



"""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
#
########################################################################

lowerCamelCase      	=						16
lowerCamelCase      	=						32
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__ = 16				):
      UpperCAmelCase_      			=							AutoTokenizer.from_pretrained("bert-base-cased"				)
      UpperCAmelCase_      			=							load_dataset("glue"     ,      "mrpc"				)

      def tokenize_function(lowerCAmelCase__				):
            # max_length=None => use the model max length (it's actually the default)
            UpperCAmelCase_      			=							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():
            UpperCAmelCase_      			=							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
      UpperCAmelCase_      			=							tokenized_datasets.rename_column("label"     ,      "labels"				)

      def collate_fn(lowerCAmelCase__				):
            # 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(
                a__     ,      padding="longest"     ,      max_length=a__     ,      pad_to_multiple_of=a__     ,      return_tensors="pt"     ,      )

      # Instantiate dataloaders.
      UpperCAmelCase_      			=							DataLoader(
          tokenized_datasets["train"]     ,      shuffle=a__     ,      collate_fn=a__     ,      batch_size=a__				)
      UpperCAmelCase_      			=							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

  lowerCamelCase      	=						mocked_dataloaders  # noqa: F811
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):





      if os.environ.get("TESTING_MOCKED_DATALOADERS"     ,      a__				) == "1":
            UpperCAmelCase_      			=							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:
            UpperCAmelCase_      			=							Accelerator(
                cpu=args.cpu     ,      mixed_precision=args.mixed_precision     ,      log_with="all"     ,      project_dir=args.project_dir				)
      else:
            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"]				)
      set_seed(a__				)

      UpperCAmelCase_			,   UpperCAmelCase_      			=							get_dataloaders(a__     ,      a__				)
      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

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

      # Instantiate optimizer
      UpperCAmelCase_      			=							AdamW(params=model.parameters()     ,      lr=a__				)

      # Instantiate scheduler
      UpperCAmelCase_      			=							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.
      UpperCAmelCase_			,   UpperCAmelCase_			,   UpperCAmelCase_			,   UpperCAmelCase_			,   UpperCAmelCase_      			=							accelerator.prepare(
          a__     ,      a__     ,      a__     ,      a__     ,      a__				)

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

      # Now we train the model
      for epoch in range(a__				):
            model.train()
            # New Code #
            # For our tracking example, we will log the total loss of each epoch
            if args.with_tracking:
                  UpperCAmelCase_      			=							0
            for step, batch in enumerate(a__				):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device				)
                  UpperCAmelCase_      			=							model(**a__				)
                  UpperCAmelCase_      			=							outputs.loss
                  # New Code #
                  if args.with_tracking:
                        total_loss += loss.detach().float()
                  UpperCAmelCase_      			=							loss / gradient_accumulation_steps
                  accelerator.backward(a__				)
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

            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():
                        UpperCAmelCase_      			=							model(**a__				)
                  UpperCAmelCase_      			=							outputs.logits.argmax(dim=-1				)
                  UpperCAmelCase_			,   UpperCAmelCase_      			=							accelerator.gather_for_metrics((predictions, batch["labels"])				)
                  metric.add_batch(
                      predictions=a__     ,      references=a__     ,      )

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

            # 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(a__				),
                          "epoch": epoch,
                      }     ,      step=a__     ,      )

    # 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__     (				):
      UpperCAmelCase_      			=							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(
          "--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=a__     ,      default="logs"     ,      help="Location on where to store experiment tracking logs` and relevent project information"     ,      )
      UpperCAmelCase_      			=							parser.parse_args()
      UpperCAmelCase_      			=							{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
      training_function(a__     ,      a__				)


if __name__ == "__main__":
  main()









"""simple docstring"""





from collections.abc import Iterable
from typing import Generic, TypeVar

lowerCamelCase       	=						TypeVar("""_T""")
class 				lowercase__       (   Generic[_T]				):







       '''simple docstring'''


       def __init__(							self      :  int		,    _UpperCAmelCase      :  Iterable[_T] | None = None   )			->						None:


             '''simple docstring'''




             UpperCAmelCase_      			=							list(iterable or []   )
             UpperCAmelCase_      			=							[]



       def __len__(							self      :  Optional[int]   )			->						int:


             '''simple docstring'''




             return len(self._stacka   ) + len(self._stacka   )



       def __repr__(							self      :  Optional[Any]   )			->						str:


             '''simple docstring'''




             return F"""Queue({tuple(self._stacka[::-1] + self._stacka   )})"""



       def 					lowercase__    (							self      :  Optional[Any]		,    _UpperCAmelCase      :  _T   )			->						None:


             '''simple docstring'''




             self._stacka.append(_UpperCAmelCase   )







       def 					lowercase__    (							self      :  Dict   )			->						_T:


             '''simple docstring'''




             UpperCAmelCase_      			=							self._stacka.pop
             UpperCAmelCase_      			=							self._stacka.append

             if not self._stacka:
                   while self._stacka:
                         stacka_append(stacka_pop()   )

             if not self._stacka:
                   raise IndexError("Queue is empty"   )
             return self._stacka.pop()


if __name__ == "__main__":
  from doctest import testmod

  testmod()




