'''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    (		a__   ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE   						=       0
       SCREAMING_SNAKE_CASE   						=       False
       SCREAMING_SNAKE_CASE   						=       3.0



class      UpperCAmelCase    (		unittest.TestCase   ):





       '''simple docstring'''



       def _lowerCAmelCase(		self					)    ->    List[Any]:
        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=2.2_5					).to_kwargs()      ,  {'''a''': 2, '''c''': 2.2_5}					)

       @require_cuda
       def _lowerCAmelCase(		self					)    ->    Dict:
        lowercase__							:       Any										=   GradScalerKwargs(init_scale=1024      ,  growth_factor=2					)
        AcceleratorState._reset_state()
        lowercase__							:       Dict										=   Accelerator(mixed_precision='''fp16'''      ,  kwargs_handlers=[scaler_handler]					)
        print(accelerator.use_fpaa					)
        lowercase__							:       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      ,  _snake_case					)


       @require_multi_gpu
       def _lowerCAmelCase(		self					)    ->    List[str]:
        lowercase__							:       str										=   ['''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__":
  __a:					List[str]			  =  DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
  __a:					Optional[Any]			  =  Accelerator(kwargs_handlers=[ddp_scaler])
  __a:					Dict			  =  torch.nn.Linear(1_00, 2_00)
  __a:					Optional[Any]			  =  accelerator.prepare(model)

  # Check the values changed in kwargs
  __a:					Dict			  =  ''''''
  __a:					Tuple			  =  model.bucket_bytes_cap // (10_24 * 10_24)
  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"""
def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str			):
 return " ".join(input_str.split()[::-1]			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()





