text: # Copyright (c) 2021 PaddlePaddle Authors. 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.
  
  import argparse
  import logging
  import os
  import sys
  import random
  import time
  import math
  import distutils.util
  from functools import partial
  
  import numpy as np
  import paddle
  from paddle.io import DataLoader
  import paddle.nn as nn
  from paddle.metric import Accuracy
  
  from paddlenlp.datasets import load_dataset
  from paddlenlp.data import Stack, Tuple, Pad, Dict
  from paddlenlp.transformers import LinearDecayWithWarmup
  from paddlenlp.transformers import BertForSequenceClassification, BertTokenizer
  from paddlenlp.transformers import ErnieForSequenceClassification, ErnieTokenizer
  from paddlenlp.transformers import RobertaForSequenceClassification, RobertaTokenizer
  
  METRIC_CLASSES = {
      "afqmc": Accuracy,
      "tnews": Accuracy,
      "iflytek": Accuracy,
      "ocnli": Accuracy,
      "cmnli": Accuracy,
      "cluewsc2020": Accuracy,
      "csl": Accuracy,
  }
  
  MODEL_CLASSES = {
      "ernie": (ErnieForSequenceClassification, ErnieTokenizer),
      "bert": (BertForSequenceClassification, BertTokenizer),
      "roberta": (RobertaForSequenceClassification, RobertaTokenizer)
  }
  
  
  def parse_args():
      parser = argparse.ArgumentParser()
  
      # Required parameters
      parser.add_argument(
          "--task_name",
          default=None,
          type=str,
          required=True,
          help="The name of the task to train selected in the list: " +
          ", ".join(METRIC_CLASSES.keys()), )
      parser.add_argument(
          "--model_type",
          default=None,
          type=str,
          required=True,
          help="Model type selected in the list: " +
          ", ".join(MODEL_CLASSES.keys()), )
      parser.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: "
          + ", ".join(
              sum([
                  list(classes[-1].pretrained_init_configuration.keys())
                  for classes in MODEL_CLASSES.values()
              ], [])), )
      parser.add_argument(
          "--output_dir",
          default="best_clue_model",
          type=str,
          help="The output directory where the model predictions and checkpoints will be written.",
      )
      parser.add_argument(
          "--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.", )
      parser.add_argument(
          "--learning_rate",
          default=1e-4,
          type=float,
          help="The initial learning rate for Adam.")
      parser.add_argument(
          "--num_train_epochs",
          default=3,
          type=int,
          help="Total number of training epochs to perform.", )
      parser.add_argument(
          "--logging_steps",
          type=int,
          default=100,
          help="Log every X updates steps.")
      parser.add_argument(
          "--save_steps",
          type=int,
          default=100,
          help="Save checkpoint every X updates steps.")
      parser.add_argument(
          "--batch_size",
          default=32,
          type=int,
          help="Batch size per GPU/CPU for training.", )
      parser.add_argument(
          "--weight_decay",
          default=0.0,
          type=float,
          help="Weight decay if we apply some.")
      parser.add_argument(
          "--warmup_steps",
          default=0,
          type=int,
          help="Linear warmup over warmup_steps. If > 0: Override warmup_proportion"
      )
      parser.add_argument(
          "--warmup_proportion",
          default=0.1,
          type=float,
          help="Linear warmup proportion over total steps.")
      parser.add_argument(
          "--adam_epsilon",
          default=1e-6,
          type=float,
          help="Epsilon for Adam optimizer.")
      parser.add_argument(
          "--do_train",
          type=distutils.util.strtobool,
          default=True,
          help="Whether do train.")
      parser.add_argument(
          "--do_eval",
          type=distutils.util.strtobool,
          default=False,
          help="Whether do train.")
      parser.add_argument(
          "--max_steps",
          default=-1,
          type=int,
          help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
      )
      parser.add_argument(
          "--seed", default=42, type=int, help="random seed for initialization")
      parser.add_argument(
          "--device",
          default="gpu",
          type=str,
          help="The device to select to train the model, is must be cpu/gpu/xpu.")
      parser.add_argument(
          "--max_grad_norm",
          default=1.0,
          type=float,
          help="The max value of grad norm.")
      args = parser.parse_args()
      return args
  
  
  def set_seed(args):
      # Use the same data seed(for data shuffle) for all procs to guarantee data
      # consistency after sharding.
      random.seed(args.seed)
      np.random.seed(args.seed)
      # Maybe different op seeds(for dropout) for different procs is better. By:
      # `paddle.seed(args.seed + paddle.distributed.get_rank())`
      paddle.seed(args.seed)
  
