


"""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 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



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







class 					__lowerCAmelCase					(   unittest.TestCase       ):


		'''simple docstring'''



		def 			__UpperCAmelCase       (  self      ):
				# 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=_a      ).to_kwargs()							, {'''a''': 2, '''b''': True}      )
				self.assertDictEqual(MockClass(a=2							, c=2.25      ).to_kwargs()							, {'''a''': 2, '''c''': 2.25}      )

		@require_cuda
		def 			__UpperCAmelCase       (  self      ):
				# If no defaults are changed, `to_kwargs` returns an empty dict.
				__a           =			GradScalerKwargs(init_scale=1_024							, growth_factor=2      )
				AcceleratorState._reset_state()
				__a           =			Accelerator(mixed_precision='''fp16'''							, kwargs_handlers=[scaler_handler]      )
				print(accelerator.use_fpaa      )
				__a           =			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							, 2_000      )
				self.assertEqual(scaler._enabled							, _a      )





		@require_multi_gpu
		def 			__UpperCAmelCase       (  self      ):
				__a           =			['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__      )]
				execute_subprocess_async(_a							, env=os.environ.copy()      )


if __name__ == "__main__":
				lowercase_			     =  DistributedDataParallelKwargs(bucket_cap_mb=1_5, find_unused_parameters=True)
				lowercase_			     =  Accelerator(kwargs_handlers=[ddp_scaler])
				lowercase_			     =  torch.nn.Linear(1_0_0, 2_0_0)
				lowercase_			     =  accelerator.prepare(model)

				# Check the values changed in kwargs
				lowercase_			     =  ""
				lowercase_			     =  model.bucket_bytes_cap // (1_0_2_4 * 1_0_2_4)
				if observed_bucket_cap_map != 1_5:
								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"""




import numpy as np

def        lowercase							(		lowerCAmelCase__ :     np.ndarray    ,					lowerCAmelCase__ :     float			) ->      np.ndarray:
		return np.where(vector > 0    ,					lowerCAmelCase__    ,					(alpha * (np.exp(lowerCAmelCase__			) - 1))			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()






