text: #!/usr/bin/env python
  # coding=utf-8
  # Copyright 2020 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 the library models for sequence classification on GLUE."""
  # You can also adapt this script on your own text classification task. Pointers for this are left as comments.
  
  import logging
  import os
  import random
  import sys
  from dataclasses import dataclass, field
  from typing import Optional
  
  import numpy as np
  from datasets import load_dataset, load_metric
  import torch
  import transformers
  from transformers import (
      AutoConfig,
      AutoModelForSequenceClassification,
      AutoTokenizer,
      DataCollatorWithPadding,
      EvalPrediction,
      HfArgumentParser,
      PretrainedConfig,
      TrainingArguments,
      default_data_collator,
      set_seed,
  )
  from models.multiplexing import RobertaSequenceClassificationMuxed
  from models.utils import get_last_checkpoint_trainerstate_robust
  from models.trainer import MuxTrainer
  import re
  
  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"),
  }
  
  logger = logging.getLogger(__name__)
  
  
  @dataclass
  class DataTrainingArguments:
      """
      Arguments pertaining to what data we are going to input our model for training and eval.
      Using `HfArgumentParser` we can turn this class
      into argparse arguments to be able to specify them on
      the command line.
      """
  
      task_name: Optional[str] = field(
          default=None,
          metadata={
              "help": "The name of the task to train on: "
              + ", ".join(task_to_keys.keys())
          },
      )
      dataset_name: Optional[str] = field(
          default=None,
          metadata={"help": "The name of the dataset to use (via the datasets library)."},
      )
      dataset_config_name: Optional[str] = field(
          default=None,
          metadata={
              "help": "The configuration name of the dataset to use (via the datasets library)."
          },
      )
      max_seq_length: int = field(
          default=128,
          metadata={
              "help": "The maximum total input sequence length after tokenization. Sequences longer "
              "than this will be truncated, sequences shorter will be padded."
          },
      )
      overwrite_cache: bool = field(
          default=False,
          metadata={"help": "Overwrite the cached preprocessed datasets or not."},
      )
      pad_to_max_length: bool = field(
          default=True,
          metadata={
              "help": "Whether to pad all samples to `max_seq_length`. "
              "If False, will pad the samples dynamically when batching to the maximum length in the batch."
          },
      )
      max_train_samples: Optional[int] = field(
          default=None,
          metadata={
              "help": "For debugging purposes or quicker training, truncate the number of training examples to this "
              "value if set."
          },
      )
      max_eval_samples: Optional[int] = field(
          default=None,
          metadata={
              "help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
              "value if set."
          },
      )
      max_predict_samples: Optional[int] = field(
          default=None,
          metadata={
              "help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
              "value if set."
          },
      )
      train_file: Optional[str] = field(
          default=None,
          metadata={"help": "A csv or a json file containing the training data."},
      )
      validation_file: Optional[str] = field(
          default=None,
          metadata={"help": "A csv or a json file containing the validation data."},
      )
      test_file: Optional[str] = field(
          default=None,
          metadata={"help": "A csv or a json file containing the test data."},
      )
  
      def __post_init__(self):
          if self.task_name is not None:
              self.task_name = self.task_name.lower()
              if self.task_name not in task_to_keys.keys():
                  raise ValueError(
                      "Unknown task, you should pick one in "
                      + ",".join(task_to_keys.keys())
                  )
          elif self.dataset_name is not None:
              pass
          elif self.train_file is None or self.validation_file is None:
              raise ValueError(
                  "Need either a GLUE task, a training/validation file or a dataset name."
              )
          else:
              train_extension = self.train_file.split(".")[-1]
              assert train_extension in [
                  "csv",
                  "json",
              ], "`train_file` should be a csv or a json file."
              validation_extension = self.validation_file.split(".")[-1]
              assert (
                  validation_extension == train_extension
              ), "`validation_file` should have the same extension (csv or json) as `train_file`."
  
