'''simple docstring'''


def   A_      (       SCREAMING_SNAKE_CASE_						)			->bool:
  if num < 0:
    return False

  lowercase_     =       num
  lowercase_     =       0
  while num > 0:
    lowercase_     =       rev_num * 10 + (num % 10)
    num //= 10

  return num_copy == rev_num


if __name__ == "__main__":
       import doctest

       doctest.testmod()


'''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 experiment tracking 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
#
########################################################################

__snake_case						  =		16
__snake_case						  =		32

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ = 16						)			->str:
  lowercase_     =       AutoTokenizer.from_pretrained("""bert-base-cased"""						)
  lowercase_     =       load_dataset("""glue""" ,	"""mrpc"""						)

  def tokenize_function(SCREAMING_SNAKE_CASE_						):
    # max_length=None => use the model max length (it's actually the default)
    lowercase_     =       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():
    lowercase_     =       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
  lowercase_     =       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.
    lowercase_     =       1_28 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      lowercase_     =       16
    elif accelerator.mixed_precision != "no":
      lowercase_     =       8
    else:
      lowercase_     =       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.
  lowercase_     =       DataLoader(
      tokenized_datasets["""train"""] ,	shuffle=SCREAMING_SNAKE_CASE_ ,	collate_fn=SCREAMING_SNAKE_CASE_ ,	batch_size=SCREAMING_SNAKE_CASE_						)
  lowercase_     =       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

       __snake_case						  =		mocked_dataloaders  # noqa: F811

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Dict:
  # For testing only
  if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,	SCREAMING_SNAKE_CASE_						) == "1":
    lowercase_     =       2
  # Initialize Accelerator

  # New Code #
  # We pass in "all" to `log_with` to grab all available trackers in the environment
  # Note: If using a custom `Tracker` class, should be passed in here such as:
  # >>> log_with = ["all", MyCustomTrackerClassInstance()]
  if args.with_tracking:
    lowercase_     =       Accelerator(
        cpu=args.cpu ,	mixed_precision=args.mixed_precision ,	log_with="""all""" ,	project_dir=args.project_dir						)
  else:
    lowercase_     =       Accelerator(cpu=args.cpu ,	mixed_precision=args.mixed_precision						)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  lowercase_     =       config["""lr"""]
  lowercase_     =       int(config["""num_epochs"""]						)
  lowercase_     =       int(config["""seed"""]						)
  lowercase_     =       int(config["""batch_size"""]						)
  set_seed(SCREAMING_SNAKE_CASE_						)

  lowercase_     ,	lowercase_     =       get_dataloaders(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
  lowercase_     =       evaluate.load("""glue""" ,	"""mrpc"""						)

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

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

  # Instantiate optimizer
  lowercase_     =       AdamW(params=model.parameters() ,	lr=SCREAMING_SNAKE_CASE_						)

  # Instantiate scheduler
  lowercase_     =       get_linear_schedule_with_warmup(
      optimizer=SCREAMING_SNAKE_CASE_ ,	num_warmup_steps=1_00 ,	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.
  lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     =       accelerator.prepare(
      SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

  # New Code #
  # We need to initialize the trackers we use. Overall configurations can also be stored
  if args.with_tracking:
    lowercase_     =       os.path.split(SCREAMING_SNAKE_CASE_						)[-1].split("""."""						)[0]
    accelerator.init_trackers(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

  # Now we train the model
  for epoch in range(SCREAMING_SNAKE_CASE_						):
    model.train()
    # New Code #
    # For our tracking example, we will log the total loss of each epoch
    if args.with_tracking:
      lowercase_     =       0
    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						)
      lowercase_     =       model(**SCREAMING_SNAKE_CASE_						)
      lowercase_     =       outputs.loss
      # New Code #
      if args.with_tracking:
        total_loss += loss.detach().float()
      lowercase_     =       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` (the default).
      batch.to(accelerator.device						)
      with torch.no_grad():
        lowercase_     =       model(**SCREAMING_SNAKE_CASE_						)
      lowercase_     =       outputs.logits.argmax(dim=-1						)
      lowercase_     ,	lowercase_     =       accelerator.gather_for_metrics((predictions, batch["""labels"""])						)
      metric.add_batch(
          predictions=SCREAMING_SNAKE_CASE_ ,	references=SCREAMING_SNAKE_CASE_ ,	)

    lowercase_     =       metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f"""epoch {epoch}:""" ,	SCREAMING_SNAKE_CASE_						)

    # New Code #
    # To actually log, we call `Accelerator.log`
    # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
    if args.with_tracking:
      accelerator.log(
          {
              """accuracy""": eval_metric["""accuracy"""],
              """f1""": eval_metric["""f1"""],
              """train_loss""": total_loss.item() / len(SCREAMING_SNAKE_CASE_						),
              """epoch""": epoch,
          } ,	step=SCREAMING_SNAKE_CASE_ ,	)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
  if args.with_tracking:
    accelerator.end_training()

def   A_      (       )			->Union[str, Any]:
  lowercase_     =       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(
      """--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(
      """--project_dir""" ,	type=SCREAMING_SNAKE_CASE_ ,	default="""logs""" ,	help="""Location on where to store experiment tracking logs` and relevent project information""" ,	)
  lowercase_     =       parser.parse_args()
  lowercase_     =       {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)


if __name__ == "__main__":
       main()

