


from __future__ import annotations

from typing import Any







def 						_lowercase       (     *__lowerCamelCase			:					list[list[int]]			)  ->   list[list[int]]:





     '''simple docstring'''





     if all(_check_not_integer(__lowerCamelCase			) for m in matrix_s			):
          for i in matrix_s[1:]:
               _verify_matrix_sizes(matrix_s[0]		,__lowerCamelCase			)
          return [[sum(__lowerCamelCase			) for t in zip(*__lowerCamelCase			)] for m in zip(*__lowerCamelCase			)]
     raise TypeError('''Expected a matrix, got int/list instead'''			)







def 						_lowercase       (     __lowerCamelCase			:					list[list[int]]		,__lowerCamelCase			:					list[list[int]]			)  ->   list[list[int]]:





     '''simple docstring'''





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







def 						_lowercase       (     __lowerCamelCase			:					list[list[int]]		,__lowerCamelCase			:					int | float			)  ->   list[list[float]]:





     '''simple docstring'''





     return [[x * n for x in row] for row in matrix]







def 						_lowercase       (     __lowerCamelCase			:					list[list[int]]		,__lowerCamelCase			:					list[list[int]]			)  ->   list[list[int]]:





     '''simple docstring'''





     if _check_not_integer(__lowerCamelCase			) and _check_not_integer(__lowerCamelCase			):
          UpperCamelCase__	,UpperCamelCase__							:  Tuple								=     _verify_matrix_sizes(__lowerCamelCase		,__lowerCamelCase			)

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







def 						_lowercase       (     __lowerCamelCase			:					int			)  ->   list[list[int]]:





     '''simple docstring'''





     UpperCamelCase__							:  Optional[Any]								=     int(__lowerCamelCase			)
     return [[int(row == column			) for column in range(__lowerCamelCase			)] for row in range(__lowerCamelCase			)]







def 						_lowercase       (     __lowerCamelCase			:					list[list[int]]		,__lowerCamelCase			:					bool = True			)  ->   list[list[int]] | map[list[int]]:





     '''simple docstring'''





     if _check_not_integer(__lowerCamelCase			):
          if return_map:
               return map(__lowerCamelCase		,zip(*__lowerCamelCase			)			)
          else:
               return list(map(__lowerCamelCase		,zip(*__lowerCamelCase			)			)			)
     raise TypeError('''Expected a matrix, got int/list instead'''			)







def 						_lowercase       (     __lowerCamelCase			:					list[list[int]]		,__lowerCamelCase			:					int		,__lowerCamelCase			:					int			)  ->   list[list[int]]:





     '''simple docstring'''





     UpperCamelCase__							:  List[Any]								=     matrix[:row] + matrix[row + 1 :]
     return [row[:column] + row[column + 1 :] for row in minor]







def 						_lowercase       (     __lowerCamelCase			:					list[list[int]]			)  ->   Any:





     '''simple docstring'''





     if len(__lowerCamelCase			) == 1:
          return matrix[0][0]

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







def 						_lowercase       (     __lowerCamelCase			:					list[list[int]]			)  ->   list[list[float]] | None:





     '''simple docstring'''





     UpperCamelCase__							:  Any								=     determinant(__lowerCamelCase			)
     if det == 0:
          return None

     UpperCamelCase__							:  Dict								=     [
         [determinant(minor(__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)			) for j in range(len(__lowerCamelCase			)			)]
         for i in range(len(__lowerCamelCase			)			)
     ]

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







def 						_lowercase       (     __lowerCamelCase			:					list[list[int]]			)  ->   bool:





     '''simple docstring'''





     return not isinstance(__lowerCamelCase		,__lowerCamelCase			) and not isinstance(matrix[0]		,__lowerCamelCase			)







def 						_lowercase       (     __lowerCamelCase			:					list[list[int]]			)  ->   tuple[int, int]:





     '''simple docstring'''





     return len(__lowerCamelCase			), len(matrix[0]			)







def 						_lowercase       (     __lowerCamelCase			:					list[list[int]]		,__lowerCamelCase			:					list[list[int]]			)  ->   tuple[tuple[int, int], tuple[int, int]]:





     '''simple docstring'''





     UpperCamelCase__							:  Union[str, Any]								=     _shape(__lowerCamelCase			) + _shape(__lowerCamelCase			)
     if shape[0] != shape[3] or shape[1] != shape[2]:
          UpperCamelCase__							:  Tuple								=     (
              '''operands could not be broadcast together with shape '''
              F'({shape[0], shape[1]}), ({shape[2], shape[3]})'
          )
          raise ValueError(__lowerCamelCase			)
     return (shape[0], shape[2]), (shape[1], shape[3])







def 						_lowercase       (     )  ->   None:





     '''simple docstring'''





     UpperCamelCase__							:  Optional[int]								=     [[12, 10], [3, 9]]
     UpperCamelCase__							:  List[Any]								=     [[3, 4], [7, 4]]
     UpperCamelCase__							:  Optional[int]								=     [[11, 12, 13, 14], [21, 22, 23, 24], [31, 32, 33, 34], [41, 42, 43, 44]]
     UpperCamelCase__							:  Union[str, Any]								=     [[3, 0, 2], [2, 0, -2], [0, 1, 1]]
     print(F'Add Operation, {add(__lowerCamelCase		,__lowerCamelCase			) = } \n'			)
     print(
         F'Multiply Operation, {multiply(__lowerCamelCase		,__lowerCamelCase			) = } \n'		,)
     print(F'Identity: {identity(5			)}\n'			)
     print(F'Minor of {matrix_c} = {minor(__lowerCamelCase		,1		,2			)} \n'			)
     print(F'Determinant of {matrix_b} = {determinant(__lowerCamelCase			)} \n'			)
     print(F'Inverse of {matrix_d} = {inverse(__lowerCamelCase			)}\n'			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()








from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE :					Optional[Any]			=	{
    """configuration_layoutlm""": ["""LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMConfig""", """LayoutLMOnnxConfig"""],
    """tokenization_layoutlm""": ["""LayoutLMTokenizer"""],
}

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

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					List[str]			=	[
       """LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """LayoutLMForMaskedLM""",
       """LayoutLMForSequenceClassification""",
       """LayoutLMForTokenClassification""",
       """LayoutLMForQuestionAnswering""",
       """LayoutLMModel""",
       """LayoutLMPreTrainedModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					str			=	[
       """TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFLayoutLMForMaskedLM""",
       """TFLayoutLMForSequenceClassification""",
       """TFLayoutLMForTokenClassification""",
       """TFLayoutLMForQuestionAnswering""",
       """TFLayoutLMMainLayer""",
       """TFLayoutLMModel""",
       """TFLayoutLMPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
   from .tokenization_layoutlm import LayoutLMTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_layoutlm import (
          LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMForMaskedLM,
          LayoutLMForQuestionAnswering,
          LayoutLMForSequenceClassification,
          LayoutLMForTokenClassification,
          LayoutLMModel,
          LayoutLMPreTrainedModel,
      )
   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_layoutlm import (
          TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForQuestionAnswering,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForTokenClassification,
          TFLayoutLMMainLayer,
          TFLayoutLMModel,
          TFLayoutLMPreTrainedModel,
      )

else:
   import sys

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




