hunk.code_window:         # *greater than* the boundary value instead.
           key_fn = key if key else lambda x: x
           comp_fn = lambda x, b: key_fn(x) > b \
               if descending else lambda x, b: key_fn(x) < b  # noqa E731
           boundary_indices = [
               len([1 for x in items if comp_fn(x, b)]) for b in boundaries
           ]
           ret = []
           prev_i = 0
           for i in boundary_indices:
               ret.append(items[prev_i:i])
               prev_i = i
  
hunk.labels: keep keep keep keep replace replace replace keep keep keep keep keep
hunk.after_edit:         # Compute the boundary indices in O(n) time via scan.
           boundary_indices = []
           remaining = boundaries.copy()
           for i, x in enumerate(items):
               while remaining and not comp_fn(x, remaining[0]):
                   remaining.pop(0)
                   boundary_indices.append(i)
           for _ in remaining:
               boundary_indices.append(len(items))
           assert len(boundary_indices) == len(boundaries)
  
hunk.file_path: python/ray/data/impl/simple_block.py
hunk.type: replace
file: # coding=utf-8
  # This is a modified example originally from The HuggingFace Inc. team.
  # Modified by Matthew Deng.
  # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """ Finetuning a 🤗 Transformers model for sequence classification on GLUE."""
  import argparse
  import logging
  import math
  import os
  import random
  from typing import Dict, Any
  
  import datasets
  import ray
  import transformers
  from accelerate import Accelerator
  from datasets import load_dataset, load_metric
  from ray.train import Trainer
  from torch.utils.data.dataloader import DataLoader
  from tqdm.auto import tqdm
  from transformers import (
      AdamW,
      AutoConfig,
      AutoModelForSequenceClassification,
      AutoTokenizer,
      DataCollatorWithPadding,
      PretrainedConfig,
      SchedulerType,
      default_data_collator,
      get_scheduler,
      set_seed,
  )
  from transformers.utils.versions import require_version
  
  logger = logging.getLogger(__name__)
  
  require_version(
      "datasets>=1.8.0",
      "To fix: pip install -r examples/pytorch/text-classification/requirements"
      ".txt")
  
  task_to_keys = {
      "cola": ("sentence", None),
      "mnli": ("premise", "hypothesis"),
      "mrpc": ("sentence1", "sentence2"),
      "qnli": ("question", "sentence"),
      "qqp": ("question1", "question2"),
      "rte": ("sentence1", "sentence2"),
      "sst2": ("sentence", None),
      "stsb": ("sentence1", "sentence2"),
      "wnli": ("sentence1", "sentence2"),
  }
  
