# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  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.
"""BERT finetuning runner."""

from __future__ import absolute_import, division, print_function

import argparse
import csv
import logging
import os
import random
import sys

import numpy as np
import torch
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import f1_score, matthews_corrcoef
from torch.nn import CrossEntropyLoss, MSELoss
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
                              TensorDataset)
from torch.utils.data.distributed import DistributedSampler
from tqdm.auto import tqdm, trange

from .. import log
from .file_utils import (CONFIG_NAME, PYTORCH_PRETRAINED_BERT_CACHE,
                         WEIGHTS_NAME)
from .modeling import BertConfig, BertForSequenceClassification
from .optimization import BertAdam, WarmupLinearSchedule
from .tokenization import BertTokenizer

logger = log


class InputExample(object):
    """A single training/test example for simple sequence classification."""

    def __init__(self, guid, text_a, text_b=None, label=None):
        """Constructs a InputExample.

        Args:
            guid: Unique id for the example.
            text_a: string. The untokenized text of the first sequence. For single
            sequence tasks, only this sequence must be specified.
            text_b: (Optional) string. The untokenized text of the second sequence.
            Only must be specified for sequence pair tasks.
            label: (Optional) string. The label of the example. This should be
            specified for train and dev examples, but not for test examples.
        """
        self.guid = guid
        self.text_a = text_a
        self.text_b = text_b
        self.label = label


class InputFeatures(object):
    """A single set of features of data."""

    def __init__(self, input_ids, input_mask, segment_ids, label_id):
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        self.label_id = label_id


class DataProcessor(object):
    """Base class for data converters for sequence classification data sets."""

    def get_train_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the train set."""
        raise NotImplementedError()

    def get_dev_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the dev set."""
        raise NotImplementedError()

    def get_labels(self):
        """Gets the list of labels for this data set."""
        raise NotImplementedError()

    @classmethod
    def _read_tsv(cls, input_file, quotechar=None):
        """Reads a tab separated value file."""
        with open(input_file, "r", encoding="utf-8") as f:
            reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
            lines = []
            for line in reader:
                if sys.version_info[0] == 2:
                    line = list(unicode(cell, "utf-8") for cell in line)
                lines.append(line)
            return lines


class MrpcProcessor(DataProcessor):
    """Processor for the MRPC data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        logger.info(
            "LOOKING AT {}".format(os.path.join(data_dir, "train.tsv"))
        )
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train"
        )

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev"
        )

    def get_labels(self):
        """See base class."""
        return ["0", "1"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, i)
            text_a = line[3]
            text_b = line[4]
            label = line[0]
            examples.append(
                InputExample(
                    guid=guid, text_a=text_a, text_b=text_b, label=label
                )
            )
        return examples


class MnliProcessor(DataProcessor):
    """Processor for the MultiNLI data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train"
        )

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")),
            "dev_matched",
        )

    def get_labels(self):
        """See base class."""
        return ["contradiction", "entailment", "neutral"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, line[0])
            text_a = line[8]
            text_b = line[9]
            label = line[-1]
            examples.append(
                InputExample(
                    guid=guid, text_a=text_a, text_b=text_b, label=label
                )
            )
        return examples


class MnliMismatchedProcessor(MnliProcessor):
    """Processor for the MultiNLI Mismatched data set (GLUE version)."""

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev_mismatched.tsv")),
            "dev_matched",
        )


class ColaProcessor(DataProcessor):
    """Processor for the CoLA data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train"
        )

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev"
        )

    def get_labels(self):
        """See base class."""
        return ["0", "1"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            guid = "%s-%s" % (set_type, i)
            text_a = line[3]
            label = line[1]
            examples.append(
                InputExample(
                    guid=guid, text_a=text_a, text_b=None, label=label
                )
            )
        return examples


class Sst2Processor(DataProcessor):
    """Processor for the SST-2 data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train"
        )

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev"
        )

    def get_labels(self):
        """See base class."""
        return ["0", "1"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, i)
            text_a = line[0]
            label = line[1]
            examples.append(
                InputExample(
                    guid=guid, text_a=text_a, text_b=None, label=label
                )
            )
        return examples


class StsbProcessor(DataProcessor):
    """Processor for the STS-B data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train"
        )

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev"
        )

    def get_labels(self):
        """See base class."""
        return [None]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, line[0])
            text_a = line[7]
            text_b = line[8]
            label = line[-1]
            examples.append(
                InputExample(
                    guid=guid, text_a=text_a, text_b=text_b, label=label
                )
            )
        return examples


class QqpProcessor(DataProcessor):
    """Processor for the QQP data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train"
        )

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev"
        )

    def get_labels(self):
        """See base class."""
        return ["0", "1"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, line[0])
            try:
                text_a = line[3]
                text_b = line[4]
                label = line[5]
            except IndexError:
                continue
            examples.append(
                InputExample(
                    guid=guid, text_a=text_a, text_b=text_b, label=label
                )
            )
        return examples


class QnliProcessor(DataProcessor):
    """Processor for the QNLI data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train"
        )

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev_matched"
        )

    def get_labels(self):
        """See base class."""
        return ["entailment", "not_entailment"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, line[0])
            text_a = line[1]
            text_b = line[2]
            label = line[-1]
            examples.append(
                InputExample(
                    guid=guid, text_a=text_a, text_b=text_b, label=label
                )
            )
        return examples


class RteProcessor(DataProcessor):
    """Processor for the RTE data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train"
        )

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev"
        )

    def get_labels(self):
        """See base class."""
        return ["entailment", "not_entailment"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, line[0])
            text_a = line[1]
            text_b = line[2]
            label = line[-1]
            examples.append(
                InputExample(
                    guid=guid, text_a=text_a, text_b=text_b, label=label
                )
            )
        return examples


class WnliProcessor(DataProcessor):
    """Processor for the WNLI data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train"
        )

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev"
        )

    def get_labels(self):
        """See base class."""
        return ["0", "1"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, line[0])
            text_a = line[1]
            text_b = line[2]
            label = line[-1]
            examples.append(
                InputExample(
                    guid=guid, text_a=text_a, text_b=text_b, label=label
                )
            )
        return examples


def convert_examples_to_features(
    examples, label_list, max_seq_length, tokenizer, output_mode
):
    """Loads a data file into a list of `InputBatch`s."""

    label_map = {label: i for i, label in enumerate(label_list)}

    features = []
    for (ex_index, example) in enumerate(examples):
        if ex_index % 10000 == 0:
            logger.info("Writing example %d of %d" % (ex_index, len(examples)))

        tokens_a = tokenizer.tokenize(example.text_a)

        tokens_b = None
        if example.text_b:
            tokens_b = tokenizer.tokenize(example.text_b)
            # Modifies `tokens_a` and `tokens_b` in place so that the total
            # length is less than the specified length.
            # Account for [CLS], [SEP], [SEP] with "- 3"
            _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
        else:
            # Account for [CLS] and [SEP] with "- 2"
            if len(tokens_a) > max_seq_length - 2:
                tokens_a = tokens_a[: (max_seq_length - 2)]

