


import argparse

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


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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
#
########################################################################


__lowerCAmelCase			=							16
__lowerCAmelCase			=							32




def        snake_case_					(  snake_case      ,							snake_case = 16					)  ->    Optional[Any]:
       lowercase__:       Optional[int]        =       AutoTokenizer.from_pretrained('bert-base-cased'					)
       lowercase__:       Any        =       load_dataset('glue'      ,							'mrpc'					)

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

       def collate_fn(snake_case					):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              lowercase__:       List[str]        =       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":
                     lowercase__:       List[Any]        =       16
              elif accelerator.mixed_precision != "no":
                     lowercase__:       List[str]        =       8
              else:
                     lowercase__:       Optional[Any]        =       None

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

       # Instantiate dataloaders.
       lowercase__:       Tuple        =       DataLoader(
           tokenized_datasets['train']      ,							shuffle=UpperCAmelCase__      ,							collate_fn=UpperCAmelCase__      ,							batch_size=UpperCAmelCase__      ,							drop_last=UpperCAmelCase__					)
       lowercase__:       str        =       DataLoader(
           tokenized_datasets['validation']      ,							shuffle=UpperCAmelCase__      ,							collate_fn=UpperCAmelCase__      ,							batch_size=UpperCAmelCase__      ,							drop_last=(accelerator.mixed_precision == 'fp8')      ,							)

       return train_dataloader, eval_dataloader




def        snake_case_					(  snake_case      ,							snake_case					)  ->    str:
       # Initialize accelerator
       lowercase__:       Optional[int]        =       Accelerator(cpu=args.cpu      ,							mixed_precision=args.mixed_precision					)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       lowercase__:       Tuple        =       config["""lr"""]
       lowercase__:       Optional[Any]        =       int(config['num_epochs']					)
       lowercase__:       List[Any]        =       int(config['seed']					)
       lowercase__:       int        =       int(config['batch_size']					)

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

       # If the batch size is too big we use gradient accumulation
       lowercase__:       List[str]        =       1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              lowercase__:       int        =       batch_size // MAX_GPU_BATCH_SIZE
              lowercase__:       Optional[int]        =       MAX_GPU_BATCH_SIZE

       set_seed(UpperCAmelCase__					)
       lowercase__:       str        =       get_dataloaders(UpperCAmelCase__      ,							UpperCAmelCase__					)
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       lowercase__:       Optional[int]        =       AutoModelForSequenceClassification.from_pretrained('bert-base-cased'      ,							return_dict=UpperCAmelCase__					)

       # 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).
       lowercase__:       Optional[int]        =       model.to(accelerator.device					)
       # Instantiate optimizer
       lowercase__:       Union[str, Any]        =       AdamW(params=model.parameters()      ,							lr=UpperCAmelCase__					)

       # Instantiate scheduler
       lowercase__:       Union[str, Any]        =       get_linear_schedule_with_warmup(
           optimizer=UpperCAmelCase__      ,							num_warmup_steps=1_00      ,							num_training_steps=(len(UpperCAmelCase__					) * num_epochs) // gradient_accumulation_steps      ,							)

       # 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.

       lowercase__:       str        =       accelerator.prepare(
           UpperCAmelCase__      ,							UpperCAmelCase__      ,							UpperCAmelCase__      ,							UpperCAmelCase__      ,							UpperCAmelCase__					)

       # Now we train the model
       for epoch in range(UpperCAmelCase__					):
              model.train()
              for step, batch in enumerate(UpperCAmelCase__					):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device					)
                     lowercase__:       Union[str, Any]        =       model(**UpperCAmelCase__					)
                     lowercase__:       int        =       outputs.loss
                     lowercase__:       Any        =       loss / gradient_accumulation_steps
                     accelerator.backward(UpperCAmelCase__					)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

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

              lowercase__:       Dict        =       metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(f'epoch {epoch}:'      ,							UpperCAmelCase__					)




def        snake_case_					(  )  ->    Tuple:
       lowercase__:       str        =       argparse.ArgumentParser(description='Simple example of training script.'					)
       parser.add_argument(
           '--mixed_precision'      ,							type=UpperCAmelCase__      ,							default=UpperCAmelCase__      ,							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.'      ,							)
       parser.add_argument('--cpu'      ,							action='store_true'      ,							help='If passed, will train on the CPU.'					)
       lowercase__:       int        =       parser.parse_args()
       lowercase__:       Optional[Any]        =       {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
       training_function(UpperCAmelCase__      ,							UpperCAmelCase__					)


if __name__ == "__main__":
 main()







from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


__lowerCAmelCase			=							logging.get_logger(__name__)


if is_torch_available():
 import torch
 from torch import nn
 from torch.utils.data import Dataset

 from transformers import Trainer





 class   __a      (   __UpperCamelCase				):
  def __init__(       self    ,			lowerCAmelCase__ = 101			) ->    Any:




