






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 		_A						( UpperCAmelCase_ ):
  lowercase_						:			int						=					0
  lowercase_						:			bool						=					False
  lowercase_						:			float						=					3.0







class 		_A						( unittest.TestCase ):

  def    a					(    self						:	Optional[int]			):
     """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    a					(    self						:	int			):
     """simple docstring"""

     __UpperCamelCase	:			Optional[Any]					=					GradScalerKwargs(init_scale=10_24   ,				growth_factor=2			)
     AcceleratorState._reset_state()
     __UpperCamelCase	:			Any					=					Accelerator(mixed_precision="""fp16"""   ,				kwargs_handlers=[scaler_handler]			)
     print(accelerator.use_fpaa			)
     __UpperCamelCase	:			Tuple					=					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   ,				20_00			)
     self.assertEqual(scaler._enabled   ,				lowerCamelCase__			)

  @require_multi_gpu
  def    a					(    self						:	Any			):
     """simple docstring"""

     __UpperCamelCase	:			Optional[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    							=      DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
       UpperCamelCase    							=      Accelerator(kwargs_handlers=[ddp_scaler])
       UpperCamelCase    							=      torch.nn.Linear(100, 200)
       UpperCamelCase    							=      accelerator.prepare(model)

       # Check the values changed in kwargs
       UpperCamelCase    							=      ''
       UpperCamelCase    							=      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 warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


UpperCamelCase    							=      logging.get_logger(__name__)







class 		_A						( UpperCAmelCase_ ):

  def __init__(    self						:	str   ,				*lowerCamelCase__						:	Optional[int]   ,				**lowerCamelCase__						:	Any			):
     """simple docstring"""

     warnings.warn(
         """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
         """ use BeitImageProcessor instead."""   ,				lowerCamelCase__   ,				)
     super().__init__(*lowerCamelCase__   ,				**lowerCamelCase__			)



