





import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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_MAPPING,
         SegformerForImageClassification,
         SegformerForSemanticSegmentation,
         SegformerModel,
     )
     from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import SegformerImageProcessor



class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''

 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict)			->Any:



   '''simple docstring'''



   lowerCamelCase__: Dict         =self.config_class(**self.inputs_dict)
   self.parent.assertTrue(hasattr(UpperCAmelCase_			,					"hidden_sizes"))
   self.parent.assertTrue(hasattr(UpperCAmelCase_			,					"num_attention_heads"))
   self.parent.assertTrue(hasattr(UpperCAmelCase_			,					"num_encoder_blocks"))



class   _SCREAMING_SNAKE_CASE		:







 '''simple docstring'''

 def __init__(self							:  Any			,					UpperCAmelCase_							:  Optional[Any]			,					UpperCAmelCase_							:  List[Any]=13			,					UpperCAmelCase_							:  Tuple=64			,					UpperCAmelCase_							:  str=3			,					UpperCAmelCase_							:  str=4			,					UpperCAmelCase_							:  Dict=[2, 2, 2, 2]			,					UpperCAmelCase_							:  Dict=[8, 4, 2, 1]			,					UpperCAmelCase_							:  Optional[Any]=[16, 32, 64, 128]			,					UpperCAmelCase_							:  str=[1, 4, 8, 16]			,					UpperCAmelCase_							:  Optional[int]=[1, 2, 4, 8]			,					UpperCAmelCase_							:  str=True			,					UpperCAmelCase_							:  List[str]=True			,					UpperCAmelCase_							:  List[str]="gelu"			,					UpperCAmelCase_							:  str=0.1			,					UpperCAmelCase_							:  Any=0.1			,					UpperCAmelCase_							:  List[str]=0.02			,					UpperCAmelCase_							:  Dict=3			,					UpperCAmelCase_							:  List[Any]=None			,					)			->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__: Optional[int]         =parent
   lowerCamelCase__: int         =batch_size
   lowerCamelCase__: Tuple         =image_size
   lowerCamelCase__: List[Any]         =num_channels
   lowerCamelCase__: Dict         =num_encoder_blocks
   lowerCamelCase__: Dict         =sr_ratios
   lowerCamelCase__: Union[str, Any]         =depths
   lowerCamelCase__: Tuple         =hidden_sizes
   lowerCamelCase__: Any         =downsampling_rates
   lowerCamelCase__: int         =num_attention_heads
   lowerCamelCase__: Tuple         =is_training
   lowerCamelCase__: Any         =use_labels
   lowerCamelCase__: str         =hidden_act
   lowerCamelCase__: Tuple         =hidden_dropout_prob
   lowerCamelCase__: Union[str, Any]         =attention_probs_dropout_prob
   lowerCamelCase__: int         =initializer_range
   lowerCamelCase__: int         =num_labels
   lowerCamelCase__: List[Any]         =scope

 def 				SCREAMING_SNAKE_CASE_   (self							:  str)			->Dict:



   '''simple docstring'''



   lowerCamelCase__: List[Any]         =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

   lowerCamelCase__: Union[str, Any]         =None
   if self.use_labels:
     lowerCamelCase__: List[Any]         =ids_tensor([self.batch_size, self.image_size, self.image_size]			,					self.num_labels)

   lowerCamelCase__: List[str]         =self.get_config()
   return config, pixel_values, labels

 def 				SCREAMING_SNAKE_CASE_   (self							:  Tuple)			->List[str]:



   '''simple docstring'''



   return SegformerConfig(
       image_size=self.image_size			,					num_channels=self.num_channels			,					num_encoder_blocks=self.num_encoder_blocks			,					depths=self.depths			,					hidden_sizes=self.hidden_sizes			,					num_attention_heads=self.num_attention_heads			,					hidden_act=self.hidden_act			,					hidden_dropout_prob=self.hidden_dropout_prob			,					attention_probs_dropout_prob=self.attention_probs_dropout_prob			,					initializer_range=self.initializer_range			,					)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any]			,					UpperCAmelCase_							:  str			,					UpperCAmelCase_							:  Tuple			,					UpperCAmelCase_							:  Union[str, Any])			->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__: str         =SegformerModel(config=UpperCAmelCase_)
   model.to(UpperCAmelCase_)
   model.eval()
   lowerCamelCase__: Union[str, Any]         =model(UpperCAmelCase_)
   lowerCamelCase__: List[str]         =self.image_size // (self.downsampling_rates[-1] * 2)
   self.parent.assertEqual(
       result.last_hidden_state.shape			,					(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width))

