run_span.py

import os
import re
import sys
import time
import json
import glob
import random
import logging
import itertools
from pathlib import Path
from argparse import ArgumentParser, Namespace
from tqdm import tqdm

import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
import numpy as np

# datasets
from transformers.data import DataProcessor

# models
import transformers
from transformers import WEIGHTS_NAME
from transformers import (
    BertConfig, 
    BertTokenizer,
    BertPreTrainedModel,
    BertModel,
)
from transformers.modeling_outputs import TokenClassifierOutput
from nezha.modeling_nezha import NeZhaModel, NeZhaPreTrainedModel
from nezha.modeling_nezha import relative_position_encoding

# trainer & training arguments
from transformers import AdamW, get_linear_schedule_with_warmup, get_cosine_with_hard_restarts_schedule_with_warmup
from lamb import Lamb

# metrics
from seqeval.metrics.sequence_labeling import (
    accuracy_score,
    classification_report, 
    performance_measure,
    f1_score, precision_score, recall_score,
    get_entities
)
from evaluate import score
from utils import LABEL_MEANING_MAP, MEANING_LABEL_MAP, get_ner_tags

PSEUDO_TAG = -1

class BertConfigSpanV2(BertConfig):

    def __init__(self, 
        max_span_length=10, 
        width_embedding_dim=150,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.max_span_length = max_span_length
        self.width_embedding_dim = width_embedding_dim

# from allennlp.nn.util import batched_index_select
def batched_index_select(input, index):
    batch_size, sequence_length, hidden_size = input.size()
    batch_size, num_spans = index.size()
    index_onehot = torch.FloatTensor(
        batch_size, num_spans, sequence_length).to(input.device)
    index_onehot.zero_()
    index_onehot.scatter_(2, index.unsqueeze(2), 1)
    output = torch.bmm(index_onehot, input)
    return output


class LabelSmoothingCE(nn.Module):
    
    def __init__(self, eps=0.1, reduction='mean', ignore_index=-100):
        super().__init__()

        self.eps = eps
        self.reduction = reduction
        self.ignore_index = ignore_index

    def forward(self, input, target):
        c = input.size()[-1]
        log_preds = F.log_softmax(input, dim=-1)
        if self.reduction == 'sum':
            loss = -log_preds.sum()
        else:
            loss = -log_preds.sum(dim=-1)
            if self.reduction == 'mean':
                loss = loss.mean()
        loss_1 = loss * self.eps / c
        loss_2 = F.nll_loss(log_preds, target, reduction=self.reduction, ignore_index=self.ignore_index)
        return loss_1 + (1 - self.eps) * loss_2


class FocalLoss(nn.Module):
    """
    Softmax and sigmoid focal loss
    """

    def __init__(self, activation_type='softmax', reduction='mean',
            gamma=2.0, alpha=0.25, epsilon=1.e-9):

        super(FocalLoss, self).__init__()
        self.gamma = gamma
        self.alpha = alpha
        self.epsilon = epsilon
        self.activation_type = activation_type
        self.reduction = reduction

    def forward(self, input, target):
        """
        Args:
            logits: pretrain_model's output, shape of [batch_size, num_cls]
            target: ground truth labels, shape of [batch_size]
        Returns:
            shape of [batch_size]
        """
        if self.activation_type == 'softmax':
            num_labels = input.size(-1)
            idx = target.view(-1, 1).long()
            one_hot_key = torch.zeros(idx.size(0), num_labels, dtype=torch.float32, device=idx.device)
            one_hot_key = one_hot_key.scatter_(1, idx, 1)
            logits = F.softmax(input, dim=-1)
            loss = -self.alpha * one_hot_key * torch.pow((1 - logits), self.gamma) * (logits + self.epsilon).log()
            loss = loss.sum(1)
        elif self.activation_type == 'sigmoid':
            multi_hot_key = target
            logits = F.sigmoid(input)
            zero_hot_key = 1 - multi_hot_key
            loss = -self.alpha * multi_hot_key * torch.pow((1 - logits), self.gamma) * (logits + self.epsilon).log()
            loss += -(1 - self.alpha) * zero_hot_key * torch.pow(logits, self.gamma) * (1 - logits + self.epsilon).log()
        if self.reduction == "mean":
            loss = loss.mean()
        elif self.reduction == "none":
            pass
        return loss


class SpanV2(nn.Module):
    
    def __init__(self, hidden_size, num_labels, max_span_length, width_embedding_dim):
        super(SpanV2, self).__init__()

        self.width_embedding = nn.Embedding(max_span_length + 1, width_embedding_dim)
        self.classifier = nn.Sequential(
            nn.Linear(hidden_size * 2 + width_embedding_dim, hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, num_labels),
        )

    def forward(self, hidden_states, spans):
        spans_start = spans[:, :, 0].view(spans.size(0), -1)
        spans_start_embedding = batched_index_select(hidden_states, spans_start)
        spans_end = spans[:, :, 1].view(spans.size(0), -1)
        spans_end_embedding = batched_index_select(hidden_states, spans_end)
        spans_width = spans[:, :, 2].view(spans.size(0), -1)
        spans_width_embedding = self.width_embedding(spans_width)
        spans_embedding = torch.cat([
            spans_start_embedding, 
            spans_end_embedding, 
            spans_width_embedding
        ], dim=-1)  # (batch_size, num_spans, num_features)

        logits = self.classifier(spans_embedding)
        return logits
    
    @staticmethod
    def decode_batch(
        batch,      # (batch_size, num_spans, num_labels)
        spans,      # (batch_size, num_spans, 3)
        span_mask,  # (batch_size, num_spans)
        is_logits:  bool=True,
        thresh:     float=0.,
    ):
        decodeds = []
        if is_logits:
            # labels = batch.argmax(dim=-1)
            probas, labels = batch.softmax(dim=-1).max(dim=-1)
        else:
            probas, labels = torch.ones_like(batch), batch
        for labels_, probas_, spans_, span_mask_ in zip(labels, probas, spans, span_mask):
            span_mask_ = span_mask_ == 1.
            labels_ = labels_[span_mask_].cpu().numpy().tolist()
            probas_ = probas_[span_mask_].cpu().numpy().tolist()
            spans_ = spans_[span_mask_].cpu().numpy().tolist()

            decoded_ = []
            for t, p, s in zip(labels_, probas_, spans_):
                if p < thresh: continue     # 置信度过低，舍去
                decoded_.append([t, s[0] - 1, s[1] - 1])
            decodeds.append(decoded_)
        
        return decodeds

class SpanV2Loss(nn.Module):

    def __init__(self):
        super().__init__()
        self.loss_fct = None
        if args.loss_type == "ce":
            self.loss_fct = nn.CrossEntropyLoss(reduction='none')
        elif args.loss_type == "lsr":
            self.loss_fct = LabelSmoothingCE(eps=args.label_smooth_eps, reduction='none')
        elif args.loss_type == "focal":
            self.loss_fct = FocalLoss(reduction='none', 
                gamma=args.focal_gamma, alpha=args.focal_alpha) # TODO:
    
    def forward(self, 
        logits=None,        # (batch_size, num_spans, num_labels)
        label=None,         # (batch_size, num_spans)
        mask=None,          # (batch_size, num_spans)
    ):
        num_labels = logits.size(-1)
        loss_mask = mask.view(-1) == 1

        if args.do_pseudo:
            proba = logits.softmax(dim=-1)
            proba, index = proba.max(dim=-1)

            is_pseudo = label == PSEUDO_TAG
            label = torch.where(is_pseudo, index, label)        # 用预测标签替换无标签
            pseudo_valid_mask = is_pseudo & (
                proba > args.pseudo_proba_thresh
            )                                                   # 有效伪标签：是伪标签、且大于阈值
            # pseudo_valid_mask = is_pseudo & (
            #     proba > args.pseudo_proba_thresh
            # ) & (
            #     index != 0
            # )                                                   # 有效伪标签：是伪标签、且大于阈值、是实体
            loss_mask = (mask == 1) & (~is_pseudo)              # 重新初始化loss_mask：真实标签
            loss_mask = loss_mask | pseudo_valid_mask           # 合并`真实标签`和`有效伪标签`
            loss_mask = loss_mask.view(-1)

        loss = self.loss_fct(logits.view(-1, num_labels), label.view(-1))
        loss = loss[loss_mask].mean()
        return loss

