


'''simple docstring'''
from __future__ import annotations

from decimal import Decimal

from numpy import array


def 			a      (			__a   )  ->	list[list[float]]:



					'''simple docstring'''






					UpperCamelCase__   :List[str]												= 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
										UpperCamelCase__   :Optional[int]												= 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
										UpperCamelCase__   :List[Any]												= [[0.0, 0.0], [0.0, 0.0]]
										UpperCamelCase__					,				UpperCamelCase__   :int												= matrix[1][1], matrix[0][0]
										UpperCamelCase__					,				UpperCamelCase__   :Union[str, Any]												= -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
										UpperCamelCase__   :Tuple												= 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
										UpperCamelCase__   :Any												= [
										    [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   )],
										]
										UpperCamelCase__   :int												= (d(matrix[1][1]   ) * d(matrix[2][2]   )) - (
										    d(matrix[1][2]   ) * d(matrix[2][1]   )
										)
										UpperCamelCase__   :Union[str, Any]												= -(
										    (d(matrix[1][0]   ) * d(matrix[2][2]   )) - (d(matrix[1][2]   ) * d(matrix[2][0]   ))
										)
										UpperCamelCase__   :Tuple												= (d(matrix[1][0]   ) * d(matrix[2][1]   )) - (
										    d(matrix[1][1]   ) * d(matrix[2][0]   )
										)
										UpperCamelCase__   :Any												= -(
										    (d(matrix[0][1]   ) * d(matrix[2][2]   )) - (d(matrix[0][2]   ) * d(matrix[2][1]   ))
										)
										UpperCamelCase__   :Dict												= (d(matrix[0][0]   ) * d(matrix[2][2]   )) - (
										    d(matrix[0][2]   ) * d(matrix[2][0]   )
										)
										UpperCamelCase__   :Tuple												= -(
										    (d(matrix[0][0]   ) * d(matrix[2][1]   )) - (d(matrix[0][1]   ) * d(matrix[2][0]   ))
										)
										UpperCamelCase__   :List[Any]												= (d(matrix[0][1]   ) * d(matrix[1][2]   )) - (
										    d(matrix[0][2]   ) * d(matrix[1][1]   )
										)
										UpperCamelCase__   :str												= -(
										    (d(matrix[0][0]   ) * d(matrix[1][2]   )) - (d(matrix[0][2]   ) * d(matrix[1][0]   ))
										)
										UpperCamelCase__   :Tuple												= (d(matrix[0][0]   ) * d(matrix[1][1]   )) - (
										    d(matrix[0][1]   ) * d(matrix[1][0]   )
										)

										# Transpose the cofactor matrix (Adjoint matrix)
										UpperCamelCase__   :Optional[int]												= array(__a   )
										for i in range(3   ):
															for j in range(3   ):
																				UpperCamelCase__   :Optional[int]												= cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
										UpperCamelCase__   :str												= 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.'''   )





'''simple docstring'''

from typing import TYPE_CHECKING

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


a  :				Tuple									=  {
    'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}

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

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a  :				str									=  ['LlamaTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a  :				Optional[Any]									=  [
							    'LlamaForCausalLM',
							    'LlamaModel',
							    'LlamaPreTrainedModel',
							    'LlamaForSequenceClassification',
							]


if TYPE_CHECKING:
							from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_llama import LlamaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_llama_fast import LlamaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
							import sys

							a  :				str									=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

