


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_   :				int						      = 16
UpperCAmelCase_   :				Any						      = 32







def  SCREAMING_SNAKE_CASE_		(    __A				:    Accelerator						,					__A				:    int = 16       )			->	Optional[int]:

       """simple docstring"""

       a_      :	List[str]							=						AutoTokenizer.from_pretrained('bert-base-cased'       )
       a_      :	Optional[int]							=						load_dataset('glue'						,					'mrpc'       )

       def tokenize_function(__A				:    List[Any]       ):
              # max_length=None => use the model max length (it's actually the default)
              a_      :	Tuple							=						tokenizer(examples['sentence1']						,					examples['sentence2']						,					truncation=__A						,					max_length=__A       )
              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():
              a_      :	Optional[int]							=						datasets.map(
                  __A						,					batched=__A						,					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
       a_      :	List[str]							=						tokenized_datasets.rename_column('label'						,					'labels'       )

       def collate_fn(__A				:    Tuple       ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              a_      :	Optional[int]							=						1_28 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     a_      :	Tuple							=						16
              elif accelerator.mixed_precision != "no":
                     a_      :	List[Any]							=						8
              else:
                     a_      :	Optional[Any]							=						None

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

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

       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_   :				Dict						      = mocked_dataloaders  # noqa: F811







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[int]						,					__A				:    str       )			->	Optional[Any]:

       """simple docstring"""

       if os.environ.get('TESTING_MOCKED_DATALOADERS'						,					__A       ) == "1":
              a_      :	Any							=						2
       # New Code #
       a_      :	Optional[Any]							=						int(args.gradient_accumulation_steps       )
       a_      :	int							=						int(args.local_sgd_steps       )
       # Initialize accelerator
       a_      :	Dict							=						Accelerator(
           cpu=args.cpu						,					mixed_precision=args.mixed_precision						,					gradient_accumulation_steps=__A       )
       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
       a_      :	Tuple							=						config['lr']
       a_      :	Optional[Any]							=						int(config['num_epochs']       )
       a_      :	Union[str, Any]							=						int(config['seed']       )
       a_      :	str							=						int(config['batch_size']       )

       a_      :	int							=						evaluate.load('glue'						,					'mrpc'       )

       set_seed(__A       )
       a_							,	a_      :	Dict							=						get_dataloaders(__A						,					__A       )
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       a_      :	Union[str, Any]							=						AutoModelForSequenceClassification.from_pretrained('bert-base-cased'						,					return_dict=__A       )

       # 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).
       a_      :	Optional[Any]							=						model.to(accelerator.device       )

       # Instantiate optimizer
       a_      :	int							=						AdamW(params=model.parameters()						,					lr=__A       )

       # Instantiate scheduler
       a_      :	Any							=						get_linear_schedule_with_warmup(
           optimizer=__A						,					num_warmup_steps=1_00						,					num_training_steps=(len(__A       ) * 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.
       a_							,	a_							,	a_							,	a_							,	a_      :	Optional[Any]							=						accelerator.prepare(
           __A						,					__A						,					__A						,					__A						,					__A       )

       # Now we train the model
       for epoch in range(__A       ):
              model.train()
              with LocalSGD(
                  accelerator=__A						,					model=__A						,					local_sgd_steps=__A						,					enabled=local_sgd_steps is not None       ) as local_sgd:
                     for step, batch in enumerate(__A       ):
                            # 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(__A       ):
                                   a_      :	str							=						model(**__A       )
                                   a_      :	str							=						output.loss
                                   accelerator.backward(__A       )
                                   optimizer.step()
                                   lr_scheduler.step()
                                   optimizer.zero_grad()
                                   # LocalSGD-specific line
                                   local_sgd.step()

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

              a_      :	Optional[Any]							=						metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F"""epoch {epoch}:"""						,					__A       )







def  SCREAMING_SNAKE_CASE_		(    )			->	Dict:

       """simple docstring"""

       a_      :	List[str]							=						argparse.ArgumentParser(description='Simple example of training script.'       )
       parser.add_argument(
           '--mixed_precision'						,					type=__A						,					default=__A						,					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=__A						,					default=1						,					help='The number of minibatches to be ran before gradients are accumulated.'						,					)
       parser.add_argument(
           '--local_sgd_steps'						,					type=__A						,					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.'       )
       a_      :	List[Any]							=						parser.parse_args()
       a_      :	Dict							=						{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       training_function(__A						,					__A       )


if __name__ == "__main__":
   main()






import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(lowercase__    )		,					'''Tatoeba directory does not exist.'''    )
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      @cached_property
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    List[Any]:
             a_      :	Optional[Any]							=						tempfile.mkdtemp()
             return TatoebaConverter(save_dir=SCREAMING_SNAKE_CASE__  )
      @slow
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Optional[Any]:
             self.resolver.convert_models(['heb-eng']  )

      @slow
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Any:
             a_							,	a_      :	Dict							=						self.resolver.write_model_card('opus-mt-he-en'  ,							dry_run=SCREAMING_SNAKE_CASE__  )
             assert mmeta["long_pair"] == "heb-eng"