def forward(
    cls,
    input_ids=None,
    attention_mask=None,
    token_type_ids=None,
    position_ids=None,
    spans=None,         # (batch_size, num_spans, 3)
    span_mask=None,     # (batch_size, num_spans)
    label=None,         # (batch_size, num_spans)
    input_len=None,     # (batch_size)
    sent_start=None,    # (batch_size)
    sent_end=None,      # (batch_size)
    head_mask=None,
    inputs_embeds=None,
    output_attentions=None,
    output_hidden_states=None,
    return_dict=True,
):
    return_dict = return_dict if return_dict is not None else cls.config.use_return_dict

    outputs = cls.base_model(
        input_ids,
        attention_mask=attention_mask,
        token_type_ids=token_type_ids,
        position_ids=position_ids,
        head_mask=head_mask,
        inputs_embeds=inputs_embeds,
        output_attentions=output_attentions,
        output_hidden_states=output_hidden_states,
        return_dict=return_dict,
    )

    sequence_output = outputs[0]
    sequence_output = cls.dropout(sequence_output)
    logits = cls.span(sequence_output, spans)  # (batch_size, num_spans, num_labels)

    total_loss = None
    if label is not None:
        loss_fct = SpanV2Loss()
        total_loss = loss_fct(logits, label, span_mask)

    if not return_dict:
        output = (logits,) + outputs[2:]
        return ((total_loss,) + output) if total_loss is not None else output

    return TokenClassifierOutput(
        loss=total_loss,
        logits=logits,
        hidden_states=outputs.hidden_states,
        attentions=outputs.attentions,
    )

def compute_kl_loss(p, q, pad_mask=None):

    batch_size, num_spans, num_labels = p.size()
    if pad_mask is None:
        pad_mask = torch.ones(batch_size, num_spans, dtype=torch.bool, device=p.device)
    pad_mask = pad_mask.unsqueeze(-1).expand(batch_size, num_spans, num_labels)
    
    p_loss = F.kl_div(F.log_softmax(p, dim=-1), F.softmax(q, dim=-1), reduction='none')
    q_loss = F.kl_div(F.log_softmax(q, dim=-1), F.softmax(p, dim=-1), reduction='none')
    
    # pad_mask is for seq-level tasks
    p_loss.masked_fill_(pad_mask, 0.)
    q_loss.masked_fill_(pad_mask, 0.)

    # You can choose whether to use function "sum" and "mean" depending on your task
    p_loss = p_loss.mean()
    q_loss = q_loss.mean()

    loss = (p_loss + q_loss) / 2
    return loss

# def compute_kl_loss(p, q, pad_mask=None):

#     batch_size, num_spans, num_labels = p.size()
#     if pad_mask is None:
#         pad_mask = torch.ones(batch_size, num_spans, dtype=torch.bool, device=p.device)
#     pad_mask = pad_mask.unsqueeze(-1).expand(batch_size, num_spans, num_labels)

#     p_loss = F.kl_div(F.log_softmax(p, dim=-1), F.softmax(q, dim=-1), reduction='none')
#     q_loss = F.kl_div(F.log_softmax(q, dim=-1), F.softmax(p, dim=-1), reduction='none')
    
#     mask_valid = ~pad_mask
#     p_loss = p_loss[mask_valid].mean()
#     q_loss = q_loss[mask_valid].mean()
#     loss = (p_loss + q_loss) / 2

#     return loss

def forward_rdrop(cls, alpha, **kwargs):
    outputs1 = forward(cls, **kwargs)
    if outputs1.loss is None or alpha <= 0.: return outputs1

    outputs2 = forward(cls, **kwargs)
    rdrop_loss = compute_kl_loss(
        outputs1["logits"], outputs2["logits"], 
        kwargs["span_mask"] == 0)
    total_loss = (outputs1["loss"] + outputs2["loss"]) / 2. + alpha * rdrop_loss
    # total_loss = (outputs1["loss"] + outputs2["loss"]) + alpha * rdrop_loss
    return TokenClassifierOutput(
        loss=total_loss,
        logits=outputs1["logits"],
        hidden_states=outputs1.hidden_states,
        attentions=outputs1.attentions,
    )

class BertSpanV2ForNer(BertPreTrainedModel):

    def __init__(self, config):
        super().__init__(config)

        self.bert = BertModel(config)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.span = SpanV2(config.hidden_size, config.num_labels, 
            config.max_span_length, config.width_embedding_dim)
        self.init_weights()

    def forward(self, **kwargs):
        if args.rdrop_alpha is not None:
            return forward_rdrop(self, args.rdrop_alpha, **kwargs)
        return forward(self, **kwargs)

class NeZhaSpanV2ForNer(NeZhaPreTrainedModel):

    def __init__(self, config):
        super().__init__(config)

        self.bert = NeZhaModel(config)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.span = SpanV2(config.hidden_size, config.num_labels, 
            config.max_span_length, config.width_embedding_dim)
        self.init_weights()

    def forward(self, **kwargs):
        if args.rdrop_alpha is not None:
            return forward_rdrop(self, args.rdrop_alpha, **kwargs)
        return forward(self, **kwargs)


class ExponentialMovingAverage(object):
    '''
    # 初始化
    ema = EMA(model, 0.999)
    # 训练过程中，更新完参数后，同步update shadow weights
    def train():
        optimizer.step()
        ema.update(model)
    # eval前，apply shadow weights；
    # eval之后（保存模型后），恢复原来模型的参数
    def evaluate():
        ema.apply_shadow(model)
        # evaluate
        ema.restore(modle)
    '''
    def __init__(self,model, decay, device):
        self.decay = decay
        self.device = device
        self.shadow = {}
        self.backup = {}
        for name, param in model.named_parameters():
            if param.requires_grad:
                # self.shadow[name] = param.data.clone().cpu()  # 显存内存数值拷贝对精度影响较大
                self.shadow[name] = param.data.clone()

    def update(self,model):
        for name, param in model.named_parameters():
            if param.requires_grad:
                assert name in self.shadow
                # new_average = (1.0 - self.decay) * param.data.cpu() + self.decay * self.shadow[name]
                new_average = (1.0 - self.decay) * param.data + self.decay * self.shadow[name]
                self.shadow[name] = new_average.clone()

    def apply_shadow(self,model):
        for name, param in model.named_parameters():
            if param.requires_grad:
                assert name in self.shadow
                # self.backup[name] = param.data.cpu()
                self.backup[name] = param.data
                param.data = self.shadow[name].to(self.device)

    def restore(self,model):
        for name, param in model.named_parameters():
            if param.requires_grad:
                assert name in self.backup
                param.data = self.backup[name].to(self.device)
        self.backup = {}


class NerArgumentParser(ArgumentParser):

    def __init__(self, **kwargs):
        super().__init__(**kwargs)

    def parse_args_from_json(self, json_file):
        data = json.loads(Path(json_file).read_text())
        return Namespace(**data)
    
    def save_args_to_json(self, json_file, args):
        Path(json_file).write_text(json.dumps(vars(args), indent=4))

    def build_arguments(self):

        # Required parameters
        self.add_argument("--version", default=None, type=str, required=True,
                            help="Version of training model.")
        self.add_argument("--device", default=None, type=str, required=False,
                            help="Device for training.")
        self.add_argument("--n_gpu", default=1, type=int, required=False,
                            help="Device for training.")
        self.add_argument("--task_name", default="ner", type=str, required=False,
                            help="The name of the task to train selected in the list: ")
        self.add_argument("--dataset_name", default="cail_ner", type=str, required=False,
                            help="The name of the dataset for the task")
        self.add_argument("--data_dir", default=None, type=str, required=True,
                            help="The input data dir. Should contain the training files for the CoNLL-2003 NER task.", )
        self.add_argument("--train_file", default=None, type=str, required=True)
        self.add_argument("--dev_file", default=None, type=str, required=True)
        self.add_argument("--test_file", default=None, type=str, required=True)
        self.add_argument("--model_type", default=None, type=str, required=True,
                            help="Model type selected in the list: ")
        self.add_argument("--model_name_or_path", default=None, type=str, required=True,
                            help="Path to pre-trained model or shortcut name selected in the list: " )
        self.add_argument("--output_dir", default="output/", type=str, required=False,
                            help="The output directory where the model predictions and checkpoints will be written.", )
        
        self.add_argument("--max_span_length", default=50, type=int)
        self.add_argument("--width_embedding_dim", default=128, type=int)
        self.add_argument("--span_proba_thresh", default=0., type=float)
        self.add_argument("--optimizer", default="adamw", type=str)
        # self.add_argument("--scheduler", default="linear", type=str)
        # self.add_argument("--context_window", default=0, type=int)
        self.add_argument("--augment_context_aware_p", default=None, type=float)
        self.add_argument("--augment_entity_replace_p", default=None, type=float)
        self.add_argument("--rdrop_alpha", default=None, type=float)
        self.add_argument("--vat_alpha", default=None, type=float)
        self.add_argument("--do_ema", action="store_true")
        self.add_argument("--ema_start_epoch", default=None, type=int)

        self.add_argument("--do_pseudo", action="store_true")
        self.add_argument("--pseudo_data_dir", default=None, type=str)
        self.add_argument("--pseudo_data_file", default=None, type=str)
        self.add_argument("--pseudo_num_sample", default=None, type=int)
        self.add_argument("--pseudo_proba_thresh", default=0.99, type=float)

