
"""simple docstring"""




import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


A__ :       str				= 1_6
A__ :       int				= 3_2




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase = 16			,					_UpperCamelCase = "bert-base-cased"    ):






  """simple docstring"""



  _lowercase:      Tuple   			=  AutoTokenizer.from_pretrained(_UpperCamelCase    )
  _lowercase:      int   			=  load_dataset('''glue'''			,					'''mrpc'''    )

  def tokenize_function(_UpperCamelCase    ):
    # max_length=None => use the model max length (it's actually the default)
    _lowercase:      int   			=  tokenizer(examples['''sentence1''']			,					examples['''sentence2''']			,					truncation=_UpperCamelCase			,					max_length=_UpperCamelCase    )
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  _lowercase:      Any   			=  datasets.map(
      _UpperCamelCase			,					batched=_UpperCamelCase			,					remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,					load_from_cache_file=_UpperCamelCase    )

  # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  # transformers library
  _lowercase:      List[str]   			=  tokenized_datasets.rename_column('''label'''			,					'''labels'''    )

  def collate_fn(_UpperCamelCase    ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    if accelerator.distributed_type == DistributedType.TPU:
      return tokenizer.pad(_UpperCamelCase			,					padding='''max_length'''			,					max_length=128			,					return_tensors='''pt'''    )
    return tokenizer.pad(_UpperCamelCase			,					padding='''longest'''			,					return_tensors='''pt'''    )

  # Instantiate dataloaders.
  _lowercase:      Tuple   			=  DataLoader(
      tokenized_datasets['''train''']			,					shuffle=_UpperCamelCase			,					collate_fn=_UpperCamelCase			,					batch_size=_UpperCamelCase    )
  _lowercase:      Optional[int]   			=  DataLoader(
      tokenized_datasets['''validation''']			,					shuffle=_UpperCamelCase			,					collate_fn=_UpperCamelCase			,					batch_size=_UpperCamelCase    )

  return train_dataloader, eval_dataloader




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  model.eval()
  _lowercase:      Tuple   			=  0
  for step, batch in enumerate(_UpperCamelCase    ):
    # We could avoid this line since we set the accelerator with `device_placement=True`.
    batch.to(accelerator.device    )
    with torch.no_grad():
      _lowercase:      Union[str, Any]   			=  model(**_UpperCamelCase    )
    _lowercase:      List[Any]   			=  outputs.logits.argmax(dim=-1    )
    # It is slightly faster to call this once, than multiple times
    _lowercase			,					_lowercase:      Optional[Any]   			=  accelerator.gather(
        (predictions, batch['''labels'''])    )  # If we are in a multiprocess environment, the last batch has duplicates
    if accelerator.use_distributed:
      if step == len(_UpperCamelCase    ) - 1:
        _lowercase:      Any   			=  predictions[: len(eval_dataloader.dataset    ) - samples_seen]
        _lowercase:      List[Any]   			=  references[: len(eval_dataloader.dataset    ) - samples_seen]
      else:
        samples_seen += references.shape[0]
    metric.add_batch(
        predictions=_UpperCamelCase			,					references=_UpperCamelCase			,					)

  _lowercase:      Union[str, Any]   			=  metric.compute()
  return eval_metric["accuracy"]




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      List[str]   			=  Accelerator()

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _lowercase:      List[str]   			=  config['''lr''']
  _lowercase:      List[str]   			=  int(config['''num_epochs''']    )
  _lowercase:      str   			=  int(config['''seed''']    )
  _lowercase:      Any   			=  int(config['''batch_size''']    )
  _lowercase:      Optional[int]   			=  args.model_name_or_path

  set_seed(_UpperCamelCase    )
  _lowercase			,					_lowercase:      List[Any]   			=  get_dataloaders(_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  _lowercase:      int   			=  AutoModelForSequenceClassification.from_pretrained(_UpperCamelCase			,					return_dict=_UpperCamelCase    )

