


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        snake_case__	(			__A							):
	UpperCAmelCase :				Optional[Any]       =			0
	UpperCAmelCase :				int       =			False
	UpperCAmelCase :				Any       =			3.0





class        snake_case__	(			unittest.TestCase							):





	def      A					(	self				)		->      Any:
								"""simple docstring"""






								self.assertDictEqual(MockClass().to_kwargs()					,     {}				)
								self.assertDictEqual(MockClass(a=2				).to_kwargs()					,     {"""a""": 2}				)
								self.assertDictEqual(MockClass(a=2					,     b=lowercase__				).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				)		->      Tuple:
								"""simple docstring"""






								a_							: str								=						GradScalerKwargs(init_scale=1024					,     growth_factor=2				)
								AcceleratorState._reset_state()
								a_							: Any								=						Accelerator(mixed_precision="""fp16"""					,     kwargs_handlers=[scaler_handler]				)
								print(accelerator.use_fpaa				)
								a_							: Optional[Any]								=						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					,     lowercase__				)





	@require_multi_gpu
	def      A					(	self				)		->      List[str]:
								"""simple docstring"""






								a_							: Tuple								=						["""torchrun""", f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__				)]
								execute_subprocess_async(lowercase__					,     env=os.environ.copy()				)


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

	# Check the values changed in kwargs
	SCREAMING_SNAKE_CASE			:			int        =     ''''''
	SCREAMING_SNAKE_CASE			:			List[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






def _lowerCamelCase  (		snake_case     , snake_case  ):

							# Validation
							if not isinstance(snake_case     , snake_case  ) or not all(isinstance(snake_case     , snake_case  ) for day in days  ):
														raise ValueError('The parameter days should be a list of integers'  )

							if len(snake_case  ) != 3 or not all(isinstance(snake_case     , snake_case  ) for cost in costs  ):
														raise ValueError('The parameter costs should be a list of three integers'  )

							if len(snake_case  ) == 0:
														return 0

							if min(snake_case  ) <= 0:
														raise ValueError('All days elements should be greater than 0'  )

							if max(snake_case  ) >= 366:
														raise ValueError('All days elements should be less than 366'  )

							_lowerCAmelCase							=							set(snake_case  )

							@functools.cache
							def dynamic_programming(snake_case  ) -> int:
														if index > 365:
																					return 0

														if index not in days_set:
																					return dynamic_programming(index + 1  )

														return min(
														    costs[0] + dynamic_programming(index + 1  )     , costs[1] + dynamic_programming(index + 7  )     , costs[2] + dynamic_programming(index + 30  )     , )

							return dynamic_programming(1  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()