  
  def parse_args():
      parser = argparse.ArgumentParser(
          description="Finetune a transformers model on a text classification "
          "task")
      parser.add_argument(
          "--task_name",
          type=str,
          default=None,
          help="The name of the glue task to train on.",
          choices=list(task_to_keys.keys()),
      )
      parser.add_argument(
          "--train_file",
          type=str,
          default=None,
          help="A csv or a json file containing the training data.")
      parser.add_argument(
          "--validation_file",
          type=str,
          default=None,
          help="A csv or a json file containing the validation data.")
      parser.add_argument(
          "--max_length",
          type=int,
          default=128,
          help=(
              "The maximum total input sequence length after tokenization. "
              "Sequences longer than this will be truncated, sequences shorter "
              "will be padded if `--pad_to_max_lengh` is passed."),
      )
      parser.add_argument(
          "--pad_to_max_length",
          action="store_true",
          help="If passed, pad all samples to `max_length`. Otherwise, dynamic "
          "padding is used.",
      )
      parser.add_argument(
          "--model_name_or_path",
          type=str,
          help="Path to pretrained model or model identifier from "
          "huggingface.co/models.",
          required=True,
      )
      parser.add_argument(
          "--use_slow_tokenizer",
          action="store_true",
          help="If passed, will use a slow tokenizer (not backed by the 🤗 "
          "Tokenizers library).",
      )
      parser.add_argument(
          "--per_device_train_batch_size",
          type=int,
          default=8,
          help="Batch size (per device) for the training dataloader.",
      )
      parser.add_argument(
          "--per_device_eval_batch_size",
          type=int,
          default=8,
          help="Batch size (per device) for the evaluation dataloader.",
      )
      parser.add_argument(
          "--learning_rate",
          type=float,
          default=5e-5,
          help="Initial learning rate (after the potential warmup period) to "
          "use.",
      )
      parser.add_argument(
          "--weight_decay", type=float, default=0.0, help="Weight decay to use.")
      parser.add_argument(
          "--num_train_epochs",
          type=int,
          default=3,
          help="Total number of training epochs to perform.")
      parser.add_argument(
          "--max_train_steps",
          type=int,
          default=None,
          help="Total number of training steps to perform. If provided, "
          "overrides num_train_epochs.",
      )
      parser.add_argument(
          "--gradient_accumulation_steps",
          type=int,
          default=1,
          help="Number of updates steps to accumulate before performing a "
          "backward/update pass.",
      )
      parser.add_argument(
          "--lr_scheduler_type",
          type=SchedulerType,
          default="linear",
          help="The scheduler type to use.",
          choices=[
              "linear", "cosine", "cosine_with_restarts", "polynomial",
              "constant", "constant_with_warmup"
          ],
      )
      parser.add_argument(
          "--num_warmup_steps",
          type=int,
          default=0,
          help="Number of steps for the warmup in the lr scheduler.")
      parser.add_argument(
          "--output_dir",
          type=str,
          default=None,
          help="Where to store the final model.")
      parser.add_argument(
          "--seed",
          type=int,
          default=None,
          help="A seed for reproducible training.")
  
      # Ray arguments.
      parser.add_argument(
          "--start_local",
          action="store_true",
          help="Starts Ray on local machine.")
      parser.add_argument(
          "--address", type=str, default=None, help="Ray address to connect to.")
      parser.add_argument(
          "--num_workers", type=int, default=1, help="Number of workers to use.")
      parser.add_argument(
          "--use_gpu",
          action="store_true",
          help="If training should be done on GPUs.")
  
      args = parser.parse_args()
  
      # Sanity checks
      if args.task_name is None and args.train_file is None and \
              args.validation_file is None:
          raise ValueError(
              "Need either a task name or a training/validation file.")
      else:
          if args.train_file is not None:
              extension = args.train_file.split(".")[-1]
              assert extension in [
                  "csv", "json"
              ], "`train_file` should be a csv or a json file."
          if args.validation_file is not None:
              extension = args.validation_file.split(".")[-1]
              assert extension in [
                  "csv", "json"
              ], "`validation_file` should be a csv or a json file."
  
      if args.output_dir is not None:
          os.makedirs(args.output_dir, exist_ok=True)
  
      return args
  
  
  def train_func(config: Dict[str, Any]):
      args = config["args"]
      # Initialize the accelerator. We will let the accelerator handle device
      # placement for us in this example.
      accelerator = Accelerator()
      # Make one log on every process with the configuration for debugging.
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
          datefmt="%m/%d/%Y %H:%M:%S",
          level=logging.INFO,
      )
      logger.info(accelerator.state)
  
      # Setup logging, we only want one process per machine to log things on
      # the screen. accelerator.is_local_main_process is only True for one
      # process per machine.
      logger.setLevel(logging.INFO
                      if accelerator.is_local_main_process else logging.ERROR)
      if accelerator.is_local_main_process:
          datasets.utils.logging.set_verbosity_warning()
          transformers.utils.logging.set_verbosity_info()
      else:
          datasets.utils.logging.set_verbosity_error()
          transformers.utils.logging.set_verbosity_error()
  
      # If passed along, set the training seed now.
      if args.seed is not None:
          set_seed(args.seed)
  
      # Get the datasets: you can either provide your own CSV/JSON training and
      # evaluation files (see below) or specify a GLUE benchmark task (the
      # dataset will be downloaded automatically from the datasets Hub).
  
