





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
#
########################################################################


_snake_case    :       Union[str, Any]		       =    16
_snake_case    :       Optional[Any]		       =    32

def     a_       (					lowerCAmelCase_ :     Accelerator,      lowerCAmelCase_ :     int = 16      ):
   __lowerCAmelCase				      =      AutoTokenizer.from_pretrained('bert-base-cased'      )
   __lowerCAmelCase				      =      load_dataset('glue',      'mrpc'      )

   def tokenize_function(lowerCAmelCase_ :     Any      ):
      # max_length=None => use the model max length (it's actually the default)
      __lowerCAmelCase				      =      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():
      __lowerCAmelCase				      =      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
   __lowerCAmelCase				      =      tokenized_datasets.rename_column('label',      'labels'      )

   def collate_fn(lowerCAmelCase_ :     str      ):
      # 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(
          lowerCAmelCase_,      padding='longest',      max_length=lowerCAmelCase_,      pad_to_multiple_of=lowerCAmelCase_,      return_tensors='pt',      )

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

     _snake_case    :       Union[str, Any]		       =    mocked_dataloaders  # noqa: F811

def     a_       (					lowerCAmelCase_ :     List[str],      lowerCAmelCase_ :     Dict      ):
   # For testing only
   if os.environ.get('TESTING_MOCKED_DATALOADERS',      lowerCAmelCase_      ) == "1":
      __lowerCAmelCase				      =      2
   # Initialize accelerator
   __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']      )

   __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

   set_seed(lowerCAmelCase_      )
   __lowerCAmelCase      ,      __lowerCAmelCase				      =      get_dataloaders(lowerCAmelCase_,      lowerCAmelCase_      )
   # 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=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).
   __lowerCAmelCase				      =      model.to(accelerator.device      )

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

   # Instantiate scheduler
   __lowerCAmelCase				      =      get_linear_schedule_with_warmup(
       optimizer=lowerCAmelCase_,      num_warmup_steps=100,      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.
   __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase				      =      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      )
         __lowerCAmelCase				      =      model(**lowerCAmelCase_      )
         __lowerCAmelCase				      =      outputs.loss
         __lowerCAmelCase				      =      loss / gradient_accumulation_steps
         accelerator.backward(lowerCAmelCase_      )
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

      model.eval()
      __lowerCAmelCase				      =      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():
            __lowerCAmelCase				      =      model(**lowerCAmelCase_      )
         __lowerCAmelCase				      =      outputs.logits.argmax(dim=-1      )
         __lowerCAmelCase      ,      __lowerCAmelCase				      =      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
               __lowerCAmelCase				      =      predictions[: len(eval_dataloader.dataset      ) - samples_seen]
               __lowerCAmelCase				      =      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_,      )

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

def     a_       (					):
   __lowerCAmelCase				      =      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.'      )
   __lowerCAmelCase				      =      parser.parse_args()
   __lowerCAmelCase				      =      {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
   training_function(lowerCAmelCase_,      lowerCAmelCase_      )


if __name__ == "__main__":
     main()








import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


_lowercase		:  int			       =logging.get_logger(__name__)






class 						UpperCamelCase_  (  snake_case__							):



					def __init__(		self     :     Tuple       ,      *lowerCamelCase     :     List[Any]       ,      **lowerCamelCase     :     Union[str, Any]						):
							warnings.warn(
							    'The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
							    ' use YolosImageProcessor instead.'       ,      lowerCamelCase       ,      )
							super().__init__(*lowerCamelCase       ,      **lowerCamelCase						)
