



"""simple docstring"""







from __future__ import annotations






class      UpperCAmelCase__		:



		def __init__( self  :    Any      ,							snake_case  :    list[list[int]]				)			->						Union[str, Any]:






									'''simple docstring'''

									A         =					TypeError(
									    'Matrices must be formed from a list of zero or more lists containing at '
									    'least one and the same number of values, each of which must be of type '
									    'int or float.'				)
									if len(snake_case				) != 0:
																A         =					len(rows[0]				)
																if cols == 0:
																							raise error
																for row in rows:
																							if len(snake_case				) != cols:
																														raise error
																							for value in row:
																														if not isinstance(snake_case      ,							(int, float)				):
																																					raise error
																A         =					rows
									else:
																A         =					[]



		def 					A_		( self  :    Any				)			->						list[list[int]]:






									'''simple docstring'''

									return [[row[i] for row in self.rows] for i in range(len(self.rows[0]				)				)]



		@property
		def 					A_		( self  :    List[Any]				)			->						int:






									'''simple docstring'''

									return len(self.rows				)



		@property
		def 					A_		( self  :    int				)			->						int:






									'''simple docstring'''

									return len(self.rows[0]				)



		@property
		def 					A_		( self  :    int				)			->						tuple[int, int]:






									'''simple docstring'''

									return (self.num_rows, self.num_columns)



		@property
		def 					A_		( self  :    Tuple				)			->						bool:






									'''simple docstring'''

									return self.order[0] == self.order[1]



		def 					A_		( self  :    Tuple				)			->						Matrix:






									'''simple docstring'''

									A         =					[
									    [0 if column_num != row_num else 1 for column_num in range(self.num_rows				)]
									    for row_num in range(self.num_rows				)
									]
									return Matrix(snake_case				)



		def 					A_		( self  :    Union[str, Any]				)			->						int:






									'''simple docstring'''

									if not self.is_square:
																return 0
									if self.order == (0, 0):
																return 1
									if self.order == (1, 1):
																return int(self.rows[0][0]				)
									if self.order == (2, 2):
																return int(
																    (self.rows[0][0] * self.rows[1][1])
																    - (self.rows[0][1] * self.rows[1][0])				)
									else:
																return sum(
																    self.rows[0][column] * self.cofactors().rows[0][column]
																    for column in range(self.num_columns				)				)



		def 					A_		( self  :    Dict				)			->						bool:






									'''simple docstring'''

									return bool(self.determinant()				)



		def 					A_		( self  :    Any      ,							snake_case  :    int      ,							snake_case  :    int				)			->						int:






									'''simple docstring'''

									A         =					[
									    [
									        self.rows[other_row][other_column]
									        for other_column in range(self.num_columns				)
									        if other_column != column
									    ]
									    for other_row in range(self.num_rows				)
									    if other_row != row
									]
									return Matrix(snake_case				).determinant()



		def 					A_		( self  :    Tuple      ,							snake_case  :    int      ,							snake_case  :    int				)			->						int:






									'''simple docstring'''

									if (row + column) % 2 == 0:
																return self.get_minor(snake_case      ,							snake_case				)
									return -1 * self.get_minor(snake_case      ,							snake_case				)



		def 					A_		( self  :    List[str]				)			->						Matrix:






									'''simple docstring'''

									return Matrix(
									    [
									        [self.get_minor(snake_case      ,							snake_case				) for column in range(self.num_columns				)]
									        for row in range(self.num_rows				)
									    ]				)



		def 					A_		( self  :    Union[str, Any]				)			->						Matrix:






									'''simple docstring'''

									return Matrix(
									    [
									        [
									            self.minors().rows[row][column]
									            if (row + column) % 2 == 0
									            else self.minors().rows[row][column] * -1
									            for column in range(self.minors().num_columns				)
									        ]
									        for row in range(self.minors().num_rows				)
									    ]				)



		def 					A_		( self  :    Union[str, Any]				)			->						Matrix:






									'''simple docstring'''

									A         =					[
									    [self.cofactors().rows[column][row] for column in range(self.num_columns				)]
									    for row in range(self.num_rows				)
									]
									return Matrix(snake_case				)



		def 					A_		( self  :    Tuple				)			->						Matrix:






									'''simple docstring'''

									A         =					self.determinant()
									if not determinant:
																raise TypeError('Only matrices with a non-zero determinant have an inverse'				)
									return self.adjugate() * (1 / determinant)



		def __repr__( self  :    Optional[Any]				)			->						str:






									'''simple docstring'''

									return str(self.rows				)



		def __str__( self  :    List[str]				)			->						str:






									'''simple docstring'''

									if self.num_rows == 0:
																return "[]"
									if self.num_rows == 1:
																return "[[" + ". ".join(str(self.rows[0]				)				) + "]]"
									return (
									    "["
									    + "\n ".join(
									        [
									            '[' + '. '.join([str(snake_case				) for value in row]				) + '.]'
									            for row in self.rows
									        ]				)
									    + "]"
									)