      # For CSV/JSON files, this script will use as labels the column called
      # 'label' and as pair of sentences the sentences in columns called
      # 'sentence1' and 'sentence2' if such column exists or the first two
      # columns not named label if at least two columns are provided.
  
      # If the CSVs/JSONs contain only one non-label column, the script does
      # single sentence classification on this single column. You can easily
      # tweak this behavior (see below)
  
      # In distributed training, the load_dataset function guarantee that only
      # one local process can concurrently download the dataset.
      if args.task_name is not None:
          # Downloading and loading a dataset from the hub.
          raw_datasets = load_dataset("glue", args.task_name)
      else:
          # Loading the dataset from local csv or json file.
          data_files = {}
          if args.train_file is not None:
              data_files["train"] = args.train_file
          if args.validation_file is not None:
              data_files["validation"] = args.validation_file
          extension = (args.train_file if args.train_file is not None else
                       args.valid_file).split(".")[-1]
          raw_datasets = load_dataset(extension, data_files=data_files)
      # See more about loading any type of standard or custom dataset at
      # https://huggingface.co/docs/datasets/loading_datasets.html.
  
      # Labels
      if args.task_name is not None:
          is_regression = args.task_name == "stsb"
          if not is_regression:
              label_list = raw_datasets["train"].features["label"].names
              num_labels = len(label_list)
          else:
              num_labels = 1
      else:
          # Trying to have good defaults here, don't hesitate to tweak to your
          # needs.
          is_regression = raw_datasets["train"].features["label"].dtype in [
              "float32", "float64"
          ]
          if is_regression:
              num_labels = 1
          else:
              # A useful fast method:
              # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.unique # noqa:E501
              label_list = raw_datasets["train"].unique("label")
              label_list.sort()  # Let's sort it for determinism
              num_labels = len(label_list)
  
      # Load pretrained model and tokenizer
      #
      # In distributed training, the .from_pretrained methods guarantee that
      # only one local process can concurrently download model & vocab.
      config = AutoConfig.from_pretrained(
          args.model_name_or_path,
          num_labels=num_labels,
          finetuning_task=args.task_name)
      tokenizer = AutoTokenizer.from_pretrained(
          args.model_name_or_path, use_fast=not args.use_slow_tokenizer)
      model = AutoModelForSequenceClassification.from_pretrained(
          args.model_name_or_path,
          from_tf=bool(".ckpt" in args.model_name_or_path),
          config=config,
      )
  
      # Preprocessing the datasets
      if args.task_name is not None:
          sentence1_key, sentence2_key = task_to_keys[args.task_name]
      else:
          # Again, we try to have some nice defaults but don't hesitate to
          # tweak to your use case.
          non_label_column_names = [
              name for name in raw_datasets["train"].column_names
              if name != "label"
          ]
          if "sentence1" in non_label_column_names and \
                  "sentence2" in non_label_column_names:
              sentence1_key, sentence2_key = "sentence1", "sentence2"
          else:
              if len(non_label_column_names) >= 2:
                  sentence1_key, sentence2_key = non_label_column_names[:2]
              else:
                  sentence1_key, sentence2_key = non_label_column_names[0], None
  
      # Some models have set the order of the labels to use,
      # so let's make sure we do use it.
      label_to_id = None
      if (model.config.label2id !=
              PretrainedConfig(num_labels=num_labels).label2id
              and args.task_name is not None and not is_regression):
          # Some have all caps in their config, some don't.
          label_name_to_id = {
              k.lower(): v
              for k, v in model.config.label2id.items()
          }
          if list(sorted(label_name_to_id.keys())) == list(  # noqa:C413
                  sorted(label_list)):  # noqa:C413
              logger.info(
                  f"The configuration of the model provided the following label "
                  f"correspondence: {label_name_to_id}. Using it!")
              label_to_id = {
                  i: label_name_to_id[label_list[i]]
                  for i in range(num_labels)
              }
          else:
              logger.warning(
                  "Your model seems to have been trained with labels, "
                  "but they don't match the dataset: ",
                  f"model labels: {list(sorted(label_name_to_id.keys()))}, "  # noqa:C413,E501
                  f"dataset labels: {list(sorted(label_list))}."  # noqa:C413
                  "\nIgnoring the model labels as a result.",
              )
      elif args.task_name is None:
          label_to_id = {v: i for i, v in enumerate(label_list)}
  
