# 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
from functools import partial

import numpy as np
import paddle
import paddle.nn as nn
from data import METRIC_MAPPING, TASK_MAPPING, InputFeatures, load_dataset
from template import ManualTemplate
from tokenizer import MLMTokenizerWrapper
from utils import (
    LinearSchedulerWarmup,
    check_args,
    convert_example,
    create_dataloader,
    set_seed,
)
from verbalizer import ManualVerbalizer
from visualdl import LogWriter

from paddlenlp.transformers import AutoModelForMaskedLM, AutoTokenizer
from paddlenlp.utils.log import logger

# yapf: disable
parser = argparse.ArgumentParser('Implementation of RGL paper.')
parser.add_argument('--seed', type=int, default=1000, help='Random seed.')
parser.add_argument('--device', type=str, default='gpu', choices=['gpu', 'cpu'], help='Device for training, default to gpu.')
parser.add_argument('--dataset', type=str, default='SST-2', help='The built-in few-shot dataset.')
parser.add_argument('--data_path', type=str, default=None, help='The path to local dataset in .tsv files.')

parser.add_argument('--model_name_or_path', type=str, default='roberta-large', help='The built-in pretrained LM or the path to local model parameters.')
parser.add_argument('--template', type=str, default="{'text':'text_a'} It was {'mask'}.", help='The input template.')
parser.add_argument('--verbalizer', type=str, default="{'0':'terrible', '1':'great'}", help='The label mapping of output.')
parser.add_argument('--alpha', type=float, default=0, help='The weight of link prediction loss in RGL.')
parser.add_argument('--max_seq_length', type=int, default=512, help='The maximum length of input text.')
parser.add_argument('--max_grad_norm', type=float, default=1.0, help='The maximum norm of all parameters.')

parser.add_argument('--num_epoch', type=int, default=0, help='The number of epoch for training.')
parser.add_argument('--max_steps', type=int, default=1000, help='Maximum steps, which overwrites num_epoch.')
parser.add_argument('--batch_size', type=int, default=32, help='The number of samples used per step.')
parser.add_argument('--learning_rate', type=float, default=1e-5, help='The learning rate of optimizer.')
parser.add_argument('--weight_decay', type=float, default=0.0, help='Weight decay if we apply some.')
parser.add_argument('--warmup_steps', type=int, default=0, help='The warmup steps for leanring rate scheduler.')
parser.add_argument('--logging_step', type=int, default=100, help='Print logs every logging_step steps.')
parser.add_argument('--eval_step', type=int, default=100, help='Evaluate model every eval_step steps.')
parser.add_argument('--save_best', action='store_true', help='Save the best model according to evaluation results. Save the last checkpoint if False.')
parser.add_argument('--output_dir', type=str, default='./checkpoints/', help='The path to save checkpoints.')
parser.add_argument('--overwrite_output', action='store_true', help='Whether overwrite the output_dir.')
args = parser.parse_args()
# yapf: enable

check_args(args)
for arg in vars(args):
    logger.info(format(arg, "<20") + format(str(getattr(args, arg)), "<"))


@paddle.no_grad()
def evaluate(model, dataloader, metric, verbalizer, task_type, bound=(0, 5)):
    if task_type == "regression":
        logsoftmax = nn.LogSoftmax(axis=-1)
        lb, ub = bound
    model.eval()
    metric.reset()
    for batch in dataloader:
        logits = model(input_ids=batch["input_ids"], attention_mask=batch["attention_mask"])
        label_logits = verbalizer.process_logits(logits, batch["mask_ids"])
        if task_type == "regression":
            label_logits = logsoftmax(label_logits)
            label_logits = paddle.exp(label_logits[..., 1].unsqueeze(-1)) * (ub - lb) + lb
        correct = metric.compute(label_logits, batch["label"])
        metric.update(correct)
    score = metric.accumulate()
    score = score if isinstance(score, (list, tuple)) else [score]
    logger.info("{:>20}".format("Evaluation results:"))
    for name, value in zip(metric.name(), score):
        logger.info("{:>20} = {:.6f}".format(name, value))
    model.train()
    return score[0]


def contrastive_loss(sentence_embeddings, labels, task_type="classification"):
    """Compute the loss proposed in RGL method."""

    def _raw_equal(x, y):
        return int(x == y)

    def _max_equal(x, y):
        return int(np.argmax(x, axis=0) == np.argmax(y, axis=0))

    equal_int = _raw_equal if task_type == "classification" else _max_equal
    bce_metric = nn.CrossEntropyLoss()
    cos_metric = nn.CosineSimilarity(axis=0, eps=1e-6)
    batch_size = sentence_embeddings.shape[0]
    loss = 0
    for i in range(batch_size):
        for j in range(batch_size):
            score = cos_metric(sentence_embeddings[i], sentence_embeddings[j])
            score = score.unsqueeze(0)
            logits = paddle.concat([(1 - score) * 50, (1 + score) * 50], axis=-1)
            label = paddle.to_tensor(equal_int(labels[i], labels[j]))
            loss += bce_metric(logits.reshape([-1, logits.shape[-1]]), label.unsqueeze(0))
    loss = loss / (batch_size * (batch_size - 1))
    loss = loss / 100
    return loss


