






from __future__ import annotations


class 			SCREAMING_SNAKE_CASE      :
     '''simple docstring'''


     def __init__(					self							:						Optional[int]       ,			UpperCAmelCase_							:						list[list[int]]   ):
       SCREAMING_SNAKE_CASE							:   List[str]						    =     TypeError(
           "Matrices must be formed from a list of zero or more lists containing at "
           "least one and the same number of values, each of which must be of type "
           "int or float."   )
       if len(UpperCAmelCase_   ) != 0:
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     len(rows[0]   )
         if cols == 0:
           raise error
         for row in rows:
           if len(UpperCAmelCase_   ) != cols:
             raise error
           for value in row:
             if not isinstance(UpperCAmelCase_       ,			(int, float)   ):
               raise error
         SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     rows
       else:
         SCREAMING_SNAKE_CASE							:   Tuple						    =     []


     def 					_A							(					self							:						Dict   ):
       return [[row[i] for row in self.rows] for i in range(len(self.rows[0]   )   )]


     @property
     def 					_A							(					self							:						List[str]   ):
       return len(self.rows   )


     @property
     def 					_A							(					self							:						List[Any]   ):
       return len(self.rows[0]   )


     @property
     def 					_A							(					self							:						Optional[Any]   ):
       return (self.num_rows, self.num_columns)


     @property
     def 					_A							(					self							:						Optional[int]   ):
       return self.order[0] == self.order[1]


     def 					_A							(					self							:						Any   ):
       SCREAMING_SNAKE_CASE							:   Dict						    =     [
           [0 if column_num != row_num else 1 for column_num in range(self.num_rows   )]
           for row_num in range(self.num_rows   )
       ]
       return Matrix(UpperCAmelCase_   )


     def 					_A							(					self							:						Union[str, Any]   ):
       if not self.is_square:
         return 0
       if self.order == (0, 0):
         return 1
       if self.order == (1, 1):
         return int(self.rows[0][0]   )
       if self.order == (2, 2):
         return int(
             (self.rows[0][0] * self.rows[1][1])
             - (self.rows[0][1] * self.rows[1][0])   )
       else:
         return sum(
             self.rows[0][column] * self.cofactors().rows[0][column]
             for column in range(self.num_columns   )   )


     def 					_A							(					self							:						List[str]   ):
       return bool(self.determinant()   )


     def 					_A							(					self							:						Optional[int]       ,			UpperCAmelCase_							:						int       ,			UpperCAmelCase_							:						int   ):
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     [
           [
               self.rows[other_row][other_column]
               for other_column in range(self.num_columns   )
               if other_column != column
           ]
           for other_row in range(self.num_rows   )
           if other_row != row
       ]
       return Matrix(UpperCAmelCase_   ).determinant()


     def 					_A							(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						int       ,			UpperCAmelCase_							:						int   ):
       if (row + column) % 2 == 0:
         return self.get_minor(UpperCAmelCase_       ,			UpperCAmelCase_   )
       return -1 * self.get_minor(UpperCAmelCase_       ,			UpperCAmelCase_   )


     def 					_A							(					self							:						List[str]   ):
       return Matrix(
           [
               [self.get_minor(UpperCAmelCase_       ,			UpperCAmelCase_   ) for column in range(self.num_columns   )]
               for row in range(self.num_rows   )
           ]   )


     def 					_A							(					self							:						Dict   ):
       return Matrix(
           [
               [
                   self.minors().rows[row][column]
                   if (row + column) % 2 == 0
                   else self.minors().rows[row][column] * -1
                   for column in range(self.minors().num_columns   )
               ]
               for row in range(self.minors().num_rows   )
           ]   )


     def 					_A							(					self							:						Any   ):
       SCREAMING_SNAKE_CASE							:   int						    =     [
           [self.cofactors().rows[column][row] for column in range(self.num_columns   )]
           for row in range(self.num_rows   )
       ]
       return Matrix(UpperCAmelCase_   )


     def 					_A							(					self							:						int   ):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.determinant()
       if not determinant:
         raise TypeError("Only matrices with a non-zero determinant have an inverse"   )
       return self.adjugate() * (1 / determinant)


     def __repr__(					self							:						Optional[int]   ):
       return str(self.rows   )


     def __str__(					self							:						int   ):
       if self.num_rows == 0:
         return "[]"
       if self.num_rows == 1:
         return "[[" + ". ".join(str(self.rows[0]   )   ) + "]]"
       return (
           "["
           + "\n ".join(
               [
                   "[" + ". ".join([str(UpperCAmelCase_   ) for value in row]   ) + ".]"
                   for row in self.rows
               ]   )
           + "]"
       )


