





"""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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


lowerCamelCase_	:		Union[str, Any]         =   16
lowerCamelCase_	:		Optional[int]         =   32





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  if args.with_tracking:
    A_    :			Union[str, Any]									=    Accelerator(
        cpu=args.cpu					,   mixed_precision=args.mixed_precision					,   log_with='all'					,   project_dir=args.project_dir   )
  else:
    A_    :			Optional[Any]									=    Accelerator(cpu=args.cpu					,   mixed_precision=args.mixed_precision   )

  if hasattr(args.checkpointing_steps					,   'isdigit'   ):
    if args.checkpointing_steps == "epoch":
      A_    :			Tuple									=    args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      A_    :			List[str]									=    int(args.checkpointing_steps   )
    else:
      raise ValueError(
          f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""   )
  else:
    A_    :			Optional[Any]									=    None
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  A_    :			str									=    config["""lr"""]
  A_    :			List[str]									=    int(config['num_epochs']   )
  A_    :			Dict									=    int(config['seed']   )
  A_    :			str									=    int(config['batch_size']   )

  # We need to initialize the trackers we use, and also store our configuration
  if args.with_tracking:
    A_    :			str									=    os.path.split(a_   )[-1].split('.'   )[0]
    accelerator.init_trackers(a_					,   a_   )

  A_    :			Union[str, Any]									=    AutoTokenizer.from_pretrained('bert-base-cased'   )
  A_    :			Optional[int]									=    load_dataset('glue'					,   'mrpc'   )
  A_    :			List[str]									=    evaluate.load('glue'					,   'mrpc'   )

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

  # If the batch size is too big we use gradient accumulation
  A_    :			Optional[Any]									=    1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    A_    :			List[str]									=    batch_size // MAX_GPU_BATCH_SIZE
    A_    :			List[str]									=    MAX_GPU_BATCH_SIZE

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

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

  # Instantiate dataloaders.
  A_    :			str									=    DataLoader(
      tokenized_datasets['train']					,   shuffle=a_					,   collate_fn=a_					,   batch_size=a_   )
  A_    :			Any									=    DataLoader(
      tokenized_datasets['validation']					,   shuffle=a_					,   collate_fn=a_					,   batch_size=a_   )

  set_seed(a_   )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  A_    :			List[Any]									=    AutoModelForSequenceClassification.from_pretrained('bert-base-cased'					,   return_dict=a_   )

  # 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_    :			Union[str, Any]									=    model.to(accelerator.device   )

  # Instantiate optimizer
  A_    :			Any									=    AdamW(params=model.parameters()					,   lr=a_   )

  # Instantiate scheduler
  A_    :			Dict									=    get_linear_schedule_with_warmup(
      optimizer=a_					,   num_warmup_steps=100					,   num_training_steps=(len(a_   ) * 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_    :			List[str]									=    accelerator.prepare(
      a_					,   a_					,   a_					,   a_					,   a_   )

  # We need to keep track of how many total steps we have iterated over
  A_    :			Optional[int]									=    0
  # We also need to keep track of the stating epoch so files are named properly
  A_    :			Union[str, Any]									=    0

  # Potentially load in the weights and states from a previous save
  if args.resume_from_checkpoint:
    if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
      accelerator.print(f"""Resumed from checkpoint: {args.resume_from_checkpoint}"""   )
      accelerator.load_state(args.resume_from_checkpoint   )
      A_    :			int									=    os.path.basename(args.resume_from_checkpoint   )
    else:
      # Get the most recent checkpoint
      A_    :			int									=    [f.name for f in os.scandir(os.getcwd()   ) if f.is_dir()]
      dirs.sort(key=os.path.getctime   )
      A_    :			Optional[Any]									=    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    A_    :			Optional[Any]									=    os.path.splitext(a_   )[0]

    if "epoch" in training_difference:
      A_    :			List[Any]									=    int(training_difference.replace('epoch_'					,   ''   )   ) + 1
      A_    :			List[str]									=    None
    else:
      A_    :			Optional[int]									=    int(training_difference.replace('step_'					,   ''   )   )
      A_    :			Optional[int]									=    resume_step // len(a_   )
      resume_step -= starting_epoch * len(a_   )