      if label_to_id is not None:
          model.config.label2id = label_to_id
          model.config.id2label = {
              id: label
              for label, id in config.label2id.items()
          }
  
      padding = "max_length" if args.pad_to_max_length else False
  
      def preprocess_function(examples):
          # Tokenize the texts
          texts = ((examples[sentence1_key], ) if sentence2_key is None else
                   (examples[sentence1_key], examples[sentence2_key]))
          result = tokenizer(
              *texts,
              padding=padding,
              max_length=args.max_length,
              truncation=True)
  
          if "label" in examples:
              if label_to_id is not None:
                  # Map labels to IDs (not necessary for GLUE tasks)
                  result["labels"] = \
                      [label_to_id[l] for l in examples["label"]]  # noqa:E741
              else:
                  # In all cases, rename the column to labels because the model
                  # will expect that.
                  result["labels"] = examples["label"]
          return result
  
      processed_datasets = raw_datasets.map(
          preprocess_function,
          batched=True,
          remove_columns=raw_datasets["train"].column_names,
          desc="Running tokenizer on dataset",
      )
  
      train_dataset = processed_datasets["train"]
      eval_dataset = processed_datasets["validation_matched" if args.task_name ==
                                        "mnli" else "validation"]
  
      # Log a few random samples from the training set:
      for index in random.sample(range(len(train_dataset)), 3):
          logger.info(
              f"Sample {index} of the training set: {train_dataset[index]}.")
  
      # DataLoaders creation:
      if args.pad_to_max_length:
          # If padding was already done ot max length, we use the default data
          # collator that will just convert everything to tensors.
          data_collator = default_data_collator
      else:
          # Otherwise, `DataCollatorWithPadding` will apply dynamic padding for
          # us (by padding to the maximum length of the samples passed). When
          # using mixed precision, we add `pad_to_multiple_of=8` to pad all
          # tensors to multiple of 8s, which will enable the use of Tensor
          # Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
          data_collator = DataCollatorWithPadding(
              tokenizer,
              pad_to_multiple_of=(8 if accelerator.use_fp16 else None))
  
      train_dataloader = DataLoader(
          train_dataset,
          shuffle=True,
          collate_fn=data_collator,
          batch_size=args.per_device_train_batch_size)
      eval_dataloader = DataLoader(
          eval_dataset,
          collate_fn=data_collator,
          batch_size=args.per_device_eval_batch_size)
  
      # Optimizer
      # Split weights in two groups, one with weight decay and the other not.
      no_decay = ["bias", "LayerNorm.weight"]
      optimizer_grouped_parameters = [
          {
              "params": [
                  p for n, p in model.named_parameters()
                  if not any(nd in n for nd in no_decay)
              ],
              "weight_decay": args.weight_decay,
          },
          {
              "params": [
                  p for n, p in model.named_parameters()
                  if any(nd in n for nd in no_decay)
              ],
              "weight_decay": 0.0,
          },
      ]
      optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
  
      # Prepare everything with our `accelerator`.
      model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(
          model, optimizer, train_dataloader, eval_dataloader)
  
      # Note -> the training dataloader needs to be prepared before we grab
      # his length below (cause its length will be shorter in multiprocess)
  
      # Scheduler and math around the number of training steps.
      num_update_steps_per_epoch = math.ceil(
          len(train_dataloader) / args.gradient_accumulation_steps)
      if args.max_train_steps is None:
          args.max_train_steps = args.num_train_epochs * \
                                 num_update_steps_per_epoch
      else:
          args.num_train_epochs = math.ceil(
              args.max_train_steps / num_update_steps_per_epoch)
  
      lr_scheduler = get_scheduler(
          name=args.lr_scheduler_type,
          optimizer=optimizer,
          num_warmup_steps=args.num_warmup_steps,
          num_training_steps=args.max_train_steps,
      )
  
