





"""simple docstring"""
from __future__ import annotations

from typing import Any




def 			__snake_case						(      *__A    )		->				list[list[int]]:
   if all(_check_not_integer(__A    ) for m in matrix_s    ):
      for i in matrix_s[1:]:
         _verify_matrix_sizes(matrix_s[0] ,__A    )
      return [[sum(__A    ) for t in zip(*__A    )] for m in zip(*__A    )]
   raise TypeError("""Expected a matrix, got int/list instead"""    )




def 			__snake_case						(      __A ,__A    )		->				list[list[int]]:
   if (
       _check_not_integer(__A    )
       and _check_not_integer(__A    )
       and _verify_matrix_sizes(__A ,__A    )
   ):
      return [[i - j for i, j in zip(*__A    )] for m in zip(__A ,__A    )]
   raise TypeError("""Expected a matrix, got int/list instead"""    )




def 			__snake_case						(      __A ,__A    )		->				list[list[float]]:
   return [[x * n for x in row] for row in matrix]




def 			__snake_case						(      __A ,__A    )		->				list[list[int]]:
   if _check_not_integer(__A    ) and _check_not_integer(__A    ):
      lowercase  ,					lowercase    :			Dict  =       _verify_matrix_sizes(__A ,__A    )

   if cols[0] != rows[1]:
      lowercase    :			Union[str, Any]  =       (
          """Cannot multiply matrix of dimensions """
          F'''({rows[0]},{cols[0]}) and ({rows[1]},{cols[1]})'''
      )
      raise ValueError(__A    )
   return [
       [sum(m * n for m, n in zip(__A ,__A    )    ) for j in zip(*__A    )] for i in matrix_a
   ]




def 			__snake_case						(      __A    )		->				list[list[int]]:
   lowercase    :			Tuple  =       int(__A    )
   return [[int(row == column    ) for column in range(__A    )] for row in range(__A    )]




def 			__snake_case						(      __A ,__A = True    )		->				list[list[int]] | map[list[int]]:
   if _check_not_integer(__A    ):
      if return_map:
         return map(__A ,zip(*__A    )    )
      else:
         return list(map(__A ,zip(*__A    )    )    )
   raise TypeError("""Expected a matrix, got int/list instead"""    )




def 			__snake_case						(      __A ,__A ,__A    )		->				list[list[int]]:
   lowercase    :			Optional[Any]  =       matrix[:row] + matrix[row + 1 :]
   return [row[:column] + row[column + 1 :] for row in minor]




def 			__snake_case						(      __A    )		->				Any:
   if len(__A    ) == 1:
      return matrix[0][0]

   return sum(
       x * determinant(minor(__A ,0 ,__A    )    ) * (-1) ** i
       for i, x in enumerate(matrix[0]    )    )




def 			__snake_case						(      __A    )		->				list[list[float]] | None:
   lowercase    :			List[str]  =       determinant(__A    )
   if det == 0:
      return None

   lowercase    :			Optional[int]  =       [
       [determinant(minor(__A ,__A ,__A    )    ) for j in range(len(__A    )    )]
       for i in range(len(__A    )    )
   ]

   lowercase    :			List[str]  =       [
       [x * (-1) ** (row + col) for col, x in enumerate(matrix_minor[row]    )]
       for row in range(len(__A    )    )
   ]
   lowercase    :			int  =       list(transpose(__A    )    )
   return scalar_multiply(__A ,1 / det    )




def 			__snake_case						(      __A    )		->				bool:
   return not isinstance(__A ,__A    ) and not isinstance(matrix[0] ,__A    )




def 			__snake_case						(      __A    )		->				tuple[int, int]:
   return len(__A    ), len(matrix[0]    )




def 			__snake_case						(      __A ,__A    )		->				tuple[tuple[int, int], tuple[int, int]]:
   lowercase    :			Any  =       _shape(__A    ) + _shape(__A    )
   if shape[0] != shape[3] or shape[1] != shape[2]:
      lowercase    :			int  =       (
          """operands could not be broadcast together with shape """
          F'''({shape[0], shape[1]}), ({shape[2], shape[3]})'''
      )
      raise ValueError(__A    )
   return (shape[0], shape[2]), (shape[1], shape[3])




def 			__snake_case						(      )		->				None:
   lowercase    :			Optional[int]  =       [[12, 10], [3, 9]]
   lowercase    :			Optional[int]  =       [[3, 4], [7, 4]]
   lowercase    :			Any  =       [[11, 12, 13, 14], [21, 22, 23, 24], [31, 32, 33, 34], [41, 42, 43, 44]]
   lowercase    :			Dict  =       [[3, 0, 2], [2, 0, -2], [0, 1, 1]]
   print(F'''Add Operation, {add(__A ,__A    ) = } \n'''    )
   print(
       F'''Multiply Operation, {multiply(__A ,__A    ) = } \n''' ,)
   print(F'''Identity: {identity(5    )}\n'''    )
   print(F'''Minor of {matrix_c} = {minor(__A ,1 ,2    )} \n'''    )
   print(F'''Determinant of {matrix_b} = {determinant(__A    )} \n'''    )
   print(F'''Inverse of {matrix_d} = {inverse(__A    )}\n'''    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()









"""simple docstring"""
from typing import TYPE_CHECKING

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


lowerCAmelCase:	Union[str, Any]							      ={
    "configuration_deberta_v2": ["DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaV2Config", "DebertaV2OnnxConfig"],
    "tokenization_deberta_v2": ["DebertaV2Tokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCAmelCase:	Dict							      =["DebertaV2TokenizerFast"]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCAmelCase:	Optional[Any]							      =[
       "TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFDebertaV2ForMaskedLM",
       "TFDebertaV2ForQuestionAnswering",
       "TFDebertaV2ForSequenceClassification",
       "TFDebertaV2ForTokenClassification",
       "TFDebertaV2Model",
       "TFDebertaV2PreTrainedModel",
   ]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCAmelCase:	str							      =[
       "DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "DebertaV2ForMaskedLM",
       "DebertaV2ForMultipleChoice",
       "DebertaV2ForQuestionAnswering",
       "DebertaV2ForSequenceClassification",
       "DebertaV2ForTokenClassification",
       "DebertaV2Model",
       "DebertaV2PreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_deberta_va import (
       DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DebertaVaConfig,
       DebertaVaOnnxConfig,
   )
   from .tokenization_deberta_va import DebertaVaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_deberta_va import (
          TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDebertaVaForMaskedLM,
          TFDebertaVaForQuestionAnswering,
          TFDebertaVaForSequenceClassification,
          TFDebertaVaForTokenClassification,
          TFDebertaVaModel,
          TFDebertaVaPreTrainedModel,
      )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_deberta_va import (
          DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
          DebertaVaForMaskedLM,
          DebertaVaForMultipleChoice,
          DebertaVaForQuestionAnswering,
          DebertaVaForSequenceClassification,
          DebertaVaForTokenClassification,
          DebertaVaModel,
          DebertaVaPreTrainedModel,
      )

else:
   import sys

   lowerCAmelCase:	Optional[Any]							      =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


