self    :						Optional[int]					)				->		Dict:


             '''simple docstring'''




             pass



       def   lowercase__       (							self    :						Dict					)				->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_				,					UpperCAmelCase_    				=							self.model_tester.prepare_config_and_inputs_for_common()

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

                   UpperCAmelCase_    				=							["pixel_values"]
                   self.assertListEqual(arg_names[:1]	,       _UpperCAmelCase					)



       def   lowercase__       (							self    :						int					)				->		Optional[Any]:


             '''simple docstring'''




             def check_hidden_states_output(_UpperCAmelCase    :						List[Any]	,       _UpperCAmelCase    :						Dict	,       _UpperCAmelCase    :						str					):
                   UpperCAmelCase_    				=							model_class(_UpperCAmelCase					)
                   model.to(_UpperCAmelCase					)
                   model.eval()

                   with torch.no_grad():
                         UpperCAmelCase_    				=							model(**self._prepare_for_class(_UpperCAmelCase	,       _UpperCAmelCase					)					)

                   UpperCAmelCase_    				=							outputs.hidden_states

                   UpperCAmelCase_    				=							len(self.model_tester.depths					) + 1
                   self.assertEqual(len(_UpperCAmelCase					)	,       _UpperCAmelCase					)

                   UpperCAmelCase_    				=							(self.model_tester.image_size, self.model_tester.image_size)
                   UpperCAmelCase_				,					UpperCAmelCase_    				=							image_size[0], image_size[1]
                   for _ in range(4					):
                         UpperCAmelCase_    				=							floor(
                             (
                                 (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                                 / self.model_tester.stride
                             )
                             + 1					)
                         UpperCAmelCase_    				=							floor(
                             (
                                 (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                                 / self.model_tester.stride
                             )
                             + 1					)
                   # verify the first hidden states (first block)
                   self.assertListEqual(
                       list(hidden_states[0].shape[-2:]					)	,       [
                           height * width,
                           self.model_tester.hidden_sizes[0],
                       ]	,       )

             UpperCAmelCase_				,					UpperCAmelCase_    				=							self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   UpperCAmelCase_    				=							True
                   check_hidden_states_output(_UpperCAmelCase	,       _UpperCAmelCase	,       _UpperCAmelCase					)

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

                   check_hidden_states_output(_UpperCAmelCase	,       _UpperCAmelCase	,       _UpperCAmelCase					)



       @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."					)
       def   lowercase__       (							self    :						int					)				->		List[Any]:


             '''simple docstring'''




             pass



       def   lowercase__       (							self    :						List[Any]	,       _UpperCAmelCase    :						Tuple	,       _UpperCAmelCase    :						int	,       _UpperCAmelCase    :						Tuple=False					)				->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							super()._prepare_for_class(_UpperCAmelCase	,       _UpperCAmelCase	,       return_labels=_UpperCAmelCase					)

             if return_labels:
                   if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                         del inputs_dict["labels"]

             return inputs_dict



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


             '''simple docstring'''




             UpperCAmelCase_    				=							self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*_UpperCAmelCase					)



       def   lowercase__       (							self    :						Dict					)				->		Any:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase					)



       def   lowercase__       (							self    :						int					)				->		Any:


             '''simple docstring'''




             if not self.model_tester.is_training:
                   return

             UpperCAmelCase_				,					UpperCAmelCase_    				=							self.model_tester.prepare_config_and_inputs_for_common()
             UpperCAmelCase_    				=							True

             for model_class in self.all_model_classes:
                   # LevitForImageClassificationWithTeacher supports inference-only
                   if (
                       model_class in get_values(_UpperCAmelCase					)
                       or model_class.__name__ == "LevitForImageClassificationWithTeacher"
                   ):
                         continue
                   UpperCAmelCase_    				=							model_class(_UpperCAmelCase					)
                   model.to(_UpperCAmelCase					)
                   model.train()
                   UpperCAmelCase_    				=							self._prepare_for_class(_UpperCAmelCase	,       _UpperCAmelCase	,       return_labels=_UpperCAmelCase					)
                   UpperCAmelCase_    				=							model(**_UpperCAmelCase					).loss
                   loss.backward()



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


             '''simple docstring'''




             UpperCAmelCase_				,					UpperCAmelCase_    				=							self.model_tester.prepare_config_and_inputs_for_common()
             if not self.model_tester.is_training:
                   return