      # Get the metric function
      if args.task_name is not None:
          metric = load_metric("glue", args.task_name)
      else:
          metric = load_metric("accuracy")
  
      # Train!
      total_batch_size = \
          args.per_device_train_batch_size * \
          accelerator.num_processes * \
          args.gradient_accumulation_steps
  
      logger.info("***** Running training *****")
      logger.info(f"  Num examples = {len(train_dataset)}")
      logger.info(f"  Num Epochs = {args.num_train_epochs}")
      logger.info(f"  Instantaneous batch size per device ="
                  f" {args.per_device_train_batch_size}")
      logger.info(
          f"  Total train batch size (w. parallel, distributed & accumulation) "
          f"= {total_batch_size}")
      logger.info(
          f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
      logger.info(f"  Total optimization steps = {args.max_train_steps}")
      # Only show the progress bar once on each machine.
      progress_bar = tqdm(
          range(args.max_train_steps),
          disable=not accelerator.is_local_main_process)
      completed_steps = 0
  
      for epoch in range(args.num_train_epochs):
          model.train()
          for step, batch in enumerate(train_dataloader):
              outputs = model(**batch)
              loss = outputs.loss
              loss = loss / args.gradient_accumulation_steps
              accelerator.backward(loss)
              if step % args.gradient_accumulation_steps == 0 or step == len(
                      train_dataloader) - 1:
                  optimizer.step()
                  lr_scheduler.step()
                  optimizer.zero_grad()
                  progress_bar.update(1)
                  completed_steps += 1
  
              if completed_steps >= args.max_train_steps:
                  break
  
          model.eval()
          for step, batch in enumerate(eval_dataloader):
              outputs = model(**batch)
              predictions = outputs.logits.argmax(
                  dim=-1) if not is_regression else outputs.logits.squeeze()
              metric.add_batch(
                  predictions=accelerator.gather(predictions),
                  references=accelerator.gather(batch["labels"]),
              )
  
          eval_metric = metric.compute()
          logger.info(f"epoch {epoch}: {eval_metric}")
  
      if args.output_dir is not None:
          accelerator.wait_for_everyone()
          unwrapped_model = accelerator.unwrap_model(model)
          unwrapped_model.save_pretrained(
              args.output_dir, save_function=accelerator.save)
  
      if args.task_name == "mnli":
          # Final evaluation on mismatched validation set
          eval_dataset = processed_datasets["validation_mismatched"]
          eval_dataloader = DataLoader(
              eval_dataset,
              collate_fn=data_collator,
              batch_size=args.per_device_eval_batch_size)
          eval_dataloader = accelerator.prepare(eval_dataloader)
  
          model.eval()
          for step, batch in enumerate(eval_dataloader):
              outputs = model(**batch)
              predictions = outputs.logits.argmax(dim=-1)
              metric.add_batch(
                  predictions=accelerator.gather(predictions),
                  references=accelerator.gather(batch["labels"]),
              )
  
          eval_metric = metric.compute()
          logger.info(f"mnli-mm: {eval_metric}")
  
  
  def main():
      args = parse_args()
      config = {"args": args}
  
      if args.start_local or args.address or \
              args.num_workers > 1 or args.use_gpu:
          if args.start_local:
              # Start a local Ray runtime.
              ray.init(num_cpus=args.num_workers)
          else:
              # Connect to a Ray cluster for distributed training.
              ray.init(address=args.address)
          trainer = Trainer(
              "torch", num_workers=args.num_workers, use_gpu=args.use_gpu)
          trainer.start()
          trainer.run(train_func, config)
      else:
          # Run training locally.
          train_func(config)
  
  
  if __name__ == "__main__":
      main()
  