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    list[int]      ,							snake_case  :    int | None = None				)			->						None:






									'''simple docstring'''

									A         =					TypeError('Row must be a list containing all ints and/or floats'				)
									if not isinstance(snake_case      ,							snake_case				):
																raise type_error
									for value in row:
																if not isinstance(snake_case      ,							(int, float)				):
																							raise type_error
									if len(snake_case				) != self.num_columns:
																raise ValueError(
																    'Row must be equal in length to the other rows in the matrix'				)
									if position is None:
																self.rows.append(snake_case				)
									else:
																A         =					self.rows[0:position] + [row] + self.rows[position:]



		def 					A_		( self  :    Tuple      ,							snake_case  :    list[int]      ,							snake_case  :    int | None = None				)			->						None:






									'''simple docstring'''

									A         =					TypeError(
									    'Column must be a list containing all ints and/or floats'				)
									if not isinstance(snake_case      ,							snake_case				):
																raise type_error
									for value in column:
																if not isinstance(snake_case      ,							(int, float)				):
																							raise type_error
									if len(snake_case				) != self.num_rows:
																raise ValueError(
																    'Column must be equal in length to the other columns in the matrix'				)
									if position is None:
																A         =					[self.rows[i] + [column[i]] for i in range(self.num_rows				)]
									else:
																A         =					[
																    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
																    for i in range(self.num_rows				)
																]



		def __eq__( self  :    str      ,							snake_case  :    object				)			->						bool:






									'''simple docstring'''

									if not isinstance(snake_case      ,							snake_case				):
																return NotImplemented
									return self.rows == other.rows



		def __ne__( self  :    int      ,							snake_case  :    object				)			->						bool:






									'''simple docstring'''

									return not self == other



		def __neg__( self  :    List[Any]				)			->						Matrix:






									'''simple docstring'''

									return self * -1



		def __add__( self  :    Tuple      ,							snake_case  :    Matrix				)			->						Matrix:






									'''simple docstring'''

									if self.order != other.order:
																raise ValueError('Addition requires matrices of the same order'				)
									return Matrix(
									    [
									        [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns				)]
									        for i in range(self.num_rows				)
									    ]				)



		def __sub__( self  :    Tuple      ,							snake_case  :    Matrix				)			->						Matrix:






									'''simple docstring'''

									if self.order != other.order:
																raise ValueError('Subtraction requires matrices of the same order'				)
									return Matrix(
									    [
									        [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns				)]
									        for i in range(self.num_rows				)
									    ]				)



		def __mul__( self  :    Tuple      ,							snake_case  :    Matrix | int | float				)			->						Matrix:






									'''simple docstring'''

									if isinstance(snake_case      ,							(int, float)				):
																return Matrix(
																    [[int(element * other				) for element in row] for row in self.rows]				)
									elif isinstance(snake_case      ,							snake_case				):
																if self.num_columns != other.num_rows:
																							raise ValueError(
																							    'The number of columns in the first matrix must '
																							    'be equal to the number of rows in the second'				)
																return Matrix(
																    [
																        [Matrix.dot_product(snake_case      ,							snake_case				) for column in other.columns()]
																        for row in self.rows
																    ]				)
									else:
																raise TypeError(
																    'A Matrix can only be multiplied by an int, float, or another matrix'				)



		def __pow__( self  :    Optional[Any]      ,							snake_case  :    int				)			->						Matrix:






									'''simple docstring'''

									if not isinstance(snake_case      ,							snake_case				):
																raise TypeError('A Matrix can only be raised to the power of an int'				)
									if not self.is_square:
																raise ValueError('Only square matrices can be raised to a power'				)
									if other == 0:
																return self.identity()
									if other < 0:
																if self.is_invertable():
																							return self.inverse() ** (-other)
																raise ValueError(
																    'Only invertable matrices can be raised to a negative power'				)
									A         =					self
									for _ in range(other - 1				):
																result *= self
									return result



		@classmethod
		def 					A_		( cls  :    Optional[int]      ,							snake_case  :    list[int]      ,							snake_case  :    list[int]				)			->						int:






									'''simple docstring'''

									return sum(row[i] * column[i] for i in range(len(snake_case				)				)				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()








"""simple docstring"""







import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


A    =			logging.get_logger(__name__)

def   lowerCAmelCase__			(     lowerCamelCase__=None				,      lowerCamelCase__=None			) ->							Dict:
							return field(default_factory=lambda: default				,      metadata=lowerCamelCase__			)






