




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 (       _lowerCamelCase ):
			snake_case_      : Union[str, Any]      =     0
			snake_case_      : Optional[Any]      =     False
			snake_case_      : Dict      =     3.0






class     __lowerCAmelCase (       unittest.TestCase ):







			def UpperCamelCase			(       self    :			Optional[Any]      ):




									"""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    :			Optional[Any]      ):




									"""simple docstring"""


									_UpperCAmelCase													=     GradScalerKwargs(init_scale=1_024    ,    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    ,    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    :			Tuple      ):




									"""simple docstring"""


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


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

				# Check the values changed in kwargs
				lowercase_	:				Optional[Any]  				= ''
				lowercase_	:				int  				= 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)











def _a				(	lowerCamelCase:   int = 2_00 )	->						int:







      '''simple docstring'''






      __A          =				[1, 2, 5, 10, 20, 50, 1_00, 2_00]
      __A          =				[0] * (pence + 1)
      __A          =				1  # base case: 1 way to make 0 pence

      for coin in coins:
            for i in range(lowerCamelCase					,				pence + 1					,				1 ):
                  number_of_ways[i] += number_of_ways[i - coin]
      return number_of_ways[pence]


if __name__ == "__main__":
       assert solution(200) == 73682