 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict			,					UpperCAmelCase_							:  Tuple			,					UpperCAmelCase_							:  Optional[Any]			,					UpperCAmelCase_							:  List[Any])			->Tuple:



   '''simple docstring'''



   lowerCamelCase__: Tuple         =self.num_labels
   lowerCamelCase__: Any         =SegformerForSemanticSegmentation(UpperCAmelCase_)
   model.to(UpperCAmelCase_)
   model.eval()
   lowerCamelCase__: Any         =model(UpperCAmelCase_)
   self.parent.assertEqual(
       result.logits.shape			,					(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4))
   lowerCamelCase__: Union[str, Any]         =model(UpperCAmelCase_			,					labels=UpperCAmelCase_)
   self.parent.assertEqual(
       result.logits.shape			,					(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4))
   self.parent.assertGreater(result.loss			,					0.0)

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



   '''simple docstring'''



   lowerCamelCase__: Optional[Any]         =1
   lowerCamelCase__: Union[str, Any]         =SegformerForSemanticSegmentation(config=UpperCAmelCase_)
   model.to(UpperCAmelCase_)
   model.eval()
   lowerCamelCase__: str         =torch.randint(0			,					1			,					(self.batch_size, self.image_size, self.image_size)).to(UpperCAmelCase_)
   lowerCamelCase__: Tuple         =model(UpperCAmelCase_			,					labels=UpperCAmelCase_)
   self.parent.assertGreater(result.loss			,					0.0)







 def 				SCREAMING_SNAKE_CASE_   (self							:  str)			->List[Any]:



   '''simple docstring'''



   lowerCamelCase__: Union[str, Any]         =self.prepare_config_and_inputs()
   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__: Union[str, Any]         =config_and_inputs
   lowerCamelCase__: List[str]         ={"pixel_values": pixel_values}
   return config, inputs_dict



@require_torch
class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ,				unittest.TestCase       ):







 '''simple docstring'''
 lowercase_						=     (
     (
         SegformerModel,
         SegformerForSemanticSegmentation,
         SegformerForImageClassification,
     )
     if is_torch_available()
     else ()
 )
 lowercase_						=     (
     {
         "feature-extraction": SegformerModel,
         "image-classification": SegformerForImageClassification,
         "image-segmentation": SegformerForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 lowercase_						=     True
 lowercase_						=     False
 lowercase_						=     False
 lowercase_						=     False

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



   '''simple docstring'''



   lowerCamelCase__: Tuple         =SegformerModelTester(self)
   lowerCamelCase__: str         =SegformerConfigTester(self			,					config_class=UpperCAmelCase_)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict)			->List[str]:



   '''simple docstring'''



   self.config_tester.run_common_tests()

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



   '''simple docstring'''



   lowerCamelCase__: str         =self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*UpperCAmelCase_)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Any)			->str:



   '''simple docstring'''



   lowerCamelCase__: List[str]         =self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_binary_image_segmentation(*UpperCAmelCase_)

 def 				SCREAMING_SNAKE_CASE_   (self							:  int)			->int:



   '''simple docstring'''



   lowerCamelCase__: Any         =self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_segmentation(*UpperCAmelCase_)

 @unittest.skip("SegFormer does not use inputs_embeds")
 def 				SCREAMING_SNAKE_CASE_   (self							:  str)			->Tuple:



   '''simple docstring'''



   pass

 @unittest.skip("SegFormer does not have get_input_embeddings method and get_output_embeddings methods")
 def 				SCREAMING_SNAKE_CASE_   (self							:  Tuple)			->Optional[Any]:



   '''simple docstring'''



   pass

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



   '''simple docstring'''



   lowerCamelCase__    ,   lowerCamelCase__: Optional[int]         =self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     lowerCamelCase__: Union[str, Any]         =model_class(UpperCAmelCase_)
     lowerCamelCase__: Dict         =inspect.signature(model.forward)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     lowerCamelCase__: int         =[*signature.parameters.keys()]

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

 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict)			->Tuple:



   '''simple docstring'''



   lowerCamelCase__    ,   lowerCamelCase__: Optional[int]         =self.model_tester.prepare_config_and_inputs_for_common()
   lowerCamelCase__: List[Any]         =True

   for model_class in self.all_model_classes:
     lowerCamelCase__: Optional[int]         =True
     lowerCamelCase__: Dict         =False
     lowerCamelCase__: Any         =True
     lowerCamelCase__: str         =model_class(UpperCAmelCase_)
     model.to(UpperCAmelCase_)
     model.eval()
     with torch.no_grad():
       lowerCamelCase__: Optional[int]         =model(**self._prepare_for_class(UpperCAmelCase_			,					UpperCAmelCase_))
     lowerCamelCase__: Tuple         =outputs.attentions

     lowerCamelCase__: List[Any]         =sum(self.model_tester.depths)
     self.assertEqual(len(UpperCAmelCase_)			,					UpperCAmelCase_)