  # Instantiate optimizer
  _lowercase:      List[str]   			=  (
      AdamW
      if accelerator.state.deepspeed_plugin is None
      or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
      else DummyOptim
  )
  _lowercase:      Tuple   			=  optimizer_cls(params=model.parameters()			,					lr=_UpperCamelCase    )

  if accelerator.state.deepspeed_plugin is not None:
    _lowercase:      List[str]   			=  accelerator.state.deepspeed_plugin.deepspeed_config[
        '''gradient_accumulation_steps'''
    ]
  else:
    _lowercase:      Optional[int]   			=  1
  _lowercase:      Union[str, Any]   			=  (len(_UpperCamelCase    ) * num_epochs) // gradient_accumulation_steps

  # Instantiate scheduler
  if (
      accelerator.state.deepspeed_plugin is None
      or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
  ):
    _lowercase:      Optional[int]   			=  get_linear_schedule_with_warmup(
        optimizer=_UpperCamelCase			,					num_warmup_steps=0			,					num_training_steps=_UpperCamelCase			,					)
  else:
    _lowercase:      List[Any]   			=  DummyScheduler(_UpperCamelCase			,					total_num_steps=_UpperCamelCase			,					warmup_num_steps=0    )

  # 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:      str   			=  accelerator.prepare(
      _UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    )

  # We need to keep track of how many total steps we have iterated over
  _lowercase:      List[Any]   			=  0
  # We also need to keep track of the stating epoch so files are named properly
  _lowercase:      Union[str, Any]   			=  0
  _lowercase:      int   			=  evaluate.load('''glue'''			,					'''mrpc'''    )
  _lowercase:      Union[str, Any]   			=  num_epochs

  if args.partial_train_epoch is not None:
    _lowercase:      List[Any]   			=  args.partial_train_epoch

  if args.resume_from_checkpoint:
    accelerator.load_state(args.resume_from_checkpoint    )
    _lowercase:      Dict   			=  args.resume_from_checkpoint.split('''epoch_'''    )[1]
    _lowercase:      Optional[int]   			=  ''''''
    for char in epoch_string:
      if char.isdigit():
        state_epoch_num += char
      else:
        break
    _lowercase:      int   			=  int(_UpperCamelCase    ) + 1
    _lowercase:      Tuple   			=  evaluation_loop(_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    )
    accelerator.print('''resumed checkpoint performance:'''			,					_UpperCamelCase    )
    accelerator.print('''resumed checkpoint\'s scheduler\'s lr:'''			,					lr_scheduler.get_lr()[0]    )
    accelerator.print('''resumed optimizers\'s lr:'''			,					optimizer.param_groups[0]['''lr''']    )
    with open(os.path.join(args.output_dir			,					f'''state_{starting_epoch-1}.json'''    )			,					'''r'''    ) as f:
      _lowercase:      Tuple   			=  json.load(_UpperCamelCase    )
      assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
      assert (
          resumed_state["lr"] == lr_scheduler.get_lr()[0]
      ), "Scheduler learning rate mismatch, loading from checkpoint failed"
      assert (
          resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
      ), "Optimizer learning rate mismatch, loading from checkpoint failed"
      assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
      return

    # Now we train the model
  _lowercase:      int   			=  {}
  for epoch in range(_UpperCamelCase			,					_UpperCamelCase    ):
    model.train()
    for step, batch in enumerate(_UpperCamelCase    ):
      _lowercase:      Optional[int]   			=  model(**_UpperCamelCase    )
      _lowercase:      str   			=  outputs.loss
      _lowercase:      Optional[int]   			=  loss / gradient_accumulation_steps
      accelerator.backward(_UpperCamelCase    )
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1
    _lowercase:      Optional[int]   			=  f'''epoch_{epoch}'''
    _lowercase:      Union[str, Any]   			=  os.path.join(args.output_dir			,					_UpperCamelCase    )
    accelerator.save_state(_UpperCamelCase    )
    _lowercase:      List[str]   			=  evaluation_loop(_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    )
    _lowercase:      List[str]   			=  accuracy
    _lowercase:      List[str]   			=  lr_scheduler.get_lr()[0]
    _lowercase:      Union[str, Any]   			=  optimizer.param_groups[0]['''lr''']
    _lowercase:      Tuple   			=  epoch
    _lowercase:      List[Any]   			=  overall_step
    accelerator.print(f'''epoch {epoch}:'''			,					_UpperCamelCase    )

    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
      with open(os.path.join(args.output_dir			,					f'''state_{epoch}.json'''    )			,					'''w'''    ) as f:
        json.dump(_UpperCamelCase			,					_UpperCamelCase    )




