text: # Copyright (c) 2022 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 os
  import sys
  import random
  import time
  import math
  import json
  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 DataCollatorWithPadding
  from paddlenlp.transformers import LinearDecayWithWarmup
  from paddlenlp.transformers import AutoModelForSequenceClassification, AutoTokenizer
  from paddlenlp.utils.log import logger
  
  METRIC_CLASSES = {
      "afqmc": Accuracy,
      "tnews": Accuracy,
      "iflytek": Accuracy,
      "ocnli": Accuracy,
      "cmnli": Accuracy,
      "cluewsc2020": Accuracy,
      "csl": Accuracy,
  }
  
  
  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_name_or_path",
          default=None,
          type=str,
          required=True,
          help="Path to pre-trained model or shortcut name.")
      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(
          '--gradient_accumulation_steps',
          type=int,
          default=1,
          help="Number of updates steps to accumualte before performing a backward/update pass."
      )
      parser.add_argument(
          "--do_train", action='store_true', help="Whether do train.")
      parser.add_argument(
          "--do_eval", action='store_true', help="Whether do train.")
      parser.add_argument(
          "--do_predict", action='store_true', help="Whether do predict.")
      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(
          "--save_best_model",
          default=True,
          type=distutils.util.strtobool,
          help="Whether to save best model.", )
      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("--dropout", default=0.1, type=float, help="dropout.")
      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:
          labels = batch.pop("labels")
          logits = model(**batch)
          loss = loss_fct(logits, labels)
          correct = metric.compute(logits, labels)
          metric.update(correct)
      res = metric.accumulate()
      logger.info("eval loss: %f, acc: %s, " % (loss.numpy(), res))
      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']}
      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:
          example["labels"] = label
      return example
  
  
  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]
  
      dev_ds = load_dataset('clue', args.task_name, splits='dev')
  
      tokenizer = AutoTokenizer.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 = DataCollatorWithPadding(tokenizer)
  
      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 = AutoModelForSequenceClassification.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:
          labels = batch.pop("labels")
          logits = model(**batch)
          correct = metric.compute(logits, labels)
          metric.update(correct)
      res = metric.accumulate()
      logger.info("acc: %s\n, " % (res))
  
  
  def do_train(args):
      assert args.batch_size % args.gradient_accumulation_steps == 0, \
          "Please make sure argmument `batch_size` must be divisible by `gradient_accumulation_steps`."
      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.batch_size = int(args.batch_size / args.gradient_accumulation_steps)
      train_ds, dev_ds = load_dataset(
          'clue', args.task_name, splits=('train', 'dev'))
  
      tokenizer = AutoTokenizer.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 = DataCollatorWithPadding(tokenizer)
  
      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 = AutoModelForSequenceClassification.from_pretrained(
          args.model_name_or_path, num_classes=num_classes)
  
      if args.dropout != 0.1:
          update_model_dropout(model, args.dropout)
  
      if paddle.distributed.get_world_size() > 1:
          model = paddle.DataParallel(model)
  
      if args.max_steps > 0:
          num_training_steps = args.max_steps / args.gradient_accumulation_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 / args.gradient_accumulation_steps
          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):
              labels = batch.pop("labels")
              logits = model(**batch)
              loss = loss_fct(logits, labels)
              if args.gradient_accumulation_steps > 1:
                  loss = loss / args.gradient_accumulation_steps
              loss.backward()
              if (step + 1) % args.gradient_accumulation_steps == 0:
                  global_step += 1
                  optimizer.step()
                  lr_scheduler.step()
                  optimizer.clear_grad()
                  if global_step % args.logging_steps == 0:
                      logger.info(
                          "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)
                      logger.info("eval done total : %s s" %
                                  (time.time() - tic_eval))
                      if acc > best_acc:
                          best_acc = acc
                          if args.save_best_model:
                              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:
                      logger.info("best_result: %.2f" % (best_acc * 100))
                      return
      logger.info("best_result: %.2f" % (best_acc * 100))
  
