"""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'''


					a   	=		0
					a   	=		False
					a   	=		3.0



class lowerCamelCase		(unittest.TestCase      ):






					'''simple docstring'''


					def 			lowerCAmelCase_					(						self			:				Dict		)							->       Tuple:
									# 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=_snake_case		).to_kwargs()   , {"a": 2, "b": True}		)
									self.assertDictEqual(MockClass(a=2   , c=2.25		).to_kwargs()   , {"a": 2, "c": 2.25}		)



					@require_cuda
					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Union[str, Any]:
									# If no defaults are changed, `to_kwargs` returns an empty dict.
									SCREAMING_SNAKE_CASE__		  =				GradScalerKwargs(init_scale=1024   , growth_factor=2		)
									AcceleratorState._reset_state()
									SCREAMING_SNAKE_CASE__		  =				Accelerator(mixed_precision="fp16"   , kwargs_handlers=[scaler_handler]		)
									print(accelerator.use_fpaa		)
									SCREAMING_SNAKE_CASE__		  =				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   , _snake_case		)



					@require_multi_gpu
					def 			lowerCAmelCase_					(						self			:				Dict		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				["torchrun", F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__		)]
									execute_subprocess_async(_snake_case   , 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)





"""simple docstring"""






import sys




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				len(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				[[0 for x in range(__UpperCAmelCase       )] for x in range(__UpperCAmelCase       )]
				SCREAMING_SNAKE_CASE__		  =				[[0 for x in range(__UpperCAmelCase       )] for x in range(__UpperCAmelCase       )]

				for chain_length in range(2  ,					__UpperCAmelCase       ):
								for a in range(1  ,					n - chain_length + 1       ):
												SCREAMING_SNAKE_CASE__		  =				a + chain_length - 1

												SCREAMING_SNAKE_CASE__		  =				sys.maxsize
												for c in range(__UpperCAmelCase  ,					__UpperCAmelCase       ):
																SCREAMING_SNAKE_CASE__		  =				(
																    matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
																)
																if cost < matrix[a][b]:
																				SCREAMING_SNAKE_CASE__		  =				cost
																				SCREAMING_SNAKE_CASE__		  =				c
				return matrix, sol




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Union[str, Any]:
				if i == j:
								print("A" + str(__UpperCAmelCase       )  ,					end=" "       )
				else:
								print("("  ,					end=" "       )
								print_optiomal_solution(__UpperCAmelCase  ,					__UpperCAmelCase  ,					optimal_solution[i][j]       )
								print_optiomal_solution(__UpperCAmelCase  ,					optimal_solution[i][j] + 1  ,					__UpperCAmelCase       )
								print(")"  ,					end=" "       )




def 						SCREAMING_SNAKE_CASE					(  )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				[30, 35, 15, 5, 10, 20, 25]
				SCREAMING_SNAKE_CASE__		  =				len(__UpperCAmelCase       )
				# Size of matrix created from above array will be
				# 30*35 35*15 15*5 5*10 10*20 20*25
				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				matrix_chain_order(__UpperCAmelCase       )

				print("No. of Operation required: " + str(matrix[1][n - 1]       )       )
				print_optiomal_solution(__UpperCAmelCase  ,					1  ,					n - 1       )


if __name__ == "__main__":
						main()