@dataclass
class      UpperCAmelCase__		:
		lowerCAmelCase_       :	List[str] 							=     list_field(
		    default=[]   ,metadata={
		        """help""": (
		            """Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"""
		            """ of all available models"""
		        )
		    }   ,)

		lowerCAmelCase_       :	List[int] 							=     list_field(
		    default=[8]   ,metadata={"""help""": """List of batch sizes for which memory and time performance will be evaluated"""}     )

		lowerCAmelCase_       :	List[int] 							=     list_field(
		    default=[8, 32, 1_28, 5_12]   ,metadata={"""help""": """List of sequence lengths for which memory and time performance will be evaluated"""}   ,)

		lowerCAmelCase_       :	bool 							=     field(
		    default=UpperCamelCase   ,metadata={"""help""": """Whether to benchmark inference of model. Inference can be disabled via --no-inference."""}   ,)
		lowerCAmelCase_       :	bool 							=     field(
		    default=UpperCamelCase   ,metadata={"""help""": """Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."""}   ,)
		lowerCAmelCase_       :	bool 							=     field(
		    default=UpperCamelCase   ,metadata={"""help""": """Whether to run on available tpu devices. TPU can be disabled via --no-tpu."""}     )
		lowerCAmelCase_       :	bool 							=     field(default=UpperCamelCase   ,metadata={"""help""": """Use FP16 to accelerate inference."""}     )
		lowerCAmelCase_       :	bool 							=     field(default=UpperCamelCase   ,metadata={"""help""": """Benchmark training of model"""}     )
		lowerCAmelCase_       :	bool 							=     field(default=UpperCamelCase   ,metadata={"""help""": """Verbose memory tracing"""}     )
		lowerCAmelCase_       :	bool 							=     field(
		    default=UpperCamelCase   ,metadata={"""help""": """Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."""}   ,)
		lowerCAmelCase_       :	bool 							=     field(
		    default=UpperCamelCase   ,metadata={
		        """help""": """Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"""
		    }   ,)
		lowerCAmelCase_       :	bool 							=     field(default=UpperCamelCase   ,metadata={"""help""": """Trace memory line by line"""}     )
		lowerCAmelCase_       :	bool 							=     field(default=UpperCamelCase   ,metadata={"""help""": """Save result to a CSV file"""}     )
		lowerCAmelCase_       :	bool 							=     field(default=UpperCamelCase   ,metadata={"""help""": """Save all print statements in a log file"""}     )
		lowerCAmelCase_       :	bool 							=     field(default=UpperCamelCase   ,metadata={"""help""": """Whether to print environment information"""}     )
		lowerCAmelCase_       :	bool 							=     field(
		    default=UpperCamelCase   ,metadata={
		        """help""": (
		            """Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"""
		            """ multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"""
		            """ for debugging / testing and on TPU."""
		        )
		    }   ,)
		lowerCAmelCase_       :	str 							=     field(
		    default=f"""inference_time_{round(time()     )}.csv"""   ,metadata={"""help""": """CSV filename used if saving time results to csv."""}   ,)
		lowerCAmelCase_       :	str 							=     field(
		    default=f"""inference_memory_{round(time()     )}.csv"""   ,metadata={"""help""": """CSV filename used if saving memory results to csv."""}   ,)
		lowerCAmelCase_       :	str 							=     field(
		    default=f"""train_time_{round(time()     )}.csv"""   ,metadata={"""help""": """CSV filename used if saving time results to csv for training."""}   ,)
		lowerCAmelCase_       :	str 							=     field(
		    default=f"""train_memory_{round(time()     )}.csv"""   ,metadata={"""help""": """CSV filename used if saving memory results to csv for training."""}   ,)
		lowerCAmelCase_       :	str 							=     field(
		    default=f"""env_info_{round(time()     )}.csv"""   ,metadata={"""help""": """CSV filename used if saving environment information."""}   ,)
		lowerCAmelCase_       :	str 							=     field(
		    default=f"""log_{round(time()     )}.csv"""   ,metadata={"""help""": """Log filename used if print statements are saved in log."""}   ,)
		lowerCAmelCase_       :	int 							=     field(default=3   ,metadata={"""help""": """Times an experiment will be run."""}     )
		lowerCAmelCase_       :	bool 							=     field(
		    default=UpperCamelCase   ,metadata={
		        """help""": (
		            """Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"""
		            """ model weights."""
		        )
		    }   ,)



		def 					A_		( self  :    Dict				)			->						Optional[int]:






									'''simple docstring'''

									warnings.warn(
									    f"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
									    ' are deprecated in general and it is advised to use external Benchmarking libraries '
									    ' to benchmark Transformer models.'      ,							snake_case      ,							)



		def 					A_		( self  :    str				)			->						List[Any]:






									'''simple docstring'''

									return json.dumps(dataclasses.asdict(self				)      ,							indent=2				)



		@property
		def 					A_		( self  :    Union[str, Any]				)			->						List[str]:






									'''simple docstring'''

									if len(self.models				) <= 0:
																raise ValueError(
																    'Please make sure you provide at least one model name / model identifier, *e.g.* `--models'
																    ' bert-base-cased` or `args.models = [\'bert-base-cased\'].'				)
									return self.models



		@property
		def 					A_		( self  :    Tuple				)			->						List[str]:






									'''simple docstring'''

									if not self.multi_process:
																return False
									elif self.is_tpu:
																logger.info('Multiprocessing is currently not possible on TPU.'				)
																return False
									else:
																return True



