






from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor
class 						lowerCamelCase_				:







    def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase=1_3       ,					__lowerCAmelCase=3_0       ,					__lowerCAmelCase=2       ,					__lowerCAmelCase=3       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=3_2       ,					__lowerCAmelCase=2       ,					__lowerCAmelCase=4       ,					__lowerCAmelCase=3_7       ,					__lowerCAmelCase="gelu"       ,					__lowerCAmelCase=0.1       ,					__lowerCAmelCase=0.1       ,					__lowerCAmelCase=1_0       ,					__lowerCAmelCase=0.02       ,					__lowerCAmelCase=3       ,					__lowerCAmelCase=0.6       ,					__lowerCAmelCase=None       ,					):
        """simple docstring"""
        __magic_name__		:Tuple        =      parent
        __magic_name__		:Dict        =      batch_size
        __magic_name__		:Any        =      image_size
        __magic_name__		:Dict        =      patch_size
        __magic_name__		:List[Any]        =      num_channels
        __magic_name__		:int        =      is_training
        __magic_name__		:Union[str, Any]        =      use_labels
        __magic_name__		:str        =      hidden_size
        __magic_name__		:Dict        =      num_hidden_layers
        __magic_name__		:Any        =      num_attention_heads
        __magic_name__		:Dict        =      intermediate_size
        __magic_name__		:Optional[Any]        =      hidden_act
        __magic_name__		:Optional[Any]        =      hidden_dropout_prob
        __magic_name__		:Optional[int]        =      attention_probs_dropout_prob
        __magic_name__		:str        =      type_sequence_label_size
        __magic_name__		:int        =      initializer_range
        __magic_name__		:Union[str, Any]        =      mask_ratio
        __magic_name__		:Union[str, Any]        =      scope

        # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
        # (we add 1 for the [CLS] token)
        __magic_name__		:Any        =      (image_size // patch_size) ** 2
        __magic_name__		:Dict        =      int(math.ceil((1 - mask_ratio) * (num_patches + 1)     )     )







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:Optional[int]        =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

        __magic_name__		:int        =      None
        if self.use_labels:
            __magic_name__		:str        =      ids_tensor([self.batch_size]       ,					self.type_sequence_label_size     )

        __magic_name__		:Optional[Any]        =      self.get_config()

        return config, pixel_values, labels







    def 			A	(						self     ):
        """simple docstring"""
        return ViTMAEConfig(
            image_size=self.image_size       ,					patch_size=self.patch_size       ,					num_channels=self.num_channels       ,					hidden_size=self.hidden_size       ,					num_hidden_layers=self.num_hidden_layers       ,					num_attention_heads=self.num_attention_heads       ,					intermediate_size=self.intermediate_size       ,					decoder_hidden_size=self.hidden_size       ,					decoder_num_hidden_layers=self.num_hidden_layers       ,					decoder_num_attention_heads=self.num_attention_heads       ,					decoder_intermediate_size=self.intermediate_size       ,					hidden_act=self.hidden_act       ,					hidden_dropout_prob=self.hidden_dropout_prob       ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,					is_decoder=__lowerCAmelCase       ,					initializer_range=self.initializer_range       ,					mask_ratio=self.mask_ratio       ,					)







    def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
        """simple docstring"""
        __magic_name__		:List[str]        =      TFViTMAEModel(config=__lowerCAmelCase     )
        __magic_name__		:Union[str, Any]        =      model(__lowerCAmelCase       ,					training=__lowerCAmelCase     )
        self.parent.assertEqual(result.last_hidden_state.shape       ,					(self.batch_size, self.seq_length, self.hidden_size)     )







    def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
        """simple docstring"""
        __magic_name__		:Optional[Any]        =      TFViTMAEForPreTraining(__lowerCAmelCase     )
        __magic_name__		:Optional[int]        =      model(__lowerCAmelCase       ,					training=__lowerCAmelCase     )
        # expected sequence length = num_patches
        __magic_name__		:Dict        =      (self.image_size // self.patch_size) ** 2
        __magic_name__		:str        =      self.patch_size**2 * self.num_channels
        self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, num_patches, expected_num_channels)     )

        # test greyscale images
        __magic_name__		:Tuple        =      1
        __magic_name__		:int        =      TFViTMAEForPreTraining(__lowerCAmelCase     )

        __magic_name__		:Optional[int]        =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
        __magic_name__		:Optional[Any]        =      model(__lowerCAmelCase       ,					training=__lowerCAmelCase     )
        __magic_name__		:List[str]        =      self.patch_size**2
        self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, num_patches, expected_num_channels)     )