        # Other parameters
        self.add_argument('--scheme', default='IOB2', type=str,
                            choices=['IOB2', 'IOBES'])
        self.add_argument('--loss_type', default='ce', type=str,
                            choices=['lsr', 'focal', 'ce'])
        self.add_argument('--label_smooth_eps', default=0.1, type=float)
        self.add_argument('--focal_gamma', default=2.0, type=float)
        self.add_argument('--focal_alpha', default=0.25, type=float)
        
        self.add_argument("--config_name", default="", type=str,
                            help="Pretrained config name or path if not the same as model_name")
        self.add_argument("--tokenizer_name", default="", type=str,
                            help="Pretrained tokenizer name or path if not the same as model_name", )
        self.add_argument("--cache_dir", default="cache/", type=str,
                            help="Where do you want to store the pre-trained models downloaded from s3", )
        self.add_argument("--train_max_seq_length", default=128, type=int,
                            help="The maximum total input sequence length after tokenization. Sequences longer "
                                "than this will be truncated, sequences shorter will be padded.", )
        self.add_argument("--eval_max_seq_length", default=512, type=int,
                            help="The maximum total input sequence length after tokenization. Sequences longer "
                                "than this will be truncated, sequences shorter will be padded.", )
        self.add_argument("--do_train", action="store_true",
                            help="Whether to run training.")
        self.add_argument("--do_eval", action="store_true",
                            help="Whether to run eval on the dev set.")
        self.add_argument("--do_predict", action="store_true",
                            help="Whether to run predictions on the test set.")
        self.add_argument("--evaluate_during_training", action="store_true",
                            help="Whether to run evaluation during training at each logging step.", )
        self.add_argument("--evaluate_each_epoch", action="store_true",
                            help="Whether to run evaluation during training at each epoch, `--logging_step` will be ignored", )
        self.add_argument("--do_lower_case", action="store_true",
                            help="Set this flag if you are using an uncased model.")
                            
        # adversarial training
        self.add_argument("--do_fgm", action="store_true",
                            help="Whether to adversarial training.")
        self.add_argument('--fgm_epsilon', default=1.0, type=float,
                            help="Epsilon for adversarial.")
        self.add_argument('--fgm_name', default='word_embeddings', type=str,
                            help="name for adversarial layer.")

        self.add_argument("--per_gpu_train_batch_size", default=8, type=int,
                            help="Batch size per GPU/CPU for training.")
        self.add_argument("--per_gpu_eval_batch_size", default=8, type=int,
                            help="Batch size per GPU/CPU for evaluation.")
        self.add_argument("--gradient_accumulation_steps", type=int, default=1,
                            help="Number of updates steps to accumulate before performing a backward/update pass.", )
        self.add_argument("--learning_rate", default=5e-5, type=float,
                            help="The initial learning rate for Adam.")
        self.add_argument("--other_learning_rate", default=5e-5, type=float,
                            help="The initial learning rate for crf and linear layer.")
        self.add_argument("--weight_decay", default=0.01, type=float,
                            help="Weight decay if we apply some.")
        self.add_argument("--adam_epsilon", default=1e-8, type=float,
                            help="Epsilon for Adam optimizer.")
        self.add_argument("--max_grad_norm", default=1.0, type=float,
                            help="Max gradient norm.")
        self.add_argument("--num_train_epochs", default=3.0, type=float,
                            help="Total number of training epochs to perform.")
        self.add_argument("--max_steps", default=-1, type=int,
                            help="If > 0: set total number of training steps to perform. Override num_train_epochs.", )

        self.add_argument("--warmup_proportion", default=0.1, type=float,
                            help="Proportion of training to perform linear learning rate warmup for,E.g., 0.1 = 10% of training.")
        self.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.")
        self.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.")
        self.add_argument("--save_best_checkpoints", action="store_true", help="Save best checkpoint each `--logging_steps`, `--save_step` will be ignore")
        self.add_argument("--eval_all_checkpoints", action="store_true", help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number", )
        self.add_argument("--predict_checkpoints", type=int, default=0,
                            help="predict checkpoints starting with the same prefix as model_name ending and ending with step number")
        self.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available")
        self.add_argument("--overwrite_output_dir", action="store_true",
                            help="Overwrite the content of the output directory")
        self.add_argument("--seed", type=int, default=42, help="random seed for initialization")
        self.add_argument("--fp16", action="store_true",
                            help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit", )
        self.add_argument("--fp16_opt_level", type=str, default="O1",
                            help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
                                "See details at https://nvidia.github.io/apex/amp.html", )
        self.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")

        return self

class NerProcessor(DataProcessor):

    def get_train_examples(self, data_dir, data_file):
        """Gets a collection of :class:`InputExample` for the train set."""
        return list(self._create_examples(data_dir, data_file, 'train'))

    def get_dev_examples(self, data_dir, data_file):
        """Gets a collection of :class:`InputExample` for the dev set."""
        return list(self._create_examples(data_dir, data_file, 'dev'))

    def get_test_examples(self, data_dir, data_file):
        """Gets a collection of :class:`InputExample` for the test set."""
        return list(self._create_examples(data_dir, data_file, 'test'))

    def get_pseudo_examples(self, data_dir, data_file):
        """Gets a collection of :class:`InputExample` for the pseudo set."""
        return list(self._create_examples(data_dir, data_file, 'pseudo'))
    
    @property
    def label2id(self):
        return {label: i for i, label in enumerate(self.get_labels())}
    
    @property
    def id2label(self):
        return {i: label for i, label in enumerate(self.get_labels())}

    def get_labels(self):
        """Gets the list of labels for this data set."""
        raise NotImplementedError()
    
    def _create_examples(self, data_dir, data_file, mode):
        raise NotImplementedError()

class CailNerProcessor(NerProcessor):

    def get_labels(self):
        return [
            "O", # "X", "O", "[START]", "[END]",
        ] + list(LABEL_MEANING_MAP.keys())
    
    def _create_examples(self, data_dir, data_file, mode):
        data_path = os.path.join(data_dir, data_file)
        logger.info(f"Creating examples from {data_path}...")
        with open(data_path, encoding="utf-8") as f:
            lines = [json.loads(line) for line in f.readlines()]
        # 无标签数据数量限制
        if mode == "pseudo" and args.pseudo_num_sample is not None:
            random.shuffle(lines)
            lines = lines[:args.pseudo_num_sample]
        logger.info(f"Totally {len(lines)} examples.")
        for sentence_counter, line in enumerate(lines):
            sentence = (
                sentence_counter,
                {
                    "id": f"{mode}-{str(line['id'])}",
                    "tokens": list(line["text"]),
                    "entities": line.get("entities", None) 
                        if mode in ["train", "dev"] else None,
                    "sent_start": line["sent_start"],
                    "sent_end": line["sent_end"],
                }
            )
            yield sentence

class NerDataset(torch.utils.data.Dataset):

    def __init__(self, examples, process_pipline=[]):
        super().__init__()
        self.examples = examples
        self.process_pipline = process_pipline

    def __getitem__(self, index):
        # get example
        example = self.examples[index]
        # preprocessing
        for proc in self.process_pipline:
            if proc is None: continue
            example = proc(example)
        # convert to features
        return example
    
    def __len__(self):
         return len(self.examples)

