



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 						UpperCAmelCase_					( _UpperCAmelCase):
       lowerCamelCase__         =	0
       lowerCamelCase__         =	False
       lowerCamelCase__         =	3.0



class 						UpperCAmelCase_					( unittest.TestCase):







       def      snake_case__   (     self):

              '''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      snake_case__   (     self):

              '''simple docstring'''

              _lowerCAmelCase  :							Optional[int]				     =					GradScalerKwargs(init_scale=1024,    growth_factor=2)
              AcceleratorState._reset_state()
              _lowerCAmelCase  :							List[Any]				     =					Accelerator(mixed_precision="fp16",    kwargs_handlers=[scaler_handler])
              print(accelerator.use_fpaa)
              _lowerCAmelCase  :							List[str]				     =					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,    2000)
              self.assertEqual(scaler._enabled,    lowercase__)







       @require_multi_gpu
       def      snake_case__   (     self):

              '''simple docstring'''

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


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

      # Check the values changed in kwargs
      _snake_case	=						""
      _snake_case	=						model.bucket_bytes_cap // (1024 * 1024)
      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						):





							'''simple docstring'''




							if length <= 0 or not isinstance(_lowerCamelCase       ,				_lowerCamelCase						):
														raise ValueError("Length must be a positive integer."						)
							return [n * (2 * n - 1) for n in range(_lowerCamelCase						)]


if __name__ == "__main__":
						print(hexagonal_numbers(length=5))
						print(hexagonal_numbers(length=10))