     # check that output_attentions also work using config
     del inputs_dict["output_attentions"]
     lowerCamelCase__: Optional[Any]         =True
     lowerCamelCase__: List[str]         =model_class(UpperCAmelCase_)
     model.to(UpperCAmelCase_)
     model.eval()
     with torch.no_grad():
       lowerCamelCase__: Optional[Any]         =model(**self._prepare_for_class(UpperCAmelCase_			,					UpperCAmelCase_))
     lowerCamelCase__: List[Any]         =outputs.attentions

     self.assertEqual(len(UpperCAmelCase_)			,					UpperCAmelCase_)

     # verify the first attentions (first block, first layer)
     lowerCamelCase__: Dict         =(self.model_tester.image_size // 4) ** 2
     lowerCamelCase__: int         =(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
     self.assertListEqual(
         list(attentions[0].shape[-3:])			,					[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]			,					)

     # verify the last attentions (last block, last layer)
     lowerCamelCase__: Any         =(self.model_tester.image_size // 32) ** 2
     lowerCamelCase__: str         =(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
     self.assertListEqual(
         list(attentions[-1].shape[-3:])			,					[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]			,					)
     lowerCamelCase__: int         =len(UpperCAmelCase_)

     # Check attention is always last and order is fine
     lowerCamelCase__: Union[str, Any]         =True
     lowerCamelCase__: List[Any]         =True
     lowerCamelCase__: Dict         =model_class(UpperCAmelCase_)
     model.to(UpperCAmelCase_)
     model.eval()
     with torch.no_grad():
       lowerCamelCase__: Union[str, Any]         =model(**self._prepare_for_class(UpperCAmelCase_			,					UpperCAmelCase_))

     self.assertEqual(out_len + 1			,					len(UpperCAmelCase_))

     lowerCamelCase__: Any         =outputs.attentions

     self.assertEqual(len(UpperCAmelCase_)			,					UpperCAmelCase_)
     # verify the first attentions (first block, first layer)
     lowerCamelCase__: Union[str, Any]         =(self.model_tester.image_size // 4) ** 2
     lowerCamelCase__: Any         =(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
     self.assertListEqual(
         list(self_attentions[0].shape[-3:])			,					[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]			,					)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict)			->Dict:



   '''simple docstring'''



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

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

     lowerCamelCase__: Any         =outputs.hidden_states

     lowerCamelCase__: Any         =self.model_tester.num_encoder_blocks
     self.assertEqual(len(UpperCAmelCase_)			,					UpperCAmelCase_)

     # verify the first hidden states (first block)
     self.assertListEqual(
         list(hidden_states[0].shape[-3:])			,					[
             self.model_tester.hidden_sizes[0],
             self.model_tester.image_size // 4,
             self.model_tester.image_size // 4,
         ]			,					)

   lowerCamelCase__    ,   lowerCamelCase__: str         =self.model_tester.prepare_config_and_inputs_for_common()

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

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     lowerCamelCase__: Any         =True

     check_hidden_states_output(UpperCAmelCase_			,					UpperCAmelCase_			,					UpperCAmelCase_)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Tuple)			->Optional[int]:



   '''simple docstring'''



   if not self.model_tester.is_training:
     return

   lowerCamelCase__    ,   lowerCamelCase__: List[str]         =self.model_tester.prepare_config_and_inputs_for_common()
   lowerCamelCase__: Tuple         =True

   for model_class in self.all_model_classes:
     if model_class in get_values(UpperCAmelCase_):
       continue

     lowerCamelCase__: List[str]         =model_class(UpperCAmelCase_)
     model.to(UpperCAmelCase_)
     model.train()
     lowerCamelCase__: Tuple         =self._prepare_for_class(UpperCAmelCase_			,					UpperCAmelCase_			,					return_labels=UpperCAmelCase_)
     lowerCamelCase__: Optional[Any]         =model(**UpperCAmelCase_).loss
     loss.backward()

 @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
 def 				SCREAMING_SNAKE_CASE_   (self							:  str)			->Tuple:



   '''simple docstring'''



   pass







 @slow
 def 				SCREAMING_SNAKE_CASE_   (self							:  Tuple)			->Optional[int]:



   '''simple docstring'''



   for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     lowerCamelCase__: Union[str, Any]         =SegformerModel.from_pretrained(UpperCAmelCase_)
     self.assertIsNotNone(UpperCAmelCase_)





def 	lowerCAmelCase_					(			)	->						Dict:







  """simple docstring"""





  lowerCamelCase__: Any         =Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
  return image






@require_torch
class   _SCREAMING_SNAKE_CASE		(      unittest.TestCase       ):







 '''simple docstring'''

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



   '''simple docstring'''



   lowerCamelCase__: Optional[Any]         =SegformerImageProcessor(
       image_scale=(512, 512)			,					keep_ratio=UpperCAmelCase_			,					align=UpperCAmelCase_			,					do_random_crop=UpperCAmelCase_)
   lowerCamelCase__: Tuple         =SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512").to(
       UpperCAmelCase_)

   lowerCamelCase__: Tuple         =prepare_img()
   lowerCamelCase__: Union[str, Any]         =image_processor(images=UpperCAmelCase_			,					return_tensors="pt")
   lowerCamelCase__: List[str]         =encoded_inputs.pixel_values.to(UpperCAmelCase_)

   with torch.no_grad():
     lowerCamelCase__: Dict         =model(UpperCAmelCase_)

   lowerCamelCase__: Optional[Any]         =torch.Size((1, model.config.num_labels, 128, 128))
   self.assertEqual(outputs.logits.shape			,					UpperCAmelCase_)

   lowerCamelCase__: Optional[Any]         =torch.tensor(
       [
           [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
           [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
           [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
       ]).to(UpperCAmelCase_)
   self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]			,					UpperCAmelCase_			,					atol=1E-4))

 @slow
 def 				SCREAMING_SNAKE_CASE_   (self							:  Any)			->str:



   '''simple docstring'''



   lowerCamelCase__: Optional[Any]         =SegformerImageProcessor(
       image_scale=(512, 512)			,					keep_ratio=UpperCAmelCase_			,					align=UpperCAmelCase_			,					do_random_crop=UpperCAmelCase_)
   lowerCamelCase__: str         =SegformerForSemanticSegmentation.from_pretrained(
       "nvidia/segformer-b1-finetuned-cityscapes-1024-1024").to(UpperCAmelCase_)

   lowerCamelCase__: Tuple         =prepare_img()
   lowerCamelCase__: Optional[Any]         =image_processor(images=UpperCAmelCase_			,					return_tensors="pt")
   lowerCamelCase__: Tuple         =encoded_inputs.pixel_values.to(UpperCAmelCase_)

   with torch.no_grad():
     lowerCamelCase__: List[Any]         =model(UpperCAmelCase_)

   lowerCamelCase__: Any         =torch.Size((1, model.config.num_labels, 128, 128))
   self.assertEqual(outputs.logits.shape			,					UpperCAmelCase_)

   lowerCamelCase__: Optional[int]         =torch.tensor(
       [
           [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
           [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
           [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
       ]).to(UpperCAmelCase_)
   self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]			,					UpperCAmelCase_			,					atol=1E-1))







 @slow
 def 				SCREAMING_SNAKE_CASE_   (self							:  str)			->Dict:



   '''simple docstring'''



   lowerCamelCase__: Union[str, Any]         =SegformerImageProcessor(
       image_scale=(512, 512)			,					keep_ratio=UpperCAmelCase_			,					align=UpperCAmelCase_			,					do_random_crop=UpperCAmelCase_)
   lowerCamelCase__: str         =SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512").to(
       UpperCAmelCase_)

   lowerCamelCase__: int         =prepare_img()
   lowerCamelCase__: Tuple         =image_processor(images=UpperCAmelCase_			,					return_tensors="pt")
   lowerCamelCase__: Any         =encoded_inputs.pixel_values.to(UpperCAmelCase_)

   with torch.no_grad():
     lowerCamelCase__: Union[str, Any]         =model(UpperCAmelCase_)

   lowerCamelCase__: Dict         =outputs.logits.detach().cpu()

   lowerCamelCase__: Any         =image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase_			,					target_sizes=[(500, 300)])
   lowerCamelCase__: List[str]         =torch.Size((500, 300))
   self.assertEqual(segmentation[0].shape			,					UpperCAmelCase_)

   lowerCamelCase__: Tuple         =image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase_)
   lowerCamelCase__: Dict         =torch.Size((128, 128))
   self.assertEqual(segmentation[0].shape			,					UpperCAmelCase_)








'''simple docstring'''






from __future__ import annotations

from typing import Any




class  _A :

		def __init__(   self					:   str  ,      __magic_name__					:   int  ,      __magic_name__					:   int  ,      __magic_name__					:   float = 0			)					->    None:


					"""simple docstring"""