    @staticmethod
    def collate_fn(batch):
        max_len = max([b["input_len"] for b in batch])[0].item()
        collated = dict()
        for k in ["input_ids", "token_type_ids", "attention_mask", "input_len", "sent_start", "sent_end"]:
            t = torch.cat([b[k] for b in batch], dim=0)
            if k not in ["input_len", "sent_start", "sent_end"]:
                t = t[:, :max_len] # dynamic batch
            collated[k] = t
        for k in ["spans", "span_mask", "label"]:
            if batch[0][k] is None:
                collated[k] = None
                continue
            t = pad_sequence([b[k][0] for b in batch], batch_first=True)
            collated[k] = t
        return collated

class AugmentContextAware:

    def __init__(self, p):
        self.p = p

        self.augment_entity_meanings = [
            # "物品价值", "被盗货币", "盗窃获利",
            # "被盗物品", "作案工具", 
            "受害人", "犯罪嫌疑人",
            # "地点", "组织机构",
        ]

    def __call__(self, example):
        id_ = example[1]["id"]
        tokens = example[1]["tokens"]
        entities = example[1]["entities"]
        sent_start = example[1]["sent_start"]
        sent_end = example[1]["sent_end"]

        random.shuffle(entities)
        for entity_type, entity_start, entity_end, entity_text in entities:
            if LABEL_MEANING_MAP[entity_type] in self.augment_entity_meanings:
                if random.random() > self.p: continue
                if any([tk == "[MASK]" for tk in tokens[entity_start: entity_end + 1]]):
                    continue
                for i in range(entity_start, entity_end + 1):
                    tokens[i] = "[MASK]"
        example[1]["tokens"] = tokens
        return example

class AugmentEntityReplace:

    def __init__(self, p, examples):
        self.p = p

        self.wordType_entityTypes_map = {
            "姓名": ["受害人", "犯罪嫌疑人", ],
            "价值": ["物品价值", "被盗货币", "盗窃获利", ],
        }
        self.entityType_wordType_map = dict()
        for word_type, entity_types in self.wordType_entityTypes_map.items():
            for entity_type in entity_types:
                self.entityType_wordType_map[entity_type] = word_type

        self.wordType_words_map = {
            "姓名": set(),
            "价值": set(),
        }
        for example in examples:
            for entity_type, entity_start, entity_end, entity_text in example[1]["entities"]:
                meaning = LABEL_MEANING_MAP[entity_type]
                if meaning not in self.entityType_wordType_map:
                    continue
                self.wordType_words_map[self.entityType_wordType_map[meaning]] \
                    .add(entity_text)
        self.wordType_words_map = {k: list(v) for k, v in self.wordType_words_map.items()}

    def __call__(self, example):
        id_ = example[1]["id"]
        tokens = example[1]["tokens"]
        entities = example[1]["entities"]
        sent_start = example[1]["sent_start"]
        sent_end = example[1]["sent_end"]

        text = "".join(tokens)
        entities = sorted(entities, key=lambda x: x[0])
        for i, (entity_type, entity_start, entity_end, entity_text) in enumerate(entities):
            if random.random() > self.p: continue
            meaning = LABEL_MEANING_MAP[entity_type]
            if meaning not in self.entityType_wordType_map:
                continue
            entity_text_new = random.choice(self.wordType_words_map[self.entityType_wordType_map[meaning]])
            len_diff = len(entity_text_new) - len(entity_text)
            text = text[: entity_start] + entity_text_new + text[entity_end + 1:]
            entity_start, entity_end = entity_start, entity_start + len(entity_text_new) - 1
            entities[i] = [entity_type, entity_start, entity_end, text[entity_start: entity_end + 1]]
            # 调整其他实体位置
            adjust_pos = lambda x: x if x <= entity_start else x + len_diff
            for j, (l, s, e, t) in enumerate(entities):
                s, e = adjust_pos(s), adjust_pos(e)
                t = text[s: e + 1]
                entities[j] = [l, s, e, t]

        example[1]["tokens"] = list(text)
        example[1]["entities"] = entities
        example[1]["sent_start"] = sent_start
        example[1]["sent_end"] = sent_start + len(text)
        return example

# TODO:
class ReDataMasking:

    def __init__(self):
        self.nc_reobj = re.compile("(现金)?(人民币)?[0-9]+(.[0-9]+)?余?元(现金)?(人民币)?")

    def __call__(self, example):
        ...


class Example2Feature:
    
    def __init__(self, tokenizer, label2id, max_seq_length, max_span_length):
        self.tokenizer = tokenizer
        self.label2id = label2id
        self.max_seq_length = max_seq_length
        self.max_span_length = max_span_length
    
    def __call__(self, example):
        return self._convert_example_to_feature(example)
    
    def _encode_span(self, max_length, input_len, sent_start, sent_end):
        spans = []; span_mask = []
        for i in range(sent_start, sent_end):
            for j in range(i, min(min(max_length, sent_end), i + self.max_span_length)):
                spans.append([i, j, j - i + 1])
                span_mask.append(0 if i >= input_len else 1)
        spans = torch.tensor([spans])               # (1, num_spans, 3) 
        span_mask = torch.tensor([span_mask])       # (1, num_spans)
        return spans, span_mask

    def _encode_label(self, entities, spans, tag_o):
        entities = {(b + 1, e + 1): self.label2id[t] for t, b, e, _ in entities}
        label = [entities.get((b, e), tag_o) for b, e, l in spans[0]]
        label = torch.tensor([label])               # (1, num_spans)
        return label

    def _convert_example_to_feature(self, example):
        id_ = example[1]["id"]
        tokens = example[1]["tokens"]
        entities = example[1]["entities"]
        sent_start = example[1]["sent_start"]
        sent_end = example[1]["sent_end"]

        # encode input
        inputs = self.tokenizer.encode_plus(
            text=tokens,
            text_pair=None, 
            add_special_tokens=True,
            padding="max_length",
            truncation="longest_first",
            max_length=self.max_seq_length,
            is_split_into_words=True,
            return_tensors="pt",
        )
        inputs["input_len"] = inputs["attention_mask"].sum(dim=1)  # for special tokens
        input_len = inputs["input_len"].item()
        inputs["spans"], inputs["span_mask"] = self._encode_span(
            input_len, input_len, sent_start + 1, sent_end + 1)  # dynamic batch
        inputs["sent_start"] = torch.tensor([sent_start])
        inputs["sent_end"] = torch.tensor([sent_end])
        
        tag_o = self.label2id["O"]

        if args.do_pseudo:
            is_pseudo_example = id_.startswith("pseudo")
            if is_pseudo_example:
                entities = []
                tag_o = PSEUDO_TAG

        if entities is None:
            inputs["label"] = None
            return inputs

        # encode label
        inputs["label"] = self._encode_label(entities, 
            inputs["spans"].cpu().numpy().tolist(), tag_o)
        return inputs

class FGM():
    def __init__(self, model, emb_name, epsilon=1.0):
        # emb_name这个参数要换成你模型中embedding的参数名
        self.model = model
        self.epsilon = epsilon
        self.emb_name = emb_name
        self.backup = {}

    def attack(self):
        for name, param in self.model.named_parameters():
            if param.requires_grad and self.emb_name in name:
                self.backup[name] = param.data.clone()
                norm = torch.norm(param.grad)
                if norm != 0 and not torch.isnan(norm):
                    r_at = self.epsilon * param.grad / norm
                    param.data.add_(r_at)

    def restore(self):
        for name, param in self.model.named_parameters():
            if param.requires_grad and self.emb_name in name:
                assert name in self.backup
                param.data = self.backup[name]
        self.backup = {}

def seed_everything(seed=None, reproducibility=True):
    '''
    init random seed for random functions in numpy, torch, cuda and cudnn
    Args:
        seed (int): random seed
        reproducibility (bool): Whether to require reproducibility
    '''
    if seed is None:
        seed = int(_select_seed_randomly())
    random.seed(seed)
    np.random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    if reproducibility:
        torch.backends.cudnn.benchmark = False
        torch.backends.cudnn.deterministic = True
    else:
        torch.backends.cudnn.benchmark = True
        torch.backends.cudnn.deterministic = False