  
  @dataclass
  class ModelArguments:
      """
      Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
      """
  
      model_name_or_path: str = field(
          default=None,
          metadata={
              "help": "Path to pretrained model or model identifier from huggingface.co/models"
          }
      )
      config_name: Optional[str] = field(
          default=None,
          metadata={
              "help": "Pretrained config name or path if not the same as model_name"
          },
      )
      tokenizer_name: Optional[str] = field(
          default=None,
          metadata={
              "help": "Pretrained tokenizer name or path if not the same as model_name"
          },
      )
      cache_dir: Optional[str] = field(
          default=None,
          metadata={
              "help": "Where do you want to store the pretrained models downloaded from huggingface.co"
          },
      )
      use_fast_tokenizer: bool = field(
          default=True,
          metadata={
              "help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."
          },
      )
      model_revision: str = field(
          default="main",
          metadata={
              "help": "The specific model version to use (can be a branch name, tag name or commit id)."
          },
      )
      use_auth_token: bool = field(
          default=False,
          metadata={
              "help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
              "with private models)."
          },
      )
      # multi instance arguments
      num_instances: Optional[int] = field(
          default=5,
          metadata={"help": "Number of instances i.e. N"},
      )
      muxing_variant: Optional[str] = field(
          default="gaussian_hadamard",
          metadata={"help": "muxing variant; choose from gaussian_hadamard or random_ortho or binary_hadamard"},
      )
      multiplex_layer_index: Optional[int] = field(
          default=0,
          metadata={"help": "The layer index at which we multiplex"},
      )
      demuxing_variant: Optional[str] = field(
          default="index",
          metadata={"help": "demuxing variant, choose from  'index' or 'mlp'"},
      )
      should_mux: Optional[int] = field(
          default=1,
          metadata={"help": "whether to mux, turn off for non-multiplexed baselines"},
      )
      retrieval_percentage: Optional[float] = field(
          default=1.0,
          metadata={"help": "percentage of tokens to retrieve during inference"},
      )
      retrieval_pretraining: Optional[int] = field(
          default=0,
          metadata={"help": "Retrieval Pretraining"},
      )
      gaussian_hadamard_norm: Optional[float] = field(
          default=1,
          metadata={"help": "Norm of sentence embeddings if we use random projections"},
      )
      binary_hadamard_epsilon: Optional[float] = field(
          default=0,
          metadata={"help": "Percentage intersection among binary vectors, default is no intersection"},
      )
      retrieval_loss_coeff: Optional[float] = field(
          default=0.1,
          metadata={"help": "Coefficient for retrieval loss"},
      )    
      task_loss_coeff: Optional[float] = field(
          default=0.9,
          metadata={"help": "Coefficient for task loss"},
      )
      learn_muxing: Optional[int] = field(
          default=0,
          metadata={"help": "whether instance embeddings are learnt or not"},
      )
  
  
  def main():
      # See all possible arguments in src/transformers/training_args.py
      # or by passing the --help flag to this script.
      # We now keep distinct sets of args, for a cleaner separation of concerns.
  
      parser = HfArgumentParser(
          (ModelArguments, DataTrainingArguments, TrainingArguments)
      )
      if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          model_args, data_args, training_args = parser.parse_json_file(
              json_file=os.path.abspath(sys.argv[1])
          )
      else:
          model_args, data_args, training_args = parser.parse_args_into_dataclasses()
  
      # Setup logging
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
          datefmt="%m/%d/%Y %H:%M:%S",
          handlers=[logging.StreamHandler(sys.stdout)],
      )
      logger.setLevel(logging.INFO)
  
      # Log on each process the small summary:
      logger.warning(
          f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
          + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
      )
      # Set the verbosity to info of the Transformers logger (on main process only):
      transformers.utils.logging.set_verbosity_info()
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()
      logger.info(f"Training/evaluation parameters {training_args}")
  
      # Detecting last checkpoint.
      last_checkpoint = None
      if (
          os.path.isdir(training_args.output_dir)
          and training_args.do_train
          and not training_args.overwrite_output_dir
      ):
          last_checkpoint = get_last_checkpoint_trainerstate_robust(training_args.output_dir)
          print("last checkpoint", last_checkpoint)
  
          # if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
          #     raise ValueError(
          #         f"Output directory ({training_args.output_dir}) already exists and is not empty. "
          #         "Use --overwrite_output_dir to overcome."
          #     )
  
