





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       (    UpperCamelCase__):
      _lowercase				:					int						=			0
      _lowercase				:					bool						=			False
      _lowercase				:					float						=			3.0







class 				__lowerCAmelCase       (    unittest.TestCase):







      def    _lowercase  (    self							)   ->						Union[str, Any]:



        '''simple docstring'''




        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    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        a__	:					int										=GradScalerKwargs(init_scale=1_0_2_4		,      growth_factor=2							)
        AcceleratorState._reset_state()
        a__	:					List[str]										=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		,      10_24.0							)
        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_0_0_0							)
        self.assertEqual(scaler._enabled		,      lowerCAmelCase__							)







      @require_multi_gpu
      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        a__	:					List[Any]										=["torchrun", F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__							)]
        execute_subprocess_async(lowerCAmelCase__		,      env=os.environ.copy()							)


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

     # Check the values changed in kwargs
     UpperCAmelCase	:	int		     =       """"""
     UpperCAmelCase	:	Union[str, Any]		     =       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)













import numpy as np
def      _A       (		SCREAMING_SNAKE_CASE     :   np.array						):





  """simple docstring"""



  return 1 / (1 + np.exp(-vector						))


if __name__ == "__main__":
     import doctest

     doctest.testmod()






