
from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
							import torch

if is_torch_tpu_available(check_device=False):
							import torch_xla.core.xla_model as xm


lowercase__		:Any			  =   logging.get_logger(__name__)







@dataclass
class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	str	 =[
							    '''no_inference''',
							    '''no_cuda''',
							    '''no_tpu''',
							    '''no_speed''',
							    '''no_memory''',
							    '''no_env_print''',
							    '''no_multi_process''',
							]




							def __init__(    self       ,**A__):
												for deprecated_arg in self.deprecated_args:
																	if deprecated_arg in kwargs:
																						lowercase						     =						deprecated_arg[3:]
																						setattr(self       ,A__       ,not kwargs.pop(A__))
																						logger.warning(
																						    f'{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'
																						    f' {positive_arg}={kwargs[positive_arg]}')

												lowercase						     =						kwargs.pop('''torchscript'''       ,self.torchscript)
												lowercase						     =						kwargs.pop('''torch_xla_tpu_print_metrics'''       ,self.torch_xla_tpu_print_metrics)
												lowercase						     =						kwargs.pop('''fp16_opt_level'''       ,self.fpaa_opt_level)
												super().__init__(**A__)

							lowercase_	:	bool	 =field(default=SCREAMING_SNAKE_CASE__	,  metadata={'''help''': '''Trace the models using torchscript'''}		)
							lowercase_	:	bool	 =field(default=SCREAMING_SNAKE_CASE__	,  metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}		)
							lowercase_	:	str	 =field(
							    default='''O1'''	,  metadata={
							        '''help''': (
							            '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
							            '''See details at https://nvidia.github.io/apex/amp.html'''
							        )
							    }	,  )




							@cached_property
							def 			A__				(    self):
												requires_backends(self       ,['''torch'''])
												logger.info('''PyTorch: setting up devices''')
												if not self.cuda:
																	lowercase						     =						torch.device('''cpu''')
																	lowercase						     =						0
												elif is_torch_tpu_available():
																	lowercase						     =						xm.xla_device()
																	lowercase						     =						0
												else:
																	lowercase						     =						torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''')
																	lowercase						     =						torch.cuda.device_count()
												return device, n_gpu




							@property
							def 			A__				(    self):
												return is_torch_tpu_available() and self.tpu




							@property
							def 			A__				(    self):
												requires_backends(self       ,['''torch'''])
												# TODO(PVP): currently only single GPU is supported
												return torch.cuda.current_device()




							@property
							def 			A__				(    self):
												requires_backends(self       ,['''torch'''])
												return self._setup_devices[0]




							@property
							def 			A__				(    self):
												requires_backends(self       ,['''torch'''])
												return self._setup_devices[1]





							@property
							def 			A__				(    self):
												return self.n_gpu > 0






from __future__ import annotations

from decimal import Decimal

from numpy import array



def 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						Decimal

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

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

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

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

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