  
  def do_predict(args):
      paddle.set_device(args.device)
      args.task_name = args.task_name.lower()
  
      train_ds, test_ds = load_dataset(
          'clue', args.task_name, splits=('train', 'test'))
      if args.task_name == "cluewsc2020" or args.task_name == "tnews":
          test_ds_10 = load_dataset('clue', args.task_name, splits="test1.0")
      tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
  
      trans_func = partial(
          convert_example,
          tokenizer=tokenizer,
          label_list=train_ds.label_list,
          max_seq_length=args.max_seq_length,
          is_test=True)
  
      batchify_fn = DataCollatorWithPadding(tokenizer)
  
      test_ds = test_ds.map(trans_func, lazy=True)
      test_batch_sampler = paddle.io.BatchSampler(
          test_ds, batch_size=args.batch_size, shuffle=False)
      test_data_loader = DataLoader(
          dataset=test_ds,
          batch_sampler=test_batch_sampler,
          collate_fn=batchify_fn,
          num_workers=0,
          return_list=True)
      if args.task_name == "cluewsc2020" or args.task_name == "tnews":
          test_ds_10 = test_ds_10.map(trans_func, lazy=True)
          test_batch_sampler_10 = paddle.io.BatchSampler(
              test_ds_10, batch_size=args.batch_size, shuffle=False)
          test_data_loader_10 = DataLoader(
              dataset=test_ds_10,
              batch_sampler=test_batch_sampler_10,
              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 = AutoModelForSequenceClassification.from_pretrained(
          args.model_name_or_path, num_classes=num_classes)
  
      if not os.path.exists(args.output_dir):
          os.makedirs(args.output_dir)
  
      prediction_filename = args.task_name
  
      if args.task_name == 'ocnli':
          prediction_filename = 'ocnli_50k'
      elif args.task_name == "cluewsc2020":
          prediction_filename = "cluewsc" + "11"
      elif args.task_name == "tnews":
          prediction_filename = args.task_name + "11"
  
      # For version 1.1
      f = open(
          os.path.join(args.output_dir, prediction_filename + "_predict.json"),
          'w')
      preds = []
      for step, batch in enumerate(test_data_loader):
          with paddle.no_grad():
              logits = model(**batch)
          pred = paddle.argmax(logits, axis=1).numpy().tolist()
          preds += pred
      for idx, pred in enumerate(preds):
          j = json.dumps({"id": idx, "label": train_ds.label_list[pred]})
          f.write(j + "\n")
  
      # For version 1.0
      if args.task_name == "cluewsc2020" or args.task_name == "tnews":
          prediction_filename = args.task_name + "10"
          if args.task_name == "cluewsc2020":
              prediction_filename = "cluewsc10"
          f = open(
              os.path.join(args.output_dir,
                           prediction_filename + "_predict.json"), 'w')
  
          preds = []
          for step, batch in enumerate(test_data_loader_10):
              with paddle.no_grad():
                  logits = model(**batch)
              pred = paddle.argmax(logits, axis=1).numpy().tolist()
              preds += pred
          for idx, pred in enumerate(preds):
              j = json.dumps({"id": idx, "label": train_ds.label_list[pred]})
              f.write(j + "\n")
  
  
  def print_arguments(args):
      """print arguments"""
      print('-----------  Configuration Arguments -----------')
      for arg, value in sorted(vars(args).items()):
          print('%s: %s' % (arg, value))
      print('------------------------------------------------')
  
  
  def update_model_dropout(model, p=0.0):
      model.base_model.embeddings.dropout.p = p
      for i in range(len(model.base_model.encoder.layers)):
          model.base_model.encoder.layers[i].dropout.p = p
          model.base_model.encoder.layers[i].dropout1.p = p
          model.base_model.encoder.layers[i].dropout2.p = p
  
  
  if __name__ == "__main__":
      args = parse_args()
      print_arguments(args)
      if args.do_train:
          do_train(args)
      if args.do_eval:
          do_eval(args)
      if args.do_predict:
          do_predict(args)
  