        # The convention in BERT is:
        # (a) For sequence pairs:
        #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
        #  type_ids: 0   0  0    0    0     0       0 0    1  1  1  1   1 1
        # (b) For single sequences:
        #  tokens:   [CLS] the dog is hairy . [SEP]
        #  type_ids: 0   0   0   0  0     0 0
        #
        # Where "type_ids" are used to indicate whether this is the first
        # sequence or the second sequence. The embedding vectors for `type=0` and
        # `type=1` were learned during pre-training and are added to the wordpiece
        # embedding vector (and position vector). This is not *strictly* necessary
        # since the [SEP] token unambiguously separates the sequences, but it makes
        # it easier for the model to learn the concept of sequences.
        #
        # For classification tasks, the first vector (corresponding to [CLS]) is
        # used as as the "sentence vector". Note that this only makes sense because
        # the entire model is fine-tuned.
        tokens = ["[CLS]"] + tokens_a + ["[SEP]"]
        segment_ids = [0] * len(tokens)

        if tokens_b:
            tokens += tokens_b + ["[SEP]"]
            segment_ids += [1] * (len(tokens_b) + 1)

        input_ids = tokenizer.convert_tokens_to_ids(tokens)

        # The mask has 1 for real tokens and 0 for padding tokens. Only real
        # tokens are attended to.
        input_mask = [1] * len(input_ids)

        # Zero-pad up to the sequence length.
        padding = [0] * (max_seq_length - len(input_ids))
        input_ids += padding
        input_mask += padding
        segment_ids += padding

        assert len(input_ids) == max_seq_length
        assert len(input_mask) == max_seq_length
        assert len(segment_ids) == max_seq_length

        if output_mode == "classification":
            label_id = label_map[example.label]
        elif output_mode == "regression":
            label_id = float(example.label)
        else:
            raise KeyError(output_mode)

        if ex_index < 5:
            logger.info("*** Example ***")
            logger.info("guid: %s" % (example.guid))
            logger.info("tokens: %s" % " ".join([str(x) for x in tokens]))
            logger.info(
                "input_ids: %s" % " ".join([str(x) for x in input_ids])
            )
            logger.info(
                "input_mask: %s" % " ".join([str(x) for x in input_mask])
            )
            logger.info(
                "segment_ids: %s" % " ".join([str(x) for x in segment_ids])
            )
            logger.info("label: %s (id = %d)" % (example.label, label_id))

        features.append(
            InputFeatures(
                input_ids=input_ids,
                input_mask=input_mask,
                segment_ids=segment_ids,
                label_id=label_id,
            )
        )
    return features


def _truncate_seq_pair(tokens_a, tokens_b, max_length):
    """Truncates a sequence pair in place to the maximum length."""

    # This is a simple heuristic which will always truncate the longer sequence
    # one token at a time. This makes more sense than truncating an equal percent
    # of tokens from each, since if one sequence is very short then each token
    # that's truncated likely contains more information than a longer sequence.
    while True:
        total_length = len(tokens_a) + len(tokens_b)
        if total_length <= max_length:
            break
        if len(tokens_a) > len(tokens_b):
            tokens_a.pop()
        else:
            tokens_b.pop()


def simple_accuracy(preds, labels):
    return (preds == labels).mean()


def acc_and_f1(preds, labels):
    acc = simple_accuracy(preds, labels)
    f1 = f1_score(y_true=labels, y_pred=preds)
    return {"acc": acc, "f1": f1, "acc_and_f1": (acc + f1) / 2}


def pearson_and_spearman(preds, labels):
    pearson_corr = pearsonr(preds, labels)[0]
    spearman_corr = spearmanr(preds, labels)[0]
    return {
        "pearson": pearson_corr,
        "spearmanr": spearman_corr,
        "corr": (pearson_corr + spearman_corr) / 2,
    }


def compute_metrics(task_name, preds, labels):
    assert len(preds) == len(labels)
    if task_name == "cola":
        return {"mcc": matthews_corrcoef(labels, preds)}
    elif task_name == "sst-2":
        return {"acc": simple_accuracy(preds, labels)}
    elif task_name == "mrpc":
        return acc_and_f1(preds, labels)
    elif task_name == "sts-b":
        return pearson_and_spearman(preds, labels)
    elif task_name == "qqp":
        return acc_and_f1(preds, labels)
    elif task_name == "mnli":
        return {"acc": simple_accuracy(preds, labels)}
    elif task_name == "mnli-mm":
        return {"acc": simple_accuracy(preds, labels)}
    elif task_name == "qnli":
        return {"acc": simple_accuracy(preds, labels)}
    elif task_name == "rte":
        return {"acc": simple_accuracy(preds, labels)}
    elif task_name == "wnli":
        return {"acc": simple_accuracy(preds, labels)}
    else:
        raise KeyError(task_name)


def main(args, attn_observer):
    # returns a dict summary of results (or none)
    #
    # attn_observer, if not none, is called on each
    # batch attn tensor (first head only)
    # batch x layer x seq x dim
    #
    # and each attention score
    # batch x layer x head x from x to
    result = {}

    if args.server_ip and args.server_port:
        # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
        import ptvsd

        print("Waiting for debugger attach")
        ptvsd.enable_attach(
            address=(args.server_ip, args.server_port), redirect_output=True
        )
        ptvsd.wait_for_attach()

    processors = {
        "cola": ColaProcessor,
        "mnli": MnliProcessor,
        "mnli-mm": MnliMismatchedProcessor,
        "mrpc": MrpcProcessor,
        "sst-2": Sst2Processor,
        "sts-b": StsbProcessor,
        "qqp": QqpProcessor,
        "qnli": QnliProcessor,
        "rte": RteProcessor,
        "wnli": WnliProcessor,
    }

    output_modes = {
        "cola": "classification",
        "mnli": "classification",
        "mrpc": "classification",
        "sst-2": "classification",
        "sts-b": "regression",
        "qqp": "classification",
        "qnli": "classification",
        "rte": "classification",
        "wnli": "classification",
    }

    if args.local_rank == -1 or args.no_cuda:
        device = torch.device(
            "cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"
        )
        n_gpu = torch.cuda.device_count()
    else:
        torch.cuda.set_device(args.local_rank)
        device = torch.device("cuda", args.local_rank)
        n_gpu = 1
        # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
        torch.distributed.init_process_group(backend="nccl")

    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,
    )

    logger.info(
        "device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
            device, n_gpu, bool(args.local_rank != -1), args.fp16
        )
    )

    if args.gradient_accumulation_steps < 1:
        raise ValueError(
            "Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
                args.gradient_accumulation_steps
            )
        )

    args.train_batch_size = (
        args.train_batch_size // args.gradient_accumulation_steps
    )

    if not args.do_train and not args.do_eval:
        raise ValueError(
            "At least one of `do_train` or `do_eval` must be True."
        )

    if (
        os.path.exists(args.output_dir)
        and os.listdir(args.output_dir)
        and args.do_train
    ):
        raise ValueError(
            "Output directory ({}) already exists and is not empty.".format(
                args.output_dir
            )
        )
    if not os.path.exists(args.output_dir):
        os.makedirs(args.output_dir)

    task_name = args.task_name.lower()

    if task_name not in processors:
        raise ValueError("Task not found: %s" % (task_name))

    processor = processors[task_name]()
    output_mode = output_modes[task_name]

    label_list = processor.get_labels()
    num_labels = len(label_list)

    tokenizer = BertTokenizer.from_pretrained(
        args.bert_model, do_lower_case=args.do_lower_case
    )

    train_examples = None
    num_train_optimization_steps = None
    if args.do_train:
        train_examples = processor.get_train_examples(args.data_dir)
        num_train_optimization_steps = (
            int(
                len(train_examples)
                / args.train_batch_size
                / args.gradient_accumulation_steps
            )
            * args.num_train_epochs
        )
        if args.local_rank != -1:
            num_train_optimization_steps = (
                num_train_optimization_steps
                // torch.distributed.get_world_size()
            )

