






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
#
########################################################################

a    =       16
a    =       32
def       a__						(  A		,				A = 16					):
   lowercase__						:	Optional[Any]         = AutoTokenizer.from_pretrained("""bert-base-cased"""					)
   lowercase__						:	Tuple         = load_dataset("""glue"""		,				"""mrpc"""					)

   def tokenize_function(A					):
      # max_length=None => use the model max length (it's actually the default)
      lowercase__						:	Optional[int]         = tokenizer(examples["""sentence1"""]		,				examples["""sentence2"""]		,				truncation=A		,				max_length=A					)
      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__						:	Any         = datasets.map(
          A		,				batched=A		,				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__						:	Tuple         = tokenized_datasets.rename_column("""label"""		,				"""labels"""					)

   def collate_fn(A					):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      lowercase__						:	Tuple         = 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":
         lowercase__						:	List[str]         = 16
      elif accelerator.mixed_precision != "no":
         lowercase__						:	Dict         = 8
      else:
         lowercase__						:	Optional[Any]         = None

      return tokenizer.pad(
          A		,				padding="""longest"""		,				max_length=A		,				pad_to_multiple_of=A		,				return_tensors="""pt"""		,				)

   # Instantiate dataloaders.
   lowercase__						:	Optional[int]         = DataLoader(
       tokenized_datasets["""train"""]		,				shuffle=A		,				collate_fn=A		,				batch_size=A					)
   lowercase__						:	Union[str, Any]         = DataLoader(
       tokenized_datasets["""validation"""]		,				shuffle=A		,				collate_fn=A		,				batch_size=A					)

   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       a__						(  A		,				A					):
   # For testing only
   if os.environ.get("""TESTING_MOCKED_DATALOADERS"""		,				A					) == "1":
      lowercase__						:	List[str]         = 2
   # Initialize accelerator
   lowercase__						:	List[Any]         = Accelerator(cpu=args.cpu		,				mixed_precision=args.mixed_precision					)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   lowercase__						:	str         = config["""lr"""]
   lowercase__						:	Dict         = int(config["""num_epochs"""]					)
   lowercase__						:	List[str]         = int(config["""seed"""]					)
   lowercase__						:	List[Any]         = 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":
         lowercase__						:	Optional[int]         = args.checkpointing_steps
      elif args.checkpointing_steps.isdigit():
         lowercase__						:	Any         = int(args.checkpointing_steps					)
      else:
         raise ValueError(
             F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''					)
   else:
      lowercase__						:	List[str]         = None

   set_seed(A					)

   lowercase__     ,					lowercase__						:	List[str]         = get_dataloaders(A		,				A					)
   lowercase__						:	Union[str, Any]         = evaluate.load("""glue"""		,				"""mrpc"""					)

   # If the batch size is too big we use gradient accumulation
   lowercase__						:	str         = 1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      lowercase__						:	List[Any]         = batch_size // MAX_GPU_BATCH_SIZE
      lowercase__						:	Union[str, Any]         = MAX_GPU_BATCH_SIZE

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   lowercase__						:	Union[str, Any]         = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""		,				return_dict=A					)

   # 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__						:	int         = model.to(accelerator.device					)

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

   # Instantiate scheduler
   lowercase__						:	str         = get_linear_schedule_with_warmup(
       optimizer=A		,				num_warmup_steps=100		,				num_training_steps=(len(A					) * 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__						:	str         = accelerator.prepare(
       A		,				A		,				A		,				A		,				A					)

   # New Code #
   # We need to keep track of how many total steps we have iterated over
   lowercase__						:	Any         = 0
   # We also need to keep track of the stating epoch so files are named properly
   lowercase__						:	str         = 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					)
         lowercase__						:	List[Any]         = os.path.basename(args.resume_from_checkpoint					)
      else:
         # Get the most recent checkpoint
         lowercase__						:	Tuple         = [f.name for f in os.scandir(os.getcwd()					) if f.is_dir()]
         dirs.sort(key=os.path.getctime					)
         lowercase__						:	Union[str, Any]         = dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
      # Extract `epoch_{i}` or `step_{i}`
      lowercase__						:	int         = os.path.splitext(A					)[0]

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

    # Now we train the model
   for epoch in range(A		,				A					):
      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
         lowercase__						:	Optional[int]         = accelerator.skip_first_batches(A		,				A					)
         overall_step += resume_step
      else:
         # After the first iteration though, we need to go back to the original dataloader
         lowercase__						:	Optional[int]         = train_dataloader
      for step, batch in enumerate(A					):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device					)
         lowercase__						:	Optional[Any]         = model(**A					)
         lowercase__						:	Optional[Any]         = outputs.loss
         lowercase__						:	List[str]         = loss / gradient_accumulation_steps
         accelerator.backward(A					)
         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(A		,				A					):
            lowercase__						:	str         = F'''step_{overall_step}'''
            if overall_step % checkpointing_steps == 0:
               if args.output_dir is not None:
                  lowercase__						:	Dict         = os.path.join(args.output_dir		,				A					)
               accelerator.save_state(A					)

      model.eval()
      for step, batch in enumerate(A					):
         # 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__						:	List[str]         = model(**A					)
         lowercase__						:	Dict         = outputs.logits.argmax(dim=-1					)
         lowercase__     ,					lowercase__						:	Optional[int]         = accelerator.gather_for_metrics((predictions, batch["""labels"""])					)
         metric.add_batch(
             predictions=A		,				references=A		,				)

      lowercase__						:	Optional[int]         = metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F'''epoch {epoch}:'''		,				A					)

      # 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":
         lowercase__						:	str         = F'''epoch_{epoch}'''
         if args.output_dir is not None:
            lowercase__						:	Any         = os.path.join(args.output_dir		,				A					)
         accelerator.save_state(A					)
def       a__						(  ):
   lowercase__						:	int         = argparse.ArgumentParser(description="""Simple example of training script."""					)
   parser.add_argument(
       """--mixed_precision"""		,				type=A		,				default=A		,				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=A		,				default=A		,				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=A		,				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=A		,				default=A		,				help="""If the training should continue from a checkpoint folder."""		,				)
   lowercase__						:	Tuple         = parser.parse_args()
   lowercase__						:	List[str]         = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
   training_function(A		,				A					)


if __name__ == "__main__":
 main()










def       a__						(  A					):

   if not isinstance(A		,				A					) or number < 0:
      raise ValueError("""Input must be a non-negative integer"""					)

   lowercase__						:	str         = number & ~(number - 1)
   lowercase__						:	Optional[int]         = 0
   while intermediate:
      intermediate >>= 1
      index += 1
   return index - 1


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)


