"""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,
# specifically showcasing the checkpointing capability,
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE = 16  )      ->Any:
  a__:	Union[str, Any]     				=      AutoTokenizer.from_pretrained('bert-base-cased'  )
  a__:	Any     				=      load_dataset('glue'						,	'mrpc'  )

  def tokenize_function(_SCREAMING_SNAKE_CASE  ):
    # max_length=None => use the model max length (it's actually the default)
    a__:	Dict     				=      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():
    a__:	str     				=      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
  a__:	Optional[int]     				=      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.
    a__:	int     				=      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__:	List[str]     				=      16
    elif accelerator.mixed_precision != "no":
      a__:	List[Any]     				=      8
    else:
      a__:	Tuple     				=      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.
  a__:	Union[str, Any]     				=      DataLoader(
      tokenized_datasets['train']						,	shuffle=_SCREAMING_SNAKE_CASE						,	collate_fn=_SCREAMING_SNAKE_CASE						,	batch_size=_SCREAMING_SNAKE_CASE  )
  a__:	Dict     				=      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 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->int:
  # For testing only
  if os.environ.get('TESTING_MOCKED_DATALOADERS'						,	_SCREAMING_SNAKE_CASE  ) == "1":
    a__:	List[str]     				=      2
  # Initialize accelerator
  a__:	Union[str, Any]     				=      Accelerator(cpu=args.cpu						,	mixed_precision=args.mixed_precision  )
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  a__:	int     				=      config['lr']
  a__:	Any     				=      int(config['num_epochs']  )
  a__:	Any     				=      int(config['seed']  )
  a__:	List[str]     				=      int(config['batch_size']  )

  # New Code #
  # Parse out whether we are saving every epoch or after a certain number of batches
  if hasattr(args.checkpointing_steps						,	'isdigit'  ):
    if args.checkpointing_steps == "epoch":
      a__:	int     				=      args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      a__:	Optional[int]     				=      int(args.checkpointing_steps  )
    else:
      raise ValueError(
          F'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'  )
  else:
    a__:	Union[str, Any]     				=      None

  set_seed(_SCREAMING_SNAKE_CASE  )

  a__				,       a__:	int     				=      get_dataloaders(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
  a__:	Dict     				=      evaluate.load('glue'						,	'mrpc'  )

  # 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__:	str     				=      batch_size // MAX_GPU_BATCH_SIZE
    a__:	Optional[int]     				=      MAX_GPU_BATCH_SIZE

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  a__:	Dict     				=      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).
  a__:	str     				=      model.to(accelerator.device  )

  # Instantiate optimizer
  a__:	Optional[Any]     				=      AdamW(params=model.parameters()						,	lr=_SCREAMING_SNAKE_CASE  )

  # Instantiate scheduler
  a__:	Optional[int]     				=      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.
  a__				,       a__				,       a__				,       a__				,       a__:	List[Any]     				=      accelerator.prepare(
      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )

  # New Code #
  # We need to keep track of how many total steps we have iterated over
  a__:	str     				=      0
  # We also need to keep track of the stating epoch so files are named properly
  a__:	Any     				=      0

  # We need to load the checkpoint back in before training here with `load_state`
  # The total number of epochs is adjusted based on where the state is being loaded from,
  # as we assume continuation of the same training script
  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__:	Union[str, Any]     				=      os.path.basename(args.resume_from_checkpoint  )
    else:
      # Get the most recent checkpoint
      a__:	Optional[Any]     				=      [f.name for f in os.scandir(os.getcwd()  ) if f.is_dir()]
      dirs.sort(key=os.path.getctime  )
      a__:	int     				=      dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    a__:	int     				=      os.path.splitext(_SCREAMING_SNAKE_CASE  )[0]

    if "epoch" in training_difference:
      a__:	Union[str, Any]     				=      int(training_difference.replace('epoch_'						,	''  )  ) + 1
      a__:	Dict     				=      None
    else:
      a__:	Optional[int]     				=      int(training_difference.replace('step_'						,	''  )  )
      a__:	Tuple     				=      resume_step // len(_SCREAMING_SNAKE_CASE  )
      resume_step -= starting_epoch * len(_SCREAMING_SNAKE_CASE  )