         '''simple docstring'''
         lowercase__:       Any        =       length
  def __len__(       self			) ->    List[Any]:




         '''simple docstring'''
         return self.length
  def __getitem__(       self    ,			lowerCAmelCase__			) ->    int:




         '''simple docstring'''
         return i





 class   __a      :
  def __call__(       self    ,			lowerCAmelCase__			) ->    List[str]:




         '''simple docstring'''
         return {"input_ids": torch.tensor(lowerCAmelCase__			), "labels": torch.tensor(lowerCAmelCase__			)}





 class   __a      (   nn.Module				):
  def __init__(       self			) ->    Tuple:




         '''simple docstring'''
         super().__init__()
         # Add some (unused) params otherwise DDP will complain.
         lowercase__:       List[str]        =       nn.Linear(120    ,			80			)
  def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=None			) ->    int:




         '''simple docstring'''
         if labels is not None:
                return torch.tensor(0.0    ,			device=input_ids.device			), input_ids
         else:
                return input_ids





class   __a      (   __UpperCamelCase				):
 @require_torch_neuroncore
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       int        =       F'--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
        lowercase__:       Tuple        =       self.get_auto_remove_tmp_dir()
        lowercase__:       Optional[int]        =       F'--output_dir {output_dir}'.split()
        lowercase__:       int        =       ['torchrun'] + distributed_args + args
        execute_subprocess_async(lowerCAmelCase__    ,			env=self.get_env()			)
        # successful return here == success - any errors would have caused an error in the sub-call





class   __a      (   __UpperCamelCase				):
 @require_torch_multi_gpu
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        lowercase__:       List[str]        =       F'--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
        lowercase__:       Tuple        =       self.get_auto_remove_tmp_dir()
        lowercase__:       List[str]        =       F'--output_dir {output_dir}'.split()
        lowercase__:       int        =       ['torchrun'] + distributed_args + args
        execute_subprocess_async(lowerCAmelCase__    ,			env=self.get_env()			)
        # successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
 # The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
 #
 # PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

 __lowerCAmelCase			=							HfArgumentParser((TrainingArguments,))
 __lowerCAmelCase			=							parser.parse_args_into_dataclasses()[0]

 logger.warning(
     F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '''
     F'''distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'''
 )

 # Essentially, what we want to verify in the distributed case is that we get all samples back,
 # in the right order. (this is crucial for prediction for instance)
 for dataset_length in [1_01, 40, 7]:
  __lowerCAmelCase			=							DummyDataset(dataset_length)




  def        snake_case_					(  snake_case					)  ->    Dict:
         lowercase__:       str        =       list(range(len(snake_case					)					)					)
         lowercase__:       Tuple        =       p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
         if not success and training_args.local_rank == 0:
                logger.warning(
                    'Predictions and/or labels do not match expected results:\n  - predictions: '
                    f'{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}'					)
         return {"success": success}

  __lowerCAmelCase			=							Trainer(
      model=DummyModel(),
      args=training_args,
      data_collator=DummyDataCollator(),
      eval_dataset=dataset,
      compute_metrics=compute_metrics,
  )
  __lowerCAmelCase			=							trainer.evaluate()
  logger.info(metrics)
  if metrics["eval_success"] is not True:
   logger.error(metrics)
   exit(1)

  __lowerCAmelCase			=							trainer.predict(dataset)
  logger.info(p.metrics)
  if p.metrics["test_success"] is not True:
   logger.error(p.metrics)
   exit(1)

  __lowerCAmelCase			=							2

  __lowerCAmelCase			=							trainer.evaluate()
  logger.info(metrics)
  if metrics["eval_success"] is not True:
   logger.error(metrics)
   exit(1)

  __lowerCAmelCase			=							trainer.predict(dataset)
  logger.info(p.metrics)
  if p.metrics["test_success"] is not True:
   logger.error(p.metrics)
   exit(1)




  __lowerCAmelCase			=							None


