


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 ):
					__SCREAMING_SNAKE_CASE							:	Union[str, Any]            = 0
					__SCREAMING_SNAKE_CASE							:	Tuple            = False
					__SCREAMING_SNAKE_CASE							:	Dict            = 3.0







class 							UpperCamelCase__ (						unittest.TestCase ):

					def 	UpperCAmelCase__							(  self							: Tuple							):






										'''simple docstring'''






										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 	UpperCAmelCase__							(  self							: List[str]							):






										'''simple docstring'''






										lowercase_					=      GradScalerKwargs(init_scale=1_024				,       growth_factor=2							)
										AcceleratorState._reset_state()
										lowercase_					=      Accelerator(mixed_precision="""fp16"""				,       kwargs_handlers=[scaler_handler]							)
										print(accelerator.use_fpaa							)
										lowercase_					=      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				,       __UpperCAmelCase							)







					@require_multi_gpu
					def 	UpperCAmelCase__							(  self							: Any							):






										'''simple docstring'''






										lowercase_					=      ["torchrun", F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__							)]
										execute_subprocess_async(__UpperCAmelCase				,       env=os.environ.copy()							)


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

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










from __future__ import annotations







def     a_   (__A      )				->						int:



					"""simple docstring"""
					__a					:						List[Any]      =					len(__A      ) // 2

					# choose the middle 3 elements
					__a					:						Any      =					lst[m - 1 : m + 2]

					# if middle element is peak
					if three[1] > three[0] and three[1] > three[2]:
										return three[1]

					# if increasing, recurse on right
					elif three[0] < three[2]:
										if len(lst[:m]      ) == 2:
															m -= 1
										return peak(lst[m:]      )

					# decreasing
					else:
										if len(lst[:m]      ) == 2:
															m += 1
										return peak(lst[:m]      )


if __name__ == "__main__":
			import doctest

			doctest.testmod()