             UpperCAmelCase_    				=							False
             UpperCAmelCase_    				=							True

             for model_class in self.all_model_classes:
                   if model_class in get_values(_UpperCAmelCase					) or not model_class.supports_gradient_checkpointing:
                         continue
                   # LevitForImageClassificationWithTeacher supports inference-only
                   if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                         continue
                   UpperCAmelCase_    				=							model_class(_UpperCAmelCase					)
                   model.gradient_checkpointing_enable()
                   model.to(_UpperCAmelCase					)
                   model.train()
                   UpperCAmelCase_    				=							self._prepare_for_class(_UpperCAmelCase	,       _UpperCAmelCase	,       return_labels=_UpperCAmelCase					)
                   UpperCAmelCase_    				=							model(**_UpperCAmelCase					).loss
                   loss.backward()



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


             '''simple docstring'''




             UpperCAmelCase_				,					UpperCAmelCase_    				=							self.model_tester.prepare_config_and_inputs_for_common()

             UpperCAmelCase_    				=							[
                 {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
                 {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
                 {"title": "regression", "num_labels": 1, "dtype": torch.float},
             ]

             for model_class in self.all_model_classes:
                   if (
                       model_class
                       not in [
                           *get_values(_UpperCAmelCase					),
                       ]
                       or model_class.__name__ == "LevitForImageClassificationWithTeacher"
                   ):
                         continue

                   for problem_type in problem_types:
                         with self.subTest(msg=F"""Testing {model_class} with {problem_type['title']}"""					):
                               UpperCAmelCase_    				=							problem_type["title"]
                               UpperCAmelCase_    				=							problem_type["num_labels"]

                               UpperCAmelCase_    				=							model_class(_UpperCAmelCase					)
                               model.to(_UpperCAmelCase					)
                               model.train()

                               UpperCAmelCase_    				=							self._prepare_for_class(_UpperCAmelCase	,       _UpperCAmelCase	,       return_labels=_UpperCAmelCase					)

                               if problem_type["num_labels"] > 1:
                                     UpperCAmelCase_    				=							inputs["labels"].unsqueeze(1					).repeat(1	,       problem_type["num_labels"]					)

                               UpperCAmelCase_    				=							inputs["labels"].to(problem_type["dtype"]					)

                               # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                               # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                               # they have the same size." which is a symptom something in wrong for the regression problem.
                               # See https://github.com/huggingface/transformers/issues/11780
                               with warnings.catch_warnings(record=_UpperCAmelCase					) as warning_list:
                                     UpperCAmelCase_    				=							model(**_UpperCAmelCase					).loss
                               for w in warning_list:
                                     if "Using a target size that is different to the input size" in str(w.message					):
                                           raise ValueError(
                                               F"""Something is going wrong in the regression problem: intercepted {w.message}"""					)

                               loss.backward()







       @slow
       def   lowercase__       (							self    :						Optional[Any]					)				->		Optional[Any]:


             '''simple docstring'''




             for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   UpperCAmelCase_    				=							LevitModel.from_pretrained(_UpperCAmelCase					)
                   self.assertIsNotNone(_UpperCAmelCase					)



def      a__			(   ):
      UpperCAmelCase_    				=							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
      return image


@require_torch
@require_vision
class    lowercase__     (					unittest.TestCase   ):







       '''simple docstring'''


       @cached_property
       def   lowercase__       (							self    :						Any					)				->		Any:


             '''simple docstring'''




             return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)







       @slow
       def   lowercase__       (							self    :						List[Any]					)				->		int:


             '''simple docstring'''




             UpperCAmelCase_    				=							LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(
                 _UpperCAmelCase					)

             UpperCAmelCase_    				=							self.default_image_processor
             UpperCAmelCase_    				=							prepare_img()
             UpperCAmelCase_    				=							image_processor(images=_UpperCAmelCase	,       return_tensors="pt"					).to(_UpperCAmelCase					)

             # forward pass
             with torch.no_grad():
                   UpperCAmelCase_    				=							model(**_UpperCAmelCase					)

             # verify the logits
             UpperCAmelCase_    				=							torch.Size((1, 1000)					)
             self.assertEqual(outputs.logits.shape	,       _UpperCAmelCase					)

             UpperCAmelCase_    				=							torch.tensor([1.0448, -0.3745, -1.8317]					).to(_UpperCAmelCase					)

             self.assertTrue(torch.allclose(outputs.logits[0, :3]	,       _UpperCAmelCase	,       atol=1e-4					)					)









"""simple docstring"""





from __future__ import annotations

from decimal import Decimal

from numpy import array
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							Decimal

      # Check if the provided matrix has 2 rows and 2 columns
      # since this implementation only works for 2x2 matrices
      if len(lowerCAmelCase__   ) == 2 and len(matrix[0]   ) == 2 and len(matrix[1]   ) == 2:
            # Calculate the determinant of the matrix
            UpperCAmelCase_    				=							float(
                d(matrix[0][0]   ) * d(matrix[1][1]   ) - d(matrix[1][0]   ) * d(matrix[0][1]   )   )
            if determinant == 0:
                  raise ValueError("This matrix has no inverse."   )