  
      # Set seed before initializing model.
      set_seed(training_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 data_args.task_name is not None:
          # Downloading and loading a dataset from the hub.
          datasets = load_dataset(
              "glue", data_args.task_name, cache_dir=model_args.cache_dir
          )
      elif data_args.dataset_name is not None:
          # Downloading and loading a dataset from the hub.
          datasets = load_dataset(
              data_args.dataset_name,
              data_args.dataset_config_name,
              cache_dir=model_args.cache_dir,
          )
      else:
          # Loading a dataset from your local files.
          # CSV/JSON training and evaluation files are needed.
          data_files = {
              "train": data_args.train_file,
              "validation": data_args.validation_file,
          }
  
          # Get the test dataset: you can provide your own CSV/JSON test file (see below)
          # when you use `do_predict` without specifying a GLUE benchmark task.
          if training_args.do_predict:
              if data_args.test_file is not None:
                  train_extension = data_args.train_file.split(".")[-1]
                  test_extension = data_args.test_file.split(".")[-1]
                  assert (
                      test_extension == train_extension
                  ), "`test_file` should have the same extension (csv or json) as `train_file`."
                  data_files["test"] = data_args.test_file
              else:
                  raise ValueError(
                      "Need either a GLUE task or a test file for `do_predict`."
                  )
  
          for key in data_files.keys():
              logger.info(f"load a local file for {key}: {data_files[key]}")
  
          if data_args.train_file.endswith(".csv"):
              # Loading a dataset from local csv files
              datasets = load_dataset(
                  "csv", data_files=data_files, cache_dir=model_args.cache_dir
              )
          else:
              # Loading a dataset from local json files
              datasets = load_dataset(
                  "json", data_files=data_files, cache_dir=model_args.cache_dir
              )
      # See more about loading any type of standard or custom dataset at
      # https://huggingface.co/docs/datasets/loading_datasets.html.
  
      # Labels
      if data_args.task_name is not None:
          is_regression = data_args.task_name == "stsb"
          if not is_regression:
              label_list = datasets["train"].features["label"].names
              num_labels = len(label_list)
          else:
              num_labels = 1
      else:
          if model_args.retrieval_pretraining:
              # add dummy value here
              num_labels = 2
              is_regression = False
              label_list = ["dummy1", "dummy2"]
          else:
              # Trying to have good defaults here, don't hesitate to tweak to your needs.
              is_regression = 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
                  label_list = 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(
          model_args.config_name
          if model_args.config_name
          else model_args.model_name_or_path,
          num_labels=num_labels,
          finetuning_task=data_args.task_name,
          cache_dir=model_args.cache_dir,
          revision=model_args.model_revision,
          use_auth_token=True if model_args.use_auth_token else None,
      )
      tokenizer = AutoTokenizer.from_pretrained(
          model_args.tokenizer_name
          if model_args.tokenizer_name
          else model_args.model_name_or_path,
          cache_dir=model_args.cache_dir,
          use_fast=model_args.use_fast_tokenizer,
          revision=model_args.model_revision,
          use_auth_token=True if model_args.use_auth_token else None,
      )
  
      config.num_instances = model_args.num_instances
      config.muxing_variant = model_args.muxing_variant
      config.demuxing_variant = model_args.demuxing_variant
      config.retrieval_percentage = model_args.retrieval_percentage
      config.gaussian_hadamard_norm = model_args.gaussian_hadamard_norm
      config.binary_hadamard_epsilon = model_args.binary_hadamard_epsilon
      config.retrieval_loss_coeff = model_args.retrieval_loss_coeff
      config.task_loss_coeff = model_args.task_loss_coeff
      config.learn_muxing = model_args.learn_muxing
      config.multiplex_layer_index = model_args.multiplex_layer_index
  
      print("the passed down mx layer index is", config.multiplex_layer_index)
      model_path_supplied = model_args.model_name_or_path is not None
      if model_args.should_mux:
          
          if model_path_supplied:
              model = RobertaSequenceClassificationMuxed.from_pretrained(model_args.model_name_or_path, config=config)
          else:
              model = RobertaSequenceClassificationMuxed(config=config)
      else:
          # non-multiplexed baseline
          if model_path_supplied:
              model = AutoModelForSequenceClassification.from_pretrained(model_args.model_name_or_path, config=config)
          else:
              model = AutoModelForSequenceClassification(config=config)
  
      if data_args.task_name is not None:
          sentence1_key, sentence2_key = task_to_keys[data_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 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
  
