






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 ):

     '''simple docstring'''






     snake_case__					=					0
     snake_case__					=					False
     snake_case__					=					3.0





class 					A__     (     unittest.TestCase ):

     '''simple docstring'''






     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           self.assertDictEqual(MockClass().to_kwargs()   ,						{} )
           self.assertDictEqual(MockClass(a=2 ).to_kwargs()   ,						{'a': 2} )
           self.assertDictEqual(MockClass(a=2   ,						b=_SCREAMING_SNAKE_CASE ).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   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any] ):






           """simple docstring"""




           UpperCamelCase					       =  GradScalerKwargs(init_scale=1024   ,						growth_factor=2 )
           AcceleratorState._reset_state()
           UpperCamelCase					       =  Accelerator(mixed_precision='fp16'   ,						kwargs_handlers=[scaler_handler] )
           print(accelerator.use_fpaa )
           UpperCamelCase					       =  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   ,						_SCREAMING_SNAKE_CASE )







     @require_multi_gpu
     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  ['torchrun', f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__ )]
           execute_subprocess_async(_SCREAMING_SNAKE_CASE   ,						env=os.environ.copy() )


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

       # Check the values changed in kwargs
       __magic_name__   :  Optional[int]        =							''''''
       __magic_name__   :  Dict        =							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)











from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
       import datasets





class 					A__     (     __snake_case ):

     '''simple docstring'''






     @slow
     @require_torch
     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           UpperCamelCase					       =  EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'   ,						'prajjwal1/bert-tiny' )
           UpperCamelCase					       =  BertTokenizer.from_pretrained('bert-base-uncased' )

           UpperCamelCase					       =  bertabert.config.encoder.vocab_size
           UpperCamelCase					       =  tokenizer.sep_token_id
           UpperCamelCase					       =  tokenizer.cls_token_id
           UpperCamelCase					       =  128

           UpperCamelCase					       =  datasets.load_dataset('cnn_dailymail'   ,						'3.0.0'   ,						split='train[:1%]' )
           UpperCamelCase					       =  datasets.load_dataset('cnn_dailymail'   ,						'3.0.0'   ,						split='validation[:1%]' )

           UpperCamelCase					       =  train_dataset.select(range(32 ) )
           UpperCamelCase					       =  val_dataset.select(range(16 ) )

           UpperCamelCase					       =  4

           def _map_to_encoder_decoder_inputs(_SCREAMING_SNAKE_CASE      :    Tuple ):
                 # Tokenizer will automatically set [BOS] <text> [EOS]
                 UpperCamelCase					       =  tokenizer(batch['article']   ,						padding='max_length'   ,						truncation=_SCREAMING_SNAKE_CASE   ,						max_length=512 )
                 UpperCamelCase					       =  tokenizer(batch['highlights']   ,						padding='max_length'   ,						truncation=_SCREAMING_SNAKE_CASE   ,						max_length=128 )
                 UpperCamelCase					       =  inputs.input_ids
                 UpperCamelCase					       =  inputs.attention_mask

                 UpperCamelCase					       =  outputs.input_ids
                 UpperCamelCase					       =  outputs.input_ids.copy()
                 UpperCamelCase					       =  [
                     [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
                 ]
                 UpperCamelCase					       =  outputs.attention_mask

                 assert all(len(_SCREAMING_SNAKE_CASE ) == 512 for x in inputs.input_ids )
                 assert all(len(_SCREAMING_SNAKE_CASE ) == 128 for x in outputs.input_ids )

                 return batch

           def _compute_metrics(_SCREAMING_SNAKE_CASE      :    str ):
                 UpperCamelCase					       =  pred.label_ids
                 UpperCamelCase					       =  pred.predictions

                 # all unnecessary tokens are removed
                 UpperCamelCase					       =  tokenizer.batch_decode(_SCREAMING_SNAKE_CASE   ,						skip_special_tokens=_SCREAMING_SNAKE_CASE )
                 UpperCamelCase					       =  tokenizer.batch_decode(_SCREAMING_SNAKE_CASE   ,						skip_special_tokens=_SCREAMING_SNAKE_CASE )

                 UpperCamelCase					       =  sum([int(pred_str[i] == label_str[i] ) for i in range(len(_SCREAMING_SNAKE_CASE ) )] ) / len(_SCREAMING_SNAKE_CASE )

                 return {"accuracy": accuracy}

           # map train dataset
           UpperCamelCase					       =  train_dataset.map(
               _map_to_encoder_decoder_inputs   ,						batched=_SCREAMING_SNAKE_CASE   ,						batch_size=_SCREAMING_SNAKE_CASE   ,						remove_columns=['article', 'highlights']   ,						)
           train_dataset.set_format(
               type='torch'   ,						columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']   ,						)

           # same for validation dataset
           UpperCamelCase					       =  val_dataset.map(
               _map_to_encoder_decoder_inputs   ,						batched=_SCREAMING_SNAKE_CASE   ,						batch_size=_SCREAMING_SNAKE_CASE   ,						remove_columns=['article', 'highlights']   ,						)
           val_dataset.set_format(
               type='torch'   ,						columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']   ,						)

           UpperCamelCase					       =  self.get_auto_remove_tmp_dir()

           UpperCamelCase					       =  SeqaSeqTrainingArguments(
               output_dir=_SCREAMING_SNAKE_CASE   ,						per_device_train_batch_size=_SCREAMING_SNAKE_CASE   ,						per_device_eval_batch_size=_SCREAMING_SNAKE_CASE   ,						predict_with_generate=_SCREAMING_SNAKE_CASE   ,						evaluation_strategy='steps'   ,						do_train=_SCREAMING_SNAKE_CASE   ,						do_eval=_SCREAMING_SNAKE_CASE   ,						warmup_steps=0   ,						eval_steps=2   ,						logging_steps=2   ,						)

           # instantiate trainer
           UpperCamelCase					       =  SeqaSeqTrainer(
               model=_SCREAMING_SNAKE_CASE   ,						args=_SCREAMING_SNAKE_CASE   ,						compute_metrics=_compute_metrics   ,						train_dataset=_SCREAMING_SNAKE_CASE   ,						eval_dataset=_SCREAMING_SNAKE_CASE   ,						tokenizer=_SCREAMING_SNAKE_CASE   ,						)

           # start training
           trainer.train()