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:Union[str, Any]        =      self.prepare_config_and_inputs()
        (__magic_name__)		:List[Any]        =      config_and_inputs
        __magic_name__		:int        =      {'''pixel_values''': pixel_values}
        return config, inputs_dict







@require_tf
class 						lowerCamelCase_				(      lowerCamelCase			,				lowerCamelCase			,				unittest.TestCase ):
    a__					       =  (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
    a__					       =  {'''feature-extraction''': TFViTMAEModel} if is_tf_available() else {}

    a__					       =  False
    a__					       =  False
    a__					       =  False
    a__					       =  False







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:int        =      TFViTMAEModelTester(self     )
        __magic_name__		:int        =      ConfigTester(self       ,					config_class=__lowerCAmelCase       ,					has_text_modality=__lowerCAmelCase       ,					hidden_size=3_7     )







    def 			A	(						self     ):
        """simple docstring"""
        self.config_tester.run_common_tests()







    @unittest.skip(reason='''ViTMAE does not use inputs_embeds'''     )
    def 			A	(						self     ):
        """simple docstring"""
        pass







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:Optional[Any]        =      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            __magic_name__		:str        =      model_class(__lowerCAmelCase     )
            self.assertIsInstance(model.get_input_embeddings()       ,					(tf.keras.layers.Layer)     )
            __magic_name__		:List[Any]        =      model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(__lowerCAmelCase       ,					tf.keras.layers.Layer     )     )







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:List[Any]        =      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            __magic_name__		:Optional[Any]        =      model_class(__lowerCAmelCase     )
            __magic_name__		:str        =      inspect.signature(model.call     )
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            __magic_name__		:Union[str, Any]        =      [*signature.parameters.keys()]

            __magic_name__		:Tuple        =      ['''pixel_values''']
            self.assertListEqual(arg_names[:1]       ,					__lowerCAmelCase     )







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:Dict        =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowerCAmelCase     )







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:int        =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*__lowerCAmelCase     )







    def 			A	(						self     ):
        """simple docstring"""
        np.random.seed(2     )

        __magic_name__		:Optional[Any]        =      self.model_tester.prepare_config_and_inputs_for_common()

        __magic_name__		:Any        =      int((config.image_size // config.patch_size) ** 2     )
        __magic_name__		:Optional[Any]        =      np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )

        for model_class in self.all_model_classes:
            __magic_name__		:Any        =      model_class(__lowerCAmelCase     )
            __magic_name__		:Tuple        =      self._prepare_for_class(__lowerCAmelCase       ,					__lowerCAmelCase     )

            __magic_name__		:Any        =      model(__lowerCAmelCase       ,					noise=__lowerCAmelCase     )

            __magic_name__		:Optional[Any]        =      copy.deepcopy(self._prepare_for_class(__lowerCAmelCase       ,					__lowerCAmelCase     )     )
            __magic_name__		:List[str]        =      model(**__lowerCAmelCase       ,					noise=__lowerCAmelCase     )
            __magic_name__		:Optional[Any]        =      outputs_dict[0].numpy()
            __magic_name__		:str        =      outputs_keywords[0].numpy()

            self.assertLess(np.sum(np.abs(output_dict - output_keywords     )     )       ,					1E-6     )







    def 			A	(						self     ):
        """simple docstring"""
        np.random.seed(2     )

        __magic_name__		:int        =      self.model_tester.prepare_config_and_inputs_for_common()

        __magic_name__		:Union[str, Any]        =      int((config.image_size // config.patch_size) ** 2     )
        __magic_name__		:Optional[int]        =      np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )

        def prepare_numpy_arrays(__lowerCAmelCase     ):
            __magic_name__		:List[Any]        =      {}
            for k, v in inputs_dict.items():
                if tf.is_tensor(__lowerCAmelCase     ):
                    __magic_name__		:Any        =      v.numpy()
                else:
                    __magic_name__		:Optional[int]        =      np.array(__lowerCAmelCase     )

            return inputs_np_dict

        for model_class in self.all_model_classes:
            __magic_name__		:Dict        =      model_class(__lowerCAmelCase     )

            __magic_name__		:Optional[int]        =      self._prepare_for_class(__lowerCAmelCase       ,					__lowerCAmelCase     )
            __magic_name__		:int        =      prepare_numpy_arrays(__lowerCAmelCase     )

