

'''simple docstring'''
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 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):
 UpperCamelCase__     =       0
 UpperCamelCase__     =       False
 UpperCamelCase__     =       3.0

class 						__magic_name__   (							unittest.TestCase ):







 def 						_A(       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=snake_case_   ).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   ):
   # If no defaults are changed, `to_kwargs` returns an empty dict.
   lowercase									=GradScalerKwargs(init_scale=10_24  , growth_factor=2   )
   AcceleratorState._reset_state()
   lowercase									=Accelerator(mixed_precision='''fp16'''  , kwargs_handlers=[scaler_handler]   )
   print(accelerator.use_fpaa   )
   lowercase									=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  , 20_00   )
   self.assertEqual(scaler._enabled  , snake_case_   )







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


if __name__ == "__main__":
     _UpperCAmelCase  :		Union[str, Any]  =		DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
     _UpperCAmelCase  :		Dict  =		Accelerator(kwargs_handlers=[ddp_scaler])
     _UpperCAmelCase  :		Tuple  =		torch.nn.Linear(1_00, 2_00)
     _UpperCAmelCase  :		Any  =		accelerator.prepare(model)

     # Check the values changed in kwargs
     _UpperCAmelCase  :		int  =		''''''
     _UpperCAmelCase  :		str  =		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)



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


SCREAMING_SNAKE_CASE		:List[Any]						=     {"""tokenization_herbert""": ["""HerbertTokenizer"""]}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Union[str, Any]						=     ["""HerbertTokenizerFast"""]


if TYPE_CHECKING:
 from .tokenization_herbert import HerbertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_herbert_fast import HerbertTokenizerFast

else:
 import sys

 SCREAMING_SNAKE_CASE		:List[str]						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