def init_logger(name, log_file='', log_file_level=logging.NOTSET):
    '''
    初始化logger
    '''
    log_format = logging.Formatter(fmt='%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
                                    datefmt='%m/%d/%Y %H:%M:%S')
    logger = logging.getLogger(name)
    logger.setLevel(logging.INFO)
    console_handler = logging.StreamHandler()
    console_handler.setFormatter(log_format)
    logger.handlers = [console_handler]
    if log_file and log_file != '':
        file_handler = logging.FileHandler(log_file)
        file_handler.setLevel(log_file_level)
        logger.addHandler(file_handler)
    return logger

def train(args, model, processor, tokenizer):
    """ Train the model """
    train_dataset = load_dataset(args, processor, tokenizer, data_type="pseudo" if args.do_pseudo else 'train')
    args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
    train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
    train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size,
                                  collate_fn=NerDataset.collate_fn)
    if args.max_steps > 0:
        t_total = args.max_steps
        args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
    else:
        t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
    if args.evaluate_each_epoch:
        args.logging_steps = args.save_steps = int(t_total // args.num_train_epochs)
    # Prepare optimizer and schedule (linear warmup and decay)
    no_decay = ["bias", "LayerNorm.weight"]
    base_model_param_optimizer = list(model.base_model.named_parameters())
    base_model_param_optimizer_ids = [id(p) for n, p in base_model_param_optimizer]
    other_param_optimizer = [(n, p) for n, p in model.named_parameters() if id(p) not in base_model_param_optimizer_ids]
    optimizer_grouped_parameters = [
        {'params': [p for n, p in base_model_param_optimizer if not any(nd in n for nd in no_decay)],
         'weight_decay': args.weight_decay, 'lr': args.learning_rate},
        {'params': [p for n, p in base_model_param_optimizer if any(nd in n for nd in no_decay)], 
         'weight_decay': 0.0, 'lr': args.learning_rate},
        {'params': [p for n, p in other_param_optimizer], 
         'weight_decay': args.weight_decay, 'lr': args.other_learning_rate}
    ]
    args.warmup_steps = int(t_total * args.warmup_proportion)
    if args.optimizer == "adamw":
        optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
    elif args.optimizer == "lamb":
        optimizer = Lamb(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
    scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps,
                                                num_training_steps=t_total)
    # if args.scheduler == "linear":
    #     scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps,
    #                                                 num_training_steps=t_total)
    # elif args.scheduler == "cosine":
    #     scheduler = get_cosine_with_hard_restarts_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps,
    #                                                 num_training_steps=t_total, num_cycles=1)
    # Check if saved optimizer or scheduler states exist
    if os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt")) and os.path.isfile(
            os.path.join(args.model_name_or_path, "scheduler.pt")):
        # Load in optimizer and scheduler states
        optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt")))
        scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt")))
    if args.fp16:
        try:
            from apex import amp
        except ImportError:
            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
        model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
    # multi-gpu training (should be after apex fp16 initialization)
    if args.n_gpu > 1:
        model = torch.nn.DataParallel(model)
    # Distributed training (should be after apex fp16 initialization)
    if args.local_rank != -1:
        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
                                                          output_device=args.local_rank,
                                                          find_unused_parameters=True)
    if args.do_fgm:
        fgm = FGM(model, emb_name=args.fgm_name, epsilon=args.fgm_epsilon)
    if args.do_ema:
        ema = None
    # Train!
    logger.info("***** Running training *****")
    logger.info("  Num examples = %d", len(train_dataset))
    logger.info("  Num Epochs = %d", args.num_train_epochs)
    logger.info("  Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
    logger.info("  Total train batch size (w. parallel, distributed & accumulation) = %d",
                args.train_batch_size
                * args.gradient_accumulation_steps
                * (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
                )
    logger.info("  Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
    logger.info("  Total optimization steps = %d", t_total)

    global_step = 0
    steps_trained_in_current_epoch = 0
    # Check if continuing training from a checkpoint
    if os.path.exists(args.model_name_or_path) and "checkpoint" in args.model_name_or_path:
        # set global_step to gobal_step of last saved checkpoint from model path
        global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0])
        epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps)
        steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps)
        logger.info("  Continuing training from checkpoint, will skip to saved global_step")
        logger.info("  Continuing training from epoch %d", epochs_trained)
        logger.info("  Continuing training from global step %d", global_step)
        logger.info("  Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch)

    tr_loss, logging_loss, best_f1 = 0.0, 0.0, 0.0
    model.zero_grad()
    seed_everything(args.seed)  # Added here for reproductibility (even between python 2 and 3)
    for epoch_no in range(int(args.num_train_epochs)):
        pbar = tqdm(enumerate(train_dataloader), total=len(train_dataloader), desc='Training...')
        if args.do_ema and ema is None and epoch_no >= args.ema_start_epoch:
            logger.info("Start doing Exponential Moving Averaging(EMA).")
            ema = ExponentialMovingAverage(model, decay=0.999, device=args.device)
        for step, batch in pbar:
            # Skip past any already trained steps if resuming training
            if steps_trained_in_current_epoch > 0:
                steps_trained_in_current_epoch -= 1
                continue
            model.train()
            batch = {k: v.to(args.device) for k, v in batch.items() if v is not None}
            if args.model_type != "distilbert":
                # XLM and RoBERTa don"t use segment_ids
                if args.model_type.split('_')[0] in ["roberta", "xlnet"]:
                    batch["token_type_ids"] = None

            outputs = model(**batch)
            loss = outputs['loss']  # model outputs are always tuple in pytorch-transformers (see doc)
            if args.n_gpu > 1:
                loss = loss.mean()  # mean() to average on multi-gpu parallel training
            if args.gradient_accumulation_steps > 1:
                loss = loss / args.gradient_accumulation_steps
            if args.fp16:
                with amp.scale_loss(loss, optimizer) as scaled_loss:
                    scaled_loss.backward(retain_graph=args.vat_alpha is not None)
            else:
                loss.backward(retain_graph=args.vat_alpha is not None)
            if args.do_fgm:
                fgm.attack()
                outputs_adv = model(**batch)
                loss_adv = outputs_adv[0]
                if args.vat_alpha is not None:
                    loss_vat = compute_kl_loss(outputs["logits"], outputs_adv["logits"], 
                        pad_mask=batch["span_mask"] == 0)
                    loss_adv = loss_adv + args.vat_alpha * loss_vat
                if args.n_gpu > 1:
                    loss_adv = loss_adv.mean()
                if args.gradient_accumulation_steps > 1:
                    loss_adv = loss_adv / args.gradient_accumulation_steps
                if args.fp16:
                    with amp.scale_loss(loss_adv, optimizer) as scaled_loss:
                        scaled_loss.backward()
                else:
                    loss_adv.backward()
                fgm.restore()
            pbar.set_description(desc=f"Training[{epoch_no}]... loss={loss.item():.6f}")
            tr_loss += loss.item()
            if (step + 1) % args.gradient_accumulation_steps == 0:
                if args.fp16:
                    torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
                else:
                    torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
                optimizer.step()
                scheduler.step()  # Update learning rate schedule
                model.zero_grad()
                if args.do_ema and ema is not None:
                    ema.update(model)
                global_step += 1
                if args.local_rank in [-1, 0] and args.evaluate_during_training and \
                    args.logging_steps > 0 and global_step % args.logging_steps == 0:
                    # Log metrics
                    print(" ")
                    if args.local_rank == -1:
                        # Only evaluate when single GPU otherwise metrics may not average well
                        eval_results = evaluate(args, model, processor, tokenizer)
                        logger.info(f"[{epoch_no}] loss={eval_results.pop('loss')}")
                        for entity, metrics in eval_results.items():
                            logger.info("{:*^50s}".format(entity))
                            logger.info("\t".join(f"{metric:s}={value:f}" 
                                for metric, value in metrics.items()))
                        if args.save_best_checkpoints:
                            if eval_results["avg"]["f"] > best_f1:
                                best_f1 = eval_results["avg"]["f"]
                                # Save model checkpoint
                                output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(
                                    global_step if not args.save_best_checkpoints else 999999))
                                if not os.path.exists(output_dir):
                                    os.makedirs(output_dir)
                                model_to_save = (
                                    model.module if hasattr(model, "module") else model
                                )  # Take care of distributed/parallel training
                                model_to_save.save_pretrained(output_dir)
                                torch.save(args, os.path.join(output_dir, "training_args.bin"))
                                logger.info("Saving model checkpoint to %s", output_dir)
                                tokenizer.save_vocabulary(output_dir)
                                torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
                                torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
                                logger.info("Saving optimizer and scheduler states to %s", output_dir)

