



"""simple docstring"""






from __future__ import annotations

from decimal import Decimal

from numpy import array


def      _lowercase							(    __snake_case      )		->  list[list[float]]:
			__lowerCAmelCase :    Dict				=			Decimal

			# Check if the provided matrix has 2 rows and 2 columns
			# since this implementation only works for 2x2 matrices
			if len(__snake_case      ) == 2 and len(matrix[0]      ) == 2 and len(matrix[1]      ) == 2:
						# Calculate the determinant of the matrix
						__lowerCAmelCase :    Dict				=			float(
						    d(matrix[0][0]      ) * d(matrix[1][1]      ) - d(matrix[1][0]      ) * d(matrix[0][1]      )      )
						if determinant == 0:
									raise ValueError("This matrix has no inverse."      )

						# Creates a copy of the matrix with swapped positions of the elements
						__lowerCAmelCase :    str				=			[[0.0, 0.0], [0.0, 0.0]]
						__lowerCAmelCase		,      __lowerCAmelCase :    Any				=			matrix[1][1], matrix[0][0]
						__lowerCAmelCase		,      __lowerCAmelCase :    int				=			-matrix[1][0], -matrix[0][1]

						# Calculate the inverse of the matrix
						return [
						    [(float(d(__snake_case      )      ) / determinant) or 0.0 for n in row] for row in swapped_matrix
						]
			elif (
			    len(__snake_case      ) == 3
			    and len(matrix[0]      ) == 3
			    and len(matrix[1]      ) == 3
			    and len(matrix[2]      ) == 3
			):
						# Calculate the determinant of the matrix using Sarrus rule
						__lowerCAmelCase :    Optional[Any]				=			float(
						    (
						        (d(matrix[0][0]      ) * d(matrix[1][1]      ) * d(matrix[2][2]      ))
						        + (d(matrix[0][1]      ) * d(matrix[1][2]      ) * d(matrix[2][0]      ))
						        + (d(matrix[0][2]      ) * d(matrix[1][0]      ) * d(matrix[2][1]      ))
						    )
						    - (
						        (d(matrix[0][2]      ) * d(matrix[1][1]      ) * d(matrix[2][0]      ))
						        + (d(matrix[0][1]      ) * d(matrix[1][0]      ) * d(matrix[2][2]      ))
						        + (d(matrix[0][0]      ) * d(matrix[1][2]      ) * d(matrix[2][1]      ))
						    )      )
						if determinant == 0:
									raise ValueError("This matrix has no inverse."      )

						# Creating cofactor matrix
						__lowerCAmelCase :    Optional[int]				=			[
						    [d(0.0      ), d(0.0      ), d(0.0      )],
						    [d(0.0      ), d(0.0      ), d(0.0      )],
						    [d(0.0      ), d(0.0      ), d(0.0      )],
						]
						__lowerCAmelCase :    List[str]				=			(d(matrix[1][1]      ) * d(matrix[2][2]      )) - (
						    d(matrix[1][2]      ) * d(matrix[2][1]      )
						)
						__lowerCAmelCase :    List[str]				=			-(
						    (d(matrix[1][0]      ) * d(matrix[2][2]      )) - (d(matrix[1][2]      ) * d(matrix[2][0]      ))
						)
						__lowerCAmelCase :    Optional[int]				=			(d(matrix[1][0]      ) * d(matrix[2][1]      )) - (
						    d(matrix[1][1]      ) * d(matrix[2][0]      )
						)
						__lowerCAmelCase :    Dict				=			-(
						    (d(matrix[0][1]      ) * d(matrix[2][2]      )) - (d(matrix[0][2]      ) * d(matrix[2][1]      ))
						)
						__lowerCAmelCase :    Any				=			(d(matrix[0][0]      ) * d(matrix[2][2]      )) - (
						    d(matrix[0][2]      ) * d(matrix[2][0]      )
						)
						__lowerCAmelCase :    Optional[int]				=			-(
						    (d(matrix[0][0]      ) * d(matrix[2][1]      )) - (d(matrix[0][1]      ) * d(matrix[2][0]      ))
						)
						__lowerCAmelCase :    int				=			(d(matrix[0][1]      ) * d(matrix[1][2]      )) - (
						    d(matrix[0][2]      ) * d(matrix[1][1]      )
						)
						__lowerCAmelCase :    List[Any]				=			-(
						    (d(matrix[0][0]      ) * d(matrix[1][2]      )) - (d(matrix[0][2]      ) * d(matrix[1][0]      ))
						)
						__lowerCAmelCase :    List[str]				=			(d(matrix[0][0]      ) * d(matrix[1][1]      )) - (
						    d(matrix[0][1]      ) * d(matrix[1][0]      )
						)

						# Transpose the cofactor matrix (Adjoint matrix)
						__lowerCAmelCase :    Any				=			array(__snake_case      )
						for i in range(3      ):
									for j in range(3      ):
												__lowerCAmelCase :    Dict				=			cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
						__lowerCAmelCase :    Any				=			array(__snake_case      )
						for i in range(3      ):
									for j in range(3      ):
												inverse_matrix[i][j] /= d(__snake_case      )

        # Calculate the inverse of the matrix
						return [[float(d(__snake_case      )      ) or 0.0 for n in row] for row in inverse_matrix]
			raise ValueError("Please provide a matrix of size 2x2 or 3x3."      )




"""simple docstring"""






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case				:  Tuple =       {
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:  Optional[Any] =       [
	    'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'AutoformerForPrediction',
	    'AutoformerModel',
	    'AutoformerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_autoformer import (
	    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    AutoformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_autoformer import (
		    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AutoformerForPrediction,
		    AutoformerModel,
		    AutoformerPreTrainedModel,
		)

else:
	import sys

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