    # Prepare model
    cache_dir = (
        args.cache_dir
        if args.cache_dir
        else os.path.join(
            str(PYTORCH_PRETRAINED_BERT_CACHE),
            "distributed_{}".format(args.local_rank),
        )
    )
    model = BertForSequenceClassification.from_pretrained(
        args.bert_model, cache_dir=cache_dir, num_labels=num_labels
    )
    if args.fp16:
        model.half()
    model.to(device)
    if args.local_rank != -1:
        try:
            from apex.parallel import DistributedDataParallel as DDP
        except ImportError:
            raise ImportError(
                "Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
            )

        model = DDP(model)
    elif n_gpu > 1:
        model = torch.nn.DataParallel(model)

    # Prepare optimizer
    if args.do_train:
        param_optimizer = list(model.named_parameters())
        no_decay = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
        optimizer_grouped_parameters = [
            {
                "params": [
                    p
                    for n, p in param_optimizer
                    if not any(nd in n for nd in no_decay)
                ],
                "weight_decay": 0.01,
            },
            {
                "params": [
                    p
                    for n, p in param_optimizer
                    if any(nd in n for nd in no_decay)
                ],
                "weight_decay": 0.0,
            },
        ]
        if args.fp16:
            try:
                from apex.optimizers import FP16_Optimizer
                from apex.optimizers import FusedAdam
            except ImportError:
                raise ImportError(
                    "Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
                )

            optimizer = FusedAdam(
                optimizer_grouped_parameters,
                lr=args.learning_rate,
                bias_correction=False,
                max_grad_norm=1.0,
            )
            if args.loss_scale == 0:
                optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
            else:
                optimizer = FP16_Optimizer(
                    optimizer, static_loss_scale=args.loss_scale
                )
            warmup_linear = WarmupLinearSchedule(
                warmup=args.warmup_proportion,
                t_total=num_train_optimization_steps,
            )

        else:
            optimizer = BertAdam(
                optimizer_grouped_parameters,
                lr=args.learning_rate,
                warmup=args.warmup_proportion,
                t_total=num_train_optimization_steps,
            )

    global_step = 0
    nb_tr_steps = 0
    tr_loss = 0
    if args.do_train:
        train_features = convert_examples_to_features(
            train_examples,
            label_list,
            args.max_seq_length,
            tokenizer,
            output_mode,
        )
        logger.info("***** Running training *****")
        logger.info("  Num examples = {}", len(train_examples))
        logger.info("  Batch size = {}", args.train_batch_size)
        logger.info("  Num steps = {}", num_train_optimization_steps)
        all_input_ids = torch.tensor(
            [f.input_ids for f in train_features], dtype=torch.long
        )
        all_input_mask = torch.tensor(
            [f.input_mask for f in train_features], dtype=torch.long
        )
        all_segment_ids = torch.tensor(
            [f.segment_ids for f in train_features], dtype=torch.long
        )

        if output_mode == "classification":
            all_label_ids = torch.tensor(
                [f.label_id for f in train_features], dtype=torch.long
            )
        elif output_mode == "regression":
            all_label_ids = torch.tensor(
                [f.label_id for f in train_features], dtype=torch.float
            )

        train_data = TensorDataset(
            all_input_ids, all_input_mask, all_segment_ids, all_label_ids
        )
        if args.local_rank == -1:
            train_sampler = RandomSampler(train_data)
        else:
            train_sampler = DistributedSampler(train_data)
        train_dataloader = DataLoader(
            train_data, sampler=train_sampler, batch_size=args.train_batch_size
        )

        model.train()
        desired_points = 1000
        points_per_epoch = max(1, desired_points // int(args.num_train_epochs))
        batches_per_epoch = max(1, len(train_data) // args.train_batch_size)
        record_every_n = max(1, batches_per_epoch // points_per_epoch)
        examples_seen = 0
        result["train_curve"] = []
        for _ in trange(int(args.num_train_epochs), desc="Epoch"):
            tr_loss = 0
            nb_tr_examples, nb_tr_steps = 0, 0
            for step, batch in enumerate(
                tqdm(train_dataloader, desc="Iteration", leave=False)
            ):
                batch = tuple(t.to(device) for t in batch)
                input_ids, input_mask, segment_ids, label_ids = batch

                # define a new function to compute loss values for both output_modes
                logits = model(input_ids, segment_ids, input_mask, labels=None)

                if output_mode == "classification":
                    loss_fct = CrossEntropyLoss()
                    loss = loss_fct(
                        logits.view(-1, num_labels), label_ids.view(-1)
                    )
                elif output_mode == "regression":
                    loss_fct = MSELoss()
                    loss = loss_fct(logits.view(-1), label_ids.view(-1))

                if n_gpu > 1:
                    loss = loss.mean()  # mean() to average on multi-gpu.
                if args.gradient_accumulation_steps > 1:
                    loss = loss / args.gradient_accumulation_steps

                if args.fp16:
                    optimizer.backward(loss)
                else:
                    loss.backward()

                loss_item = loss.item()
                if (step + 1) % record_every_n == 0:
                    examples_seen += record_every_n * args.train_batch_size
                    result["train_curve"].append((examples_seen, loss_item))
                tr_loss += loss_item
                nb_tr_examples += input_ids.size(0)
                nb_tr_steps += 1
                if (step + 1) % args.gradient_accumulation_steps == 0:
                    if args.fp16:
                        # modify learning rate with special warm up BERT uses
                        # if args.fp16 is False, BertAdam is used that handles this automatically
                        lr_this_step = (
                            args.learning_rate
                            * warmup_linear.get_lr(
                                global_step, args.warmup_proportion
                            )
                        )
                        for param_group in optimizer.param_groups:
                            param_group["lr"] = lr_this_step
                    optimizer.step()
                    optimizer.zero_grad()
                    global_step += 1

    if args.do_train and (
        args.local_rank == -1 or torch.distributed.get_rank() == 0
    ):
        # Save a trained model, configuration and tokenizer
        model_to_save = (
            model.module if hasattr(model, "module") else model
        )  # Only save the model it-self

        # If we save using the predefined names, we can load using `from_pretrained`
        output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
        output_config_file = os.path.join(args.output_dir, CONFIG_NAME)

        torch.save(model_to_save.state_dict(), output_model_file)
        model_to_save.config.to_json_file(output_config_file)
        tokenizer.save_vocabulary(args.output_dir)

        # Load a trained model and vocabulary that you have fine-tuned
        model = BertForSequenceClassification.from_pretrained(
            args.output_dir, num_labels=num_labels
        )
        tokenizer = BertTokenizer.from_pretrained(
            args.output_dir, do_lower_case=args.do_lower_case
        )
    else:
        if args.load_dir:
            # Load a trained model and vocabulary that you have fine-tuned
            model = BertForSequenceClassification.from_pretrained(
                args.load_dir, num_labels=num_labels
            )
            tokenizer = BertTokenizer.from_pretrained(
                args.load_dir, do_lower_case=args.do_lower_case
            )
        else:
            model = BertForSequenceClassification.from_pretrained(
                args.bert_model, num_labels=num_labels
            )
    model.to(device)

    if args.do_eval and (
        args.local_rank == -1 or torch.distributed.get_rank() == 0
    ):
        eval_examples = get_eval(args.data_dir, processor, args.eval_set_name)
        eval_features = convert_examples_to_features(
            eval_examples,
            label_list,
            args.max_seq_length,
            tokenizer,
            output_mode,
        )
        logger.info("***** Running evaluation *****")
        logger.info("  Num examples = {}", len(eval_examples))
        logger.info("  Batch size = {}", args.eval_batch_size)
        all_input_ids = torch.tensor(
            [f.input_ids for f in eval_features], dtype=torch.long
        )
        all_input_mask = torch.tensor(
            [f.input_mask for f in eval_features], dtype=torch.long
        )
        all_segment_ids = torch.tensor(
            [f.segment_ids for f in eval_features], dtype=torch.long
        )