def main():
    paddle.set_device(args.device)
    set_seed(args.seed)

    task_type = TASK_MAPPING[args.dataset]
    model = AutoModelForMaskedLM.from_pretrained(args.model_name_or_path)
    tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
    tokenizer_wrapper = MLMTokenizerWrapper(args.max_seq_length, tokenizer)

    train_ds, dev_ds, test_ds, label_list = load_dataset(
        args.dataset, data_path=args.data_path, splits=["train", "dev", "test"]
    )

    template = ManualTemplate(tokenizer, args.template)
    logger.info("Set template: {}".format(template.template))
    verbalizer = ManualVerbalizer(tokenizer, labels=label_list, label_to_words=eval(args.verbalizer), prefix=" ")
    logger.info("Set verbalizer: {}".format(args.verbalizer))

    trans_fn = partial(convert_example, template=template, verbalizer=verbalizer, tokenizer_wrapper=tokenizer_wrapper)

    train_loader = create_dataloader(train_ds, "train", args.batch_size, InputFeatures.collate_fn, trans_fn)
    dev_loader = create_dataloader(dev_ds, "dev", args.batch_size, InputFeatures.collate_fn, trans_fn)
    test_loader = create_dataloader(test_ds, "test", args.batch_size, InputFeatures.collate_fn, trans_fn)
    if args.max_steps > 0:
        num_epoch = args.max_steps // len(train_loader) + int(args.max_steps % len(train_loader) > 0)
        max_steps = args.max_steps
    else:
        num_epoch = args.num_epoch
        max_steps = args.num_epoch * len(train_loader)

    lr_scheduler = LinearSchedulerWarmup(args.learning_rate, args.warmup_steps, max_steps)
    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,
        parameters=model.parameters(),
        weight_decay=args.weight_decay,
        grad_clip=paddle.nn.ClipGradByGlobalNorm(args.max_grad_norm),
        apply_decay_param_fun=lambda x: x in decay_params,
    )

    metric_fn = METRIC_MAPPING[args.dataset]
    if task_type == "regression":
        loss_fn = nn.KLDivLoss()
        lb, ub = 0, 5
        logsoftmax = nn.LogSoftmax(axis=-1)
    else:
        loss_fn = nn.CrossEntropyLoss()
    with LogWriter(logdir="./log/pet/train") as writer:
        best_metric = -float("inf")
        global_step = 1
        global_loss = 0
        for epoch in range(1, num_epoch + 1):
            for step, batch in enumerate(train_loader, start=1):
                writer.add_scalar("train/lr", lr_scheduler.get_lr(), global_step)

                logits = model(input_ids=batch["input_ids"], attention_mask=batch["attention_mask"])
                label_logits = verbalizer.process_logits(logits, batch["mask_ids"])
                if task_type == "regression":
                    label_logits = logsoftmax(label_logits)

                    labels = paddle.stack(
                        [
                            1 - (batch["label"].reshape([-1]) - lb) / (ub - lb),
                            (batch["label"].reshape([-1]) - lb) / (ub - lb),
                        ],
                        axis=-1,
                    )
                    loss = loss_fn(label_logits.reshape([-1, 2]), labels)
                else:
                    labels = paddle.to_tensor(batch["label"], dtype="int64")
                    loss = loss_fn(label_logits.reshape([-1, label_logits.shape[-1]]), labels.reshape([-1]))
                if args.alpha > 0:
                    con_loss = contrastive_loss(logits, labels, task_type=task_type)
                    loss += args.alpha * con_loss
                global_loss += loss.item()

                loss.backward()
                optimizer.step()
                lr_scheduler.step()
                optimizer.clear_grad()

                writer.add_scalar("train/loss", loss.item(), global_step)

                if global_step % args.logging_step == 0:
                    avg_loss = global_loss / args.logging_step
                    logger.info(
                        "Epoch: {:3d}/{:3d}, Global Step: {:4d}, Loss: {:e}".format(
                            epoch, num_epoch, global_step, avg_loss
                        )
                    )
                    global_loss = 0

                if global_step % args.eval_step == 0:
                    logger.info("{0:-^30}".format(" Validate "))
                    value = evaluate(model, dev_loader, metric_fn, verbalizer, task_type)
                    if args.save_best and value > best_metric:
                        best_metric = value
                        save_path = os.path.join(args.output_dir, "model_best")
                        if not os.path.exists(save_path):
                            os.makedirs(save_path)
                        model.save_pretrained(save_path)
                        tokenizer.save_pretrained(save_path)

                global_step += 1
                if global_step > max_steps:
                    break

        logger.info("{0:-^30}".format(" Test "))
        evaluate(model, test_loader, metric_fn, verbalizer, task_type)
        if not args.save_best:
            save_path = os.path.join(args.output_dir, "model_last")
            if not os.path.exists(save_path):
                os.makedirs(save_path)
            model.save_pretrained(save_path)
            tokenizer.save_pretrained(save_path)


if __name__ == "__main__":
    main()
