





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_					     =	16
lowercase_					     =	32




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = 16	)       ->     Optional[int]:
  lowercase__			    =						AutoTokenizer.from_pretrained('bert-base-cased'	)
  lowercase__			    =						load_dataset('glue'	,		'mrpc'	)

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

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

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

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

  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_					     =	mocked_dataloaders  # noqa: F811




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

  lowercase__			    =						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=_SCREAMING_SNAKE_CASE	)
  def inner_training_loop(_SCREAMING_SNAKE_CASE	):
    # 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(_SCREAMING_SNAKE_CASE	)

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

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

    # Instantiate optimizer
    lowercase__			    =						AdamW(params=model.parameters()	,		lr=_SCREAMING_SNAKE_CASE	)
    lowercase__    ,					lowercase__			    =						get_dataloaders(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)

    # Instantiate scheduler
    lowercase__			    =						get_linear_schedule_with_warmup(
        optimizer=_SCREAMING_SNAKE_CASE	,		num_warmup_steps=100	,		num_training_steps=(len(_SCREAMING_SNAKE_CASE	) * 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.
    lowercase__    ,					lowercase__    ,					lowercase__    ,					lowercase__    ,					lowercase__			    =						accelerator.prepare(
        _SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)

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

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

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

    # 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     __UpperCamelCase						()       ->     Optional[Any]:
  lowercase__			    =						argparse.ArgumentParser(description='Simple example of training script.'	)
  parser.add_argument(
      '--mixed_precision'	,		type=_SCREAMING_SNAKE_CASE	,		default=_SCREAMING_SNAKE_CASE	,		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.'	)
  lowercase__			    =						parser.parse_args()
  lowercase__			    =						{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  training_function(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
      main()









import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import DonutImageProcessor
class        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):


   def __init__(					self    :       Any    ,  a    :       str    ,  a    :       List[Any]=7    ,  a    :       int=3    ,  a    :       int=18    ,  a    :       Optional[Any]=30    ,  a    :       Optional[int]=400    ,  a    :       int=True    ,  a    :       Tuple=None    ,  a    :       Optional[Any]=True    ,  a    :       str=False    ,  a    :       str=True    ,  a    :       int=True    ,  a    :       Tuple=[0.5, 0.5, 0.5]    ,  a    :       Any=[0.5, 0.5, 0.5]    ,  )->     Optional[int]:





     """simple docstring"""







     lowercase__			    =						parent
     lowercase__			    =						batch_size
     lowercase__			    =						num_channels
     lowercase__			    =						image_size
     lowercase__			    =						min_resolution
     lowercase__			    =						max_resolution
     lowercase__			    =						do_resize
     lowercase__			    =						size if size is not None else {'height': 18, 'width': 20}
     lowercase__			    =						do_thumbnail
     lowercase__			    =						do_align_axis
     lowercase__			    =						do_pad
     lowercase__			    =						do_normalize
     lowercase__			    =						image_mean
     lowercase__			    =						image_std


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Tuple:





     """simple docstring"""







     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_thumbnail": self.do_thumbnail,
         "do_align_long_axis": self.do_align_axis,
         "do_pad": self.do_pad,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
     }






@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase    ,			unittest.TestCase   ):
   _UpperCamelCase					:	Optional[Any]				      = DonutImageProcessor if is_vision_available() else None


   def  SCREAMING_SNAKE_CASE_							(					self    :       int )->     List[Any]:





     """simple docstring"""







     lowercase__			    =						DonutImageProcessingTester(self )


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     Optional[int]:





     """simple docstring"""







     return self.image_processor_tester.prepare_image_processor_dict()


   def  SCREAMING_SNAKE_CASE_							(					self    :       Any )->     int:





     """simple docstring"""







     lowercase__			    =						self.image_processing_class(**self.image_processor_dict )
     self.assertTrue(hasattr(a    ,  'do_resize' ) )
     self.assertTrue(hasattr(a    ,  'size' ) )
     self.assertTrue(hasattr(a    ,  'do_thumbnail' ) )
     self.assertTrue(hasattr(a    ,  'do_align_long_axis' ) )
     self.assertTrue(hasattr(a    ,  'do_pad' ) )
     self.assertTrue(hasattr(a    ,  'do_normalize' ) )
     self.assertTrue(hasattr(a    ,  'image_mean' ) )
     self.assertTrue(hasattr(a    ,  'image_std' ) )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Dict:





     """simple docstring"""







     lowercase__			    =						self.image_processing_class.from_dict(self.image_processor_dict )
     self.assertEqual(image_processor.size    ,  {'height': 18, 'width': 20} )

     lowercase__			    =						self.image_processing_class.from_dict(self.image_processor_dict    ,  size=42 )
     self.assertEqual(image_processor.size    ,  {'height': 42, 'width': 42} )

     # Previous config had dimensions in (width, height) order
     lowercase__			    =						self.image_processing_class.from_dict(self.image_processor_dict    ,  size=(42, 84) )
     self.assertEqual(image_processor.size    ,  {'height': 84, 'width': 42} )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Dict:





     """simple docstring"""







     pass


   @is_flaky()
   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     Optional[int]:





     """simple docstring"""







     lowercase__			    =						self.image_processing_class(**self.image_processor_dict )
     # create random PIL images
     lowercase__			    =						prepare_image_inputs(self.image_processor_tester    ,  equal_resolution=a )
     for image in image_inputs:
       self.assertIsInstance(a    ,  Image.Image )

     # Test not batched input
     lowercase__			    =						image_processing(image_inputs[0]    ,  return_tensors='pt' ).pixel_values
     self.assertEqual(
         encoded_images.shape    ,  (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )    ,  )

     # Test batched
     lowercase__			    =						image_processing(a    ,  return_tensors='pt' ).pixel_values
     self.assertEqual(
         encoded_images.shape    ,  (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )    ,  )


   @is_flaky()
   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Tuple:





     """simple docstring"""







     lowercase__			    =						self.image_processing_class(**self.image_processor_dict )
     # create random numpy tensors
     lowercase__			    =						prepare_image_inputs(self.image_processor_tester    ,  equal_resolution=a    ,  numpify=a )
     for image in image_inputs:
       self.assertIsInstance(a    ,  np.ndarray )

     # Test not batched input
     lowercase__			    =						image_processing(image_inputs[0]    ,  return_tensors='pt' ).pixel_values
     self.assertEqual(
         encoded_images.shape    ,  (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )    ,  )

     # Test batched
     lowercase__			    =						image_processing(a    ,  return_tensors='pt' ).pixel_values
     self.assertEqual(
         encoded_images.shape    ,  (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )    ,  )


   @is_flaky()
   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str] )->     Dict:





     """simple docstring"""







     lowercase__			    =						self.image_processing_class(**self.image_processor_dict )
     # create random PyTorch tensors
     lowercase__			    =						prepare_image_inputs(self.image_processor_tester    ,  equal_resolution=a    ,  torchify=a )
     for image in image_inputs:
       self.assertIsInstance(a    ,  torch.Tensor )

     # Test not batched input
     lowercase__			    =						image_processing(image_inputs[0]    ,  return_tensors='pt' ).pixel_values
     self.assertEqual(
         encoded_images.shape    ,  (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )    ,  )

     # Test batched
     lowercase__			    =						image_processing(a    ,  return_tensors='pt' ).pixel_values
     self.assertEqual(
         encoded_images.shape    ,  (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )    ,  )


