





"""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  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				int     						=  0
       SCREAMING_SNAKE_CASE_       :				bool     						=  False
       SCREAMING_SNAKE_CASE_       :				float     						=  3.0






class  _SCREAMING_SNAKE_CASE(       unittest.TestCase  ):




       def    _UpperCamelCase					(						self							)      ->				Tuple:





             """simple docstring"""






             self.assertDictEqual(MockClass().to_kwargs()		,{}							)
             self.assertDictEqual(MockClass(a=2							).to_kwargs()		,{'''a''': 2}							)
             self.assertDictEqual(MockClass(a=2		,b=SCREAMING_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    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  GradScalerKwargs(init_scale=10_24		,growth_factor=2							)
             AcceleratorState._reset_state()
             __SCREAMING_SNAKE_CASE       :int		    =  Accelerator(mixed_precision='''fp16'''		,kwargs_handlers=[scaler_handler]							)
             print(accelerator.use_fpaa							)
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  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		,20_00							)
             self.assertEqual(scaler._enabled		,SCREAMING_SNAKE_CASE__							)




       @require_multi_gpu
       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  ['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__							)]
             execute_subprocess_async(SCREAMING_SNAKE_CASE__		,env=os.environ.copy()							)


if __name__ == "__main__":
  lowerCamelCase_       =		DistributedDataParallelKwargs(bucket_cap_mb=1_5, find_unused_parameters=True)
  lowerCamelCase_       =		Accelerator(kwargs_handlers=[ddp_scaler])
  lowerCamelCase_       =		torch.nn.Linear(1_0_0, 2_0_0)
  lowerCamelCase_       =		accelerator.prepare(model)

  # Check the values changed in kwargs
  lowerCamelCase_       =		""
  lowerCamelCase_       =		model.bucket_bytes_cap // (1_0_2_4 * 1_0_2_4)
  if observed_bucket_cap_map != 1_5:
    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 					__lowerCamelCase       (							a_					:		int = 10			,       a_					:		int = 22							)      ->				int:
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  range(1			,       a_							)
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  range(1			,       a_							)
      return sum(
          1 for power in powers for base in bases if len(str(base**power							)							) == power							)


if __name__ == "__main__":
  print(f'{solution(1_0, 2_2) = }')