                            if args.do_ema and ema is not None:
                                logger.info("{:*^50s}".format("EMA"))
                                ema.apply_shadow(model)
                                eval_results_ema = evaluate(args, model, processor, tokenizer)
                                logger.info(f"[{epoch_no}] loss={eval_results_ema.pop('loss')}")
                                for entity, metrics in eval_results_ema.items():
                                    logger.info("{:*^50s}".format(entity))
                                    logger.info("\t".join(f"{metric:s}={value:f}" 
                                        for metric, value in metrics.items()))
                                if eval_results_ema["avg"]["f"] > best_f1:
                                    model_to_save = (
                                        model.module if hasattr(model, "module") else model
                                    )  # Take care of distributed/parallel training
                                    model_to_save.save_pretrained(output_dir)
                                    logger.info("Saving model checkpoint to %s", output_dir)
                                ema.restore(model)
                                
                elif args.local_rank in [-1, 0] and not args.evaluate_during_training and \
                        args.save_steps > 0 and global_step % args.save_steps == 0:
                    # Save model checkpoint
                    output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(
                        global_step if not args.save_best_checkpoints else 999999))
                    if not os.path.exists(output_dir):
                        os.makedirs(output_dir)
                    model_to_save = (
                        model.module if hasattr(model, "module") else model
                    )  # Take care of distributed/parallel training
                    model_to_save.save_pretrained(output_dir)
                    torch.save(args, os.path.join(output_dir, "training_args.bin"))
                    logger.info("Saving model checkpoint to %s", output_dir)
                    tokenizer.save_vocabulary(output_dir)
                    torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
                    torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
                    logger.info("Saving optimizer and scheduler states to %s", output_dir)
        logger.info("\n")
        if 'cuda' in str(args.device):
            torch.cuda.empty_cache()
    return global_step, tr_loss / global_step

def evaluate(args, model, processor, tokenizer, prefix=""):
    eval_output_dir = args.output_dir
    if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
        os.makedirs(eval_output_dir)
    eval_dataset = load_dataset(args, processor, tokenizer, data_type='dev')
    args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
    # Note that DistributedSampler samples randomly
    eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)
    eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size,
                                 collate_fn=NerDataset.collate_fn)
    # Eval!
    logger.info("***** Running evaluation %s *****", prefix)
    logger.info("  Num examples = %d", len(eval_dataset))
    logger.info("  Batch size = %d", args.eval_batch_size)
    eval_loss = 0.0
    nb_eval_steps = 0
    pbar = tqdm(enumerate(eval_dataloader), total=len(eval_dataloader), desc='Eval...')
    if isinstance(model, nn.DataParallel):
        model = model.module
    model.eval()

    y_true = []; y_pred = []
    id2label = processor.id2label
    for step, batch in pbar:
        # forward step
        with torch.no_grad():
            batch = {k: v.to(args.device) for k, v in batch.items() if v is not None}
            if args.model_type != "distilbert":
                # XLM and RoBERTa don"t use segment_ids
                if args.model_type.split('_')[0] in ["roberta", "xlnet"]:
                    batch["token_type_ids"] = None
            outputs = model(**batch)
            tmp_eval_loss = outputs['loss']
            logits = outputs['logits']
        if args.n_gpu > 1:
            tmp_eval_loss = tmp_eval_loss.mean()  # mean() to average on multi-gpu parallel evaluating
        eval_loss += tmp_eval_loss.item()
        nb_eval_steps += 1
        
        # calculate metrics
        # preds = SpanV2.decode_batch(logits, batch["spans"], batch["span_mask"])
        # for pred_no, (pred, input_len, start, end) in enumerate(zip(
        #         preds, batch["input_len"], batch["sent_start"], batch["sent_end"])):
        #     pred = [(LABEL_MEANING_MAP[id2label[t]], b, e) for t, b, e in pred if id2label[t] != "O"]
        #     pred = [(t, b - start, e - start) for t, b, e in pred]
        #     sample = eval_dataset.examples[args.eval_batch_size * step + pred_no][1]
        #     label_entities_map = {v: [] for v in LABEL_MEANING_MAP.values()}
        #     for t, b, e in pred:
        #         label_entities_map[t].append(f"{b};{e+1}")
        #     entities = [{"label": k, "span": v} for k, v in label_entities_map.items()]
        #     y_pred.append({"id": sample["id"].split("-")[-1], "entities": entities})

        # evaluate函数解码替换为与test一致
        for pred_no in range(logits.size(0)):
            example_ = eval_dataset.examples[args.eval_batch_size * step + pred_no][1]
            batch_ = {k: v[[pred_no]] for k,v in batch.items()}
            batch_["logits"] = logits[[pred_no]]
            y_pred_ = predict_decode_batch(example_, batch_, id2label, post_process=True)
            for entity_no, entity in enumerate(y_pred_["entities"]):
                y_pred_["entities"][entity_no] = {
                    "label": LABEL_MEANING_MAP[entity["label"]],
                    "span": entity["span"]}
            y_pred.append(y_pred_)

        labels = SpanV2.decode_batch(batch["label"], batch["spans"], batch["span_mask"], is_logits=False)
        for label_no, (label, input_len, start, end) in enumerate(zip(
                labels, batch["input_len"], batch["sent_start"], batch["sent_end"])):
            label = [(LABEL_MEANING_MAP[id2label[t]], b, e) for t, b, e in label if id2label[t] != "O"]
            label = [(t, b - start, e - start) for t, b, e in label]
            sample = eval_dataset.examples[args.eval_batch_size * step + label_no][1]
            label_entities_map = {v: [] for v in LABEL_MEANING_MAP.values()}
            for t, b, e in label:
                label_entities_map[t].append(f"{b};{e+1}")
            entities = [{"label": k, "span": v} for k, v in label_entities_map.items()]
            y_true.append({"id": sample["id"].split("-")[-1], "entities": entities})

    y_true = {y["id"]: {"entities": y["entities"]} for y in y_true}
    y_pred = {y["id"]: {"entities": y["entities"]} for y in y_pred}
    results = dict()
    results["avg"] = score(y_true, y_pred)
    for label in LABEL_MEANING_MAP.values():
        results[label] = score(y_true, y_pred, [label])
    results['loss'] = eval_loss / nb_eval_steps
    return results
    
def predict_decode_batch(example, batch, id2label, thresh=0., post_process=True):
    # if example["id"].split("-")[-1] == "033522d9bdf796d13c4b594cbdf03184":
    #     print()
    is_intersect = lambda a, b: min(a[1], b[1]) - max(a[0], b[0]) > 0
    is_a_included_by_b = lambda a, b: min(a[1], b[1]) - max(a[0], b[0]) == a[1] - a[0]
    is_contain_special_char = lambda x: any([c in text[x[0]: x[1]] for c in ["，", "。", "、", ",", ".",]])
    is_length_le_n = lambda x, n: x[1] - x[0] < n
    entities2spans = lambda entities: [(int(e.split(";")[0]), int(e.split(";")[1])) for e in entities]
    spans2entities = lambda spans: [f"{b};{e}" for b, e in spans]
    def merge_spans(spans, keep_type="short"):
        spans = sorted(spans, key=lambda x: (x[0], x[1] - x[0]))    # (起始位置， 区间长度)
        spans_new = []
        for span in spans:
            if not spans_new:
                spans_new.append(span)
            else:
                spans_last = spans_new[-1]
                if not is_intersect(spans_last, span):
                    spans_new.append(span)
                else:
                    if keep_type == "long":
                        if is_a_included_by_b(spans_last, span):
                            spans_new.pop(-1)
                            spans_new.append(span)
                        elif is_a_included_by_b(span, spans_last):
                            pass
                        else:
                            spans_new.append(span)
                    elif keep_type == "short":
                        if is_a_included_by_b(spans_last, span):
                            pass
                        elif is_a_included_by_b(span, spans_last):
                            spans_new.pop(-1)
                            spans_new.append(span)
                        else:
                            spans_new.append(span)
        # if len(spans_new) < len(spans): print(spans, "->", spans_new)
        return spans_new

    text = "".join(example["tokens"])
    logits = batch["logits"]
    preds = SpanV2.decode_batch(logits, batch["spans"], batch["span_mask"], thresh=thresh)
    pred, input_len = preds[0], batch["input_len"][0]
    start, end = batch["sent_start"].item(), batch["sent_end"].item()
    pred = [(id2label[t], b, e) for t, b, e in pred if id2label[t] != "O"]
    pred = [(t, b - start, e - start) for t, b, e in pred]
    label_entities_map = {label: [] for label in LABEL_MEANING_MAP.keys()}
    for t, b, e in pred: label_entities_map[t].append(f"{b};{e+1}")
    if post_process:
        # 若存在以下实体重叠，则保留较长的
        for meaning in [
            "时间", "地点",
        ]:
            label = MEANING_LABEL_MAP[meaning]
            entities = label_entities_map[label]                        # 左闭右开
            if entities:
                spans = entities2spans(entities)
                spans = list(filter(lambda x: not is_contain_special_char(x), spans))
                spans = merge_spans(spans, keep_type="long")
                entities = spans2entities(spans)
                label_entities_map[label] = entities