  
  @paddle.no_grad()
  def evaluate(model, loss_fct, metric, data_loader):
      model.eval()
      metric.reset()
      for batch in data_loader:
          input_ids, segment_ids, labels = batch
          logits = model(input_ids, segment_ids)
          loss = loss_fct(logits, labels)
          correct = metric.compute(logits, labels)
          metric.update(correct)
      res = metric.accumulate()
      print("eval loss: %f, acc: %s, " % (loss.numpy(), res), end='')
      model.train()
      return res
  
  
  def convert_example(example,
                      tokenizer,
                      label_list,
                      is_test=False,
                      max_seq_length=512):
      """convert a glue example into necessary features"""
      if not is_test:
          # `label_list == None` is for regression task
          label_dtype = "int64" if label_list else "float32"
          # Get the label
          label = np.array(example["label"], dtype="int64")
      # Convert raw text to feature
      if 'keyword' in example:  # CSL
          sentence1 = " ".join(example['keyword'])
          example = {
              'sentence1': sentence1,
              'sentence2': example['abst'],
              'label': example['label']
          }
      elif 'target' in example:  # wsc
          text, query, pronoun, query_idx, pronoun_idx = example['text'], example[
              'target']['span1_text'], example['target']['span2_text'], example[
                  'target']['span1_index'], example['target']['span2_index']
          text_list = list(text)
          assert text[pronoun_idx:(pronoun_idx + len(pronoun)
                                   )] == pronoun, "pronoun: {}".format(pronoun)
          assert text[query_idx:(query_idx + len(query)
                                 )] == query, "query: {}".format(query)
          if pronoun_idx > query_idx:
              text_list.insert(query_idx, "_")
              text_list.insert(query_idx + len(query) + 1, "_")
              text_list.insert(pronoun_idx + 2, "[")
              text_list.insert(pronoun_idx + len(pronoun) + 2 + 1, "]")
          else:
              text_list.insert(pronoun_idx, "[")
              text_list.insert(pronoun_idx + len(pronoun) + 1, "]")
              text_list.insert(query_idx + 2, "_")
              text_list.insert(query_idx + len(query) + 2 + 1, "_")
          text = "".join(text_list)
          example['sentence'] = text
      if 'sentence' in example:
          example = tokenizer(example['sentence'], max_seq_len=max_seq_length)
      elif 'sentence1' in example:
          example = tokenizer(
              example['sentence1'],
              text_pair=example['sentence2'],
              max_seq_len=max_seq_length)
      if not is_test:
          return example['input_ids'], example['token_type_ids'], label
      else:
          return example['input_ids'], example['token_type_ids']
  
  
  def do_eval(args):
      paddle.set_device(args.device)
      if paddle.distributed.get_world_size() > 1:
          paddle.distributed.init_parallel_env()
  
      set_seed(args)
  
      args.task_name = args.task_name.lower()
      metric_class = METRIC_CLASSES[args.task_name]
      args.model_type = args.model_type.lower()
      model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
  
      dev_ds = load_dataset('clue', args.task_name, splits='dev')
  
      tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
      trans_func = partial(
          convert_example,
          label_list=dev_ds.label_list,
          tokenizer=tokenizer,
          max_seq_length=args.max_seq_length)
  
      dev_ds = dev_ds.map(trans_func, lazy=True)
      dev_batch_sampler = paddle.io.BatchSampler(
          dev_ds, batch_size=args.batch_size, shuffle=False)
  
      batchify_fn = lambda samples, fn=Tuple(
          Pad(axis=0, pad_val=tokenizer.pad_token_id),  # input
          Pad(axis=0, pad_val=tokenizer.pad_token_type_id),  # segment
          Stack(dtype="int64" if dev_ds.label_list else "float32")  # label
      ): fn(samples)
  
      dev_data_loader = DataLoader(
          dataset=dev_ds,
          batch_sampler=dev_batch_sampler,
          collate_fn=batchify_fn,
          num_workers=0,
          return_list=True)
  
      num_classes = 1 if dev_ds.label_list == None else len(dev_ds.label_list)
  
      model = model_class.from_pretrained(
          args.model_name_or_path, num_classes=num_classes)
      if paddle.distributed.get_world_size() > 1:
          model = paddle.DataParallel(model)
  
      metric = metric_class()
      best_acc = 0.0
      global_step = 0
      tic_train = time.time()
      model.eval()
      metric.reset()
      for batch in dev_data_loader:
          input_ids, segment_ids, labels = batch
          logits = model(input_ids, segment_ids)
          correct = metric.compute(logits, labels)
          metric.update(correct)
      res = metric.accumulate()
      print("acc: %s\n, " % (res), end='')
  
  
  def do_train(args):
      paddle.set_device(args.device)
      if paddle.distributed.get_world_size() > 1:
          paddle.distributed.init_parallel_env()
  
      set_seed(args)
  
      args.task_name = args.task_name.lower()
      metric_class = METRIC_CLASSES[args.task_name]
      args.model_type = args.model_type.lower()
      model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
  
      train_ds, dev_ds = load_dataset(
          'clue', args.task_name, splits=('train', 'dev'))
  
      tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
  
      trans_func = partial(
          convert_example,
          label_list=train_ds.label_list,
          tokenizer=tokenizer,
          max_seq_length=args.max_seq_length)
  