        if output_mode == "classification":
            all_label_ids = torch.tensor(
                [f.label_id for f in eval_features], dtype=torch.long
            )
        elif output_mode == "regression":
            all_label_ids = torch.tensor(
                [f.label_id for f in eval_features], dtype=torch.float
            )

        eval_data = TensorDataset(
            all_input_ids, all_input_mask, all_segment_ids, all_label_ids
        )
        # Run prediction for full data
        eval_sampler = SequentialSampler(eval_data)
        eval_dataloader = DataLoader(
            eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size
        )

        model.eval()
        eval_loss = 0
        nb_eval_steps = 0
        preds = []

        for input_ids, input_mask, segment_ids, label_ids in tqdm(
            eval_dataloader, desc="Evaluating"
        ):
            input_ids = input_ids.to(device)
            input_mask = input_mask.to(device)
            segment_ids = segment_ids.to(device)
            label_ids = label_ids.to(device)

            with torch.no_grad():
                logits = model(
                    input_ids,
                    segment_ids,
                    input_mask,
                    labels=None,
                    attn_observer=attn_observer,
                )

            # create eval loss and other metric required by the task
            if output_mode == "classification":
                loss_fct = CrossEntropyLoss()
                tmp_eval_loss = loss_fct(
                    logits.view(-1, num_labels), label_ids.view(-1)
                )
            elif output_mode == "regression":
                loss_fct = MSELoss()
                tmp_eval_loss = loss_fct(logits.view(-1), label_ids.view(-1))

            eval_loss += tmp_eval_loss.mean().item()
            nb_eval_steps += 1
            if len(preds) == 0:
                preds.append(logits.detach().cpu().numpy())
            else:
                preds[0] = np.append(
                    preds[0], logits.detach().cpu().numpy(), axis=0
                )

        eval_loss = eval_loss / nb_eval_steps
        preds = preds[0]
        if output_mode == "classification":
            preds = np.argmax(preds, axis=1)
        elif output_mode == "regression":
            preds = np.squeeze(preds)
        result.update(compute_metrics(task_name, preds, all_label_ids.numpy()))

        result["eval_loss"] = eval_loss

        # hack for MNLI-MM
        if task_name == "mnli":
            task_name = "mnli-mm"
            processor = processors[task_name]()

            if (
                os.path.exists(args.output_dir + "-MM")
                and os.listdir(args.output_dir + "-MM")
                and args.do_train
            ):
                raise ValueError(
                    "Output directory ({}) already exists and is not empty.".format(
                        args.output_dir
                    )
                )
            if not os.path.exists(args.output_dir + "-MM"):
                os.makedirs(args.output_dir + "-MM")

            eval_examples = get_eval(
                args.data_dir, processor, args.eval_set_name
            )
            eval_features = convert_examples_to_features(
                eval_examples,
                label_list,
                args.max_seq_length,
                tokenizer,
                output_mode,
            )
            logger.info("***** Running evaluation *****")
            logger.info("  Num examples = {}", len(eval_examples))
            logger.info("  Batch size = {}", args.eval_batch_size)
            all_input_ids = torch.tensor(
                [f.input_ids for f in eval_features], dtype=torch.long
            )
            all_input_mask = torch.tensor(
                [f.input_mask for f in eval_features], dtype=torch.long
            )
            all_segment_ids = torch.tensor(
                [f.segment_ids for f in eval_features], dtype=torch.long
            )
            all_label_ids = torch.tensor(
                [f.label_id for f in eval_features], dtype=torch.long
            )

            eval_data = TensorDataset(
                all_input_ids, all_input_mask, all_segment_ids, all_label_ids
            )
            # Run prediction for full data
            eval_sampler = SequentialSampler(eval_data)
            eval_dataloader = DataLoader(
                eval_data,
                sampler=eval_sampler,
                batch_size=args.eval_batch_size,
            )

            model.eval()
            eval_loss = 0
            nb_eval_steps = 0
            preds = []

            for input_ids, input_mask, segment_ids, label_ids in tqdm(
                eval_dataloader, desc="Evaluating"
            ):
                input_ids = input_ids.to(device)
                input_mask = input_mask.to(device)
                segment_ids = segment_ids.to(device)
                label_ids = label_ids.to(device)

                with torch.no_grad():
                    logits = model(
                        input_ids, segment_ids, input_mask, labels=None
                    )

                loss_fct = CrossEntropyLoss()
                tmp_eval_loss = loss_fct(
                    logits.view(-1, num_labels), label_ids.view(-1)
                )

                eval_loss += tmp_eval_loss.mean().item()
                nb_eval_steps += 1
                if len(preds) == 0:
                    preds.append(logits.detach().cpu().numpy())
                else:
                    preds[0] = np.append(
                        preds[0], logits.detach().cpu().numpy(), axis=0
                    )

            eval_loss = eval_loss / nb_eval_steps
            preds = preds[0]
            preds = np.argmax(preds, axis=1)
            result.update(
                compute_metrics(task_name, preds, all_label_ids.numpy())
            )
            result["loss"] = eval_loss
    return result


def get_eval(data_dir, processor, eval_set_name):
    """
    get evaluation examples, can be dev or train
    """

    if eval_set_name == "dev":
        return processor.get_dev_examples(data_dir)

    if eval_set_name == "train":
        return processor.get_train_examples(data_dir)

    raise KeyError(eval_set_name)

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  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.
"""BERT finetuning runner."""

from __future__ import absolute_import, division, print_function

import argparse
import csv
import logging
import os
import random
import sys

import numpy as np
import torch
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import f1_score, matthews_corrcoef
from torch.nn import CrossEntropyLoss, MSELoss
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
                              TensorDataset)
from torch.utils.data.distributed import DistributedSampler
from tqdm.auto import tqdm, trange

from .. import log
from .file_utils import (CONFIG_NAME, PYTORCH_PRETRAINED_BERT_CACHE,
                         WEIGHTS_NAME)
from .modeling import BertConfig, BertForSequenceClassification
from .optimization import BertAdam, WarmupLinearSchedule
from .tokenization import BertTokenizer

logger = log


class InputExample(object):
    """A single training/test example for simple sequence classification."""

    def __init__(self, guid, text_a, text_b=None, label=None):
        """Constructs a InputExample.