      # Padding strategy
      if data_args.pad_to_max_length:
          padding = "max_length"
      else:
          # We will pad later, dynamically at batch creation, to the max sequence length in each batch
          padding = False
  
      # 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 data_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(sorted(label_list)):
              label_to_id = {
                  i: int(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()))}, dataset labels: {list(sorted(label_list))}."
                  "\nIgnoring the model labels as a result.",
              )
      elif data_args.task_name is None and not is_regression:
          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()}
  
      if data_args.max_seq_length > tokenizer.model_max_length:
          logger.warning(
              f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
              f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
          )
      max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
  
      def preprocess_function(examples):
          # Tokenize the texts
          args = (
              (examples[sentence1_key],)
              if sentence2_key is None
              else (examples[sentence1_key], examples[sentence2_key])
          )
          result = tokenizer(
              *args, padding=padding, max_length=max_seq_length, truncation=True
          )
  
          # Map labels to IDs (not necessary for GLUE tasks)
          if label_to_id is not None and "label" in examples:
              result["label"] = [
                  (label_to_id[l] if l != -1 else -1) for l in examples["label"]
              ]
          return result
  
      if model_args.retrieval_pretraining:
          # process wikitext dataset
          column_names = datasets["train"].column_names
  
          text_column_name = "text"
          def tokenize_function(examples):
              examples["text"] = [line for line in examples["text"] if line is not None and len(line) > 0 and not line.isspace()]
              return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
  
          tokenized_datasets = datasets.map(
              tokenize_function,
              batched=True,
              # num_proc=data_args.preprocessing_num_workers,
              remove_columns=column_names,
              load_from_cache_file=True,
          )
  
          # Main data processing function that will concatenate all texts from our dataset and generate chunks of
          # max_seq_length.
          def group_texts(examples):
              # Concatenate all texts.
              concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
              total_length = len(concatenated_examples[list(examples.keys())[0]])
              # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
              # customize this part to your needs.
              total_length = (total_length // max_seq_length) * max_seq_length
              # Split by chunks of max_len.
              result = {
                  k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)]
                  for k, t in concatenated_examples.items()
              }
              random_key = list(result.keys())[0]
              len_result = len(result[random_key])
              result["label"] = [random.randint(0, num_labels-1) for _ in range(len_result)]
              return result
  
          # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a
          # remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value
          # might be slower to preprocess.
          #
          # To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
          # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
  
          tokenized_datasets = tokenized_datasets.map(
              group_texts,
              batched=True,
              # num_proc=data_args.preprocessing_num_workers,
              load_from_cache_file=True,
          )
          datasets = tokenized_datasets
  
      else:
          datasets = datasets.map(
              preprocess_function,
              batched=True,
              load_from_cache_file=not data_args.overwrite_cache,
              # desc="Running tokenizer on dataset",
          )
      # if training_args.do_train:
      if "train" not in datasets:
          raise ValueError("--do_train requires a train dataset")
      train_dataset = datasets["train"]
      if data_args.max_train_samples is not None:
          train_dataset = train_dataset.select(range(data_args.max_train_samples))
  
      # if training_args.do_eval:
      if "validation" not in datasets and "validation_matched" not in datasets:
          raise ValueError("--do_eval requires a validation dataset")
      eval_dataset = datasets[
          "validation_matched" if data_args.task_name == "mnli" else "validation"
      ]
      if data_args.max_eval_samples is not None:
          eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
  
      if (
          training_args.do_predict
          or data_args.task_name is not None
          or data_args.test_file is not None
      ):
          if "test" not in datasets and "test_matched" not in datasets:
              raise ValueError("--do_predict requires a test dataset")
          predict_dataset = datasets[
              "test_matched" if data_args.task_name == "mnli" else "test"
          ]
          if data_args.max_predict_samples is not None:
              predict_dataset = predict_dataset.select(
                  range(data_args.max_predict_samples)
              )
  
      # Log a few random samples from the training set:
      if training_args.do_train:
          for index in random.sample(range(len(train_dataset)), 3):
              logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
  
