


"""simple docstring"""





import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowerCamelCase       	=						16
lowerCamelCase       	=						32
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__ = 16     ,      lowerCAmelCase__ = "bert-base-cased"				):
      UpperCAmelCase_      			=							AutoTokenizer.from_pretrained(lowerCAmelCase__				)
      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=lowerCAmelCase__     ,      max_length=lowerCAmelCase__				)
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      UpperCAmelCase_      			=							datasets.map(
          lowerCAmelCase__     ,      batched=lowerCAmelCase__     ,      remove_columns=["idx", "sentence1", "sentence2"]     ,      load_from_cache_file=lowerCAmelCase__				)

      # 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.
            if accelerator.distributed_type == DistributedType.TPU:
                  return tokenizer.pad(lowerCAmelCase__     ,      padding="max_length"     ,      max_length=128     ,      return_tensors="pt"				)
            return tokenizer.pad(lowerCAmelCase__     ,      padding="longest"     ,      return_tensors="pt"				)

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

      return train_dataloader, eval_dataloader
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__				):
      model.eval()
      UpperCAmelCase_      			=							0
      for step, batch in enumerate(lowerCAmelCase__				):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device				)
            with torch.no_grad():
                  UpperCAmelCase_      			=							model(**lowerCAmelCase__				)
            UpperCAmelCase_      			=							outputs.logits.argmax(dim=-1				)
            # It is slightly faster to call this once, than multiple times
            UpperCAmelCase_      			=							accelerator.gather(
                (predictions, batch["labels"])				)  # If we are in a multiprocess environment, the last batch has duplicates
            if accelerator.use_distributed:
                  if step == len(lowerCAmelCase__				) - 1:
                        UpperCAmelCase_      			=							predictions[: len(eval_dataloader.dataset				) - samples_seen]
                        UpperCAmelCase_      			=							references[: len(eval_dataloader.dataset				) - samples_seen]
                  else:
                        samples_seen += references.shape[0]
            metric.add_batch(
                predictions=lowerCAmelCase__     ,      references=lowerCAmelCase__     ,      )

      UpperCAmelCase_      			=							metric.compute()
      return eval_metric["accuracy"]
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      # Initialize accelerator
      UpperCAmelCase_      			=							Accelerator()

      # 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"]				)
      UpperCAmelCase_      			=							args.model_name_or_path

      set_seed(lowerCAmelCase__				)
      UpperCAmelCase_      			=							get_dataloaders(lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__				)

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      UpperCAmelCase_      			=							AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase__     ,      return_dict=lowerCAmelCase__				)

      # Instantiate optimizer
      UpperCAmelCase_      			=							(
          AdamW
          if accelerator.state.deepspeed_plugin is None
          or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
          else DummyOptim
      )
      UpperCAmelCase_      			=							optimizer_cls(params=model.parameters()     ,      lr=lowerCAmelCase__				)

      if accelerator.state.deepspeed_plugin is not None:
            UpperCAmelCase_      			=							accelerator.state.deepspeed_plugin.deepspeed_config[
                """gradient_accumulation_steps"""
            ]
      else:
            UpperCAmelCase_      			=							1
      UpperCAmelCase_      			=							(len(lowerCAmelCase__				) * num_epochs) // gradient_accumulation_steps

      # Instantiate scheduler
      if (
          accelerator.state.deepspeed_plugin is None
          or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
      ):
            UpperCAmelCase_      			=							get_linear_schedule_with_warmup(
                optimizer=lowerCAmelCase__     ,      num_warmup_steps=0     ,      num_training_steps=lowerCAmelCase__     ,      )
      else:
            UpperCAmelCase_      			=							DummyScheduler(lowerCAmelCase__     ,      total_num_steps=lowerCAmelCase__     ,      warmup_num_steps=0				)

      # 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_      			=							accelerator.prepare(
          lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__				)

