





"""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_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							int									=		0
				__snake_case :							bool									=		False
				__snake_case :							float									=		3.0

class 		SCREAMING_SNAKE_CASE_				(     unittest.TestCase ):
				"""simple docstring"""




				def        __lowercase	(  self	:Any      ):
											# 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=2.25      ).to_kwargs()    ,     {'''a''': 2, '''c''': 2.25}      )




				@require_cuda
				def        __lowercase	(  self	:List[Any]      ):
											# If no defaults are changed, `to_kwargs` returns an empty dict.
											__lowerCamelCase   :   Optional[Any]       =GradScalerKwargs(init_scale=1024    ,     growth_factor=2      )
											AcceleratorState._reset_state()
											__lowerCamelCase   :   Union[str, Any]       =Accelerator(mixed_precision='''fp16'''    ,     kwargs_handlers=[scaler_handler]      )
											print(accelerator.use_fpaa      )
											__lowerCamelCase   :   Dict       =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    ,     __lowercase      )




				@require_multi_gpu
				def        __lowercase	(  self	:int      ):
											__lowerCamelCase   :   Union[str, Any]       =['''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					      =   DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
							_UpperCamelCase					      =   Accelerator(kwargs_handlers=[ddp_scaler])
							_UpperCamelCase					      =   torch.nn.Linear(100, 200)
							_UpperCamelCase					      =   accelerator.prepare(model)

							# Check the values changed in kwargs
							_UpperCamelCase					      =   ''
							_UpperCamelCase					      =   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)











"""simple docstring"""





from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
							import accelerate



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str ):


							'''simple docstring'''







							if not is_accelerate_available():
														return method
							__lowerCamelCase   :   Dict       =version.parse(accelerate.__version__ ).base_version
							if version.parse(SCREAMING_SNAKE_CASE ) < version.parse('''0.17.0''' ):
														return method

							def wrapper(self							:				List[Any]     ,    *SCREAMING_SNAKE_CASE							:				Any     ,    **SCREAMING_SNAKE_CASE							:				List[str] ):
														if hasattr(self     ,    '''_hf_hook''' ) and hasattr(self._hf_hook     ,    '''pre_forward''' ):
																					self._hf_hook.pre_forward(self )
														return method(self     ,    *SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE )

							return wrapper




