
from __future__ import annotations

from typing import Any




class 					_lowerCamelCase			:





     """simple docstring"""







     def __init__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = 0	)	-> None:




           """simple docstring"""







           UpperCamelCase__					,UpperCamelCase__     : str											=					row, column
           UpperCamelCase__     : List[Any]											=					[[default_value for c in range(__SCREAMING_SNAKE_CASE	)] for r in range(__SCREAMING_SNAKE_CASE	)]






     def __str__(				self	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					F'''Matrix consist of {self.row} rows and {self.column} columns\n'''

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

           # Make string and return
           def single_line(__SCREAMING_SNAKE_CASE	) -> str:
                 nonlocal string_format_identifier
                 UpperCamelCase__     : Union[str, 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:




           """simple docstring"""







           return str(self	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> bool:




           """simple docstring"""







           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:




           """simple docstring"""







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






     def __setitem__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> None:




           """simple docstring"""







           assert self.validate_indicies(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : List[Any]											=					value






     def __add__(				self    ,    __SCREAMING_SNAKE_CASE	)	-> Matrix:




           """simple docstring"""







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

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






     def __neg__(				self	)	-> Matrix:




           """simple docstring"""







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






     def __sub__(				self    ,    __SCREAMING_SNAKE_CASE	)	-> Matrix:




           """simple docstring"""







           return self + (-another)






     def __mul__(				self    ,    __SCREAMING_SNAKE_CASE	)	-> Matrix:




           """simple docstring"""







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






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Matrix:




           """simple docstring"""







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






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> Any:




           """simple docstring"""







           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__     : Dict											=					v.transpose()
           UpperCamelCase__     : 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 				SCREAMING_SNAKE_CASE_   (      ):
          # a^(-1)
          UpperCamelCase__     : Any											=					Matrix(3			,		3			,		0		)
          for i in range(3		):
                UpperCamelCase__     : Union[str, Any]											=					1
          print(f'''a^(-1) is {ainv}'''		)
          # u, v
          UpperCamelCase__     : Union[str, Any]											=					Matrix(3			,		1			,		0		)
          UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__     : Optional[Any]											=					1, 2, -3
          UpperCamelCase__     : Any											=					Matrix(3			,		1			,		0		)
          UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__     : Optional[Any]											=					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(UpperCamelCase__			,		UpperCamelCase__		)}'''		)


    def 				SCREAMING_SNAKE_CASE_   (      ):
          import doctest

          doctest.testmod()
    testa()




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
    from transformers import TvltImageProcessor

if is_speech_available():
    from transformers import TvltFeatureExtractor

from transformers import TvltProcessor




@require_torch
class 					_lowerCamelCase			(   unittest.TestCase			):





     """simple docstring"""







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




           """simple docstring"""







           UpperCamelCase__     : int											=					'''ZinengTang/tvlt-base'''
           UpperCamelCase__     : Optional[Any]											=					tempfile.mkdtemp()






     def  __SCREAMING_SNAKE_CASE      (				self    ,    **__SCREAMING_SNAKE_CASE	)	-> Tuple:




           """simple docstring"""







           return TvltImageProcessor.from_pretrained(self.checkpoint    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    **__SCREAMING_SNAKE_CASE	)	-> str:




           """simple docstring"""







           return TvltFeatureExtractor.from_pretrained(self.checkpoint    ,    **__SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           shutil.rmtree(self.tmpdirname	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					self.get_image_processor()
           UpperCamelCase__     : Tuple											=					self.get_feature_extractor()

           UpperCamelCase__     : Dict											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)
           processor.save_pretrained(self.tmpdirname	)
           UpperCamelCase__     : Optional[Any]											=					TvltProcessor.from_pretrained(self.tmpdirname	)

           self.assertIsInstance(processor.feature_extractor    ,    __SCREAMING_SNAKE_CASE	)
           self.assertIsInstance(processor.image_processor    ,    __SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[str]:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					self.get_image_processor()
           UpperCamelCase__     : int											=					self.get_feature_extractor()

           UpperCamelCase__     : Union[str, Any]											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : str											=					np.ones([1_2_0_0_0]	)

           UpperCamelCase__     : Dict											=					feature_extractor(__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)
           UpperCamelCase__     : Dict											=					processor(audio=__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)

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






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




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					self.get_image_processor()
           UpperCamelCase__     : Tuple											=					self.get_feature_extractor()

           UpperCamelCase__     : str											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					np.ones([3, 2_2_4, 2_2_4]	)

           UpperCamelCase__     : Optional[int]											=					image_processor(__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)
           UpperCamelCase__     : Tuple											=					processor(images=__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)

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






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					self.get_image_processor()
           UpperCamelCase__     : Optional[Any]											=					self.get_feature_extractor()

           UpperCamelCase__     : Union[str, Any]											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Dict											=					np.ones([1_2_0_0_0]	)
           UpperCamelCase__     : str											=					np.ones([3, 2_2_4, 2_2_4]	)

           UpperCamelCase__     : List[Any]											=					processor(audio=__SCREAMING_SNAKE_CASE    ,    images=__SCREAMING_SNAKE_CASE	)

           self.assertListEqual(list(inputs.keys()	)    ,    ['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']	)

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






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







           UpperCamelCase__     : int											=					self.get_image_processor()
           UpperCamelCase__     : str											=					self.get_feature_extractor()

           UpperCamelCase__     : Dict											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           self.assertListEqual(
               processor.model_input_names    ,    image_processor.model_input_names + feature_extractor.model_input_names    ,    msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''    ,    )