					__snake_case      ,     __snake_case					:      Optional[Any]							      =     row, column
					__snake_case					:      Dict							      =     [[default_value for c in range(__magic_name__			)] for r in range(__magic_name__			)]

		def __str__(   self					:   List[Any]			)					->    str:


					"""simple docstring"""

					__snake_case					:      Dict							      =     f'''Matrix consist of {self.row} rows and {self.column} columns\n'''

					# Make string identifier
					__snake_case					:      Optional[int]							      =     0
					for row_vector in self.array:
								for obj in row_vector:
											__snake_case					:      Optional[int]							      =     max(__magic_name__  ,      len(str(__magic_name__			)			)			)
					__snake_case					:      str							      =     f'''%{max_element_length}s'''

					# Make string and return
					def single_line(__magic_name__					:   list[float]			) -> str:
								nonlocal string_format_identifier
								__snake_case					:      Union[str, Any]							      =     """["""
								line += ", ".join(string_format_identifier % (obj,) for obj in row_vector			)
								line += "]"
								return line

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

		def __repr__(   self					:   Optional[int]			)					->    str:


					"""simple docstring"""

					return str(self			)

		def 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   tuple[int, int]			)					->    bool:


					"""simple docstring"""

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

		def __getitem__(   self					:   int  ,      __magic_name__					:   tuple[int, int]			)					->    Any:


					"""simple docstring"""

					assert self.validate_indicies(__magic_name__			)
					return self.array[loc[0]][loc[1]]

		def __setitem__(   self					:   List[str]  ,      __magic_name__					:   tuple[int, int]  ,      __magic_name__					:   float			)					->    None:


					"""simple docstring"""

					assert self.validate_indicies(__magic_name__			)
					__snake_case					:      Optional[int]							      =     value

		def __add__(   self					:   Any  ,      __magic_name__					:   Matrix			)					->    Matrix:


					"""simple docstring"""

					assert isinstance(__magic_name__  ,      __magic_name__			)
					assert self.row == another.row and self.column == another.column

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

		def __neg__(   self					:   Tuple			)					->    Matrix:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     Matrix(self.row  ,      self.column			)
					for r in range(self.row			):
								for c in range(self.column			):
											__snake_case					:      List[Any]							      =     -self[r, c]
					return result

		def __sub__(   self					:   Optional[int]  ,      __magic_name__					:   Matrix			)					->    Matrix:


					"""simple docstring"""

					return self + (-another)

		def __mul__(   self					:   List[Any]  ,      __magic_name__					:   int | float | Matrix			)					->    Matrix:


					"""simple docstring"""

					if isinstance(__magic_name__  ,      (int, float)			):  # Scalar multiplication
								__snake_case					:      Optional[int]							      =     Matrix(self.row  ,      self.column			)
								for r in range(self.row			):
											for c in range(self.column			):
														__snake_case					:      Tuple							      =     self[r, c] * another
								return result
					elif isinstance(__magic_name__  ,      __magic_name__			):  # Matrix multiplication
								assert self.column == another.row
								__snake_case					:      Dict							      =     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:
								__snake_case					:      Optional[int]							      =     f'''Unsupported type given for another ({type(__magic_name__			)})'''
								raise TypeError(__magic_name__			)

		def 	lowercase__     (   self					:   str			)					->    Matrix:


					"""simple docstring"""

					__snake_case					:      Any							      =     Matrix(self.column  ,      self.row			)
					for r in range(self.row			):
								for c in range(self.column			):
											__snake_case					:      str							      =     self[r, c]
					return result

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


					"""simple docstring"""

					assert isinstance(__magic_name__  ,      __magic_name__			) and isinstance(__magic_name__  ,      __magic_name__			)
					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
					__snake_case					:      List[str]							      =     v.transpose()
					__snake_case					:      Tuple							      =     (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 					_a						(       ) ->				None:



				"""simple docstring"""


				__snake_case					:      Tuple							      =     Matrix(3    ,	3    ,	0 )
				for i in range(3 ):
							__snake_case					:      Any							      =     1
				print(F'''a^(-1) is {ainv}''' )
				# u, v
				__snake_case					:      Dict							      =     Matrix(3    ,	1    ,	0 )
				__snake_case      ,     __snake_case      ,     __snake_case					:      Union[str, Any]							      =     1, 2, -3
				__snake_case					:      str							      =     Matrix(3    ,	1    ,	0 )
				__snake_case      ,     __snake_case      ,     __snake_case					:      Tuple							      =     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(_lowerCamelCase    ,	_lowerCamelCase )}''' )







	def 					_a						(       ) ->				None:



				"""simple docstring"""


				import doctest

				doctest.testmod()

	testa()


