
"""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   _lowercase						(       _UpperCAmelCase				):


     '''simple docstring'''





     _A								=							0
     _A								=							False
     _A								=							3.0







class   _lowercase						(       unittest.TestCase				):


     '''simple docstring'''





     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          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=2.25  ).to_kwargs() ,			{"a": 2, "c": 2.25}  )



     @require_cuda
     def  lowerCAmelCase__       (      self  )->					int:
          UpperCAmelCase__							: Optional[int] 							=						GradScalerKwargs(init_scale=10_24 ,			growth_factor=2  )
          AcceleratorState._reset_state()
          UpperCAmelCase__							: List[Any] 							=						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 ,			20_00  )
          self.assertEqual(scaler._enabled ,			lowercase__  )



     @require_multi_gpu
     def  lowerCAmelCase__       (      self  )->					List[Any]:
          UpperCAmelCase__							: 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__":
    A__      :	Union[str, Any]          =    DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
    A__      :	Dict          =    Accelerator(kwargs_handlers=[ddp_scaler])
    A__      :	Optional[int]          =    torch.nn.Linear(100, 200)
    A__      :	int          =    accelerator.prepare(model)

    # Check the values changed in kwargs
    A__      :	Dict          =    """"""
    A__      :	Tuple          =    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)





"""simple docstring"""




def 							a__ (				lowerCAmelCase	:       list       ,    lowerCAmelCase	:       list		):






     '''simple docstring'''
     _validate_point(lowerCAmelCase		)
     _validate_point(lowerCAmelCase		)
     if len(lowerCAmelCase		) != len(lowerCAmelCase		):
          raise ValueError("Both points must be in the same n-dimensional space"		)

     return float(sum(abs(a - b		) for a, b in zip(lowerCAmelCase       ,    lowerCAmelCase		)		)		)



def 							a__ (				lowerCAmelCase	:       list[float]		):






     '''simple docstring'''
     if point:
          if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
               for item in point:
                    if not isinstance(lowerCAmelCase       ,    (int, float)		):
                         UpperCAmelCase__							: Tuple 							=						(
                             "Expected a list of numbers as input, found "
                             F"{type(lowerCAmelCase		).__name__}"
                         )
                         raise TypeError(lowerCAmelCase		)
          else:
               UpperCAmelCase__							: Dict 							=						F"Expected a list of numbers as input, found {type(lowerCAmelCase		).__name__}"
               raise TypeError(lowerCAmelCase		)
     else:
          raise ValueError("Missing an input"		)



def 							a__ (				lowerCAmelCase	:       list       ,    lowerCAmelCase	:       list		):






     '''simple docstring'''
     _validate_point(lowerCAmelCase		)
     _validate_point(lowerCAmelCase		)
     if len(lowerCAmelCase		) != len(lowerCAmelCase		):
          raise ValueError("Both points must be in the same n-dimensional space"		)

     return float(sum(abs(x - y		) for x, y in zip(lowerCAmelCase       ,    lowerCAmelCase		)		)		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()