        Args:
            guid: Unique id for the example.
            text_a: string. The untokenized text of the first sequence. For single
            sequence tasks, only this sequence must be specified.
            text_b: (Optional) string. The untokenized text of the second sequence.
            Only must be specified for sequence pair tasks.
            label: (Optional) string. The label of the example. This should be
            specified for train and dev examples, but not for test examples.
        """
        self.guid = guid
        self.text_a = text_a
        self.text_b = text_b
        self.label = label


class InputFeatures(object):
    """A single set of features of data."""

    def __init__(self, input_ids, input_mask, segment_ids, label_id):
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        self.label_id = label_id


class DataProcessor(object):
    """Base class for data converters for sequence classification data sets."""

    def get_train_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the train set."""
        raise NotImplementedError()

    def get_dev_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the dev set."""
        raise NotImplementedError()

    def get_labels(self):
        """Gets the list of labels for this data set."""
        raise NotImplementedError()

    @classmethod
    def _read_tsv(cls, input_file, quotechar=None):
        """Reads a tab separated value file."""
        with open(input_file, "r", encoding="utf-8") as f:
            reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
            lines = []
            for line in reader:
                if sys.version_info[0] == 2:
                    line = list(unicode(cell, "utf-8") for cell in line)
                lines.append(line)
            return lines


class MrpcProcessor(DataProcessor):
    """Processor for the MRPC data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        logger.info(
            "LOOKING AT {}".format(os.path.join(data_dir, "train.tsv"))
        )
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train"
        )

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev"
        )

    def get_labels(self):
        """See base class."""
        return ["0", "1"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, i)
            text_a = line[3]
            text_b = line[4]
            label = line[0]
            examples.append(
                InputExample(
                    guid=guid, text_a=text_a, text_b=text_b, label=label
                )
            )
        return examples


class MnliProcessor(DataProcessor):
    """Processor for the MultiNLI data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train"
        )

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")),
            "dev_matched",
        )

    def get_labels(self):
        """See base class."""
        return ["contradiction", "entailment", "neutral"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, line[0])
            text_a = line[8]
            text_b = line[9]
            label = line[-1]
            examples.append(
                InputExample(
                    guid=guid, text_a=text_a, text_b=text_b, label=label
                )
            )
        return examples


class MnliMismatchedProcessor(MnliProcessor):
    """Processor for the MultiNLI Mismatched data set (GLUE version)."""

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev_mismatched.tsv")),
            "dev_matched",
        )


class ColaProcessor(DataProcessor):
    """Processor for the CoLA data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train"
        )

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev"
        )

    def get_labels(self):
        """See base class."""
        return ["0", "1"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            guid = "%s-%s" % (set_type, i)
            text_a = line[3]
            label = line[1]
            examples.append(
                InputExample(
                    guid=guid, text_a=text_a, text_b=None, label=label
                )
            )
        return examples


class Sst2Processor(DataProcessor):
    """Processor for the SST-2 data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train"
        )

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev"
        )

    def get_labels(self):
        """See base class."""
        return ["0", "1"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, i)
            text_a = line[0]
            label = line[1]
            examples.append(
                InputExample(
                    guid=guid, text_a=text_a, text_b=None, label=label
                )
            )
        return examples


class StsbProcessor(DataProcessor):
    """Processor for the STS-B data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train"
        )

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev"
        )

    def get_labels(self):
        """See base class."""
        return [None]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, line[0])
            text_a = line[7]
            text_b = line[8]
            label = line[-1]
            examples.append(
                InputExample(
                    guid=guid, text_a=text_a, text_b=text_b, label=label
                )
            )
        return examples


class QqpProcessor(DataProcessor):
    """Processor for the QQP data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train"
        )

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev"
        )

    def get_labels(self):
        """See base class."""
        return ["0", "1"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, line[0])
            try:
                text_a = line[3]
                text_b = line[4]
                label = line[5]
            except IndexError:
                continue
            examples.append(
                InputExample(
                    guid=guid, text_a=text_a, text_b=text_b, label=label
                )
            )
        return examples


class QnliProcessor(DataProcessor):
    """Processor for the QNLI data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train"
        )

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev_matched"
        )

    def get_labels(self):
        """See base class."""
        return ["entailment", "not_entailment"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, line[0])
            text_a = line[1]
            text_b = line[2]
            label = line[-1]
            examples.append(
                InputExample(
                    guid=guid, text_a=text_a, text_b=text_b, label=label
                )
            )
        return examples


class RteProcessor(DataProcessor):
    """Processor for the RTE data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train"
        )

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev"
        )

    def get_labels(self):
        """See base class."""
        return ["entailment", "not_entailment"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, line[0])
            text_a = line[1]
            text_b = line[2]
            label = line[-1]
            examples.append(
                InputExample(
                    guid=guid, text_a=text_a, text_b=text_b, label=label
                )
            )
        return examples


class WnliProcessor(DataProcessor):
    """Processor for the WNLI data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train"
        )

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev"
        )

    def get_labels(self):
        """See base class."""
        return ["0", "1"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, line[0])
            text_a = line[1]
            text_b = line[2]
            label = line[-1]
            examples.append(
                InputExample(
                    guid=guid, text_a=text_a, text_b=text_b, label=label
                )
            )
        return examples


def convert_examples_to_features(
    examples, label_list, max_seq_length, tokenizer, output_mode
):
    """Loads a data file into a list of `InputBatch`s."""

    label_map = {label: i for i, label in enumerate(label_list)}

    features = []
    for (ex_index, example) in enumerate(examples):
        if ex_index % 10000 == 0:
            logger.info("Writing example %d of %d" % (ex_index, len(examples)))

        tokens_a = tokenizer.tokenize(example.text_a)

        tokens_b = None
        if example.text_b:
            tokens_b = tokenizer.tokenize(example.text_b)
            # Modifies `tokens_a` and `tokens_b` in place so that the total
            # length is less than the specified length.
            # Account for [CLS], [SEP], [SEP] with "- 3"
            _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
        else:
            # Account for [CLS] and [SEP] with "- 2"
            if len(tokens_a) > max_seq_length - 2:
                tokens_a = tokens_a[: (max_seq_length - 2)]

        # The convention in BERT is:
        # (a) For sequence pairs:
        #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
        #  type_ids: 0   0  0    0    0     0       0 0    1  1  1  1   1 1
        # (b) For single sequences:
        #  tokens:   [CLS] the dog is hairy . [SEP]
        #  type_ids: 0   0   0   0  0     0 0
        #
        # Where "type_ids" are used to indicate whether this is the first
        # sequence or the second sequence. The embedding vectors for `type=0` and
        # `type=1` were learned during pre-training and are added to the wordpiece
        # embedding vector (and position vector). This is not *strictly* necessary
        # since the [SEP] token unambiguously separates the sequences, but it makes
        # it easier for the model to learn the concept of sequences.
        #
        # For classification tasks, the first vector (corresponding to [CLS]) is
        # used as as the "sentence vector". Note that this only makes sense because
        # the entire model is fine-tuned.
        tokens = ["[CLS]"] + tokens_a + ["[SEP]"]
        segment_ids = [0] * len(tokens)

        if tokens_b:
            tokens += tokens_b + ["[SEP]"]
            segment_ids += [1] * (len(tokens_b) + 1)

        input_ids = tokenizer.convert_tokens_to_ids(tokens)

        # The mask has 1 for real tokens and 0 for padding tokens. Only real
        # tokens are attended to.
        input_mask = [1] * len(input_ids)

        # Zero-pad up to the sequence length.
        padding = [0] * (max_seq_length - len(input_ids))
        input_ids += padding
        input_mask += padding
        segment_ids += padding

        assert len(input_ids) == max_seq_length
        assert len(input_mask) == max_seq_length
        assert len(segment_ids) == max_seq_length

        if output_mode == "classification":
            label_id = label_map[example.label]
        elif output_mode == "regression":
            label_id = float(example.label)
        else:
            raise KeyError(output_mode)

        if ex_index < 5:
            logger.info("*** Example ***")
            logger.info("guid: %s" % (example.guid))
            logger.info("tokens: %s" % " ".join([str(x) for x in tokens]))
            logger.info(
                "input_ids: %s" % " ".join([str(x) for x in input_ids])
            )
            logger.info(
                "input_mask: %s" % " ".join([str(x) for x in input_mask])
            )
            logger.info(
                "segment_ids: %s" % " ".join([str(x) for x in segment_ids])
            )
            logger.info("label: %s (id = %d)" % (example.label, label_id))

        features.append(
            InputFeatures(
                input_ids=input_ids,
                input_mask=input_mask,
                segment_ids=segment_ids,
                label_id=label_id,
            )
        )
    return features


def _truncate_seq_pair(tokens_a, tokens_b, max_length):
    """Truncates a sequence pair in place to the maximum length."""

