
"""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__    ):
							lowercase_  :  str      =				0
							lowercase_  :  List[str]      =				False
							lowercase_  :  Optional[int]      =				3.0





class  _lowerCAmelCase    (    unittest.TestCase    ):



							def    _a (   self )				->				List[str]:
								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    _a (   self )				->				str:
								_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			,    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			,    a_ )



							@require_multi_gpu
							def    _a (   self )				->				List[Any]:
								_UpperCAmelCase			=    ["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__ )]
								execute_subprocess_async(a_			,    env=os.environ.copy() )


if __name__ == "__main__":
			__magic_name__									=					DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
			__magic_name__									=					Accelerator(kwargs_handlers=[ddp_scaler])
			__magic_name__									=					torch.nn.Linear(1_00, 2_00)
			__magic_name__									=					accelerator.prepare(model)

			# Check the values changed in kwargs
			__magic_name__									=					""
			__magic_name__									=					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)










from __future__ import annotations

from typing import Any

def lowercase    (  SCREAMING_SNAKE_CASE )			->							None:


			'''simple docstring'''







			create_state_space_tree(SCREAMING_SNAKE_CASE       ,    []       ,    0 )

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							None:


			'''simple docstring'''







			if index == len(SCREAMING_SNAKE_CASE ):
						print(SCREAMING_SNAKE_CASE )
						return

			create_state_space_tree(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    index + 1 )
			current_subsequence.append(sequence[index] )
			create_state_space_tree(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    index + 1 )
			current_subsequence.pop()


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     list[Any]		    =       [3, 1, 2, 4]
				generate_all_subsequences(seq)

				seq.clear()
				seq.extend(["A", "B", "C"])
				generate_all_subsequences(seq)
