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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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_								=							16
UpperCAmelCase_								=							32

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_ = 16   )		->						Any:




       '''simple docstring'''






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

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

       def collate_fn(lowerCAmelCase_   ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              _A=	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=	16
              elif accelerator.mixed_precision != "no":
                     _A=	8
              else:
                     _A=	None

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

       # Instantiate dataloaders.
       _A=	DataLoader(
           tokenized_datasets['train']       ,					shuffle=lowerCAmelCase_       ,					collate_fn=lowerCAmelCase_       ,					batch_size=lowerCAmelCase_   )
       _A=	DataLoader(
           tokenized_datasets['validation']       ,					shuffle=lowerCAmelCase_       ,					collate_fn=lowerCAmelCase_       ,					batch_size=lowerCAmelCase_   )

       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				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						Dict:




       '''simple docstring'''






       if os.environ.get('TESTING_MOCKED_DATALOADERS'       ,					lowerCAmelCase_   ) == "1":
              _A=	2
       # Initialize accelerator
       _A=	Accelerator(cpu=args.cpu       ,					mixed_precision=args.mixed_precision   )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       _A=	config['lr']
       _A=	int(config['num_epochs']   )
       _A=	int(config['seed']   )
       _A=	int(config['batch_size']   )

       _A=	evaluate.load('glue'       ,					'mrpc'   )

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

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

       # 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=	model.to(accelerator.device   )

       # Instantiate optimizer
       _A=	AdamW(params=model.parameters()       ,					lr=lowerCAmelCase_   )

       # Instantiate scheduler
       _A=	get_linear_schedule_with_warmup(
           optimizer=lowerCAmelCase_       ,					num_warmup_steps=1_00       ,					num_training_steps=(len(lowerCAmelCase_   ) * 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.
       _A,			_A,			_A,			_A,			_A=	accelerator.prepare(
           lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )

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

              model.eval()
              _A=	0
              for step, batch in enumerate(lowerCAmelCase_   ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device   )
                     with torch.no_grad():
                            _A=	model(**lowerCAmelCase_   )
                     _A=	outputs.logits.argmax(dim=-1   )
                     _A,			_A=	accelerator.gather((predictions, batch['labels'])   )
                     # New Code #
                     # First we check if it's a distributed system
                     if accelerator.use_distributed:
                            # Then see if we're on the last batch of our eval dataloader
                            if step == len(lowerCAmelCase_   ) - 1:
                                   # Last batch needs to be truncated on distributed systems as it contains additional samples
                                   _A=	predictions[: len(eval_dataloader.dataset   ) - samples_seen]
                                   _A=	references[: len(eval_dataloader.dataset   ) - samples_seen]
                            else:
                                   # Otherwise we add the number of samples seen
                                   samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
                     metric.add_batch(
                         predictions=lowerCAmelCase_       ,					references=lowerCAmelCase_       ,					)

              _A=	metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F"epoch {epoch}:"       ,					lowerCAmelCase_   )

def 							UpperCamelCase				(    )		->						Tuple:




       '''simple docstring'''






       _A=	argparse.ArgumentParser(description='Simple example of training script.'   )
       parser.add_argument(
           '--mixed_precision'       ,					type=lowerCAmelCase_       ,					default=lowerCAmelCase_       ,					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.'   )
       _A=	parser.parse_args()
       _A=	{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       training_function(lowerCAmelCase_       ,					lowerCAmelCase_   )


if __name__ == "__main__":
      main()
from ..utils import DummyObject, requires_backends





class      lowerCAmelCase						(					metaclass=_a			):
     _SCREAMING_SNAKE_CASE     :     Optional[int]		=["""transformers""", """torch""", """note_seq"""]



     def __init__(					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            requires_backends(self				,      ['transformers', 'torch', 'note_seq']       )



     @classmethod
     def 			a__  (					cls				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            requires_backends(cls				,      ['transformers', 'torch', 'note_seq']       )





     @classmethod
     def 			a__  (					cls				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            requires_backends(cls				,      ['transformers', 'torch', 'note_seq']       )