





'''simple docstring'''

import warnings

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


lowercase__						:      Optional[int]	=    logging.get_logger(__name__)
class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""





       def __init__(							self       :		List[Any]	,			*lowerCAmelCase__       :		Any	,			**lowerCAmelCase__       :		Tuple					)   ->					None:

            '''simple docstring'''




            warnings.warn(
                '''The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
                ''' use DonutImageProcessor instead.'''	,			lowerCAmelCase__	,			)
            super().__init__(*lowerCAmelCase__	,			**lowerCAmelCase__					)










'''simple docstring'''

import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


lowercase__						:      str	=    16
lowercase__						:      int	=    32







def  a__    (    lowercase :					Accelerator, lowercase :					int = 16				)		->		List[str]:





     """simple docstring"""

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

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

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

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

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

     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

   lowercase__						:      str	=    mocked_dataloaders  # noqa: F811







def  a__    (    lowercase :					List[Any], lowercase :					List[str]				)		->		Any:





     """simple docstring"""

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

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

     # New Code #
     # We now can define an inner training loop function. It should take a batch size as the only parameter,
     # and build the dataloaders in there.
     # It also gets our decorator
     @find_executable_batch_size(starting_batch_size=lowercase				)
     def inner_training_loop(lowercase :					List[Any]				):
          # And now just move everything below under this function
          # We need to bring in the Accelerator object from earlier
          nonlocal accelerator
          # And reset all of its attributes that could hold onto any memory:
          accelerator.free_memory()

          # Then we can declare the model, optimizer, and everything else:
          set_seed(lowercase				)

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

          # 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).
          _UpperCamelCase       =	model.to(accelerator.device				)

          # Instantiate optimizer
          _UpperCamelCase       =	AdamW(params=model.parameters(), lr=lowercase				)
          _UpperCamelCase   ,    _UpperCamelCase       =	get_dataloaders(lowercase, lowercase				)

          # Instantiate scheduler
          _UpperCamelCase       =	get_linear_schedule_with_warmup(
              optimizer=lowercase, num_warmup_steps=100, num_training_steps=(len(lowercase				) * num_epochs), )

          # 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.
          _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	accelerator.prepare(
              lowercase, lowercase, lowercase, lowercase, lowercase				)

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

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

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

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
     inner_training_loop()







def  a__    (    )		->		str:





     """simple docstring"""

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


if __name__ == "__main__":
   main()