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      List[str]   			=  argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''    )
  parser.add_argument(
      '''--model_name_or_path'''			,					type=_UpperCamelCase			,					default='''bert-base-cased'''			,					help='''Path to pretrained model or model identifier from huggingface.co/models.'''			,					required=_UpperCamelCase			,					)
  parser.add_argument(
      '''--output_dir'''			,					type=_UpperCamelCase			,					default='''.'''			,					help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''			,					)
  parser.add_argument(
      '''--resume_from_checkpoint'''			,					type=_UpperCamelCase			,					default=_UpperCamelCase			,					help='''If the training should continue from a checkpoint folder.'''			,					)
  parser.add_argument(
      '''--partial_train_epoch'''			,					type=_UpperCamelCase			,					default=_UpperCamelCase			,					help='''If passed, the training will stop after this number of epochs.'''			,					)
  parser.add_argument(
      '''--num_epochs'''			,					type=_UpperCamelCase			,					default=2			,					help='''Number of train epochs.'''			,					)
  _lowercase:      Tuple   			=  parser.parse_args()
  _lowercase:      List[str]   			=  {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

  training_function(_UpperCamelCase			,					_UpperCamelCase    )


if __name__ == "__main__":
  main()


"""simple docstring"""




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       __magic_name__							(							unittest.TestCase			):
       def __init__(  self			,					A_			,					A_=7			,					A_=3			,					A_=18			,					A_=30			,					A_=400			,					A_=True			,					A_=None			,					A_=True			,					A_=False			,					A_=True			,					A_=True			,					A_=[0.5, 0.5, 0.5]			,					A_=[0.5, 0.5, 0.5]			,					)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      Optional[int]   			=  parent
         _lowercase:      str   			=  batch_size
         _lowercase:      Any   			=  num_channels
         _lowercase:      Any   			=  image_size
         _lowercase:      Any   			=  min_resolution
         _lowercase:      int   			=  max_resolution
         _lowercase:      Union[str, Any]   			=  do_resize
         _lowercase:      List[Any]   			=  size if size is not None else {'''height''': 18, '''width''': 20}
         _lowercase:      Optional[int]   			=  do_thumbnail
         _lowercase:      List[str]   			=  do_align_axis
         _lowercase:      int   			=  do_pad
         _lowercase:      Optional[Any]   			=  do_normalize
         _lowercase:      List[Any]   			=  image_mean
         _lowercase:      Union[str, Any]   			=  image_std



       def 				lowercase_				(  self							)  ->						List[str]:





         """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       __magic_name__							(							SCREAMING_SNAKE_CASE__ ,       unittest.TestCase			):
       UpperCamelCase_ =		DonutImageProcessor if is_vision_available() else None
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      str   			=  DonutImageProcessingTester(self							)
       @property
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         return self.image_processor_tester.prepare_image_processor_dict()
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         _lowercase:      Optional[int]   			=  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 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         _lowercase:      Optional[Any]   			=  self.image_processing_class.from_dict(self.image_processor_dict							)
         self.assertEqual(image_processor.size			,					{'''height''': 18, '''width''': 20}							)

         _lowercase:      List[str]   			=  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:      Any   			=  self.image_processing_class.from_dict(self.image_processor_dict			,					size=(42, 84)							)
         self.assertEqual(image_processor.size			,					{'''height''': 84, '''width''': 42}							)
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         pass
       @is_flaky()
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         _lowercase:      str   			=  self.image_processing_class(**self.image_processor_dict							)
         # create random PIL images
         _lowercase:      Dict   			=  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:      Tuple   			=  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:      List[Any]   			=  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 				lowercase_				(  self							)  ->						Any:





         """simple docstring"""




         _lowercase:      str   			=  self.image_processing_class(**self.image_processor_dict							)
         # create random numpy tensors
         _lowercase:      str   			=  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:      Union[str, Any]   			=  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:      Dict   			=  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 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      List[Any]   			=  self.image_processing_class(**self.image_processor_dict							)
         # create random PyTorch tensors
         _lowercase:      List[Any]   			=  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:      Union[str, Any]   			=  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:      Dict   			=  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'''],
             )			,					)