            __magic_name__		:Union[str, Any]        =      model(__lowerCAmelCase       ,					noise=__lowerCAmelCase     )
            __magic_name__		:Any        =      model(**__lowerCAmelCase       ,					noise=__lowerCAmelCase     )
            self.assert_outputs_same(__lowerCAmelCase       ,					__lowerCAmelCase     )







    def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
        """simple docstring"""
        np.random.seed(2     )

        __magic_name__		:List[str]        =      int((tf_model.config.image_size // tf_model.config.patch_size) ** 2     )
        __magic_name__		:Union[str, Any]        =      np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )
        __magic_name__		:Optional[int]        =      tf.constant(__lowerCAmelCase     )

        # Add `noise` argument.
        # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
        __magic_name__		:str        =      tf_noise

        super().check_pt_tf_models(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )







    def 			A	(						self     ):
        """simple docstring"""
        np.random.seed(2     )

        __magic_name__		:Dict        =      self.model_tester.prepare_config_and_inputs_for_common()

        __magic_name__		:Optional[int]        =      {
            module_member
            for model_class in self.all_model_classes
            for module in (import_module(model_class.__module__     ),)
            for module_member_name in dir(__lowerCAmelCase     )
            if module_member_name.endswith('''MainLayer'''     )
            # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
            and module_member_name[: -len('''MainLayer'''     )] == model_class.__name__[: -len('''Model'''     )]
            for module_member in (getattr(__lowerCAmelCase       ,					__lowerCAmelCase     ),)
            if isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     )
            and tf.keras.layers.Layer in module_member.__bases__
            and getattr(__lowerCAmelCase       ,					'''_keras_serializable'''       ,					__lowerCAmelCase     )
        }

        __magic_name__		:Union[str, Any]        =      int((config.image_size // config.patch_size) ** 2     )
        __magic_name__		:Union[str, Any]        =      np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )
        __magic_name__		:Dict        =      tf.convert_to_tensor(__lowerCAmelCase     )
        inputs_dict.update({'''noise''': noise}     )

        for main_layer_class in tf_main_layer_classes:
            __magic_name__		:Tuple        =      main_layer_class(__lowerCAmelCase     )

            __magic_name__		:Optional[int]        =      {
                name: tf.keras.Input(tensor.shape[1:]       ,					dtype=tensor.dtype     ) for name, tensor in inputs_dict.items()
            }

            __magic_name__		:List[Any]        =      tf.keras.Model(__lowerCAmelCase       ,					outputs=main_layer(__lowerCAmelCase     )     )
            __magic_name__		:int        =      model(__lowerCAmelCase     )

            with tempfile.TemporaryDirectory() as tmpdirname:
                __magic_name__		:Optional[Any]        =      os.path.join(__lowerCAmelCase       ,					'''keras_model.h5'''     )
                model.save(__lowerCAmelCase     )
                __magic_name__		:Any        =      tf.keras.models.load_model(
                    __lowerCAmelCase       ,					custom_objects={main_layer_class.__name__: main_layer_class}     )
                assert isinstance(__lowerCAmelCase       ,					tf.keras.Model     )
                __magic_name__		:int        =      model(__lowerCAmelCase     )
                self.assert_outputs_same(__lowerCAmelCase       ,					__lowerCAmelCase     )







    @slow
    def 			A	(						self     ):
        """simple docstring"""
        np.random.seed(2     )

        __magic_name__		:List[Any]        =      self.model_tester.prepare_config_and_inputs_for_common()

        __magic_name__		:Union[str, Any]        =      int((config.image_size // config.patch_size) ** 2     )
        __magic_name__		:Union[str, Any]        =      np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )

        for model_class in self.all_model_classes:
            __magic_name__		:str        =      model_class(__lowerCAmelCase     )
            __magic_name__		:Optional[int]        =      self._prepare_for_class(__lowerCAmelCase       ,					__lowerCAmelCase     )
            __magic_name__		:List[Any]        =      model(__lowerCAmelCase       ,					noise=__lowerCAmelCase     )

            if model_class.__name__ == "TFViTMAEModel":
                __magic_name__		:Optional[int]        =      outputs.last_hidden_state.numpy()
                __magic_name__		:Union[str, Any]        =      0
            else:
                __magic_name__		:Any        =      outputs.logits.numpy()
                __magic_name__		:Union[str, Any]        =      0

