'''simple docstring'''





def     __lowerCAmelCase						(       a_		)      ->	Optional[Any]:





     '''simple docstring'''





     assert column_title.isupper()
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       0
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       len(_lowerCAmelCase		) - 1
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       0

     while index >= 0:
          SCREAMING_SNAKE_CASE					:			List[str]    	=       (ord(column_title[index]		) - 64) * pow(26       ,      _lowerCAmelCase		)
          answer += value
          power += 1
          index -= 1

     return answer


if __name__ == "__main__":
 from doctest import testmod

 testmod()




'''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 (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







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






class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> Tuple:
         # 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=lowercase__ ).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 )							-> Dict:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       GradScalerKwargs(init_scale=1_024    ,							growth_factor=2 )
         AcceleratorState._reset_state()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       Accelerator(mixed_precision='fp16'    ,							kwargs_handlers=[scaler_handler] )
         print(accelerator.use_fpaa )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       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    ,							2_000 )
         self.assertEqual(scaler._enabled    ,							lowercase__ )






    @require_multi_gpu
    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__ )]
         execute_subprocess_async(lowercase__    ,							env=os.environ.copy() )


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

 # Check the values changed in kwargs
 _lowerCAmelCase  :Dict  				=    """"""
 _lowerCAmelCase  :Optional[int]  				=    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)