    # This is a simple heuristic which will always truncate the longer sequence
    # one token at a time. This makes more sense than truncating an equal percent
    # of tokens from each, since if one sequence is very short then each token
    # that's truncated likely contains more information than a longer sequence.
    while True:
        total_length = len(tokens_a) + len(tokens_b)
        if total_length <= max_length:
            break
        if len(tokens_a) > len(tokens_b):
            tokens_a.pop()
        else:
            tokens_b.pop()


def simple_accuracy(preds, labels):
    return (preds == labels).mean()


def acc_and_f1(preds, labels):
    acc = simple_accuracy(preds, labels)
    f1 = f1_score(y_true=labels, y_pred=preds)
    return {"acc": acc, "f1": f1, "acc_and_f1": (acc + f1) / 2}


def pearson_and_spearman(preds, labels):
    pearson_corr = pearsonr(preds, labels)[0]
    spearman_corr = spearmanr(preds, labels)[0]
    return {
        "pearson": pearson_corr,
        "spearmanr": spearman_corr,
        "corr": (pearson_corr + spearman_corr) / 2,
    }


def compute_metrics(task_name, preds, labels):
    assert len(preds) == len(labels)
    if task_name == "cola":
        return {"mcc": matthews_corrcoef(labels, preds)}
    elif task_name == "sst-2":
        return {"acc": simple_accuracy(preds, labels)}
    elif task_name == "mrpc":
        return acc_and_f1(preds, labels)
    elif task_name == "sts-b":
        return pearson_and_spearman(preds, labels)
    elif task_name == "qqp":
        return acc_and_f1(preds, labels)
    elif task_name == "mnli":
        return {"acc": simple_accuracy(preds, labels)}
    elif task_name == "mnli-mm":
        return {"acc": simple_accuracy(preds, labels)}
    elif task_name == "qnli":
        return {"acc": simple_accuracy(preds, labels)}
    elif task_name == "rte":
        return {"acc": simple_accuracy(preds, labels)}
    elif task_name == "wnli":
        return {"acc": simple_accuracy(preds, labels)}
    else:
        raise KeyError(task_name)


def main(args, attn_observer):
    # returns a dict summary of results (or none)
    #
    # attn_observer, if not none, is called on each
    # batch attn tensor (first head only)
    # batch x layer x seq x dim
    #
    # and each attention score
    # batch x layer x head x from x to
    result = {}

    if args.server_ip and args.server_port:
        # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
        import ptvsd

        print("Waiting for debugger attach")
        ptvsd.enable_attach(
            address=(args.server_ip, args.server_port), redirect_output=True
        )
        ptvsd.wait_for_attach()

    processors = {
        "cola": ColaProcessor,
        "mnli": MnliProcessor,
        "mnli-mm": MnliMismatchedProcessor,
        "mrpc": MrpcProcessor,
        "sst-2": Sst2Processor,
        "sts-b": StsbProcessor,
        "qqp": QqpProcessor,
        "qnli": QnliProcessor,
        "rte": RteProcessor,
        "wnli": WnliProcessor,
    }

    output_modes = {
        "cola": "classification",
        "mnli": "classification",
        "mrpc": "classification",
        "sst-2": "classification",
        "sts-b": "regression",
        "qqp": "classification",
        "qnli": "classification",
        "rte": "classification",
        "wnli": "classification",
    }

    if args.local_rank == -1 or args.no_cuda:
        device = torch.device(
            "cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"
        )
        n_gpu = torch.cuda.device_count()
    else:
        torch.cuda.set_device(args.local_rank)
        device = torch.device("cuda", args.local_rank)
        n_gpu = 1
        # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
        torch.distributed.init_process_group(backend="nccl")

    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,
    )

    logger.info(
        "device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
            device, n_gpu, bool(args.local_rank != -1), args.fp16
        )
    )

    if args.gradient_accumulation_steps < 1:
        raise ValueError(
            "Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
                args.gradient_accumulation_steps
            )
        )

    args.train_batch_size = (
        args.train_batch_size // args.gradient_accumulation_steps
    )

    if not args.do_train and not args.do_eval:
        raise ValueError(
            "At least one of `do_train` or `do_eval` must be True."
        )

    if (
        os.path.exists(args.output_dir)
        and os.listdir(args.output_dir)
        and args.do_train
    ):
        raise ValueError(
            "Output directory ({}) already exists and is not empty.".format(
                args.output_dir
            )
        )
    if not os.path.exists(args.output_dir):
        os.makedirs(args.output_dir)

    task_name = args.task_name.lower()

    if task_name not in processors:
        raise ValueError("Task not found: %s" % (task_name))

    processor = processors[task_name]()
    output_mode = output_modes[task_name]

    label_list = processor.get_labels()
    num_labels = len(label_list)

    tokenizer = BertTokenizer.from_pretrained(
        args.bert_model, do_lower_case=args.do_lower_case
    )

    train_examples = None
    num_train_optimization_steps = None
    if args.do_train:
        train_examples = processor.get_train_examples(args.data_dir)
        num_train_optimization_steps = (
            int(
                len(train_examples)
                / args.train_batch_size
                / args.gradient_accumulation_steps
            )
            * args.num_train_epochs
        )
        if args.local_rank != -1:
            num_train_optimization_steps = (
                num_train_optimization_steps
                // torch.distributed.get_world_size()
            )

    # Prepare model
    cache_dir = (
        args.cache_dir
        if args.cache_dir
        else os.path.join(
            str(PYTORCH_PRETRAINED_BERT_CACHE),
            "distributed_{}".format(args.local_rank),
        )
    )
    model = BertForSequenceClassification.from_pretrained(
        args.bert_model, cache_dir=cache_dir, num_labels=num_labels
    )
    if args.fp16:
        model.half()
    model.to(device)
    if args.local_rank != -1:
        try:
            from apex.parallel import DistributedDataParallel as DDP
        except ImportError:
            raise ImportError(
                "Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
            )

        model = DDP(model)
    elif n_gpu > 1:
        model = torch.nn.DataParallel(model)

    # Prepare optimizer
    if args.do_train:
        param_optimizer = list(model.named_parameters())
        no_decay = ["bias", "LayerNorm.bias", "LayerNorm.weight"]
        optimizer_grouped_parameters = [
            {
                "params": [
                    p
                    for n, p in param_optimizer
                    if not any(nd in n for nd in no_decay)
                ],
                "weight_decay": 0.01,
            },
            {
                "params": [
                    p
                    for n, p in param_optimizer
                    if any(nd in n for nd in no_decay)
                ],
                "weight_decay": 0.0,
            },
        ]
        if args.fp16:
            try:
                from apex.optimizers import FP16_Optimizer
                from apex.optimizers import FusedAdam
            except ImportError:
                raise ImportError(
                    "Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
                )

            optimizer = FusedAdam(
                optimizer_grouped_parameters,
                lr=args.learning_rate,
                bias_correction=False,
                max_grad_norm=1.0,
            )
            if args.loss_scale == 0:
                optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
            else:
                optimizer = FP16_Optimizer(
                    optimizer, static_loss_scale=args.loss_scale
                )
            warmup_linear = WarmupLinearSchedule(
                warmup=args.warmup_proportion,
                t_total=num_train_optimization_steps,
            )

        else:
            optimizer = BertAdam(
                optimizer_grouped_parameters,
                lr=args.learning_rate,
                warmup=args.warmup_proportion,
                t_total=num_train_optimization_steps,
            )

