

"""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        _lowercase					(     __UpperCAmelCase     ):
     _lowerCamelCase     =       0
     _lowerCamelCase     =       False
     _lowerCamelCase     =       3.0


class        _lowercase					(     unittest.TestCase     ):







     def 	lowerCAmelCase__    (      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=UpperCamelCase_       ).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 	lowerCAmelCase__    (      self       ):
      # If no defaults are changed, `to_kwargs` returns an empty dict.
      __magic_name__     =      GradScalerKwargs(init_scale=1024 ,						growth_factor=2       )
      AcceleratorState._reset_state()
      __magic_name__     =      Accelerator(mixed_precision='''fp16''' ,						kwargs_handlers=[scaler_handler]       )
      print(accelerator.use_fpaa       )
      __magic_name__     =      accelerator.scaler

      # Check the kwargs have been applied
      self.assertEqual(scaler._init_scale ,						1_0_2_4.0       )
      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 ,						UpperCamelCase_       )







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


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

 # Check the values changed in kwargs
 __lowerCamelCase										=		""
 __lowerCamelCase										=		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)




"""simple docstring"""






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
 from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class        _lowercase					(     unittest.TestCase     ):







     @slow
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' ,						return_dict=UpperCamelCase_       ).to(UpperCamelCase_       )
      __magic_name__     =      AutoTokenizer.from_pretrained('''google/mt5-small'''       )

      __magic_name__     =      tokenizer('''Hello there''' ,						return_tensors='''pt'''       ).input_ids
      __magic_name__     =      tokenizer('''Hi I am''' ,						return_tensors='''pt'''       ).input_ids

      __magic_name__     =      model(input_ids.to(UpperCamelCase_       ) ,						labels=labels.to(UpperCamelCase_       )       ).loss
      __magic_name__     =      -(labels.shape[-1] * loss.item())

      __magic_name__     =      -8_4.9_1_2_7
      self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 1E-4       )

