


'''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 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
#
########################################################################


a_					:    Union[str, Any]          =    1_6
a_					:    Tuple          =    3_2






def 	_A				(lowerCAmelCase__  :Accelerator				,						lowerCAmelCase__  :int = 16		)   ->  Union[str, Any]:




     '''simple docstring'''



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

     def tokenize_function(lowerCAmelCase__  :Tuple		):
          # 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__  :Optional[int]		):
          # 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

 a_					:    Optional[int]          =    mocked_dataloaders  # noqa: F811






def 	_A				(lowerCAmelCase__  :Tuple				,						lowerCAmelCase__  :Optional[int]		)   ->  Optional[Any]:




     '''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 	_A				()   ->  str:




     '''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()





'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


a_					:    Optional[Any]          =    logging.get_logger(__name__)




class  a     (     _SCREAMING_SNAKE_CASE       ):
 def __init__( self      ,				*__magic_name__      ,				**__magic_name__						)					->							None:
      warnings.warn(
          'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
          ' use DonutImageProcessor instead.'      ,				__magic_name__      ,				)
      super().__init__(*__magic_name__      ,				**__magic_name__						)