    # Now we train the model
  for epoch in range(a_					,   a_   ):
    model.train()
    if args.with_tracking:
      A_    :			List[str]									=    0
    if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
      # We need to skip steps until we reach the resumed step
      A_    :			List[Any]									=    accelerator.skip_first_batches(a_					,   a_   )
      overall_step += resume_step
    else:
      # After the first iteration though, we need to go back to the original dataloader
      A_    :			Tuple									=    train_dataloader
    for step, batch in enumerate(a_   ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device   )
      A_    :			List[Any]									=    model(**a_   )
      A_    :			str									=    outputs.loss
      A_    :			Tuple									=    loss / gradient_accumulation_steps
      # We keep track of the loss at each epoch
      if args.with_tracking:
        total_loss += loss.detach().float()
      accelerator.backward(a_   )
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1

      if isinstance(a_					,   a_   ):
        A_    :			str									=    f"""step_{overall_step}"""
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            A_    :			Dict									=    os.path.join(args.output_dir					,   a_   )
          accelerator.save_state(a_   )

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

    A_    :			Union[str, Any]									=    metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f"""epoch {epoch}:"""					,   a_   )
    if args.with_tracking:
      accelerator.log(
          {
              'accuracy': eval_metric['accuracy'],
              'f1': eval_metric['f1'],
              'train_loss': total_loss.item() / len(a_   ),
              'epoch': epoch,
          }					,   step=a_					,   )

    if checkpointing_steps == "epoch":
      A_    :			str									=    f"""epoch_{epoch}"""
      if args.output_dir is not None:
        A_    :			Dict									=    os.path.join(args.output_dir					,   a_   )
      accelerator.save_state(a_   )

  if args.with_tracking:
    accelerator.end_training()





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  A_    :			List[Any]									=    argparse.ArgumentParser(description='Simple example of training script.'   )
  parser.add_argument(
      '--mixed_precision'					,   type=a_					,   default=a_					,   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.'   )
  parser.add_argument(
      '--checkpointing_steps'					,   type=a_					,   default=a_					,   help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'					,   )
  parser.add_argument(
      '--resume_from_checkpoint'					,   type=a_					,   default=a_					,   help='If the training should continue from a checkpoint folder.'					,   )
  parser.add_argument(
      '--with_tracking'					,   action='store_true'					,   help='Whether to load in all available experiment trackers from the environment and use them for logging.'					,   )
  parser.add_argument(
      '--output_dir'					,   type=a_					,   default='.'					,   help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'					,   )
  parser.add_argument(
      '--project_dir'					,   type=a_					,   default='logs'					,   help='Location on where to store experiment tracking logs` and relevent project information'					,   )
  A_    :			Tuple									=    parser.parse_args()
  A_    :			Union[str, Any]									=    {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(a_					,   a_   )


if __name__ == "__main__":
   main()




'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import 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 ViltImageProcessor


class 							_lowerCAmelCase	(  unittest.TestCase							):


 """simple docstring"""

 def __init__(      self					,    _lowerCamelCase					,    _lowerCamelCase=7					,    _lowerCamelCase=3					,    _lowerCamelCase=18					,    _lowerCamelCase=30					,    _lowerCamelCase=400					,    _lowerCamelCase=True					,    _lowerCamelCase=None					,    _lowerCamelCase=2					,    _lowerCamelCase=True					,    _lowerCamelCase=[0.5, 0.5, 0.5]					,    _lowerCamelCase=[0.5, 0.5, 0.5]					,    )    ->    Tuple:
    A_  :		Dict						=					size if size is not None else {"""shortest_edge""": 30}
    A_  :		List[Any]						=					parent
    A_  :		Optional[Any]						=					batch_size
    A_  :		Tuple						=					num_channels
    A_  :		Any						=					image_size
    A_  :		str						=					min_resolution
    A_  :		str						=					max_resolution
    A_  :		Any						=					do_resize
    A_  :		List[Any]						=					size
    A_  :		Union[str, Any]						=					size_divisor
    A_  :		Dict						=					do_normalize
    A_  :		Dict						=					image_mean
    A_  :		Optional[Any]						=					image_std







 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    return {
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_normalize": self.do_normalize,
        "do_resize": self.do_resize,
        "size": self.size,
        "size_divisor": self.size_divisor,
    }







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase=False  )    ->    Union[str, Any]:
    if not batched:
       A_  :		List[str]						=					self.size["""shortest_edge"""]
       A_  :		str						=					image_inputs[0]
       if isinstance(_lowerCamelCase					,    Image.Image  ):
          A_						,  A_  :		Dict						=					image.size
       else:
          A_						,  A_  :		Dict						=					image.shape[1], image.shape[2]
       A_  :		Dict						=					size / min(_lowerCamelCase					,    _lowerCamelCase  )
       if h < w:
          A_						,  A_  :		Any						=					size, scale * w
       else:
          A_						,  A_  :		List[str]						=					scale * h, size

