'''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        __A      ( __A	):
    a__       :						int          =  0
    a__       :						bool          =  False
    a__       :						float          =  3.0




class        __A      ( unittest.TestCase	):



    def 					_lowercase						(self    :    Optional[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=_UpperCamelCase ).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    :    Optional[int] ):
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         UpperCAmelCase_           =   GradScalerKwargs(init_scale=1024   ,			growth_factor=2 )
         AcceleratorState._reset_state()
         UpperCAmelCase_           =   Accelerator(mixed_precision="fp16"   ,			kwargs_handlers=[scaler_handler] )
         print(accelerator.use_fpaa )
         UpperCAmelCase_           =   accelerator.scaler

         # Check the kwargs have been applied
         self.assertEqual(scaler._init_scale   ,			10_24.0 )
         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   ,			_UpperCamelCase )






    @require_multi_gpu
    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_           =   ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__ )]
         execute_subprocess_async(_UpperCamelCase   ,			env=os.environ.copy() )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:     Optional[int]  =DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
    SCREAMING_SNAKE_CASE_:     Optional[Any]  =Accelerator(kwargs_handlers=[ddp_scaler])
    SCREAMING_SNAKE_CASE_:     Union[str, Any]  =torch.nn.Linear(1_00, 2_00)
    SCREAMING_SNAKE_CASE_:     List[str]  =accelerator.prepare(model)

    # Check the values changed in kwargs
    SCREAMING_SNAKE_CASE_:     Tuple  =''
    SCREAMING_SNAKE_CASE_:     Optional[Any]  =model.bucket_bytes_cap // (10_24 * 10_24)
    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)







import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available


def   __SCREAMING_SNAKE_CASE					(*SCREAMING_SNAKE_CASE__       ):
						if not isinstance(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
												snake_case_   =	list(SCREAMING_SNAKE_CASE__       )
						for i in range(len(SCREAMING_SNAKE_CASE__       )       ):
												snake_case_   =	None
						gc.collect()
						if is_xpu_available():
												torch.xpu.empty_cache()
						elif is_npu_available():
												torch.npu.empty_cache()
						else:
												torch.cuda.empty_cache()
						return objects


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	[
						    '''CUDA out of memory.''',  # CUDA OOM
						    '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
						    '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
						]
						if isinstance(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ) and len(exception.args       ) == 1:
												return any(err in exception.args[0] for err in _statements       )
						return False


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__ = None  ,		SCREAMING_SNAKE_CASE__ = 128       ):
						if function is None:
												return functools.partial(SCREAMING_SNAKE_CASE__  ,		starting_batch_size=SCREAMING_SNAKE_CASE__       )

						snake_case_   =	starting_batch_size

						def decorator(*SCREAMING_SNAKE_CASE__  ,		**SCREAMING_SNAKE_CASE__       ):
												nonlocal batch_size
												gc.collect()
												if is_xpu_available():
																		torch.xpu.empty_cache()
												elif is_npu_available():
																		torch.npu.empty_cache()
												else:
																		torch.cuda.empty_cache()
												snake_case_   =	list(inspect.signature(SCREAMING_SNAKE_CASE__       ).parameters.keys()       )
												# Guard against user error
												if len(SCREAMING_SNAKE_CASE__       ) < (len(SCREAMING_SNAKE_CASE__       ) + 1):
																		snake_case_   =	''', '''.join([F'''{arg}={value}''' for arg, value in zip(params[1:]  ,		args[1:]       )]       )
																		raise TypeError(
																		    F'''Batch size was passed into `{function.__name__}` as the first argument when called.'''
																		    F'''Remove this as the decorator already does so: `{function.__name__}({arg_str})`'''       )
												while True:
																		if batch_size == 0:
																								raise RuntimeError('''No executable batch size found, reached zero.'''       )
																		try:
																								return function(SCREAMING_SNAKE_CASE__  ,		*SCREAMING_SNAKE_CASE__  ,		**SCREAMING_SNAKE_CASE__       )
																		except Exception as e:
																								if should_reduce_batch_size(SCREAMING_SNAKE_CASE__       ):
																														gc.collect()
																														if is_xpu_available():
																																				torch.xpu.empty_cache()
																														elif is_npu_available():
																																				torch.npu.empty_cache()
																														else:
																																				torch.cuda.empty_cache()
																														batch_size //= 2
																								else:
																														raise

						return decorator