


"""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 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      int        =   0
       __A			:      bool        =   False
       __A			:      float        =   3.0







class 					__magic_name__		(			unittest.TestCase     ):






       def  __snake_case						(   self  :    str				):

              '''simple docstring'''






              self.assertDictEqual(MockClass().to_kwargs()  ,     {}				)
              self.assertDictEqual(MockClass(a=2				).to_kwargs()  ,     {'''a''': 2}				)
              self.assertDictEqual(MockClass(a=2  ,     b=snake_case__				).to_kwargs()  ,     {'''a''': 2, '''b''': True}				)
              self.assertDictEqual(MockClass(a=2  ,     c=2.25				).to_kwargs()  ,     {'''a''': 2, '''c''': 2.25}				)






       @require_cuda
       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              lowercase		:str       							=				GradScalerKwargs(init_scale=1_0_2_4  ,     growth_factor=2				)
              AcceleratorState._reset_state()
              lowercase		:Optional[int]       							=				Accelerator(mixed_precision='''fp16'''  ,     kwargs_handlers=[scaler_handler]				)
              print(accelerator.use_fpaa				)
              lowercase		:Dict       							=				accelerator.scaler

              # Check the kwargs have been applied
              self.assertEqual(scaler._init_scale  ,     10_24.0				)
              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_0_0_0				)
              self.assertEqual(scaler._enabled  ,     snake_case__				)







       @require_multi_gpu
       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              lowercase		:Tuple       							=				['''torchrun''', f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__				)]
              execute_subprocess_async(snake_case__  ,     env=os.environ.copy()				)


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

 # Check the values changed in kwargs
 UpperCAmelCase      		=			''''''
 UpperCAmelCase      		=			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)





"""simple docstring"""



import unittest

import numpy as np






def       lowerCamelCase					(a_						:np.ndarray				,				a_						:np.ndarray				,				a_						:np.ndarray				,				a_						:np.ndarray | None = None				,				)     ->							np.ndarray:
       lowercase		:str       							=				np.shape(a_)
       lowercase		:Dict       							=				np.shape(a_)
       lowercase		:Optional[int]       							=				np.shape(a_)

       if shape_a[0] != shape_b[0]:
              lowercase		:List[str]       							=				(
                  '''Expected the same number of rows for A and B. '''
                  F"""Instead found A of size {shape_a} and B of size {shape_b}"""
              )
              raise ValueError(a_)

       if shape_b[1] != shape_c[1]:
              lowercase		:List[str]       							=				(
                  '''Expected the same number of columns for B and C. '''
                  F"""Instead found B of size {shape_b} and C of size {shape_c}"""
              )
              raise ValueError(a_)

       lowercase		:Optional[Any]       							=				pseudo_inv
       if a_inv is None:
              try:
                     lowercase		:int       							=				np.linalg.inv(a_)
              except np.linalg.LinAlgError:
                     raise ValueError(
                         '''Input matrix A is not invertible. Cannot compute Schur complement.''')

       return mat_c - mat_b.T @ a_inv @ mat_b
class 					__magic_name__		(			unittest.TestCase     ):






       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              lowercase		:Dict       							=				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]				)
              lowercase		:List[str]       							=				np.array([[0, 3], [3, 0], [2, 3]]				)
              lowercase		:List[str]       							=				np.array([[2, 1], [6, 3]]				)

              lowercase		:Any       							=				schur_complement(snake_case__  ,     snake_case__  ,     snake_case__				)

              lowercase		:int       							=				np.block([[a, b], [b.T, c]]				)

              lowercase		:Tuple       							=				np.linalg.det(snake_case__				)
              lowercase		:Any       							=				np.linalg.det(snake_case__				)
              lowercase		:List[Any]       							=				np.linalg.det(snake_case__				)

              self.assertAlmostEqual(snake_case__  ,     det_a * det_s				)






       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]				)
              lowercase		:Union[str, Any]       							=				np.array([[0, 3], [3, 0], [2, 3]]				)
              lowercase		:List[str]       							=				np.array([[2, 1], [6, 3]]				)

              with self.assertRaises(snake_case__				):
                     schur_complement(snake_case__  ,     snake_case__  ,     snake_case__				)







       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              lowercase		:List[str]       							=				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]				)
              lowercase		:int       							=				np.array([[0, 3], [3, 0], [2, 3]]				)
              lowercase		:List[Any]       							=				np.array([[2, 1, 3], [6, 3, 5]]				)

              with self.assertRaises(snake_case__				):
                     schur_complement(snake_case__  ,     snake_case__  ,     snake_case__				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 unittest.main()