            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(__lowerCAmelCase       ,					saved_model=__lowerCAmelCase     )
                __magic_name__		:Union[str, Any]        =      model_class.from_pretrained(__lowerCAmelCase     )
                __magic_name__		:List[str]        =      model(__lowerCAmelCase       ,					noise=__lowerCAmelCase     )

                if model_class.__name__ == "TFViTMAEModel":
                    __magic_name__		:Tuple        =      after_outputs['''last_hidden_state'''].numpy()
                    __magic_name__		:Optional[int]        =      0
                else:
                    __magic_name__		:int        =      after_outputs['''logits'''].numpy()
                    __magic_name__		:Union[str, Any]        =      0

                __magic_name__		:Optional[int]        =      np.amax(np.abs(out_a - out_a     )     )
                self.assertLessEqual(__lowerCAmelCase       ,					1E-5     )







    def 			A	(						self     ):
        """simple docstring"""
        np.random.seed(2     )

        __magic_name__		:str        =      self.model_tester.prepare_config_and_inputs_for_common()

        __magic_name__		:Optional[int]        =      int((config.image_size // config.patch_size) ** 2     )
        __magic_name__		:List[Any]        =      np.random.uniform(size=(self.model_tester.batch_size, num_patches)     )

        for model_class in self.all_model_classes:
            __magic_name__		:List[Any]        =      model_class(__lowerCAmelCase     )
            __magic_name__		:Union[str, Any]        =      self._prepare_for_class(__lowerCAmelCase       ,					__lowerCAmelCase     )

            __magic_name__		:List[str]        =      model(__lowerCAmelCase       ,					noise=__lowerCAmelCase     )
            __magic_name__		:List[Any]        =      model.get_config()
            # make sure that returned config is jsonifiable, which is required by keras
            json.dumps(__lowerCAmelCase     )
            __magic_name__		:int        =      model_class.from_config(model.get_config()     )
            # make sure it also accepts a normal config
            __magic_name__		:Dict        =      model_class.from_config(model.config     )
            __magic_name__		:Any        =      new_model(__lowerCAmelCase     )  # Build model
            new_model.set_weights(model.get_weights()     )
            __magic_name__		:Any        =      new_model(__lowerCAmelCase       ,					noise=__lowerCAmelCase     )

            self.assert_outputs_same(__lowerCAmelCase       ,					__lowerCAmelCase     )







    @unittest.skip(
        reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''     )
    def 			A	(						self     ):
        """simple docstring"""
        pass







    @unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'''     )
    def 			A	(						self     ):
        """simple docstring"""
        pass







    @slow
    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:Optional[Any]        =      TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224'''     )
        self.assertIsNotNone(__lowerCAmelCase     )







def      __lowercase					( ):







    """simple docstring"""






    __magic_name__		:Optional[Any]        =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
    return image

@require_tf
@require_vision
class 						lowerCamelCase_				(      unittest.TestCase ):







    @cached_property
    def 			A	(						self     ):
        """simple docstring"""
        return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''     ) if is_vision_available() else None







    @slow
    def 			A	(						self     ):
        """simple docstring"""
        np.random.seed(2     )

        __magic_name__		:Union[str, Any]        =      TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''     )

        __magic_name__		:str        =      self.default_image_processor
        __magic_name__		:str        =      prepare_img()
        __magic_name__		:List[str]        =      image_processor(images=__lowerCAmelCase       ,					return_tensors='''tf'''     )

        # prepare a noise vector that will be also used for testing the TF model
        # (this way we can ensure that the PT and TF models operate on the same inputs)
        __magic_name__		:List[str]        =      ViTMAEConfig()
        __magic_name__		:Dict        =      int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2     )
        __magic_name__		:Dict        =      np.random.uniform(size=(1, num_patches)     )

        # forward pass
        __magic_name__		:Tuple        =      model(**__lowerCAmelCase       ,					noise=__lowerCAmelCase     )

        # verify the logits
        __magic_name__		:Tuple        =      tf.convert_to_tensor([1, 1_9_6, 7_6_8]     )
        self.assertEqual(outputs.logits.shape       ,					__lowerCAmelCase     )

        __magic_name__		:Any        =      tf.convert_to_tensor(
            [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]     )

        tf.debugging.assert_near(outputs.logits[0, :3, :3]       ,					__lowerCAmelCase       ,					atol=1E-4     )














from __future__ import annotations

from typing import Any
class 						lowerCamelCase_				:







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = 0     ):
								"""simple docstring"""
								__magic_name__     ,				__magic_name__		:Any        =      row, column
								__magic_name__		:Union[str, Any]        =      [[default_value for c in range(__lowerCAmelCase     )] for r in range(__lowerCAmelCase     )]







				def __str__(						self     ):
								"""simple docstring"""
								__magic_name__		:str        =      F'''Matrix consist of {self.row} rows and {self.column} columns\n'''

