
from __future__ import annotations

from typing import Any

class   __a:






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = 0  )			->		None:
         UpperCAmelCase_      :							int       =      row, column
         UpperCAmelCase_      :							Optional[Any]       =      [[default_value for c in range(_SCREAMING_SNAKE_CASE  )] for r in range(_SCREAMING_SNAKE_CASE  )]





    def __str__(				self  )			->		str:
         UpperCAmelCase_      :							str       =      f'''Matrix consist of {self.row} rows and {self.column} columns\n'''

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

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

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





    def __repr__(				self  )			->		str:
         return str(self  )





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





    def __getitem__(				self       ,_SCREAMING_SNAKE_CASE  )			->		Any:
         assert self.validate_indicies(_SCREAMING_SNAKE_CASE  )
         return self.array[loc[0]][loc[1]]





    def __setitem__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		None:
         assert self.validate_indicies(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							int       =      value





    def __add__(				self       ,_SCREAMING_SNAKE_CASE  )			->		Matrix:
         assert isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
         assert self.row == another.row and self.column == another.column

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





    def __neg__(				self  )			->		Matrix:
         UpperCAmelCase_      :							int       =      Matrix(self.row       ,self.column  )
         for r in range(self.row  ):
              for c in range(self.column  ):
                   UpperCAmelCase_      :							Dict       =      -self[r, c]
         return result





    def __sub__(				self       ,_SCREAMING_SNAKE_CASE  )			->		Matrix:
         return self + (-another)





    def __mul__(				self       ,_SCREAMING_SNAKE_CASE  )			->		Matrix:

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





    def 							a__				(				self  )			->		Matrix:
         UpperCAmelCase_      :							List[Any]       =      Matrix(self.column       ,self.row  )
         for r in range(self.row  ):
              for c in range(self.column  ):
                   UpperCAmelCase_      :							int       =      self[r, c]
         return result





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Any:
         assert isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ) and isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
         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
         UpperCAmelCase_      :							List[str]       =      v.transpose()
         UpperCAmelCase_      :							int       =      (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 lowerCamelCase__       (      ):

          '''simple docstring'''


          UpperCAmelCase_      :							Tuple       =      Matrix(3			,						3			,						0				)
          for i in range(3				):
               UpperCAmelCase_      :							Any       =      1
          print(f'''a^(-1) is {ainv}'''				)
          # u, v
          UpperCAmelCase_      :							List[str]       =      Matrix(3			,						1			,						0				)
          UpperCAmelCase_      :							List[Any]       =      1, 2, -3
          UpperCAmelCase_      :							List[Any]       =      Matrix(3			,						1			,						0				)
          UpperCAmelCase_      :							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(_lowercase			,						_lowercase				)}'''				)







     def lowerCamelCase__       (      ):

          '''simple docstring'''


          import doctest

          doctest.testmod()



     testa()





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast

@require_vision
class   __a(					unittest.TestCase       ):






    """simple docstring"""





    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							Optional[int]       =      tempfile.mkdtemp()

         UpperCAmelCase_      :							str       =      BlipImageProcessor()
         UpperCAmelCase_      :							Dict       =      GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''  )

         UpperCAmelCase_      :							Optional[Any]       =      BlipaProcessor(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )

         processor.save_pretrained(self.tmpdirname  )





    def 							a__				(				self       ,**_SCREAMING_SNAKE_CASE  )			->		Optional[int]:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,**_SCREAMING_SNAKE_CASE  ).tokenizer





    def 							a__				(				self       ,**_SCREAMING_SNAKE_CASE  )			->		Dict:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,**_SCREAMING_SNAKE_CASE  ).image_processor





    def 							a__				(				self  )			->		List[Any]:
         shutil.rmtree(self.tmpdirname  )





    def 							a__				(				self  )			->		Tuple:
         UpperCAmelCase_      :							Tuple       =      [np.random.randint(255       ,size=(3, 30, 400)       ,dtype=np.uinta  )]

         UpperCAmelCase_      :							Optional[Any]       =      [Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE       ,0       ,-1  )  ) for x in image_inputs]

         return image_inputs





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							Dict       =      BlipaProcessor(tokenizer=self.get_tokenizer()       ,image_processor=self.get_image_processor()  )
         processor.save_pretrained(self.tmpdirname  )

