





'''simple docstring'''




from __future__ import annotations

from typing import Any


class  SCREAMING_SNAKE_CASE    :


     def __init__(						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase = 0):

          '''simple docstring'''






          __A						: int      =       row, column
          __A						: Any      =       [[default_value for c in range(_snake_case)] for r in range(_snake_case)]


     def __str__(						self):

          '''simple docstring'''






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

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

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

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


     def __repr__(						self):

          '''simple docstring'''






          return str(self)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          if not (isinstance(_snake_case						,	(list, tuple)) and len(_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						,	_UpperCAmelCase):

          '''simple docstring'''






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


     def __setitem__(						self						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          assert self.validate_indicies(_snake_case)
          __A						: Any      =       value


     def __add__(						self						,	_UpperCAmelCase):

          '''simple docstring'''






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

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


     def __neg__(						self):

          '''simple docstring'''






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


     def __sub__(						self						,	_UpperCAmelCase):

          '''simple docstring'''






          return self + (-another)


     def __mul__(						self						,	_UpperCAmelCase):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: int      =       Matrix(self.column						,	self.row)
          for r in range(self.row):
               for c in range(self.column):
                    __A						: Dict      =       self[r, c]
          return result


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          assert isinstance(_snake_case						,	_snake_case) and isinstance(_snake_case						,	_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
          __A						: Dict      =       v.transpose()
          __A						: Union[str, Any]      =       (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			(  )     ->     None:
       # a^(-1)
       __A						: Tuple      =       Matrix(3 ,							3 ,							0			)
       for i in range(3			):
            __A						: List[Any]      =       1
       print(f'a^(-1) is {ainv}'			)
       # u, v
       __A						: Tuple      =       Matrix(3 ,							1 ,							0			)
       __A						: Dict      =       1, 2, -3
       __A						: List[Any]      =       Matrix(3 ,							1 ,							0			)
       __A						: 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(__lowerCamelCase ,							__lowerCamelCase			)}'			)






  def        _lowerCAmelCase			(  )     ->     None:
       import doctest

       doctest.testmod()







  testa()




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


lowerCAmelCase_   						=   {
    'configuration_speecht5': [
        'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP',
        'SpeechT5Config',
        'SpeechT5HifiGanConfig',
    ],
    'feature_extraction_speecht5': ['SpeechT5FeatureExtractor'],
    'processing_speecht5': ['SpeechT5Processor'],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   ['SpeechT5Tokenizer']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   [
							    'SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SpeechT5ForSpeechToText',
							    'SpeechT5ForSpeechToSpeech',
							    'SpeechT5ForTextToSpeech',
							    'SpeechT5Model',
							    'SpeechT5PreTrainedModel',
							    'SpeechT5HifiGan',
							]

if TYPE_CHECKING:
							from .configuration_speechta import (
							    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
							    SpeechTaConfig,
							    SpeechTaHifiGanConfig,
							)
							from .feature_extraction_speechta import SpeechTaFeatureExtractor
							from .processing_speechta import SpeechTaProcessor

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_speechta import SpeechTaTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_speechta import (
														    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SpeechTaForSpeechToSpeech,
														    SpeechTaForSpeechToText,
														    SpeechTaForTextToSpeech,
														    SpeechTaHifiGan,
														    SpeechTaModel,
														    SpeechTaPreTrainedModel,
														)

else:
							import sys

							lowerCAmelCase_   						=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
