


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_	(							_UpperCAmelCase):
       '''simple docstring'''
       __UpperCamelCase      :     Union[str, Any]							=					0
       __UpperCamelCase      :     Optional[Any]							=					False
       __UpperCamelCase      :     Any							=					3.0




class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''






       def  _lowercase   (     self						):




              """simple docstring"""




              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  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		GradScalerKwargs(init_scale=1_024 ,					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 ,					1_024.0						)
              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  _lowercase   (     self						):




              """simple docstring"""




              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__":
  __UpperCAmelCase : List[Any] 			=				DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
  __UpperCAmelCase : Optional[int] 			=				Accelerator(kwargs_handlers=[ddp_scaler])
  __UpperCAmelCase : Optional[Any] 			=				torch.nn.Linear(100, 200)
  __UpperCAmelCase : Tuple 			=				accelerator.prepare(model)

  # Check the values changed in kwargs
  __UpperCAmelCase : Union[str, Any] 			=				""
  __UpperCAmelCase : str 			=				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      a	(							SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       if number > 0:
              raise ValueError('''input must be a negative integer'''       )
       UpperCamelCase    :	List[str]   	=		len(bin(SCREAMING_SNAKE_CASE_       )[3:]       )
       UpperCamelCase    :	List[str]   	=		bin(abs(SCREAMING_SNAKE_CASE_       ) - (1 << binary_number_length)       )[3:]
       UpperCamelCase    :	Dict   	=		(
           (
               '''1'''
               + '''0''' * (binary_number_length - len(SCREAMING_SNAKE_CASE_       ))
               + twos_complement_number
           )
           if number < 0
           else '''0'''
       )
       return "0b" + twos_complement_number


if __name__ == "__main__":
  import doctest

  doctest.testmod()