         UpperCAmelCase_      :							str       =      self.get_tokenizer(bos_token='''(BOS)'''       ,eos_token='''(EOS)'''  )
         UpperCAmelCase_      :							int       =      self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE       ,padding_value=1.0  )

         UpperCAmelCase_      :							Union[str, Any]       =      BlipaProcessor.from_pretrained(
             self.tmpdirname       ,bos_token='''(BOS)'''       ,eos_token='''(EOS)'''       ,do_normalize=_SCREAMING_SNAKE_CASE       ,padding_value=1.0  )

         self.assertEqual(processor.tokenizer.get_vocab()       ,tokenizer_add_kwargs.get_vocab()  )
         self.assertIsInstance(processor.tokenizer       ,_SCREAMING_SNAKE_CASE  )

         self.assertEqual(processor.image_processor.to_json_string()       ,image_processor_add_kwargs.to_json_string()  )
         self.assertIsInstance(processor.image_processor       ,_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Any:
         UpperCAmelCase_      :							Dict       =      self.get_image_processor()
         UpperCAmelCase_      :							Any       =      self.get_tokenizer()

         UpperCAmelCase_      :							str       =      BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,image_processor=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Union[str, Any]       =      self.prepare_image_inputs()

         UpperCAmelCase_      :							Optional[Any]       =      image_processor(_SCREAMING_SNAKE_CASE       ,return_tensors='''np'''  )
         UpperCAmelCase_      :							int       =      processor(images=_SCREAMING_SNAKE_CASE       ,return_tensors='''np'''  )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()       ,input_processor[key].sum()       ,delta=1e-2  )





    def 							a__				(				self  )			->		int:
         UpperCAmelCase_      :							str       =      self.get_image_processor()
         UpperCAmelCase_      :							List[Any]       =      self.get_tokenizer()

         UpperCAmelCase_      :							Any       =      BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,image_processor=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Tuple       =      '''lower newer'''

         UpperCAmelCase_      :							Optional[int]       =      processor(text=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							int       =      tokenizer(_SCREAMING_SNAKE_CASE       ,return_token_type_ids=_SCREAMING_SNAKE_CASE  )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]       ,encoded_processor[key]  )





    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							str       =      self.get_image_processor()
         UpperCAmelCase_      :							List[Any]       =      self.get_tokenizer()

         UpperCAmelCase_      :							Tuple       =      BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,image_processor=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							int       =      '''lower newer'''
         UpperCAmelCase_      :							int       =      self.prepare_image_inputs()

         UpperCAmelCase_      :							List[str]       =      processor(text=_SCREAMING_SNAKE_CASE       ,images=_SCREAMING_SNAKE_CASE  )

         self.assertListEqual(list(inputs.keys()  )       ,['''pixel_values''', '''input_ids''', '''attention_mask''']  )

         # test if it raises when no input is passed
         with pytest.raises(_SCREAMING_SNAKE_CASE  ):
              processor()





    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							Tuple       =      self.get_image_processor()
         UpperCAmelCase_      :							Dict       =      self.get_tokenizer()

         UpperCAmelCase_      :							List[str]       =      BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,image_processor=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Optional[Any]       =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         UpperCAmelCase_      :							List[str]       =      processor.batch_decode(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Union[str, Any]       =      tokenizer.batch_decode(_SCREAMING_SNAKE_CASE  )

         self.assertListEqual(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		str:
         UpperCAmelCase_      :							Union[str, Any]       =      self.get_image_processor()
         UpperCAmelCase_      :							int       =      self.get_tokenizer()

         UpperCAmelCase_      :							Any       =      BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,image_processor=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Dict       =      '''lower newer'''
         UpperCAmelCase_      :							Union[str, Any]       =      self.prepare_image_inputs()

         UpperCAmelCase_      :							Any       =      processor(text=_SCREAMING_SNAKE_CASE       ,images=_SCREAMING_SNAKE_CASE  )

         # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
         self.assertListEqual(list(inputs.keys()  )       ,['''pixel_values''', '''input_ids''', '''attention_mask''']  )