


import unittest

import numpy as np

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : np.ndarray      ,					SCREAMING_SNAKE_CASE__      : np.ndarray      ,					SCREAMING_SNAKE_CASE__      : np.ndarray      ,					SCREAMING_SNAKE_CASE__      : np.ndarray | None = None      ,					):
     __UpperCamelCase   							=np.shape(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=np.shape(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=np.shape(SCREAMING_SNAKE_CASE__    )

     if shape_a[0] != shape_b[0]:
          __UpperCamelCase   							=(
              '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(SCREAMING_SNAKE_CASE__    )

     if shape_b[1] != shape_c[1]:
          __UpperCamelCase   							=(
              '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(SCREAMING_SNAKE_CASE__    )

     __UpperCamelCase   							=pseudo_inv
     if a_inv is None:
          try:
               __UpperCamelCase   							=np.linalg.inv(SCREAMING_SNAKE_CASE__    )
          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    UpperCAmelCase__	( unittest.TestCase		):



    """simple docstring"""






    def _a (      self   )  ->	None:
         __UpperCamelCase   							=np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
         __UpperCamelCase   							=np.array([[0, 3], [3, 0], [2, 3]]   )
         __UpperCamelCase   							=np.array([[2, 1], [6, 3]]   )

         __UpperCamelCase   							=schur_complement(A_ ,       A_ ,       A_   )

         __UpperCamelCase   							=np.block([[a, b], [b.T, c]]   )

         __UpperCamelCase   							=np.linalg.det(A_   )
         __UpperCamelCase   							=np.linalg.det(A_   )
         __UpperCamelCase   							=np.linalg.det(A_   )

         self.assertAlmostEqual(A_ ,       det_a * det_s   )






    def _a (      self   )  ->	None:
         __UpperCamelCase   							=np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
         __UpperCamelCase   							=np.array([[0, 3], [3, 0], [2, 3]]   )
         __UpperCamelCase   							=np.array([[2, 1], [6, 3]]   )

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






    def _a (      self   )  ->	None:
         __UpperCamelCase   							=np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
         __UpperCamelCase   							=np.array([[0, 3], [3, 0], [2, 3]]   )
         __UpperCamelCase   							=np.array([[2, 1, 3], [6, 3, 5]]   )

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


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   unittest.main()









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__	( A_		):



    """simple docstring"""






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






class    UpperCAmelCase__	( unittest.TestCase		):



    """simple docstring"""






    def _a (      self   )  ->	Any:
         # 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=A_   ).to_kwargs() ,       {'a': 2, 'b': True}   )
         self.assertDictEqual(MockClass(a=2 ,       c=2.25   ).to_kwargs() ,       {'a': 2, 'c': 2.25}   )






    @require_cuda
    def _a (      self   )  ->	Union[str, Any]:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         __UpperCamelCase   							=GradScalerKwargs(init_scale=1024 ,       growth_factor=2   )
         AcceleratorState._reset_state()
         __UpperCamelCase   							=Accelerator(mixed_precision='fp16' ,       kwargs_handlers=[scaler_handler]   )
         print(accelerator.use_fpaa   )
         __UpperCamelCase   							=accelerator.scaler

         # Check the kwargs have been applied
         self.assertEqual(scaler._init_scale ,       1024.0   )
         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 ,       2000   )
         self.assertEqual(scaler._enabled ,       A_   )






    @require_multi_gpu
    def _a (      self   )  ->	Optional[int]:
         __UpperCamelCase   							=['torchrun', f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__   )]
         execute_subprocess_async(A_ ,       env=os.environ.copy()   )


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

   # Check the values changed in kwargs
   _A			=						''
   _A			=						model.bucket_bytes_cap // (1024 * 1024)
   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)