      # We need to keep track of how many total steps we have iterated over
      UpperCAmelCase_      			=							0
      # We also need to keep track of the stating epoch so files are named properly
      UpperCAmelCase_      			=							0
      UpperCAmelCase_      			=							evaluate.load("glue"     ,      "mrpc"				)
      UpperCAmelCase_      			=							num_epochs

      if args.partial_train_epoch is not None:
            UpperCAmelCase_      			=							args.partial_train_epoch

      if args.resume_from_checkpoint:
            accelerator.load_state(args.resume_from_checkpoint				)
            UpperCAmelCase_      			=							args.resume_from_checkpoint.split("epoch_"				)[1]
            UpperCAmelCase_      			=							""""""
            for char in epoch_string:
                  if char.isdigit():
                        state_epoch_num += char
                  else:
                        break
            UpperCAmelCase_      			=							int(lowerCAmelCase__				) + 1
            UpperCAmelCase_      			=							evaluation_loop(lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__				)
            accelerator.print("resumed checkpoint performance:"     ,      lowerCAmelCase__				)
            accelerator.print("resumed checkpoint's scheduler's lr:"     ,      lr_scheduler.get_lr()[0]				)
            accelerator.print("resumed optimizers's lr:"     ,      optimizer.param_groups[0]["lr"]				)
            with open(os.path.join(args.output_dir     ,      f"""state_{starting_epoch-1}.json"""				)     ,      "r"				) as f:
                  UpperCAmelCase_      			=							json.load(lowerCAmelCase__				)
                  assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
                  assert (
                      resumed_state["lr"] == lr_scheduler.get_lr()[0]
                  ), "Scheduler learning rate mismatch, loading from checkpoint failed"
                  assert (
                      resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
                  ), "Optimizer learning rate mismatch, loading from checkpoint failed"
                  assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
                  return

                   # Now we train the model
      UpperCAmelCase_      			=							{}
      for epoch in range(lowerCAmelCase__     ,      lowerCAmelCase__				):
            model.train()
            for step, batch in enumerate(lowerCAmelCase__				):
                  UpperCAmelCase_      			=							model(**lowerCAmelCase__				)
                  UpperCAmelCase_      			=							outputs.loss
                  UpperCAmelCase_      			=							loss / gradient_accumulation_steps
                  accelerator.backward(lowerCAmelCase__				)
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

                  overall_step += 1
            UpperCAmelCase_      			=							f"""epoch_{epoch}"""
            UpperCAmelCase_      			=							os.path.join(args.output_dir     ,      lowerCAmelCase__				)
            accelerator.save_state(lowerCAmelCase__				)
            UpperCAmelCase_      			=							evaluation_loop(lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__				)
            UpperCAmelCase_      			=							accuracy
            UpperCAmelCase_      			=							lr_scheduler.get_lr()[0]
            UpperCAmelCase_      			=							optimizer.param_groups[0]["""lr"""]
            UpperCAmelCase_      			=							epoch
            UpperCAmelCase_      			=							overall_step
            accelerator.print(f"""epoch {epoch}:"""     ,      lowerCAmelCase__				)

            accelerator.wait_for_everyone()
            if accelerator.is_main_process:
                  with open(os.path.join(args.output_dir     ,      f"""state_{epoch}.json"""				)     ,      "w"				) as f:
                        json.dump(lowerCAmelCase__     ,      lowerCAmelCase__				)
def        a__     (				):
      UpperCAmelCase_      			=							argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."				)
      parser.add_argument(
          "--model_name_or_path"     ,      type=lowerCAmelCase__     ,      default="bert-base-cased"     ,      help="Path to pretrained model or model identifier from huggingface.co/models."     ,      required=lowerCAmelCase__     ,      )
      parser.add_argument(
          "--output_dir"     ,      type=lowerCAmelCase__     ,      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=lowerCAmelCase__     ,      default=lowerCAmelCase__     ,      help="If the training should continue from a checkpoint folder."     ,      )
      parser.add_argument(
          "--partial_train_epoch"     ,      type=lowerCAmelCase__     ,      default=lowerCAmelCase__     ,      help="If passed, the training will stop after this number of epochs."     ,      )
      parser.add_argument(
          "--num_epochs"     ,      type=lowerCAmelCase__     ,      default=2     ,      help="Number of train epochs."     ,      )
      UpperCAmelCase_      			=							parser.parse_args()
      UpperCAmelCase_      			=							{"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

      training_function(lowerCAmelCase__     ,      lowerCAmelCase__				)


if __name__ == "__main__":
  main()









"""simple docstring"""





def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      return [sentence[i : i + ngram_size] for i in range(len(lowerCAmelCase__				) - ngram_size + 1				)]


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

  testmod()