     def 					_A							(					self							:						int       ,			UpperCAmelCase_							:						list[int]       ,			UpperCAmelCase_							:						int | None = None   ):
       SCREAMING_SNAKE_CASE							:   int						    =     TypeError("Row must be a list containing all ints and/or floats"   )
       if not isinstance(UpperCAmelCase_       ,			UpperCAmelCase_   ):
         raise type_error
       for value in row:
         if not isinstance(UpperCAmelCase_       ,			(int, float)   ):
           raise type_error
       if len(UpperCAmelCase_   ) != self.num_columns:
         raise ValueError(
             "Row must be equal in length to the other rows in the matrix"   )
       if position is None:
         self.rows.append(UpperCAmelCase_   )
       else:
         SCREAMING_SNAKE_CASE							:   List[str]						    =     self.rows[0:position] + [row] + self.rows[position:]


     def 					_A							(					self							:						Optional[Any]       ,			UpperCAmelCase_							:						list[int]       ,			UpperCAmelCase_							:						int | None = None   ):
       SCREAMING_SNAKE_CASE							:   List[str]						    =     TypeError(
           "Column must be a list containing all ints and/or floats"   )
       if not isinstance(UpperCAmelCase_       ,			UpperCAmelCase_   ):
         raise type_error
       for value in column:
         if not isinstance(UpperCAmelCase_       ,			(int, float)   ):
           raise type_error
       if len(UpperCAmelCase_   ) != self.num_rows:
         raise ValueError(
             "Column must be equal in length to the other columns in the matrix"   )
       if position is None:
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     [self.rows[i] + [column[i]] for i in range(self.num_rows   )]
       else:
         SCREAMING_SNAKE_CASE							:   List[str]						    =     [
             self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
             for i in range(self.num_rows   )
         ]


     def __eq__(					self							:						Any       ,			UpperCAmelCase_							:						object   ):
       if not isinstance(UpperCAmelCase_       ,			UpperCAmelCase_   ):
         return NotImplemented
       return self.rows == other.rows


     def __ne__(					self							:						List[Any]       ,			UpperCAmelCase_							:						object   ):
       return not self == other


     def __neg__(					self							:						Tuple   ):
       return self * -1


     def __add__(					self							:						str       ,			UpperCAmelCase_							:						Matrix   ):
       if self.order != other.order:
         raise ValueError("Addition requires matrices of the same order"   )
       return Matrix(
           [
               [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns   )]
               for i in range(self.num_rows   )
           ]   )


     def __sub__(					self							:						Any       ,			UpperCAmelCase_							:						Matrix   ):
       if self.order != other.order:
         raise ValueError("Subtraction requires matrices of the same order"   )
       return Matrix(
           [
               [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns   )]
               for i in range(self.num_rows   )
           ]   )


     def __mul__(					self							:						Any       ,			UpperCAmelCase_							:						Matrix | int | float   ):
       if isinstance(UpperCAmelCase_       ,			(int, float)   ):
         return Matrix(
             [[int(element * other   ) for element in row] for row in self.rows]   )
       elif isinstance(UpperCAmelCase_       ,			UpperCAmelCase_   ):
         if self.num_columns != other.num_rows:
           raise ValueError(
               "The number of columns in the first matrix must "
               "be equal to the number of rows in the second"   )
         return Matrix(
             [
                 [Matrix.dot_product(UpperCAmelCase_       ,			UpperCAmelCase_   ) for column in other.columns()]
                 for row in self.rows
             ]   )
       else:
         raise TypeError(
             "A Matrix can only be multiplied by an int, float, or another matrix"   )


     def __pow__(					self							:						Any       ,			UpperCAmelCase_							:						int   ):
       if not isinstance(UpperCAmelCase_       ,			UpperCAmelCase_   ):
         raise TypeError("A Matrix can only be raised to the power of an int"   )
       if not self.is_square:
         raise ValueError("Only square matrices can be raised to a power"   )
       if other == 0:
         return self.identity()
       if other < 0:
         if self.is_invertable():
           return self.inverse() ** (-other)
         raise ValueError(
             "Only invertable matrices can be raised to a negative power"   )
       SCREAMING_SNAKE_CASE							:   Tuple						    =     self
       for _ in range(other - 1   ):
         result *= self
       return result






     @classmethod
     def 					_A							(					cls							:						List[Any]       ,			UpperCAmelCase_							:						list[int]       ,			UpperCAmelCase_							:						list[int]   ):
       return sum(row[i] * column[i] for i in range(len(UpperCAmelCase_   )   )   )


if __name__ == "__main__":
   import doctest

   doctest.testmod()











"""simple docstring"""






from typing import TYPE_CHECKING

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


__snake_case				:  int =       {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:  Optional[Any] =       ['PLBartTokenizer']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:  List[str] =       [
	    'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'PLBartForCausalLM',
	    'PLBartForConditionalGeneration',
	    'PLBartForSequenceClassification',
	    'PLBartModel',
	    'PLBartPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_plbart import PLBartTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_plbart import (
		    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
		    PLBartForCausalLM,
		    PLBartForConditionalGeneration,
		    PLBartForSequenceClassification,
		    PLBartModel,
		    PLBartPreTrainedModel,
		)


else:
	import sys

	__snake_case				:  Any =       _LazyModule(__name__, globals()['__file__'], _import_structure)