


'''simple docstring'''







from __future__ import annotations

from decimal import Decimal

from numpy import array



def     _UpperCamelCase				(			UpperCamelCase__		):






				"""simple docstring"""



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

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

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
								__magic_name__					:       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

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)