



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   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				0
       _lowerCamelCase			    =				False
       _lowerCamelCase			    =				3.0


class   snake_case_ (unittest.TestCase  ):




       """simple docstring"""






       def  A_   (     self):

             """simple docstring"""





             self.assertDictEqual(MockClass().to_kwargs()     ,{})
             self.assertDictEqual(MockClass(a=2).to_kwargs()     ,{"a": 2})
             self.assertDictEqual(MockClass(a=2     ,b=UpperCamelCase_).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):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		GradScalerKwargs(init_scale=1024     ,growth_factor=2)
             AcceleratorState._reset_state()
             UpperCAmelCase_  :	int				      =		Accelerator(mixed_precision="fp16"     ,kwargs_handlers=[scaler_handler])
             print(accelerator.use_fpaa)
             UpperCAmelCase_  :	List[str]				      =		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     ,UpperCamelCase_)



       @require_multi_gpu
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[Any]				      =		['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__)]
             execute_subprocess_async(UpperCamelCase_     ,env=os.environ.copy())


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

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





def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case    )     ->     list:






      '''simple docstring'''







      UpperCAmelCase_  :	Any				      =		len(__snake_case    )
      UpperCAmelCase_  :	Tuple				      =		[[0] * n for i in range(__snake_case    )]
      for i in range(__snake_case    ):
            UpperCAmelCase_  :	Optional[Any]				      =		y_points[i]

      for i in range(2					,  __snake_case    ):
            for j in range(__snake_case					,  __snake_case    ):
                  UpperCAmelCase_  :	int				      =		(
                      (xa - x_points[j - i + 1]) * q[j][i - 1]
                      - (xa - x_points[j]) * q[j - 1][i - 1]
                  ) / (x_points[j] - x_points[j - i + 1])

      return [q[n - 1][n - 1], q]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