      train_ds = train_ds.map(trans_func, lazy=True)
      train_batch_sampler = paddle.io.DistributedBatchSampler(
          train_ds, batch_size=args.batch_size, shuffle=True)
  
      dev_ds = dev_ds.map(trans_func, lazy=True)
      dev_batch_sampler = paddle.io.BatchSampler(
          dev_ds, batch_size=args.batch_size, shuffle=False)
  
      batchify_fn = lambda samples, fn=Tuple(
          Pad(axis=0, pad_val=tokenizer.pad_token_id),  # input
          Pad(axis=0, pad_val=tokenizer.pad_token_type_id),  # segment
          Stack(dtype="int64" if train_ds.label_list else "float32")  # label
      ): fn(samples)
  
      train_data_loader = DataLoader(
          dataset=train_ds,
          batch_sampler=train_batch_sampler,
          collate_fn=batchify_fn,
          num_workers=0,
          return_list=True)
      dev_data_loader = DataLoader(
          dataset=dev_ds,
          batch_sampler=dev_batch_sampler,
          collate_fn=batchify_fn,
          num_workers=0,
          return_list=True)
  
      num_classes = 1 if train_ds.label_list == None else len(train_ds.label_list)
      model = model_class.from_pretrained(
          args.model_name_or_path, num_classes=num_classes)
      if paddle.distributed.get_world_size() > 1:
          model = paddle.DataParallel(model)
  
      if args.max_steps > 0:
          num_training_steps = args.max_steps
          num_train_epochs = math.ceil(num_training_steps /
                                       len(train_data_loader))
      else:
          num_training_steps = len(train_data_loader) * args.num_train_epochs
          num_train_epochs = args.num_train_epochs
  
      warmup = args.warmup_steps if args.warmup_steps > 0 else args.warmup_proportion
  
      lr_scheduler = LinearDecayWithWarmup(args.learning_rate, num_training_steps,
                                           warmup)
  
      # Generate parameter names needed to perform weight decay.
      # All bias and LayerNorm parameters are excluded.
      decay_params = [
          p.name for n, p in model.named_parameters()
          if not any(nd in n for nd in ["bias", "norm"])
      ]
      optimizer = paddle.optimizer.AdamW(
          learning_rate=lr_scheduler,
          beta1=0.9,
          beta2=0.999,
          epsilon=args.adam_epsilon,
          parameters=model.parameters(),
          weight_decay=args.weight_decay,
          apply_decay_param_fun=lambda x: x in decay_params,
          grad_clip=nn.ClipGradByGlobalNorm(args.max_grad_norm))
  
      loss_fct = paddle.nn.loss.CrossEntropyLoss(
      ) if train_ds.label_list else paddle.nn.loss.MSELoss()
  
      metric = metric_class()
      best_acc = 0.0
      global_step = 0
      tic_train = time.time()
      for epoch in range(num_train_epochs):
          for step, batch in enumerate(train_data_loader):
              global_step += 1
              input_ids, segment_ids, labels = batch
              logits = model(input_ids, segment_ids)
              loss = loss_fct(logits, labels)
              loss.backward()
              optimizer.step()
              lr_scheduler.step()
              optimizer.clear_grad()
              if global_step % args.logging_steps == 0:
                  print(
                      "global step %d/%d, epoch: %d, batch: %d, rank_id: %s, loss: %f, lr: %.10f, speed: %.4f step/s"
                      % (global_step, num_training_steps, epoch, step,
                         paddle.distributed.get_rank(), loss, optimizer.get_lr(),
                         args.logging_steps / (time.time() - tic_train)))
                  tic_train = time.time()
              if global_step % args.save_steps == 0 or global_step == num_training_steps:
                  tic_eval = time.time()
                  acc = evaluate(model, loss_fct, metric, dev_data_loader)
                  print("eval done total : %s s" % (time.time() - tic_eval))
                  if acc > best_acc:
                      best_acc = acc
                      output_dir = args.output_dir
                      if not os.path.exists(output_dir):
                          os.makedirs(output_dir)
                      # Need better way to get inner model of DataParallel
                      model_to_save = model._layers if isinstance(
                          model, paddle.DataParallel) else model
                      model_to_save.save_pretrained(output_dir)
                      tokenizer.save_pretrained(output_dir)
              if global_step >= num_training_steps:
                  print("best_acc: ", best_acc)
                  return
      print("best_acc: ", best_acc)
  
  
  def print_arguments(args):
      """print arguments"""
      print('-----------  Configuration Arguments -----------')
      for arg, value in sorted(vars(args).items()):
          print('%s: %s' % (arg, value))
      print('------------------------------------------------')
  
  
  if __name__ == "__main__":
      args = parse_args()
      print_arguments(args)
      if args.do_train:
          do_train(args)
      if args.do_eval:
          do_eval(args)
  