

'''simple docstring'''



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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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						:  List[Any]							       =				16
_UpperCamelCase						:  Optional[int]							       =				32


def       snake_case   (	snake_case      : Accelerator					,		snake_case      : int = 16			) ->  str:


    """simple docstring"""


    lowerCAmelCase 						=							AutoTokenizer.from_pretrained('bert-base-cased'			)
    lowerCAmelCase 						=							load_dataset('glue'					,		'mrpc'			)

    def tokenize_function(snake_case      : Optional[Any]			):
        # max_length=None => use the model max length (it's actually the default)
        lowerCAmelCase 						=							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():
        lowerCAmelCase 						=							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
    lowerCAmelCase 						=							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.
        lowerCAmelCase 						=							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":
            lowerCAmelCase 						=							16
        elif accelerator.mixed_precision != "no":
            lowerCAmelCase 						=							8
        else:
            lowerCAmelCase 						=							None

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

    # Instantiate dataloaders.
    lowerCAmelCase 						=							DataLoader(
        tokenized_datasets['train']					,		shuffle=snake_case					,		collate_fn=snake_case					,		batch_size=snake_case			)
    lowerCAmelCase 						=							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						:  List[Any]							       =				mocked_dataloaders  # noqa: F811


def       snake_case   (	snake_case      : Optional[int]					,		snake_case      : Tuple			) ->  Dict:


    """simple docstring"""


    if os.environ.get('TESTING_MOCKED_DATALOADERS'					,		snake_case			) == "1":
        lowerCAmelCase 						=							2
    # Initialize Accelerator

    # New Code #
    # We pass in "all" to `log_with` to grab all available trackers in the environment
    # Note: If using a custom `Tracker` class, should be passed in here such as:
    # >>> log_with = ["all", MyCustomTrackerClassInstance()]
    if args.with_tracking:
        lowerCAmelCase 						=							Accelerator(
            cpu=args.cpu					,		mixed_precision=args.mixed_precision					,		log_with='all'					,		project_dir=args.project_dir			)
    else:
        lowerCAmelCase 						=							Accelerator(cpu=args.cpu					,		mixed_precision=args.mixed_precision			)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    lowerCAmelCase 						=							config['lr']
    lowerCAmelCase 						=							int(config['num_epochs']			)
    lowerCAmelCase 						=							int(config['seed']			)
    lowerCAmelCase 						=							int(config['batch_size']			)
    set_seed(snake_case			)

    lowerCAmelCase						,   lowerCAmelCase 						=							get_dataloaders(snake_case					,		snake_case			)
    lowerCAmelCase 						=							evaluate.load('glue'					,		'mrpc'			)

    # If the batch size is too big we use gradient accumulation
    lowerCAmelCase 						=							1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        lowerCAmelCase 						=							batch_size // MAX_GPU_BATCH_SIZE
        lowerCAmelCase 						=							MAX_GPU_BATCH_SIZE

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    lowerCAmelCase 						=							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).
    lowerCAmelCase 						=							model.to(accelerator.device			)

    # Instantiate optimizer
    lowerCAmelCase 						=							AdamW(params=model.parameters()					,		lr=snake_case			)

    # Instantiate scheduler
    lowerCAmelCase 						=							get_linear_schedule_with_warmup(
        optimizer=snake_case					,		num_warmup_steps=100					,		num_training_steps=(len(snake_case			) * 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.
    lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase 						=							accelerator.prepare(
        snake_case					,		snake_case					,		snake_case					,		snake_case					,		snake_case			)

    # New Code #
    # We need to initialize the trackers we use. Overall configurations can also be stored
    if args.with_tracking:
        lowerCAmelCase 						=							os.path.split(snake_case			)[-1].split('.'			)[0]
        accelerator.init_trackers(snake_case					,		snake_case			)

    # Now we train the model
    for epoch in range(snake_case			):
        model.train()
        # New Code #
        # For our tracking example, we will log the total loss of each epoch
        if args.with_tracking:
            lowerCAmelCase 						=							0
        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			)
            lowerCAmelCase 						=							model(**snake_case			)
            lowerCAmelCase 						=							outputs.loss
            # New Code #
            if args.with_tracking:
                total_loss += loss.detach().float()
            lowerCAmelCase 						=							loss / gradient_accumulation_steps
            accelerator.backward(snake_case			)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

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

        lowerCAmelCase 						=							metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'epoch {epoch}:'					,		snake_case			)

        # New Code #
        # To actually log, we call `Accelerator.log`
        # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
        if args.with_tracking:
            accelerator.log(
                {
                    'accuracy': eval_metric['accuracy'],
                    'f1': eval_metric['f1'],
                    'train_loss': total_loss.item() / len(snake_case			),
                    'epoch': epoch,
                }					,		step=snake_case					,		)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
    if args.with_tracking:
        accelerator.end_training()


def       snake_case   (	) ->  Any:


    """simple docstring"""


    lowerCAmelCase 						=							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.'					,		)
    parser.add_argument('--cpu'					,		action='store_true'					,		help='If passed, will train on the CPU.'			)
    parser.add_argument(
        '--with_tracking'					,		action='store_true'					,		help='Whether to load in all available experiment trackers from the environment and use them for logging.'					,		)
    parser.add_argument(
        '--project_dir'					,		type=snake_case					,		default='logs'					,		help='Location on where to store experiment tracking logs` and relevent project information'					,		)
    lowerCAmelCase 						=							parser.parse_args()
    lowerCAmelCase 						=							{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
    training_function(snake_case					,		snake_case			)


if __name__ == "__main__":
     main()








'''simple docstring'''



from ..utils import DummyObject, requires_backends



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       int				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       List[Any]				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       int				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       Dict				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       List[Any]				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       Optional[Any]				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)





