


'''simple docstring'''




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


__snake_case    						=16
__snake_case    						=32





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

   def tokenize_function(lowerCamelCase	:     Union[str, 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	:     int				):
      # 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	, drop_last=lowerCamelCase				)
   lowerCAmelCase     =			DataLoader(
       tokenized_datasets['validation']	, shuffle=lowerCamelCase	, collate_fn=lowerCamelCase	, batch_size=lowerCamelCase	, drop_last=(accelerator.mixed_precision == 'fp8')	, )

   return train_dataloader, eval_dataloader





def 	a_					( lowerCamelCase	:     Tuple	, lowerCamelCase	:     Optional[int]				):
   # 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()
      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_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()









'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


__snake_case    						=logging.get_logger(__name__)

class 		UpperCAmelCase_      (  __lowercase		):





     def __init__( self       :      Dict    ,      *UpperCAmelCase__       :      Dict    ,      **UpperCAmelCase__       :      List[str]    )				->  None:
        warnings.warn(
            'The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
            ' use FlavaImageProcessor instead.'    ,      UpperCAmelCase__    ,      )
        super().__init__(*UpperCAmelCase__    ,      **UpperCAmelCase__    )