        # 1. 若存在被盗物品实体重叠，保留最短的；2. 被盗物品要和人名联系
        meaning = "被盗物品"
        label = MEANING_LABEL_MAP[meaning]
        entities = label_entities_map[label]                            # 左闭右开
        if entities:
            spans = entities2spans(entities)
            spans = list(filter(lambda x: not is_contain_special_char(x), spans))
            # >>> 姓名处理 >>>
            entities_name = label_entities_map[MEANING_LABEL_MAP["受害人"]]
            spans_name = entities2spans(entities_name)
            # 加入`受害人+被盗物品`的组合
            spans.extend([(a[0], b[1]) for a, b in itertools.product(
                spans_name, spans) if a[1] - b[0] in [-1, 0]])
            # `受害人+被盗物品`、`被盗物品`，优先保留`受害人+被盗物品`
            is_todel = [False] * len(spans)
            for i, a in enumerate(spans_name):
                for j, b in enumerate(spans):
                    u = (a[0], b[1])
                    if u in spans and u != b:
                        is_todel[j] = True
            spans = [span for flag, span in zip(is_todel, spans) if not flag]
            # <<< 姓名处理 <<<
            # # TODO: >>> 地点处理 >>>
            # entities_name = label_entities_map[MEANING_LABEL_MAP["地点"]]
            # spans_name = entities2spans(entities_name)
            # # 加入`地点+被盗物品`的组合
            # spans.extend([(a[0], b[1]) for a, b in itertools.product(
            #     spans_name, spans) if a[1] - b[0] in [-1, 0]])
            # # `地点+被盗物品`、`被盗物品`，优先保留`地点+被盗物品`
            # is_todel = [False] * len(spans)
            # for i, a in enumerate(spans_name):
            #     for j, b in enumerate(spans):
            #         u = (a[0], b[1])
            #         if u in spans and u != b:
            #             is_todel[j] = True
            # spans = [span for flag, span in zip(is_todel, spans) if not flag]
            # # <<< 地点处理 <<<
            spans = merge_spans(spans, keep_type="short")
            entities = spans2entities(spans)
            label_entities_map[label] = entities

        # 1. 若存在被盗货币实体重叠，保留最长的；2. 被盗货币要和人名联系
        meaning = "被盗货币"
        label = MEANING_LABEL_MAP[meaning]
        entities = label_entities_map[label]                            # 左闭右开
        if entities:
            spans = entities2spans(entities)
            spans = list(filter(lambda x: not is_contain_special_char(x), spans))
            # # TODO: >>> 姓名处理 >>>
            # entities_name = label_entities_map[MEANING_LABEL_MAP["受害人"]]
            # spans_name = entities2spans(entities_name)
            # # 加入`受害人+被盗货币`的组合
            # spans.extend([(a[0], b[1]) for a, b in itertools.product(
            #     spans_name, spans) if a[1] - b[0] in [-1, 0]])
            # # `受害人+被盗货币`、`被盗货币`，优先保留`受害人+被盗货币`
            # is_todel = [False] * len(spans)
            # for i, a in enumerate(spans_name):
            #     for j, b in enumerate(spans):
            #         u = (a[0], b[1])
            #         if u in spans and u != b:
            #             is_todel[j] = True
            # spans = [span for flag, span in zip(is_todel, spans) if not flag]
            # # <<< 姓名处理 <<<
            spans = merge_spans(spans, keep_type="long")
            entities = spans2entities(spans)
            label_entities_map[label] = entities

        # 受害人和犯罪嫌疑人设置最长实体限制(10)
        for meaning in ["受害人", "犯罪嫌疑人"]:
            label = MEANING_LABEL_MAP[meaning]
            entities = label_entities_map[label]
            if entities:
                spans = entities2spans(entities)
                spans = list(filter(lambda x: (not is_contain_special_char(x)) and is_length_le_n(x, 10), spans))
                entities = spans2entities(spans)
                label_entities_map[label] = entities
        
        # # TODO: 元现金
        # for meaning in [
        #     "被盗货币",
        #     "物品价值",
        #     "盗窃获利",
        # ]:
        #     label = MEANING_LABEL_MAP[meaning]
        #     entities = label_entities_map[label]
        #     if entities:
        #         spans = entities2spans(entities)
        #         for i, (l, r) in enumerate(spans):
        #             if text[r - 1] == "元" and text[r: r + 2] == "现金":
        #                 spans[i] = (l, r + 2)
        #         entities = spans2entities(spans)
        #         label_entities_map[label] = entities
                
    entities = [{"label": label, "span": label_entities_map[label]} \
        for label in LABEL_MEANING_MAP.keys()]
    # 预测结果文件为一个json格式的文件，包含两个字段，分别为``id``和``entities``
    return {"id": example["id"].split("-")[1], "entities": entities}

def predict(args, model, processor, tokenizer, prefix=""):
    pred_output_dir = args.output_dir
    if not os.path.exists(pred_output_dir) and args.local_rank in [-1, 0]:
        os.makedirs(pred_output_dir)
    test_dataset = load_dataset(args, processor, tokenizer, data_type='test')
    # Note that DistributedSampler samples randomly
    test_sampler = SequentialSampler(test_dataset) if args.local_rank == -1 else DistributedSampler(test_dataset)
    test_dataloader = DataLoader(test_dataset, sampler=test_sampler, batch_size=1, collate_fn=NerDataset.collate_fn)
    id2label = processor.id2label
    # Eval!
    logger.info("***** Running prediction %s *****", prefix)
    logger.info("  Num examples = %d", len(test_dataset))
    logger.info("  Batch size = %d", 1)
    results = []
    output_predict_file = os.path.join(pred_output_dir, prefix, "test_prediction.json")

    if isinstance(model, nn.DataParallel):
        model = model.module
    pbar = tqdm(enumerate(test_dataloader), total=len(test_dataloader), desc="Predicting...")
    batch_all = []
    for step, batch in pbar:
        model.eval()
        with torch.no_grad():
            batch = {k: v.to(args.device) for k, v in batch.items() if v is not None}
            if args.model_type != "distilbert":
                # XLM and RoBERTa don"t use segment_ids
                if args.model_type.split('_')[0] in ["roberta", "xlnet"]:
                    batch["token_type_ids"] = None
            outputs = model(**batch)
            logits = outputs['logits']      # (batch_size=1, num_spans, num_labels)
            batch["logits"] = logits.detach()
            batch.pop("input_ids")
            batch.pop("attention_mask")
            batch.pop("token_type_ids")
        # 解码输出
        example = test_dataset.examples[step][1]
        results.append(predict_decode_batch(example, batch, id2label, args.span_proba_thresh, post_process=True))
        # for k-fold
        batch_all.append({k: v.detach().cpu() for k, v in batch.items()})
    logger.info("\n")
    with open(output_predict_file, "w") as writer:
        for record in results:
            writer.write(json.dumps(record) + '\n')
    # for k-fold
    torch.save(batch_all, os.path.join(args.output_dir, "test_batches.pkl"))
    torch.save(test_dataset.examples, os.path.join(args.output_dir, "test_examples.pkl"))