      # Get the metric function
      if data_args.task_name is not None:
          metric = load_metric("glue", data_args.task_name)
      else:
          metric = load_metric("accuracy")
  
      # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
      # predictions and label_ids field) and has to return a dictionary string to float.
      def compute_metrics(p: EvalPrediction):
          preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
          preds = np.squeeze(preds) if is_regression else np.argmax(preds, axis=1)
          if data_args.task_name is not None:
              result = metric.compute(predictions=preds, references=p.label_ids)
              if len(result) > 1:
                  result["combined_score"] = np.mean(list(result.values())).item()
              return result
          elif is_regression:
              return {"mse": ((preds - p.label_ids) ** 2).mean().item()}
          else:
              return {"accuracy": (preds == p.label_ids).astype(np.float32).mean().item()}
  
      # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
      if data_args.pad_to_max_length:
          data_collator = default_data_collator
      elif training_args.fp16:
          data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
      else:
          data_collator = None
  
      trainer = MuxTrainer(
          model=model,
          args=training_args,
          train_dataset=train_dataset,
          eval_dataset=eval_dataset,
          compute_metrics=compute_metrics,
          tokenizer=tokenizer,
          data_collator=data_collator,
      )
      # Training
      if training_args.do_train:
          logger.info("*** Train ***")
  
          checkpoint = None
          if last_checkpoint is not None:
              checkpoint = last_checkpoint
          print("checkpoint", checkpoint)
          train_result = trainer.train(resume_from_checkpoint=checkpoint)
          metrics = train_result.metrics
          max_train_samples = (
              data_args.max_train_samples
              if data_args.max_train_samples is not None
              else len(train_dataset)
          )
          metrics["train_samples"] = min(max_train_samples, len(train_dataset))
  
          trainer.save_model()  # Saves the tokenizer too for easy upload
  
          trainer.log_metrics("train", metrics)
          trainer.save_metrics("train", metrics)
          trainer.save_state()
  
      # Evaluation
      if training_args.do_eval:
          logger.info("*** Evaluate ***")
  
          # Loop to handle MNLI double evaluation (matched, mis-matched)
          tasks = [data_args.task_name]
          eval_datasets = [eval_dataset]
          if data_args.task_name == "mnli":
              tasks.append("mnli-mm")
              eval_datasets.append(datasets["validation_mismatched"])
  
          for eval_dataset, task in zip(eval_datasets, tasks):
              metrics = trainer.evaluate(eval_dataset=eval_dataset)
              max_eval_samples = (
                  data_args.max_eval_samples
                  if data_args.max_eval_samples is not None
                  else len(eval_dataset)
              )
              metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
  
              trainer.log_metrics("eval", metrics)
              trainer.save_metrics("eval", metrics)
  
      if training_args.do_predict:
          logger.info("*** Predict ***")
  
          # Loop to handle MNLI double evaluation (matched, mis-matched)
          tasks = [data_args.task_name]
          predict_datasets = [predict_dataset]
          if data_args.task_name == "mnli":
              tasks.append("mnli-mm")
              predict_datasets.append(datasets["test_mismatched"])
  
          for predict_dataset, task in zip(predict_datasets, tasks):
              # Removing the `label` columns because it contains -1 and Trainer won't like that.
              predict_dataset.remove_columns_("label")
              predictions = trainer.predict(
                  predict_dataset, metric_key_prefix="predict"
              ).predictions
              predictions = (
                  np.squeeze(predictions)
                  if is_regression
                  else np.argmax(predictions, axis=1)
              )
  
              output_predict_file = os.path.join(
                  training_args.output_dir, f"predict_results_{task}.txt"
              )
              if trainer.is_world_process_zero():
                  with open(output_predict_file, "w") as writer:
                      logger.info(f"***** Predict results {task} *****")
                      writer.write("index\tprediction\n")
                      for index, item in enumerate(predictions):
                          if is_regression:
                              writer.write(f"{index}\t{item:3.3f}\n")
                          else:
                              item = label_list[item]
                              writer.write(f"{index}\t{item}\n")
  
  
  def _mp_fn(index):
      # For xla_spawn (TPUs)
      main()
  
  
  if __name__ == "__main__":
      main()
  