    # Now we train the model
  for epoch in range(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  ):
    model.train()
    # New Code #
    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(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
      overall_step += resume_step
    else:
      # After the first iteration though, we need to go back to the original dataloader
      a__:	Optional[int]     				=      train_dataloader
    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  )
      a__:	List[str]     				=      model(**_SCREAMING_SNAKE_CASE  )
      a__:	Any     				=      outputs.loss
      a__:	List[str]     				=      loss / gradient_accumulation_steps
      accelerator.backward(_SCREAMING_SNAKE_CASE  )
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()
      # New Code #
      overall_step += 1

      # New Code #
      # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
      # These are saved to folders named `step_{overall_step}`
      # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
      # If mixed precision was used, will also save a "scalar.bin" file
      if isinstance(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  ):
        a__:	List[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						,	_SCREAMING_SNAKE_CASE  )
          accelerator.save_state(_SCREAMING_SNAKE_CASE  )

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

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

    # New Code #
    # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
    # These are saved to folders named `epoch_{epoch}`
    # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
    # If mixed precision was used, will also save a "scalar.bin" file
    if checkpointing_steps == "epoch":
      a__:	Dict     				=      F'epoch_{epoch}'
      if args.output_dir is not None:
        a__:	Optional[Any]     				=      os.path.join(args.output_dir						,	_SCREAMING_SNAKE_CASE  )
      accelerator.save_state(_SCREAMING_SNAKE_CASE  )







def 		__a  (      )      ->List[Any]:
  a__:	Optional[Any]     				=      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.'  )
  parser.add_argument(
      '--checkpointing_steps'						,	type=_SCREAMING_SNAKE_CASE						,	default=_SCREAMING_SNAKE_CASE						,	help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'						,	)
  parser.add_argument(
      '--output_dir'						,	type=_SCREAMING_SNAKE_CASE						,	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=_SCREAMING_SNAKE_CASE						,	default=_SCREAMING_SNAKE_CASE						,	help='If the training should continue from a checkpoint folder.'						,	)
  a__:	Any     				=      parser.parse_args()
  a__:	Optional[Any]     				=      {'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 MobileNetVaImageProcessor
class SCREAMING_SNAKE_CASE       (      unittest.TestCase					):






	'''simple docstring'''







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

			'''simple docstring'''






			a__   		=     size if size is not None else {'''shortest_edge''': 20}
			a__   		=     crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
			a__   		=     parent
			a__   		=     batch_size
			a__   		=     num_channels
			a__   		=     image_size
			a__   		=     min_resolution
			a__   		=     max_resolution
			a__   		=     do_resize
			a__   		=     size
			a__   		=     do_center_crop
			a__   		=     crop_size






	def    _UpperCamelCase							(  self :Tuple			)			->   Any:

			'''simple docstring'''






			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_center_crop": self.do_center_crop,
			    "crop_size": self.crop_size,
			}

@require_torch
@require_vision
class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					,   unittest.TestCase					):






	'''simple docstring'''







	snake_case__							:		str         =		MobileNetVaImageProcessor if is_vision_available() else None


	def    _UpperCamelCase							(  self :Union[str, Any]			)			->   str:

			'''simple docstring'''






			a__   		=     MobileNetVaImageProcessingTester(self			)


	@property
	def    _UpperCamelCase							(  self :List[str]			)			->   Optional[Any]:

			'''simple docstring'''






			return self.image_processor_tester.prepare_image_processor_dict()


	def    _UpperCamelCase							(  self :Tuple			)			->   Tuple:

			'''simple docstring'''






			a__   		=     self.image_processing_class(**self.image_processor_dict			)
			self.assertTrue(hasattr(__magic_name__       ,    '''do_resize'''			)			)
			self.assertTrue(hasattr(__magic_name__       ,    '''size'''			)			)
			self.assertTrue(hasattr(__magic_name__       ,    '''do_center_crop'''			)			)
			self.assertTrue(hasattr(__magic_name__       ,    '''center_crop'''			)			)


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

			'''simple docstring'''






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

			a__   		=     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 :Optional[int]			)			->   Any:

			'''simple docstring'''






			pass


	def    _UpperCamelCase							(  self :Dict			)			->   Tuple:

			'''simple docstring'''






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

			# Test not batched input
			a__   		=     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
			a__   		=     image_processing(__magic_name__       ,    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 :Optional[Any]			)			->   Union[str, Any]:

			'''simple docstring'''






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

			# Test not batched input
			a__   		=     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
			a__   		=     image_processing(__magic_name__       ,    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 :List[Any]			)			->   str:

			'''simple docstring'''






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

			# Test not batched input
			a__   		=     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
			a__   		=     image_processing(__magic_name__       ,    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'''],
			    )       ,    )




