'''simple docstring'''





from __future__ import annotations






class 				_A :




    '''simple docstring'''







    def __init__(       self    ,							lowercase__ )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Dict    	=       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(lowercase__ ) != 0:
              SCREAMING_SNAKE_CASE					:			int    	=       len(rows[0] )
              if cols == 0:
                   raise error
              for row in rows:
                   if len(lowercase__ ) != cols:
                        raise error
                   for value in row:
                        if not isinstance(lowercase__    ,							(int, float) ):
                             raise error
              SCREAMING_SNAKE_CASE					:			Tuple    	=       rows
         else:
              SCREAMING_SNAKE_CASE					:			Tuple    	=       []






    def       _UpperCamelCase    (       self )							-> list[list[int]]:
         return [[row[i] for row in self.rows] for i in range(len(self.rows[0] ) )]






    @property
    def       _UpperCamelCase    (       self )							-> int:
         return len(self.rows )






    @property
    def       _UpperCamelCase    (       self )							-> int:
         return len(self.rows[0] )






    @property
    def       _UpperCamelCase    (       self )							-> tuple[int, int]:
         return (self.num_rows, self.num_columns)






    @property
    def       _UpperCamelCase    (       self )							-> bool:
         return self.order[0] == self.order[1]






    def       _UpperCamelCase    (       self )							-> Matrix:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       [
             [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(lowercase__ )






    def       _UpperCamelCase    (       self )							-> int:
         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       _UpperCamelCase    (       self )							-> bool:
         return bool(self.determinant() )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ )							-> int:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       [
             [
                 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(lowercase__ ).determinant()






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ )							-> int:
         if (row + column) % 2 == 0:
              return self.get_minor(lowercase__    ,							lowercase__ )
         return -1 * self.get_minor(lowercase__    ,							lowercase__ )






    def       _UpperCamelCase    (       self )							-> Matrix:
         return Matrix(
             [
                 [self.get_minor(lowercase__    ,							lowercase__ ) for column in range(self.num_columns )]
                 for row in range(self.num_rows )
             ] )






    def       _UpperCamelCase    (       self )							-> Matrix:
         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       _UpperCamelCase    (       self )							-> Matrix:
         SCREAMING_SNAKE_CASE					:			Any    	=       [
             [self.cofactors().rows[column][row] for column in range(self.num_columns )]
             for row in range(self.num_rows )
         ]
         return Matrix(lowercase__ )






    def       _UpperCamelCase    (       self )							-> Matrix:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       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 )							-> str:
         return str(self.rows )






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






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None )							-> None:
         SCREAMING_SNAKE_CASE					:			str    	=       TypeError('Row must be a list containing all ints and/or floats' )
         if not isinstance(lowercase__    ,							lowercase__ ):
              raise type_error
         for value in row:
              if not isinstance(lowercase__    ,							(int, float) ):
                   raise type_error
         if len(lowercase__ ) != 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(lowercase__ )
         else:
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.rows[0:position] + [row] + self.rows[position:]






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






    def __eq__(       self    ,							lowercase__ )							-> bool:
         if not isinstance(lowercase__    ,							lowercase__ ):
              return NotImplemented
         return self.rows == other.rows






    def __ne__(       self    ,							lowercase__ )							-> bool:
         return not self == other






    def __neg__(       self )							-> Matrix:
         return self * -1






    def __add__(       self    ,							lowercase__ )							-> Matrix:
         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    ,							lowercase__ )							-> Matrix:
         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    ,							lowercase__ )							-> Matrix:
         if isinstance(lowercase__    ,							(int, float) ):
              return Matrix(
                  [[int(element * other ) for element in row] for row in self.rows] )
         elif isinstance(lowercase__    ,							lowercase__ ):
              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(lowercase__    ,							lowercase__ ) 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    ,							lowercase__ )							-> Matrix:
         if not isinstance(lowercase__    ,							lowercase__ ):
              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' )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self
         for _ in range(other - 1 ):
              result *= self
         return result






    @classmethod
    def       _UpperCamelCase    (       cls    ,							lowercase__    ,							lowercase__ )							-> int:
         return sum(row[i] * column[i] for i in range(len(lowercase__ ) ) )


if __name__ == "__main__":
 import doctest

 doctest.testmod()




'''simple docstring'''





import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler






@dataclass
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   int							=						0
    snake_case__							:   bool							=						False
    snake_case__							:   float							=						3.0






class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> Tuple:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         self.assertDictEqual(MockClass().to_kwargs()    ,							{} )
         self.assertDictEqual(MockClass(a=2 ).to_kwargs()    ,							{'a': 2} )
         self.assertDictEqual(MockClass(a=2    ,							b=lowercase__ ).to_kwargs()    ,							{'a': 2, 'b': True} )
         self.assertDictEqual(MockClass(a=2    ,							c=2.2_5 ).to_kwargs()    ,							{'a': 2, 'c': 2.2_5} )






    @require_cuda
    def       _UpperCamelCase    (       self )							-> Dict:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       GradScalerKwargs(init_scale=1_024    ,							growth_factor=2 )
         AcceleratorState._reset_state()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       Accelerator(mixed_precision='fp16'    ,							kwargs_handlers=[scaler_handler] )
         print(accelerator.use_fpaa )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       accelerator.scaler

         # Check the kwargs have been applied
         self.assertEqual(scaler._init_scale    ,							1_0_2_4.0 )
         self.assertEqual(scaler._growth_factor    ,							2.0 )

         # Check the other values are at the default
         self.assertEqual(scaler._backoff_factor    ,							0.5 )
         self.assertEqual(scaler._growth_interval    ,							2_000 )
         self.assertEqual(scaler._enabled    ,							lowercase__ )






    @require_multi_gpu
    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__ )]
         execute_subprocess_async(lowercase__    ,							env=os.environ.copy() )


if __name__ == "__main__":
 _lowerCAmelCase  :int  				=    DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
 _lowerCAmelCase  :int  				=    Accelerator(kwargs_handlers=[ddp_scaler])
 _lowerCAmelCase  :str  				=    torch.nn.Linear(100, 200)
 _lowerCAmelCase  :Optional[Any]  				=    accelerator.prepare(model)

 # Check the values changed in kwargs
 _lowerCAmelCase  :Dict  				=    """"""
 _lowerCAmelCase  :Optional[int]  				=    model.bucket_bytes_cap // (1_024 * 1_024)
 if observed_bucket_cap_map != 15:
  error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
 if model.find_unused_parameters is not True:
  error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

 # Check the values of the defaults
 if model.dim != 0:
  error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
 if model.broadcast_buffers is not True:
  error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
 if model.gradient_as_bucket_view is not False:
  error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

 # Raise error at the end to make sure we don't stop at the first failure.
 if len(error_msg) > 0:
  raise ValueError(error_msg)



