





'''simple docstring'''






import argparse

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)
#
# 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						: Optional[int]		=   16
__lowerCamelCase						: Tuple		=   32


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE = 16   ):


  """simple docstring"""

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

  def tokenize_function(__SCREAMING_SNAKE_CASE   ):
    # max_length=None => use the model max length (it's actually the default)
    _UpperCamelCase       			=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():
    _UpperCamelCase       			=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
  _UpperCamelCase       			=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.
    _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(
        __SCREAMING_SNAKE_CASE  ,      padding='''longest'''  ,      max_length=__SCREAMING_SNAKE_CASE  ,      pad_to_multiple_of=__SCREAMING_SNAKE_CASE  ,      return_tensors='''pt'''  ,      )

  # Instantiate dataloaders.
  _UpperCamelCase       			=DataLoader(
      tokenized_datasets['''train''']  ,      shuffle=__SCREAMING_SNAKE_CASE  ,      collate_fn=__SCREAMING_SNAKE_CASE  ,      batch_size=__SCREAMING_SNAKE_CASE  ,      drop_last=__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=DataLoader(
      tokenized_datasets['''validation''']  ,      shuffle=__SCREAMING_SNAKE_CASE  ,      collate_fn=__SCREAMING_SNAKE_CASE  ,      batch_size=__SCREAMING_SNAKE_CASE  ,      drop_last=(accelerator.mixed_precision == '''fp8''')  ,      )

  return train_dataloader, eval_dataloader


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _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'''   )

  # If the batch size is too big we use gradient accumulation
  _UpperCamelCase       			=1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    _UpperCamelCase       			=batch_size // MAX_GPU_BATCH_SIZE
    _UpperCamelCase       			=MAX_GPU_BATCH_SIZE

  set_seed(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase			,     _UpperCamelCase       			=get_dataloaders(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
  # 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=__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).
  _UpperCamelCase       			=model.to(accelerator.device   )
  # Instantiate optimizer
  _UpperCamelCase       			=AdamW(params=model.parameters()  ,      lr=__SCREAMING_SNAKE_CASE   )

  # Instantiate scheduler
  _UpperCamelCase       			=get_linear_schedule_with_warmup(
      optimizer=__SCREAMING_SNAKE_CASE  ,      num_warmup_steps=100  ,      num_training_steps=(len(__SCREAMING_SNAKE_CASE   ) * 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.

  _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase       			=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   )
      _UpperCamelCase       			=model(**__SCREAMING_SNAKE_CASE   )
      _UpperCamelCase       			=outputs.loss
      _UpperCamelCase       			=loss / gradient_accumulation_steps
      accelerator.backward(__SCREAMING_SNAKE_CASE   )
      if step % gradient_accumulation_steps == 0:
        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():
        _UpperCamelCase       			=model(**__SCREAMING_SNAKE_CASE   )
      _UpperCamelCase       			=outputs.logits.argmax(dim=-1   )
      _UpperCamelCase			,     _UpperCamelCase       			=accelerator.gather_for_metrics((predictions, batch['''labels'''])   )
      metric.add_batch(
          predictions=__SCREAMING_SNAKE_CASE  ,      references=__SCREAMING_SNAKE_CASE  ,      )

    _UpperCamelCase       			=metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'''epoch {epoch}:'''  ,      __SCREAMING_SNAKE_CASE   )


def 					_a   ():


  """simple docstring"""

  _UpperCamelCase       			=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.'''   )
  _UpperCamelCase       			=parser.parse_args()
  _UpperCamelCase       			={'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
  training_function(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )


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 PoolFormerImageProcessor







class 		UpperCAmelCase   (				unittest.TestCase):


  """simple docstring"""







  def __init__(				self    :					Optional[int]							,	UpperCamelCase__    :					Union[str, Any]							,	UpperCamelCase__    :					Any=7							,	UpperCamelCase__    :					List[Any]=3							,	UpperCamelCase__    :					Dict=30							,	UpperCamelCase__    :					Union[str, Any]=400							,	UpperCamelCase__    :					List[Any]=True							,	UpperCamelCase__    :					List[str]=None							,	UpperCamelCase__    :					List[str]=0.9							,	UpperCamelCase__    :					Any=None							,	UpperCamelCase__    :					Any=True							,	UpperCamelCase__    :					Optional[Any]=[0.5, 0.5, 0.5]							,	UpperCamelCase__    :					List[str]=[0.5, 0.5, 0.5]							,	)     ->						Dict:
    _UpperCamelCase       			=size if size is not None else {'''shortest_edge''': 30}
    _UpperCamelCase       			=crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
    _UpperCamelCase       			=parent
    _UpperCamelCase       			=batch_size
    _UpperCamelCase       			=num_channels
    _UpperCamelCase       			=min_resolution
    _UpperCamelCase       			=max_resolution
    _UpperCamelCase       			=do_resize_and_center_crop
    _UpperCamelCase       			=size
    _UpperCamelCase       			=crop_pct
    _UpperCamelCase       			=crop_size
    _UpperCamelCase       			=do_normalize
    _UpperCamelCase       			=image_mean
    _UpperCamelCase       			=image_std




  def   UpperCamelCase__      (				self    :					str  )     ->						Union[str, Any]:
    return {
        "size": self.size,
        "do_resize_and_center_crop": self.do_resize_and_center_crop,
        "crop_pct": self.crop_pct,
        "crop_size": self.crop_size,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
    }







@require_torch
@require_vision
class 		UpperCAmelCase   (				lowercase_   ,     unittest.TestCase):


  """simple docstring"""







  lowerCAmelCase_					=				PoolFormerImageProcessor if is_vision_available() else None


  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						int:
    _UpperCamelCase       			=PoolFormerImageProcessingTester(self  )


  @property
  def   UpperCamelCase__      (				self    :					Tuple  )     ->						Optional[int]:
    return self.image_processor_tester.prepare_image_processor_dict()


  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						int:
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''do_resize_and_center_crop'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''size'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''crop_pct'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''do_normalize'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''image_mean'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''image_std'''  )  )


  def   UpperCamelCase__      (				self    :					Any  )     ->						Dict:
    _UpperCamelCase       			=self.image_processing_class.from_dict(self.image_processor_dict  )
    self.assertEqual(image_processor.size							,	{'''shortest_edge''': 30}  )
    self.assertEqual(image_processor.crop_size							,	{'''height''': 30, '''width''': 30}  )

    _UpperCamelCase       			=self.image_processing_class.from_dict(self.image_processor_dict							,	size=42							,	crop_size=84  )
    self.assertEqual(image_processor.size							,	{'''shortest_edge''': 42}  )
    self.assertEqual(image_processor.crop_size							,	{'''height''': 84, '''width''': 84}  )


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


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

    # Test not batched input
    _UpperCamelCase       			=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.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )							,	)

    # Test batched
    _UpperCamelCase       			=image_processing(UpperCamelCase__							,	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.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )							,	)


  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						Optional[int]:
    # Initialize image_processing
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    # create random numpy tensors
    _UpperCamelCase       			=prepare_image_inputs(self.image_processor_tester							,	equal_resolution=UpperCamelCase__							,	numpify=UpperCamelCase__  )
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase__							,	np.ndarray  )

    # Test not batched input
    _UpperCamelCase       			=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.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )							,	)

    # Test batched
    _UpperCamelCase       			=image_processing(UpperCamelCase__							,	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.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )							,	)




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

    # Test not batched input
    _UpperCamelCase       			=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.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )							,	)

    # Test batched
    _UpperCamelCase       			=image_processing(UpperCamelCase__							,	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.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )							,	)