       A_  :		Dict						=					int((1333 / 800) * size  )
       if max(_lowerCamelCase					,    _lowerCamelCase  ) > max_size:
          A_  :		Dict						=					max_size / max(_lowerCamelCase					,    _lowerCamelCase  )
          A_  :		str						=					newh * scale
          A_  :		Tuple						=					neww * scale

       A_						,  A_  :		str						=					int(newh + 0.5  ), int(neww + 0.5  )
       A_						,  A_  :		Union[str, Any]						=					(
           newh // self.size_divisor * self.size_divisor,
           neww // self.size_divisor * self.size_divisor,
       )

    else:
       A_  :		Union[str, Any]						=					[]
       for image in image_inputs:
          A_						,  A_  :		Any						=					self.get_expected_values([image]  )
          expected_values.append((expected_height, expected_width)  )
       A_  :		List[str]						=					max(_lowerCamelCase					,    key=lambda _lowerCamelCase  : item[0]  )[0]
       A_  :		List[str]						=					max(_lowerCamelCase					,    key=lambda _lowerCamelCase  : item[1]  )[1]

    return expected_height, expected_width


@require_torch
@require_vision
class 							_lowerCAmelCase	(  __A,							unittest.TestCase							):


 """simple docstring"""

 lowerCamelCase									=     ViltImageProcessor if is_vision_available() else None







 def       UpperCAmelCase_       (      self  )    ->    int:
    A_  :		Optional[int]						=					ViltImageProcessingTester(self  )







 @property
 def       UpperCAmelCase_       (      self  )    ->    Dict:
    return self.image_processor_tester.prepare_image_processor_dict()







 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    A_  :		List[str]						=					self.image_processing_class(**self.image_processor_dict  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """image_mean"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """image_std"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """do_normalize"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """do_resize"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """size"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """size_divisor"""  )  )







 def       UpperCAmelCase_       (      self  )    ->    List[str]:
    A_  :		Optional[int]						=					self.image_processing_class.from_dict(self.image_processor_dict  )
    self.assertEqual(image_processor.size					,    {"""shortest_edge""": 30}  )

    A_  :		Union[str, Any]						=					self.image_processing_class.from_dict(self.image_processor_dict					,    size=42  )
    self.assertEqual(image_processor.size					,    {"""shortest_edge""": 42}  )







 def       UpperCAmelCase_       (      self  )    ->    Any:
    pass







 def       UpperCAmelCase_       (      self  )    ->    List[Any]:
    # Initialize image_processing
    A_  :		Any						=					self.image_processing_class(**self.image_processor_dict  )
    # create random PIL images
    A_  :		Any						=					prepare_image_inputs(self.image_processor_tester					,    equal_resolution=_lowerCamelCase  )
    for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase					,    Image.Image  )

    # Test not batched input
    A_  :		Tuple						=					image_processing(image_inputs[0]					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		Union[str, Any]						=					self.image_processor_tester.get_expected_values(_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,    )

    # Test batched
    A_  :		List[Any]						=					image_processing(_lowerCamelCase					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		Union[str, Any]						=					self.image_processor_tester.get_expected_values(_lowerCamelCase					,    batched=_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )					,    )







 def       UpperCAmelCase_       (      self  )    ->    str:
    # Initialize image_processing
    A_  :		Tuple						=					self.image_processing_class(**self.image_processor_dict  )
    # create random numpy tensors
    A_  :		List[str]						=					prepare_image_inputs(self.image_processor_tester					,    equal_resolution=_lowerCamelCase					,    numpify=_lowerCamelCase  )
    for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase					,    np.ndarray  )

    # Test not batched input
    A_  :		Any						=					image_processing(image_inputs[0]					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		int						=					self.image_processor_tester.get_expected_values(_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,    )

    # Test batched
    A_  :		List[str]						=					image_processing(_lowerCamelCase					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		Any						=					self.image_processor_tester.get_expected_values(_lowerCamelCase					,    batched=_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )					,    )







 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    # Initialize image_processing
    A_  :		List[str]						=					self.image_processing_class(**self.image_processor_dict  )
    # create random PyTorch tensors
    A_  :		str						=					prepare_image_inputs(self.image_processor_tester					,    equal_resolution=_lowerCamelCase					,    torchify=_lowerCamelCase  )
    for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase					,    torch.Tensor  )

    # Test not batched input
    A_  :		Optional[int]						=					image_processing(image_inputs[0]					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		List[str]						=					self.image_processor_tester.get_expected_values(_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,    )

    # Test batched
    A_  :		Any						=					image_processing(_lowerCamelCase					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		Any						=					self.image_processor_tester.get_expected_values(_lowerCamelCase					,    batched=_lowerCamelCase  )
    self.assertEqual(
        encoded_images.shape					,    (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )					,    )