								# Make string identifier
								__magic_name__		:Dict        =      0
								for row_vector in self.array:
												for obj in row_vector:
																__magic_name__		:Dict        =      max(__lowerCAmelCase       ,					len(str(__lowerCAmelCase     )     )     )
								__magic_name__		:List[str]        =      F'''%{max_element_length}s'''

								# Make string and return
								def single_line(__lowerCAmelCase     ) -> str:
												nonlocal string_format_identifier
												__magic_name__		:int        =      '''['''
												line += ", ".join(string_format_identifier % (obj,) for obj in row_vector     )
												line += "]"
												return line

								s += "\n".join(single_line(__lowerCAmelCase     ) for row_vector in self.array     )
								return s







				def __repr__(						self     ):
								"""simple docstring"""
								return str(self     )







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								if not (isinstance(__lowerCAmelCase       ,					(list, tuple)     ) and len(__lowerCAmelCase     ) == 2):
												return False
								elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
												return False
								else:
												return True







				def __getitem__(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								assert self.validate_indicies(__lowerCAmelCase     )
								return self.array[loc[0]][loc[1]]







				def __setitem__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								assert self.validate_indicies(__lowerCAmelCase     )
								__magic_name__		:List[Any]        =      value







				def __add__(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								assert isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     )
								assert self.row == another.row and self.column == another.column

								# Add
								__magic_name__		:List[Any]        =      Matrix(self.row       ,					self.column     )
								for r in range(self.row     ):
												for c in range(self.column     ):
																__magic_name__		:Any        =      self[r, c] + another[r, c]
								return result







				def __neg__(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      Matrix(self.row       ,					self.column     )
								for r in range(self.row     ):
												for c in range(self.column     ):
																__magic_name__		:Any        =      -self[r, c]
								return result







				def __sub__(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								return self + (-another)







				def __mul__(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""

								if isinstance(__lowerCAmelCase       ,					(int, float)     ):  # Scalar multiplication
												__magic_name__		:List[str]        =      Matrix(self.row       ,					self.column     )
												for r in range(self.row     ):
																for c in range(self.column     ):
																				__magic_name__		:Tuple        =      self[r, c] * another
												return result
								elif isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ):  # Matrix multiplication
												assert self.column == another.row
												__magic_name__		:List[Any]        =      Matrix(self.row       ,					another.column     )
												for r in range(self.row     ):
																for c in range(another.column     ):
																				for i in range(self.column     ):
																								result[r, c] += self[r, i] * another[i, c]
												return result
								else:
												__magic_name__		:List[str]        =      F'''Unsupported type given for another ({type(__lowerCAmelCase     )})'''
												raise TypeError(__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      Matrix(self.column       ,					self.row     )
								for r in range(self.row     ):
												for c in range(self.column     ):
																__magic_name__		:List[str]        =      self[r, c]
								return result







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								assert isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ) and isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     )
								assert self.row == self.column == u.row == v.row  # u, v should be column vector
								assert u.column == v.column == 1  # u, v should be column vector

								# Calculate
								__magic_name__		:int        =      v.transpose()
								__magic_name__		:str        =      (v_t * self * u)[0, 0] + 1
								if numerator_factor == 0:
												return None  # It's not invertable
								return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":

					def      __lowercase					( ):







									"""simple docstring"""






									__magic_name__		:Optional[int]        =      Matrix(3,  3,  0 )
									for i in range(3 ):
													__magic_name__		:Tuple        =      1
									print(f'''a^(-1) is {ainv}''' )
									# u, v
									__magic_name__		:Optional[int]        =      Matrix(3,  1,  0 )
									__magic_name__     ,				__magic_name__     ,				__magic_name__		:Optional[int]        =      1, 2, -3
									__magic_name__		:int        =      Matrix(3,  1,  0 )
									__magic_name__     ,				__magic_name__     ,				__magic_name__		:str        =      4, -2, 5
									print(f'''u is {u}''' )
									print(f'''v is {v}''' )
									print(f'''uv^T is {u * v.transpose()}''' )
									# Sherman Morrison
									print(f'''(a + uv^T)^(-1) is {ainv.sherman_morrison(snake_case,  snake_case )}''' )

					def      __lowercase					( ):







									"""simple docstring"""






									import doctest

									doctest.testmod()

					testa()






