





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        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):
 __SCREAMING_SNAKE_CASE							=      0
 __SCREAMING_SNAKE_CASE							=      False
 __SCREAMING_SNAKE_CASE							=      3.0




class        SCREAMING_SNAKE_CASE__					(		unittest.TestCase ):



 def 							UpperCamelCase     (				self					):
        # 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=__lowerCamelCase					).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					):
        # If no defaults are changed, `to_kwargs` returns an empty dict.
        A__		   =		GradScalerKwargs(init_scale=1024,growth_factor=2					)
        AcceleratorState._reset_state()
        A__		   =		Accelerator(mixed_precision='''fp16''',kwargs_handlers=[scaler_handler]					)
        print(accelerator.use_fpaa					)
        A__		   =		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,__lowerCamelCase					)




 @require_multi_gpu
 def 							UpperCamelCase     (				self					):
        A__		   =		['''torchrun''', f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__					)]
        execute_subprocess_async(__lowerCamelCase,env=os.environ.copy()					)


if __name__ == "__main__":
       a__: int							   =						DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
       a__: List[Any]							   =						Accelerator(kwargs_handlers=[ddp_scaler])
       a__: List[str]							   =						torch.nn.Linear(100, 200)
       a__: Tuple							   =						accelerator.prepare(model)

       # Check the values changed in kwargs
       a__: str							   =						''
       a__: List[Any]							   =						model.bucket_bytes_cap // (1_024 * 1_024)
       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       UpperCamelCase__(			UpperCamelCase__     :			str					,	UpperCamelCase__     :			str    )->str:
       assert x is not None
       assert y is not None

       A__		   =		len(UpperCamelCase__    )
       A__		   =		len(UpperCamelCase__    )

       # declaring the array for storing the dp values
       A__		   =		[[0] * (n + 1) for _ in range(m + 1    )]  # noqa: E741

       for i in range(1					,	m + 1    ):
              for j in range(1					,	n + 1    ):
                     A__		   =		1 if x[i - 1] == y[j - 1] else 0

                     A__		   =		max(l[i - 1][j]					,	l[i][j - 1]					,	l[i - 1][j - 1] + match    )

       A__		   =		''''''
       A__   ,      A__		   =		m, n
       while i > 0 and j > 0:
              A__		   =		1 if x[i - 1] == y[j - 1] else 0

              if l[i][j] == l[i - 1][j - 1] + match:
                     if match == 1:
                            A__		   =		x[i - 1] + seq
                     i -= 1
                     j -= 1
              elif l[i][j] == l[i - 1][j]:
                     i -= 1
              else:
                     j -= 1

       return l[m][n], seq


if __name__ == "__main__":
       a__: Tuple							   =						'AGGTAB'
       a__: Tuple							   =						'GXTXAYB'
       a__: List[Any]							   =						4
       a__: Optional[Any]							   =						'GTAB'

       a__   ,     a__: List[str]							   =						longest_common_subsequence(a, b)
       print('len =', ln, ', sub-sequence =', subseq)
       import doctest

       doctest.testmod()





