


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_A		:				List[Any]									=       {
    '''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
    '''tokenization_convbert''': ['''ConvBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:				int									=       ['''ConvBertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:				List[Any]									=       [
       '''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ConvBertForMaskedLM''',
       '''ConvBertForMultipleChoice''',
       '''ConvBertForQuestionAnswering''',
       '''ConvBertForSequenceClassification''',
       '''ConvBertForTokenClassification''',
       '''ConvBertLayer''',
       '''ConvBertModel''',
       '''ConvBertPreTrainedModel''',
       '''load_tf_weights_in_convbert''',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:				List[Any]									=       [
       '''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFConvBertForMaskedLM''',
       '''TFConvBertForMultipleChoice''',
       '''TFConvBertForQuestionAnswering''',
       '''TFConvBertForSequenceClassification''',
       '''TFConvBertForTokenClassification''',
       '''TFConvBertLayer''',
       '''TFConvBertModel''',
       '''TFConvBertPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
   from .tokenization_convbert import ConvBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_convbert_fast import ConvBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_convbert import (
          CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ConvBertForMaskedLM,
          ConvBertForMultipleChoice,
          ConvBertForQuestionAnswering,
          ConvBertForSequenceClassification,
          ConvBertForTokenClassification,
          ConvBertLayer,
          ConvBertModel,
          ConvBertPreTrainedModel,
          load_tf_weights_in_convbert,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_convbert import (
          TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFConvBertForMaskedLM,
          TFConvBertForMultipleChoice,
          TFConvBertForQuestionAnswering,
          TFConvBertForSequenceClassification,
          TFConvBertForTokenClassification,
          TFConvBertLayer,
          TFConvBertModel,
          TFConvBertPreTrainedModel,
      )


else:
   import sys

   _A		:				int									=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






from __future__ import annotations

from typing import Any



class        a					:





     def __init__(    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				float = 0							):
        __lowerCamelCase   ,     __lowerCamelCase:				Dict     =	row, column
        __lowerCamelCase:				Optional[Any]     =	[[default_value for c in range(SCREAMING_SNAKE_CASE_							)] for r in range(SCREAMING_SNAKE_CASE_							)]





     def __str__(    self					:				Union[str, Any]							):
        __lowerCamelCase:				str     =	F'Matrix consist of {self.row} rows and {self.column} columns\n'

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

        # Make string and return
        def single_line(SCREAMING_SNAKE_CASE_					:				list[float]							) -> str:
           nonlocal string_format_identifier
           __lowerCamelCase:				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					:				Union[str, Any]							):
        return str(self							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				tuple[int, int]							):
        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					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				tuple[int, int]							):
        assert self.validate_indicies(SCREAMING_SNAKE_CASE_							)
        return self.array[loc[0]][loc[1]]





     def __setitem__(    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				tuple[int, int]    ,	SCREAMING_SNAKE_CASE_					:				float							):
        assert self.validate_indicies(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[Any]     =	value





     def __add__(    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Matrix							):
        assert isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
        assert self.row == another.row and self.column == another.column

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





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





     def __sub__(    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Matrix							):
        return self + (-another)





     def __mul__(    self					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				int | float | Matrix							):

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





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







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



   def     __lowerCAmelCase						(							)       ->							None:
      import doctest

      doctest.testmod()







   testa()


