



from ..utils import DummyObject, requires_backends



class     lowerCAmelCase_		(  metaclass=lowerCAmelCase_							):
       UpperCAmelCase__    : List[str]       					=  ["""speech"""]
       def __init__(      self,      *SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)					->		List[str]:
          requires_backends(self,      ['speech']	)






class     lowerCAmelCase_		(  metaclass=lowerCAmelCase_							):
       UpperCAmelCase__    : Union[str, Any]       					=  ["""speech"""]
       def __init__(      self,      *SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)					->		List[Any]:
          requires_backends(self,      ['speech']	)








import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__UpperCAmelCase	       =	16
__UpperCAmelCase	       =	32
def 		UpperCamelCase     (       snake_case__	:   Accelerator	,					snake_case__	:   int = 16     )     ->					Dict:
   UpperCamelCase   :  str						     =     AutoTokenizer.from_pretrained('bert-base-cased'     )
   UpperCamelCase   :  str						     =     load_dataset('glue'	,					'mrpc'     )

   def tokenize_function(snake_case__	:   Dict     ):
      # max_length=None => use the model max length (it's actually the default)
      UpperCamelCase   :  Optional[Any]						     =     tokenizer(examples['sentence1']	,					examples['sentence2']	,					truncation=snake_case__	,					max_length=snake_case__     )
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   # starting with the main process first:
   with accelerator.main_process_first():
      UpperCamelCase   :  List[str]						     =     datasets.map(
          snake_case__	,					batched=snake_case__	,					remove_columns=['idx', 'sentence1', 'sentence2']	,					)

   # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
   # transformers library
   UpperCamelCase   :  Dict						     =     tokenized_datasets.rename_column('label'	,					'labels'     )

   def collate_fn(snake_case__	:   Tuple     ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      UpperCamelCase   :  List[str]						     =     128 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         UpperCamelCase   :  str						     =     16
      elif accelerator.mixed_precision != "no":
         UpperCamelCase   :  Dict						     =     8
      else:
         UpperCamelCase   :  Union[str, Any]						     =     None

      return tokenizer.pad(
          snake_case__	,					padding='longest'	,					max_length=snake_case__	,					pad_to_multiple_of=snake_case__	,					return_tensors='pt'	,					)

   # Instantiate dataloaders.
   UpperCamelCase   :  Optional[int]						     =     DataLoader(
       tokenized_datasets['train']	,					shuffle=snake_case__	,					collate_fn=snake_case__	,					batch_size=snake_case__     )
   UpperCamelCase   :  List[Any]						     =     DataLoader(
       tokenized_datasets['validation']	,					shuffle=snake_case__	,					collate_fn=snake_case__	,					batch_size=snake_case__     )

   return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
    from accelerate.test_utils.training import mocked_dataloaders

    __UpperCAmelCase	       =	mocked_dataloaders  # noqa: F811
def 		UpperCamelCase     (       snake_case__	:   str	,					snake_case__	:   List[Any]     )     ->					List[str]:
   # For testing only
   if os.environ.get('TESTING_MOCKED_DATALOADERS'	,					snake_case__     ) == "1":
      UpperCamelCase   :  List[str]						     =     2
   # New Code #
   UpperCamelCase   :  Optional[int]						     =     int(args.gradient_accumulation_steps     )
   UpperCamelCase   :  List[str]						     =     int(args.local_sgd_steps     )
   # Initialize accelerator
   UpperCamelCase   :  Optional[Any]						     =     Accelerator(
       cpu=args.cpu	,					mixed_precision=args.mixed_precision	,					gradient_accumulation_steps=snake_case__     )
   if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
      raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'     )
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   UpperCamelCase   :  Union[str, Any]						     =     config['lr']
   UpperCamelCase   :  Optional[int]						     =     int(config['num_epochs']     )
   UpperCamelCase   :  List[Any]						     =     int(config['seed']     )
   UpperCamelCase   :  List[str]						     =     int(config['batch_size']     )

   UpperCamelCase   :  Optional[Any]						     =     evaluate.load('glue'	,					'mrpc'     )

   set_seed(snake_case__     )
   UpperCamelCase     ,					UpperCamelCase   :  int						     =     get_dataloaders(snake_case__	,					snake_case__     )
   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   UpperCamelCase   :  Tuple						     =     AutoModelForSequenceClassification.from_pretrained('bert-base-cased'	,					return_dict=snake_case__     )

   # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
   # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
   # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
   UpperCamelCase   :  Dict						     =     model.to(accelerator.device     )

   # Instantiate optimizer
   UpperCamelCase   :  List[str]						     =     AdamW(params=model.parameters()	,					lr=snake_case__     )

   # Instantiate scheduler
   UpperCamelCase   :  Any						     =     get_linear_schedule_with_warmup(
       optimizer=snake_case__	,					num_warmup_steps=100	,					num_training_steps=(len(snake_case__     ) * num_epochs)	,					)

   # Prepare everything
   # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
   # prepare method.
   UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase   :  str						     =     accelerator.prepare(
       snake_case__	,					snake_case__	,					snake_case__	,					snake_case__	,					snake_case__     )

   # Now we train the model
   for epoch in range(snake_case__     ):
      model.train()
      with LocalSGD(
          accelerator=snake_case__	,					model=snake_case__	,					local_sgd_steps=snake_case__	,					enabled=local_sgd_steps is not None     ) as local_sgd:
         for step, batch in enumerate(snake_case__     ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device     )
            # New code #
            # We use the new `accumulate` context manager to perform gradient accumulation
            # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
            with accelerator.accumulate(snake_case__     ):
               UpperCamelCase   :  int						     =     model(**snake_case__     )
               UpperCamelCase   :  Union[str, Any]						     =     output.loss
               accelerator.backward(snake_case__     )
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()
               # LocalSGD-specific line
               local_sgd.step()

      model.eval()
      for step, batch in enumerate(snake_case__     ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device     )
         with torch.no_grad():
            UpperCamelCase   :  Dict						     =     model(**snake_case__     )
         UpperCamelCase   :  Any						     =     outputs.logits.argmax(dim=-1     )
         UpperCamelCase     ,					UpperCamelCase   :  Any						     =     accelerator.gather_for_metrics((predictions, batch['labels'])     )
         metric.add_batch(
             predictions=snake_case__	,					references=snake_case__	,					)

      UpperCamelCase   :  List[Any]						     =     metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"""epoch {epoch}:"""	,					snake_case__     )
def 		UpperCamelCase     (       )     ->					Dict:
   UpperCamelCase   :  Any						     =     argparse.ArgumentParser(description='Simple example of training script.'     )
   parser.add_argument(
       '--mixed_precision'	,					type=snake_case__	,					default=snake_case__	,					choices=['no', 'fp16', 'bf16', 'fp8']	,					help='Whether to use mixed precision. Choose'
       'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
       'and an Nvidia Ampere GPU.'	,					)
   # New Code #
   parser.add_argument(
       '--gradient_accumulation_steps'	,					type=snake_case__	,					default=1	,					help='The number of minibatches to be ran before gradients are accumulated.'	,					)
   parser.add_argument(
       '--local_sgd_steps'	,					type=snake_case__	,					default=8	,					help='Number of local SGD steps or None to disable local SGD'     )
   parser.add_argument('--cpu'	,					action='store_true'	,					help='If passed, will train on the CPU.'     )
   UpperCamelCase   :  str						     =     parser.parse_args()
   UpperCamelCase   :  Tuple						     =     {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
   training_function(snake_case__	,					snake_case__     )


if __name__ == "__main__":
    main()



