# 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.

from functools import partial

import argparse

import os

import random

import numpy as np

import paddle

from paddlenlp.datasets import load_dataset

from paddlenlp.data import JiebaTokenizer, Pad, Stack, Tuple, Vocab

from data import create_dataloader, convert_example, read_custom_data

from model import TextCNNModel

# yapf: disable

parser = argparse.ArgumentParser(__doc__)

parser.add_argument("--epochs", type=int, default=10, help="Number of epoches for training.")

parser.add_argument('--device', choices=['cpu', 'gpu', 'xpu'], default="gpu", help="Select which device to train model, defaults to gpu.")

parser.add_argument("--lr", type=float, default=5e-5, help="Learning rate used to train.")

parser.add_argument("--save_dir", type=str, default='checkpoints/', help="Directory to save model checkpoint")

parser.add_argument("--data_path", type=str, default='./RobotChat', help="The path of datasets to be loaded")

parser.add_argument("--batch_size", type=int, default=64, help="Total examples' number of a batch for training.")

parser.add_argument("--vocab_path", type=str, default="./robot_chat_word_dict.txt", help="The directory to dataset.")

parser.add_argument("--init_from_ckpt", type=str, default=None, help="The path of checkpoint to be loaded.")

args = parser.parse_args()

# yapf: enable

def set_seed(seed=1000):

"""Sets random seed."""

random.seed(seed)

np.random.seed(seed)

paddle.seed(seed)

if __name__ == "__main__":

paddle.set_device(args.device)

set_seed()

# Load vocab.

if not os.path.exists(args.vocab_path):

raise RuntimeError('The vocab_path can not be found in the path %s' %

args.vocab_path)

vocab = Vocab.load_vocabulary(

args.vocab_path, unk_token='[UNK]', pad_token='[PAD]')

# Load datasets.

dataset_names = ['train.tsv', 'dev.tsv', 'test.tsv']

train_ds, dev_ds, test_ds = [load_dataset(read_custom_data, \

filename=os.path.join(args.data_path, dataset_name), lazy=False) for dataset_name in dataset_names]

tokenizer = JiebaTokenizer(vocab)

trans_fn = partial(convert_example, tokenizer=tokenizer)

batchify_fn = lambda samples, fn=Tuple(

Pad(axis=0, pad_val=vocab.token_to_idx.get('[PAD]', 0)),

Stack(dtype='int64') # label

): [data for data in fn(samples)]

train_loader = create_dataloader(

train_ds,

batch_size=args.batch_size,

mode='train',

batchify_fn=batchify_fn,

trans_fn=trans_fn)

dev_loader = create_dataloader(

dev_ds,

batch_size=args.batch_size,

mode='validation',

batchify_fn=batchify_fn,

trans_fn=trans_fn)

test_loader = create_dataloader(

test_ds,

batch_size=args.batch_size,

mode='test',

batchify_fn=batchify_fn,

trans_fn=trans_fn)

label_map = {0: 'negative', 1: 'neutral', 2: 'positive'}

vocab_size = len(vocab)

num_classes = len(label_map)

pad_token_id = vocab.to_indices('[PAD]')

model = TextCNNModel(

vocab_size,

num_classes,

padding_idx=pad_token_id,

ngram_filter_sizes=(1, 2, 3))

if args.init_from_ckpt and os.path.isfile(args.init_from_ckpt):

state_dict = paddle.load(args.init_from_ckpt)

model.set_dict(state_dict)

model = paddle.Model(model)

optimizer = paddle.optimizer.Adam(

parameters=model.parameters(), learning_rate=args.lr)

# Define loss and metric.

criterion = paddle.nn.CrossEntropyLoss()

metric = paddle.metric.Accuracy()

model.prepare(optimizer, criterion, metric)

# Start training and evaluating.

callback = paddle.callbacks.ProgBarLogger(log_freq=10, verbose=3)

model.fit(train_loader,

dev_loader,

epochs=args.epochs,

save_dir=args.save_dir,

callbacks=callback)

# Evaluate on test dataset

print('Start to evaluate on test dataset...')

model.evaluate(test_loader, log_freq=len(test_loader))