

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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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
#
########################################################################

__A     						=      32



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase = 16	)				->      Tuple:



  """simple docstring"""






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

  def tokenize_function(_UpperCamelCase	):
    # max_length=None => use the model max length (it's actually the default)
    _snake_case		=				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
  # starting with the main process first:
  with accelerator.main_process_first():
    _snake_case		=				datasets.map(
        _UpperCamelCase					,			batched=_UpperCamelCase					,			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
  _snake_case		=				tokenized_datasets.rename_column('''label'''					,			'''labels'''	)

  def collate_fn(_UpperCamelCase	):
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      _snake_case		=				16
    elif accelerator.mixed_precision != "no":
      _snake_case		=				8
    else:
      _snake_case		=				None

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

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

  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

 __A     						=      mocked_dataloaders  # noqa: F811



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



  """simple docstring"""






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

  _snake_case		=				evaluate.load('''glue'''					,			'''mrpc'''	)

  # New Code #
  # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
  # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
  # half each time. From this, we can calculate the number of gradient accumulation steps needed
  # and modify the Accelerator object as a result
  @find_executable_batch_size(starting_batch_size=int(_UpperCamelCase	)	)
  def inner_training_loop(_UpperCamelCase	):
    # Since we need to modify the outside accelerator object, we need to bring it
    # to the local scope
    nonlocal accelerator

    # We can calculate the number of gradient accumulation steps based on the current
    # batch size vs the starting batch size
    _snake_case		=				observed_batch_size // batch_size

    # And then set it in the Accelerator directly:
    _snake_case		=				num_gradient_accumulation_steps

    # Next we need to free all of the stored model references in the Accelerator each time
    accelerator.free_memory()

    # And set the seed so our results are reproducable each reset
    set_seed(_UpperCamelCase	)

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

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

    # Instantiate optimizer
    _snake_case		=				AdamW(params=model.parameters()					,			lr=_UpperCamelCase	)
    _snake_case,	_snake_case		=				get_dataloaders(_UpperCamelCase					,			_UpperCamelCase	)

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

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

      model.eval()
      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():
          _snake_case		=				model(**_UpperCamelCase	)
        _snake_case		=				outputs.logits.argmax(dim=-1	)
        _snake_case,	_snake_case		=				accelerator.gather_for_metrics((predictions, batch['''labels'''])	)
        metric.add_batch(
            predictions=_UpperCamelCase					,			references=_UpperCamelCase					,			)

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

    # 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  snake_case_()				->      Optional[Any]:



  """simple docstring"""






  _snake_case		=				argparse.ArgumentParser(description='''Simple example of training script.'''	)
  parser.add_argument(
      '''--mixed_precision'''					,			type=_UpperCamelCase					,			default=_UpperCamelCase					,			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.'''	)
  _snake_case		=				parser.parse_args()
  # New Code #
  # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
  _snake_case		=				{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 256}
  training_function(_UpperCamelCase					,			_UpperCamelCase	)


if __name__ == "__main__":
 main()




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__A     						=      {'''configuration_hubert''': ['''HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''HubertConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A     						=      [
     '''HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''HubertForCTC''',
     '''HubertForSequenceClassification''',
     '''HubertModel''',
     '''HubertPreTrainedModel''',
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A     						=      [
     '''TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFHubertForCTC''',
     '''TFHubertModel''',
     '''TFHubertPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_hubert import (
      HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      HubertForCTC,
      HubertForSequenceClassification,
      HubertModel,
      HubertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_hubert import (
      TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFHubertForCTC,
      TFHubertModel,
      TFHubertPreTrainedModel,
  )


else:
 import sys

 __A     						=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

