

'''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 	lowerCAmelCase  (   __snake_case ):
       lowerCAmelCase_												=			0
       lowerCAmelCase_												=			False
       lowerCAmelCase_												=			3.0






class 	lowerCAmelCase  (   unittest.TestCase ):







       def       snake_case (				self      :			Optional[Any]   ):







            """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=2.2_5   ).to_kwargs()    ,   {'a': 2, 'c': 2.2_5}   )







       @require_cuda
       def       snake_case (				self      :			Union[str, Any]   ):







            """simple docstring"""







            __lowercase      		=GradScalerKwargs(init_scale=1024    ,   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    ,   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    ,   2000   )
            self.assertEqual(scaler._enabled    ,   A_   )







       @require_multi_gpu
       def       snake_case (				self      :			Tuple   ):







            """simple docstring"""







            __lowercase      		=["torchrun", f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__   )]
            execute_subprocess_async(A_    ,   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 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_  )
