
from __future__ import annotations

from decimal import Decimal

from numpy import array


def 						UpperCAmelCase      (     a_						)		->     list[list[float]]:







							"""simple docstring"""



							__A             =			Decimal

							# Check if the provided matrix has 2 rows and 2 columns
							# since this implementation only works for 2x2 matrices
							if len(a_						) == 2 and len(matrix[0]						) == 2 and len(matrix[1]						) == 2:
														# Calculate the determinant of the matrix
														__A             =			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
														__A             =			[[0.0, 0.0], [0.0, 0.0]]
														__A      ,							__A             =			matrix[1][1], matrix[0][0]
														__A      ,							__A             =			-matrix[1][0], -matrix[0][1]

														# Calculate the inverse of the matrix
														return [
														    [(float(d(a_						)						) / determinant) or 0.0 for n in row] for row in swapped_matrix
														]
							elif (
							    len(a_						) == 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
														__A             =			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
														__A             =			[
														    [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						)],
														]
														__A             =			(d(matrix[1][1]						) * d(matrix[2][2]						)) - (
														    d(matrix[1][2]						) * d(matrix[2][1]						)
														)
														__A             =			-(
														    (d(matrix[1][0]						) * d(matrix[2][2]						)) - (d(matrix[1][2]						) * d(matrix[2][0]						))
														)
														__A             =			(d(matrix[1][0]						) * d(matrix[2][1]						)) - (
														    d(matrix[1][1]						) * d(matrix[2][0]						)
														)
														__A             =			-(
														    (d(matrix[0][1]						) * d(matrix[2][2]						)) - (d(matrix[0][2]						) * d(matrix[2][1]						))
														)
														__A             =			(d(matrix[0][0]						) * d(matrix[2][2]						)) - (
														    d(matrix[0][2]						) * d(matrix[2][0]						)
														)
														__A             =			-(
														    (d(matrix[0][0]						) * d(matrix[2][1]						)) - (d(matrix[0][1]						) * d(matrix[2][0]						))
														)
														__A             =			(d(matrix[0][1]						) * d(matrix[1][2]						)) - (
														    d(matrix[0][2]						) * d(matrix[1][1]						)
														)
														__A             =			-(
														    (d(matrix[0][0]						) * d(matrix[1][2]						)) - (d(matrix[0][2]						) * d(matrix[1][0]						))
														)
														__A             =			(d(matrix[0][0]						) * d(matrix[1][1]						)) - (
														    d(matrix[0][1]						) * d(matrix[1][0]						)
														)

														# Transpose the cofactor matrix (Adjoint matrix)
														__A             =			array(a_						)
														for i in range(3						):
																					for j in range(3						):
																												__A             =			cofactor_matrix[j][i]

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

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


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE							:Any										=    {
    'configuration_rag': ['RagConfig'],
    'retrieval_rag': ['RagRetriever'],
    'tokenization_rag': ['RagTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:List[Any]										=    [
					    'RagModel',
					    'RagPreTrainedModel',
					    'RagSequenceForGeneration',
					    'RagTokenForGeneration',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Any										=    [
					    'TFRagModel',
					    'TFRagPreTrainedModel',
					    'TFRagSequenceForGeneration',
					    'TFRagTokenForGeneration',
					]


if TYPE_CHECKING:
					from .configuration_rag import RagConfig
					from .retrieval_rag import RagRetriever
					from .tokenization_rag import RagTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_rag import (
										    TFRagModel,
										    TFRagPreTrainedModel,
										    TFRagSequenceForGeneration,
										    TFRagTokenForGeneration,
										)

else:
					import sys

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