





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__  (			__snake_case     ):
      _UpperCAmelCase      :int		       =  0
      _UpperCAmelCase      :bool		       =  False
      _UpperCAmelCase      :float		       =  3.0



class    A__  (			unittest.TestCase     ):





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       self.assertDictEqual(MockClass().to_kwargs()			,				{}  )
       self.assertDictEqual(MockClass(a=2  ).to_kwargs()			,				{"a": 2}  )
       self.assertDictEqual(MockClass(a=2			,				b=A_  ).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  ):
       '''simple docstring'''




       UpperCamelCase      :   int			      =  GradScalerKwargs(init_scale=1024			,				growth_factor=2  )
       AcceleratorState._reset_state()
       UpperCamelCase      :   Optional[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			,				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			,				2000  )
       self.assertEqual(scaler._enabled			,				A_  )





      @require_multi_gpu
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




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


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

    # Check the values changed in kwargs
    __lowerCamelCase			:	Tuple					    =	""""""
    __lowerCamelCase			:	str					    =	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 json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format



class    A__  :
      _UpperCAmelCase      :Union[str, Any]		       =  None





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   int			      =  self.feature_extraction_class(**self.feat_extract_dict  )
       UpperCamelCase      :   Union[str, Any]			      =  json.loads(feat_extract.to_json_string()  )
       for key, value in self.feat_extract_dict.items():
        self.assertEqual(obj[key]			,				A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   str			      =  self.feature_extraction_class(**self.feat_extract_dict  )

       with tempfile.TemporaryDirectory() as tmpdirname:
        UpperCamelCase      :   List[Any]			      =  os.path.join(A_			,				"feat_extract.json"  )
        feat_extract_first.to_json_file(A_  )
        UpperCamelCase      :   str			      =  self.feature_extraction_class.from_json_file(A_  )

       self.assertEqual(feat_extract_second.to_dict()			,				feat_extract_first.to_dict()  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Union[str, Any]			      =  self.feature_extraction_class(**self.feat_extract_dict  )

       with tempfile.TemporaryDirectory() as tmpdirname:
        UpperCamelCase      :   int			      =  feat_extract_first.save_pretrained(A_  )[0]
        check_json_file_has_correct_format(A_  )
        UpperCamelCase      :   Optional[Any]			      =  self.feature_extraction_class.from_pretrained(A_  )

       self.assertEqual(feat_extract_second.to_dict()			,				feat_extract_first.to_dict()  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Any			      =  self.feature_extraction_class()
       self.assertIsNotNone(A_  )