            # Creates a copy of the matrix with swapped positions of the elements
            UpperCAmelCase_    				=							[[0.0, 0.0], [0.0, 0.0]]
            UpperCAmelCase_				,					UpperCAmelCase_    				=							matrix[1][1], matrix[0][0]
            UpperCAmelCase_				,					UpperCAmelCase_    				=							-matrix[1][0], -matrix[0][1]

            # Calculate the inverse of the matrix
            return [
                [(float(d(lowerCAmelCase__   )   ) / determinant) or 0.0 for n in row] for row in swapped_matrix
            ]
      elif (
          len(lowerCAmelCase__   ) == 3
          and len(matrix[0]   ) == 3
          and len(matrix[1]   ) == 3
          and len(matrix[2]   ) == 3
      ):
            # Calculate the determinant of the matrix using Sarrus rule
            UpperCAmelCase_    				=							float(
                (
                    (d(matrix[0][0]   ) * d(matrix[1][1]   ) * d(matrix[2][2]   ))
                    + (d(matrix[0][1]   ) * d(matrix[1][2]   ) * d(matrix[2][0]   ))
                    + (d(matrix[0][2]   ) * d(matrix[1][0]   ) * d(matrix[2][1]   ))
                )
                - (
                    (d(matrix[0][2]   ) * d(matrix[1][1]   ) * d(matrix[2][0]   ))
                    + (d(matrix[0][1]   ) * d(matrix[1][0]   ) * d(matrix[2][2]   ))
                    + (d(matrix[0][0]   ) * d(matrix[1][2]   ) * d(matrix[2][1]   ))
                )   )
            if determinant == 0:
                  raise ValueError("This matrix has no inverse."   )

            # Creating cofactor matrix
            UpperCAmelCase_    				=							[
                [d(0.0   ), d(0.0   ), d(0.0   )],
                [d(0.0   ), d(0.0   ), d(0.0   )],
                [d(0.0   ), d(0.0   ), d(0.0   )],
            ]
            UpperCAmelCase_    				=							(d(matrix[1][1]   ) * d(matrix[2][2]   )) - (
                d(matrix[1][2]   ) * d(matrix[2][1]   )
            )
            UpperCAmelCase_    				=							-(
                (d(matrix[1][0]   ) * d(matrix[2][2]   )) - (d(matrix[1][2]   ) * d(matrix[2][0]   ))
            )
            UpperCAmelCase_    				=							(d(matrix[1][0]   ) * d(matrix[2][1]   )) - (
                d(matrix[1][1]   ) * d(matrix[2][0]   )
            )
            UpperCAmelCase_    				=							-(
                (d(matrix[0][1]   ) * d(matrix[2][2]   )) - (d(matrix[0][2]   ) * d(matrix[2][1]   ))
            )
            UpperCAmelCase_    				=							(d(matrix[0][0]   ) * d(matrix[2][2]   )) - (
                d(matrix[0][2]   ) * d(matrix[2][0]   )
            )
            UpperCAmelCase_    				=							-(
                (d(matrix[0][0]   ) * d(matrix[2][1]   )) - (d(matrix[0][1]   ) * d(matrix[2][0]   ))
            )
            UpperCAmelCase_    				=							(d(matrix[0][1]   ) * d(matrix[1][2]   )) - (
                d(matrix[0][2]   ) * d(matrix[1][1]   )
            )
            UpperCAmelCase_    				=							-(
                (d(matrix[0][0]   ) * d(matrix[1][2]   )) - (d(matrix[0][2]   ) * d(matrix[1][0]   ))
            )
            UpperCAmelCase_    				=							(d(matrix[0][0]   ) * d(matrix[1][1]   )) - (
                d(matrix[0][1]   ) * d(matrix[1][0]   )
            )

            # Transpose the cofactor matrix (Adjoint matrix)
            UpperCAmelCase_    				=							array(lowerCAmelCase__   )
            for i in range(3   ):
                  for j in range(3   ):
                        UpperCAmelCase_    				=							cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
            UpperCAmelCase_    				=							array(lowerCAmelCase__   )
            for i in range(3   ):
                  for j in range(3   ):
                        inverse_matrix[i][j] /= d(lowerCAmelCase__   )

        # Calculate the inverse of the matrix
            return [[float(d(lowerCAmelCase__   )   ) or 0.0 for n in row] for row in inverse_matrix]
      raise ValueError("Please provide a matrix of size 2x2 or 3x3."   )