    global_step = 0
    nb_tr_steps = 0
    tr_loss = 0
    if args.do_train:
        train_features = convert_examples_to_features(
            train_examples,
            label_list,
            args.max_seq_length,
            tokenizer,
            output_mode,
        )
        logger.info("***** Running training *****")
        logger.info("  Num examples = {}", len(train_examples))
        logger.info("  Batch size = {}", args.train_batch_size)
        logger.info("  Num steps = {}", num_train_optimization_steps)
        all_input_ids = torch.tensor(
            [f.input_ids for f in train_features], dtype=torch.long
        )
        all_input_mask = torch.tensor(
            [f.input_mask for f in train_features], dtype=torch.long
        )
        all_segment_ids = torch.tensor(
            [f.segment_ids for f in train_features], dtype=torch.long
        )

        if output_mode == "classification":
            all_label_ids = torch.tensor(
                [f.label_id for f in train_features], dtype=torch.long
            )
        elif output_mode == "regression":
            all_label_ids = torch.tensor(
                [f.label_id for f in train_features], dtype=torch.float
            )

        train_data = TensorDataset(
            all_input_ids, all_input_mask, all_segment_ids, all_label_ids
        )
        if args.local_rank == -1:
            train_sampler = RandomSampler(train_data)
        else:
            train_sampler = DistributedSampler(train_data)
        train_dataloader = DataLoader(
            train_data, sampler=train_sampler, batch_size=args.train_batch_size
        )

        model.train()
        desired_points = 1000
        points_per_epoch = max(1, desired_points // int(args.num_train_epochs))
        batches_per_epoch = max(1, len(train_data) // args.train_batch_size)
        record_every_n = max(1, batches_per_epoch // points_per_epoch)
        examples_seen = 0
        result["train_curve"] = []
        for _ in trange(int(args.num_train_epochs), desc="Epoch"):
            tr_loss = 0
            nb_tr_examples, nb_tr_steps = 0, 0
            for step, batch in enumerate(
                tqdm(train_dataloader, desc="Iteration", leave=False)
            ):
                batch = tuple(t.to(device) for t in batch)
                input_ids, input_mask, segment_ids, label_ids = batch

                # define a new function to compute loss values for both output_modes
                logits = model(input_ids, segment_ids, input_mask, labels=None)

                if output_mode == "classification":
                    loss_fct = CrossEntropyLoss()
                    loss = loss_fct(
                        logits.view(-1, num_labels), label_ids.view(-1)
                    )
                elif output_mode == "regression":
                    loss_fct = MSELoss()
                    loss = loss_fct(logits.view(-1), label_ids.view(-1))

                if n_gpu > 1:
                    loss = loss.mean()  # mean() to average on multi-gpu.
                if args.gradient_accumulation_steps > 1:
                    loss = loss / args.gradient_accumulation_steps

                if args.fp16:
                    optimizer.backward(loss)
                else:
                    loss.backward()

                loss_item = loss.item()
                if (step + 1) % record_every_n == 0:
                    examples_seen += record_every_n * args.train_batch_size
                    result["train_curve"].append((examples_seen, loss_item))
                tr_loss += loss_item
                nb_tr_examples += input_ids.size(0)
                nb_tr_steps += 1
                if (step + 1) % args.gradient_accumulation_steps == 0:
                    if args.fp16:
                        # modify learning rate with special warm up BERT uses
                        # if args.fp16 is False, BertAdam is used that handles this automatically
                        lr_this_step = (
                            args.learning_rate
                            * warmup_linear.get_lr(
                                global_step, args.warmup_proportion
                            )
                        )
                        for param_group in optimizer.param_groups:
                            param_group["lr"] = lr_this_step
                    optimizer.step()
                    optimizer.zero_grad()
                    global_step += 1

    if args.do_train and (
        args.local_rank == -1 or torch.distributed.get_rank() == 0
    ):
        # Save a trained model, configuration and tokenizer
        model_to_save = (
            model.module if hasattr(model, "module") else model
        )  # Only save the model it-self

        # If we save using the predefined names, we can load using `from_pretrained`
        output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
        output_config_file = os.path.join(args.output_dir, CONFIG_NAME)

        torch.save(model_to_save.state_dict(), output_model_file)
        model_to_save.config.to_json_file(output_config_file)
        tokenizer.save_vocabulary(args.output_dir)

        # Load a trained model and vocabulary that you have fine-tuned
        model = BertForSequenceClassification.from_pretrained(
            args.output_dir, num_labels=num_labels
        )
        tokenizer = BertTokenizer.from_pretrained(
            args.output_dir, do_lower_case=args.do_lower_case
        )
    else:
        if args.load_dir:
            # Load a trained model and vocabulary that you have fine-tuned
            model = BertForSequenceClassification.from_pretrained(
                args.load_dir, num_labels=num_labels
            )
            tokenizer = BertTokenizer.from_pretrained(
                args.load_dir, do_lower_case=args.do_lower_case
            )
        else:
            model = BertForSequenceClassification.from_pretrained(
                args.bert_model, num_labels=num_labels
            )
    model.to(device)

    if args.do_eval and (
        args.local_rank == -1 or torch.distributed.get_rank() == 0
    ):
        eval_examples = get_eval(args.data_dir, processor, args.eval_set_name)
        eval_features = convert_examples_to_features(
            eval_examples,
            label_list,
            args.max_seq_length,
            tokenizer,
            output_mode,
        )
        logger.info("***** Running evaluation *****")
        logger.info("  Num examples = {}", len(eval_examples))
        logger.info("  Batch size = {}", args.eval_batch_size)
        all_input_ids = torch.tensor(
            [f.input_ids for f in eval_features], dtype=torch.long
        )
        all_input_mask = torch.tensor(
            [f.input_mask for f in eval_features], dtype=torch.long
        )
        all_segment_ids = torch.tensor(
            [f.segment_ids for f in eval_features], dtype=torch.long
        )

        if output_mode == "classification":
            all_label_ids = torch.tensor(
                [f.label_id for f in eval_features], dtype=torch.long
            )
        elif output_mode == "regression":
            all_label_ids = torch.tensor(
                [f.label_id for f in eval_features], dtype=torch.float
            )

        eval_data = TensorDataset(
            all_input_ids, all_input_mask, all_segment_ids, all_label_ids
        )
        # Run prediction for full data
        eval_sampler = SequentialSampler(eval_data)
        eval_dataloader = DataLoader(
            eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size
        )

        model.eval()
        eval_loss = 0
        nb_eval_steps = 0
        preds = []

        for input_ids, input_mask, segment_ids, label_ids in tqdm(
            eval_dataloader, desc="Evaluating"
        ):
            input_ids = input_ids.to(device)
            input_mask = input_mask.to(device)
            segment_ids = segment_ids.to(device)
            label_ids = label_ids.to(device)

            with torch.no_grad():
                logits = model(
                    input_ids,
                    segment_ids,
                    input_mask,
                    labels=None,
                    attn_observer=attn_observer,
                )