PROCESSER_CLASS = {
    "cail_ner": CailNerProcessor,
}

MODEL_CLASSES = {
    "bert_span": (BertConfigSpanV2, BertSpanV2ForNer, BertTokenizer),
    "nezha_span": (BertConfigSpanV2, NeZhaSpanV2ForNer, BertTokenizer),
}

def load_dataset(args, processor, tokenizer, data_type='train'):
    if args.local_rank not in [-1, 0] and not evaluate:
        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
    if data_type == 'train':
        examples = processor.get_train_examples(args.data_dir, args.train_file)
    elif data_type == 'dev':
        examples = processor.get_dev_examples(args.data_dir, args.dev_file)
    elif data_type == "test":
        examples = processor.get_test_examples(args.data_dir, args.test_file)
    elif data_type == 'pseudo':
        examples = processor.get_train_examples(args.data_dir, args.train_file)
        examples_pseudo = processor.get_pseudo_examples(args.pseudo_data_dir, args.pseudo_data_file)
        examples.extend(examples_pseudo)
    if args.local_rank == 0 and not evaluate:
        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
    max_seq_length = args.train_max_seq_length if data_type == 'train' else args.eval_max_seq_length
    return NerDataset(examples, process_pipline=[
        AugmentEntityReplace(args.augment_entity_replace_p, examples,
            ) if (data_type == 'train' and args.augment_entity_replace_p is not None) else None,
        AugmentContextAware(args.augment_context_aware_p,
            ) if (data_type == 'train' and args.augment_context_aware_p is not None) else None,
        Example2Feature(tokenizer, processor.label2id, max_seq_length, config.max_span_length),
    ])


if __name__ == "__main__":

    parser = NerArgumentParser()
    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.
        args = parser.parse_args_from_json(json_file=os.path.abspath(sys.argv[1]))
    else:
        args = parser.build_arguments().parse_args()
    # args = parser.parse_args_from_json(json_file="args/pred.1.json")

    # Set seed before initializing model.
    seed_everything(args.seed)
    
    # User-defined post initialization
    output_dir = f"{args.task_name}-{args.dataset_name}-{args.model_type}-{args.version}-{args.seed}"
    if not args.output_dir.endswith(output_dir):
        output_dir = os.path.join(args.output_dir, output_dir)
        os.makedirs(output_dir, exist_ok=True)
        parser.save_args_to_json(os.path.join(output_dir, "training_args.json"), args)
        args.output_dir = output_dir
    args.logging_dir = args.output_dir
    os.makedirs(args.cache_dir, exist_ok=True)

    # Setup logging
    time_ = time.strftime("%Y-%m-%d-%H:%M:%S", time.localtime())
    logger = init_logger(__name__, log_file=os.path.join(args.output_dir, f'{time_}.log'))
    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {args.local_rank}, device: {args.device}, n_gpu: {args.n_gpu}"
        + f"distributed training: {bool(args.local_rank != -1)}, 16-bits training: {args.fp16}"
    )
    logger.info(f"Training/evaluation parameters {args}")

    # Setup CUDA, GPU & distributed training
    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")
        args.n_gpu = torch.cuda.device_count()
    else:  # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
        torch.cuda.set_device(args.local_rank)
        device = torch.device("cuda", args.local_rank)
        torch.distributed.init_process_group(backend="nccl")
        args.n_gpu = 1
    args.device = device
    # args.device, args.n_gpu = torch.device(args.device), 1

    logger.warning(
        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
        args.local_rank, args.device, args.n_gpu, bool(args.local_rank != -1), args.fp16, )

    # Prepare NER task
    args.dataset_name = args.dataset_name.lower()
    if args.dataset_name not in PROCESSER_CLASS:
        raise ValueError("Task not found: %s" % (args.dataset_name))
    processor = PROCESSER_CLASS[args.dataset_name]()
    label_list = processor.get_labels()
    args.id2label = {i: label for i, label in enumerate(label_list)}
    args.label2id = {label: i for i, label in enumerate(label_list)}
    num_labels = len(label_list)

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    if args.local_rank not in [-1, 0]:
        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
    args.model_type = args.model_type.lower()
    config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
    logger.info("Training/evaluation parameters %s", args)
    # Training
    if args.do_train:
        config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path,
                                            num_labels=num_labels, max_span_length=args.max_span_length,
                                            width_embedding_dim=args.width_embedding_dim,
                                            cache_dir=args.cache_dir if args.cache_dir else None, )
        tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
                                                    do_lower_case=args.do_lower_case,
                                                    cache_dir=args.cache_dir if args.cache_dir else None, )
        model = model_class.from_pretrained(args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path),
                                            config=config, cache_dir=args.cache_dir if args.cache_dir else None)
        if args.local_rank == 0:
            torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
        model.to(args.device)
        global_step, tr_loss = train(args, model, processor, tokenizer)
        logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
    # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
    if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
        # 将early stop模型保存到输出目录下
        if args.save_best_checkpoints:
            best_checkpoints = os.path.join(args.output_dir, "checkpoint-999999")
            logger.info("Loading model checkpoint from %s", best_checkpoints)
            config = config_class.from_pretrained(best_checkpoints,
                                                  num_labels=num_labels, max_span_length=args.max_span_length,
                                                  width_embedding_dim=args.width_embedding_dim,
                                                  cache_dir=args.cache_dir if args.cache_dir else None, )
            tokenizer = tokenizer_class.from_pretrained(best_checkpoints,
                                                        do_lower_case=args.do_lower_case, 
                                                        cache_dir=args.cache_dir if args.cache_dir else None, )
            model = model_class.from_pretrained(best_checkpoints, config=config, 
                                                cache_dir=args.cache_dir if args.cache_dir else None)
        # Create output directory if needed
        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
            os.makedirs(args.output_dir)
        logger.info("Saving model checkpoint to %s", args.output_dir)
        # Save a trained model, configuration and tokenizer using `save_pretrained()`.
        # They can then be reloaded using `from_pretrained()`
        model_to_save = (
            model.module if hasattr(model, "module") else model
        )  # Take care of distributed/parallel training
        model_to_save.save_pretrained(args.output_dir)
        tokenizer.save_vocabulary(args.output_dir)
        # Good practice: save your training arguments together with the trained model
        torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
    # Evaluation
    results = {}
    if args.do_eval and args.local_rank in [-1, 0]:
        config = config_class.from_pretrained(args.output_dir,
                                              num_labels=num_labels, max_span_length=args.max_span_length,
                                              width_embedding_dim=args.width_embedding_dim,
                                              cache_dir=args.cache_dir if args.cache_dir else None, )
        tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
        checkpoints = [args.output_dir]
        if args.eval_all_checkpoints:
            checkpoints = list(
                os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True))
            )
            logging.getLogger("pytorch_transformers.modeling_utils").setLevel(logging.WARN)  # Reduce logging
        logger.info("Evaluate the following checkpoints: %s", checkpoints)
        for checkpoint in checkpoints:
            global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
            prefix = checkpoint.split('/')[-1] if checkpoint.find('checkpoint') != -1 else ""
            model = model_class.from_pretrained(checkpoint, config=config)
            model.to(args.device)
            result = evaluate(args, model, processor, tokenizer, prefix=prefix)
            if global_step:
                result = {"{}_{}".format(global_step, k): v for k, v in result.items()}
            results.update(result)
        output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
        with open(output_eval_file, "w") as writer:
            for key in sorted(results.keys()):
                writer.write("{} = {}\n".format(key, str(results[key])))

    if args.do_predict and args.local_rank in [-1, 0]:
        config = config_class.from_pretrained(args.output_dir,
                                              num_labels=num_labels, max_span_length=args.max_span_length,
                                              cache_dir=args.cache_dir if args.cache_dir else None, )
        tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
        checkpoints = [args.output_dir]
        if args.predict_checkpoints > 0:
            checkpoints = list(
                os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
            logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN)  # Reduce logging
            checkpoints = [x for x in checkpoints if x.split('-')[-1] == str(args.predict_checkpoints)]
        logger.info("Predict the following checkpoints: %s", checkpoints)
        for checkpoint in checkpoints:
            prefix = checkpoint.split('/')[-1] if checkpoint.find('checkpoint') != -1 else ""
            model = model_class.from_pretrained(checkpoint, config=config)
            model.to(args.device)
            predict(args, model, processor, tokenizer, prefix=prefix)