            # create eval loss and other metric required by the task
            if output_mode == "classification":
                loss_fct = CrossEntropyLoss()
                tmp_eval_loss = loss_fct(
                    logits.view(-1, num_labels), label_ids.view(-1)
                )
            elif output_mode == "regression":
                loss_fct = MSELoss()
                tmp_eval_loss = loss_fct(logits.view(-1), label_ids.view(-1))

            eval_loss += tmp_eval_loss.mean().item()
            nb_eval_steps += 1
            if len(preds) == 0:
                preds.append(logits.detach().cpu().numpy())
            else:
                preds[0] = np.append(
                    preds[0], logits.detach().cpu().numpy(), axis=0
                )

        eval_loss = eval_loss / nb_eval_steps
        preds = preds[0]
        if output_mode == "classification":
            preds = np.argmax(preds, axis=1)
        elif output_mode == "regression":
            preds = np.squeeze(preds)
        result.update(compute_metrics(task_name, preds, all_label_ids.numpy()))

        result["eval_loss"] = eval_loss

        # hack for MNLI-MM
        if task_name == "mnli":
            task_name = "mnli-mm"
            processor = processors[task_name]()

            if (
                os.path.exists(args.output_dir + "-MM")
                and os.listdir(args.output_dir + "-MM")
                and args.do_train
            ):
                raise ValueError(
                    "Output directory ({}) already exists and is not empty.".format(
                        args.output_dir
                    )
                )
            if not os.path.exists(args.output_dir + "-MM"):
                os.makedirs(args.output_dir + "-MM")

            eval_examples = get_eval(
                args.data_dir, processor, args.eval_set_name
            )
            eval_features = convert_examples_to_features(
                eval_examples,
                label_list,
                args.max_seq_length,
                tokenizer,
                output_mode,
            )
            logger.info("***** Running evaluation *****")
            logger.info("  Num examples = {}", len(eval_examples))
            logger.info("  Batch size = {}", args.eval_batch_size)
            all_input_ids = torch.tensor(
                [f.input_ids for f in eval_features], dtype=torch.long
            )
            all_input_mask = torch.tensor(
                [f.input_mask for f in eval_features], dtype=torch.long
            )
            all_segment_ids = torch.tensor(
                [f.segment_ids for f in eval_features], dtype=torch.long
            )
            all_label_ids = torch.tensor(
                [f.label_id for f in eval_features], dtype=torch.long
            )

            eval_data = TensorDataset(
                all_input_ids, all_input_mask, all_segment_ids, all_label_ids
            )
            # Run prediction for full data
            eval_sampler = SequentialSampler(eval_data)
            eval_dataloader = DataLoader(
                eval_data,
                sampler=eval_sampler,
                batch_size=args.eval_batch_size,
            )

            model.eval()
            eval_loss = 0
            nb_eval_steps = 0
            preds = []

            for input_ids, input_mask, segment_ids, label_ids in tqdm(
                eval_dataloader, desc="Evaluating"
            ):
                input_ids = input_ids.to(device)
                input_mask = input_mask.to(device)
                segment_ids = segment_ids.to(device)
                label_ids = label_ids.to(device)

                with torch.no_grad():
                    logits = model(
                        input_ids, segment_ids, input_mask, labels=None
                    )

                loss_fct = CrossEntropyLoss()
                tmp_eval_loss = loss_fct(
                    logits.view(-1, num_labels), label_ids.view(-1)
                )

                eval_loss += tmp_eval_loss.mean().item()
                nb_eval_steps += 1
                if len(preds) == 0:
                    preds.append(logits.detach().cpu().numpy())
                else:
                    preds[0] = np.append(
                        preds[0], logits.detach().cpu().numpy(), axis=0
                    )

            eval_loss = eval_loss / nb_eval_steps
            preds = preds[0]
            preds = np.argmax(preds, axis=1)
            result.update(
                compute_metrics(task_name, preds, all_label_ids.numpy())
            )
            result["loss"] = eval_loss
    return result


def get_eval(data_dir, processor, eval_set_name):
    """
    get evaluation examples, can be dev or train
    """

    if eval_set_name == "dev":
        return processor.get_dev_examples(data_dir)

    if eval_set_name == "train":
        return processor.get_train_examples(data_dir)

    raise KeyError(eval_set_name)

# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION.  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. BERT finetuning runner. A single training/test example for simple sequence classification. Constructs a InputExample.          Args:             guid: Unique id for the example.             text_a: string. The untokenized text of the first sequence. For single             sequence tasks, only this sequence must be specified.             text_b: (Optional) string. The untokenized text of the second sequence.             Only must be specified for sequence pair tasks.             label: (Optional) string. The label of the example. This should be             specified for train and dev examples, but not for test examples. A single set of features of data. Base class for data converters for sequence classification data sets. Gets a collection of `InputExample`s for the train set. Gets a collection of `InputExample`s for the dev set. Gets the list of labels for this data set. Reads a tab separated value file. Processor for the MRPC data set (GLUE version). See base class. See base class. See base class. Creates examples for the training and dev sets. Processor for the MultiNLI data set (GLUE version). See base class. See base class. See base class. Creates examples for the training and dev sets. Processor for the MultiNLI Mismatched data set (GLUE version). See base class. Processor for the CoLA data set (GLUE version). See base class. See base class. See base class. Creates examples for the training and dev sets. Processor for the SST-2 data set (GLUE version). See base class. See base class. See base class. Creates examples for the training and dev sets. Processor for the STS-B data set (GLUE version). See base class. See base class. See base class. Creates examples for the training and dev sets. Processor for the QQP data set (GLUE version). See base class. See base class. See base class. Creates examples for the training and dev sets. Processor for the QNLI data set (GLUE version). See base class. See base class. See base class. Creates examples for the training and dev sets. Processor for the RTE data set (GLUE version). See base class. See base class. See base class. Creates examples for the training and dev sets. Processor for the WNLI data set (GLUE version). See base class. See base class. See base class. Creates examples for the training and dev sets. Loads a data file into a list of `InputBatch`s. # Modifies `tokens_a` and `tokens_b` in place so that the total # length is less than the specified length. # Account for [CLS], [SEP], [SEP] with "- 3" # Account for [CLS] and [SEP] with "- 2" # The convention in BERT is: # (a) For sequence pairs: #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP] #  type_ids: 0   0  0    0    0     0       0 0    1  1  1  1   1 1 # (b) For single sequences: #  tokens:   [CLS] the dog is hairy . [SEP] #  type_ids: 0   0   0   0  0     0 0 # # Where "type_ids" are used to indicate whether this is the first # sequence or the second sequence. The embedding vectors for `type=0` and # `type=1` were learned during pre-training and are added to the wordpiece # embedding vector (and position vector). This is not *strictly* necessary # since the [SEP] token unambiguously separates the sequences, but it makes # it easier for the model to learn the concept of sequences. # # For classification tasks, the first vector (corresponding to [CLS]) is # used as as the "sentence vector". Note that this only makes sense because # the entire model is fine-tuned. # The mask has 1 for real tokens and 0 for padding tokens. Only real # tokens are attended to. # Zero-pad up to the sequence length. Truncates a sequence pair in place to the maximum length. # This is a simple heuristic which will always truncate the longer sequence # one token at a time. This makes more sense than truncating an equal percent # of tokens from each, since if one sequence is very short then each token # that's truncated likely contains more information than a longer sequence. # returns a dict summary of results (or none) # # attn_observer, if not none, is called on each # batch attn tensor (first head only) # batch x layer x seq x dim # # and each attention score # batch x layer x head x from x to # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script # Initializes the distributed backend which will take care of sychronizing nodes/GPUs # Prepare model # Prepare optimizer # define a new function to compute loss values for both output_modes # mean() to average on multi-gpu. # modify learning rate with special warm up BERT uses # if args.fp16 is False, BertAdam is used that handles this automatically # Save a trained model, configuration and tokenizer # Only save the model it-self # If we save using the predefined names, we can load using `from_pretrained` # Load a trained model and vocabulary that you have fine-tuned # Load a trained model and vocabulary that you have fine-tuned # Run prediction for full data # create eval loss and other metric required by the task # hack for MNLI-MM # Run prediction for full data get evaluation examples, can be dev or train