# 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 os
import random
import time
from functools import partial

import numpy as np
import paddle
import paddle.nn as nn
from data import (
    build_index,
    convert_example,
    create_dataloader,
    gen_id2corpus,
    gen_text_file,
    label2ids,
    read_text_pair,
)
from metric import MetricReport
from model import SemanticIndexBatchNeg

from paddlenlp.data import Pad, Tuple
from paddlenlp.datasets import MapDataset, load_dataset
from paddlenlp.transformers import AutoModel, AutoTokenizer, LinearDecayWithWarmup

# fmt: off
parser = argparse.ArgumentParser()
parser.add_argument("--save_dir", default='./checkpoint', type=str, help="The output directory where the model checkpoints will be written.")
parser.add_argument("--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.")
parser.add_argument("--batch_size", default=32, type=int, help="Batch size per GPU/CPU for training.")
parser.add_argument("--output_emb_size", default=256, type=int, help="output_embedding_size")
parser.add_argument("--learning_rate", default=5E-5, type=float, help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
parser.add_argument("--epochs", default=10, type=int, help="Total number of training epochs to perform.")
parser.add_argument("--warmup_proportion", default=0.0, type=float, help="Linear warmup proportion over the training process.")
parser.add_argument("--init_from_ckpt", type=str, default=None, help="The path of checkpoint to be loaded.")
parser.add_argument("--seed", type=int, default=1000, help="random seed for initialization")
parser.add_argument('--device', choices=['cpu', 'gpu'], default="cpu", help="Select which device to train model, defaults to gpu.")
parser.add_argument('--save_steps', type=int, default=10000, help="Inteval steps to save checkpoint")
parser.add_argument('--log_steps', type=int, default=10, help="Inteval steps to print log")
parser.add_argument("--train_set_file", type=str, default='./data/train.txt', help="The full path of train_set_file.")
parser.add_argument("--margin", default=0.2, type=float, help="Margin between pos_sample and neg_samples")
parser.add_argument("--scale", default=30, type=int, help="Scale for pair-wise margin_rank_loss")
parser.add_argument("--corpus_file", type=str, default='./data/label.txt', help="The full path of input file")
parser.add_argument("--similar_text_pair_file", type=str, default='./data/dev.txt', help="The full path of similar text pair file")
parser.add_argument("--recall_result_dir", type=str, default='./recall_result_dir', help="The full path of recall result file to save")
parser.add_argument("--recall_result_file", type=str, default='recall_result_init.txt', help="The file name of recall result")
parser.add_argument("--recall_num", default=50, type=int, help="Recall number for each query from Ann index.")
parser.add_argument("--hnsw_m", default=100, type=int, help="Recall number for each query from Ann index.")
parser.add_argument("--hnsw_ef", default=100, type=int, help="Recall number for each query from Ann index.")
parser.add_argument("--hnsw_max_elements", default=1000000, type=int, help="Recall number for each query from Ann index.")
parser.add_argument("--evaluate_result", type=str, default='evaluate_result.txt', help="evaluate_result")
parser.add_argument('--evaluate', default=True, type=eval, choices=[True, False], help='whether evaluate while training')
parser.add_argument("--model_name_or_path", default='rocketqa-zh-dureader-query-encoder', type=str, help='The pretrained model used for training')
parser.add_argument("--threshold", default=0.5, type=float, help="The threshold for selection the labels")
args = parser.parse_args()
# fmt: on


def set_seed(seed):
    """sets random seed"""
    random.seed(seed)
    np.random.seed(seed)
    paddle.seed(seed)


@paddle.no_grad()
def evaluate(model, corpus_data_loader, query_data_loader, recall_result_file, text_list, id2corpus, label2id):
    metric = MetricReport()
    # Load pretrained semantic model
    inner_model = model._layers
    final_index = build_index(
        corpus_data_loader,
        inner_model,
        output_emb_size=args.output_emb_size,
        hnsw_max_elements=args.hnsw_max_elements,
        hnsw_ef=args.hnsw_ef,
        hnsw_m=args.hnsw_m,
    )
    query_embedding = inner_model.get_semantic_embedding(query_data_loader)
    with open(recall_result_file, "w", encoding="utf-8") as f:
        for batch_index, batch_query_embedding in enumerate(query_embedding):
            recalled_idx, cosine_sims = final_index.knn_query(batch_query_embedding.numpy(), args.recall_num)
            batch_size = len(cosine_sims)
            for row_index in range(batch_size):
                text_index = args.batch_size * batch_index + row_index
                for idx, doc_idx in enumerate(recalled_idx[row_index]):
                    f.write(
                        "{}\t{}\t{}\n".format(
                            text_list[text_index]["text"], id2corpus[doc_idx], 1.0 - cosine_sims[row_index][idx]
                        )
                    )
    text2similar = {}
    with open(args.similar_text_pair_file, "r", encoding="utf-8") as f:
        for line in f:
            text_arr = line.rstrip().rsplit("\t")
            text, similar_text = text_arr[0], text_arr[1]
            text2similar[text] = np.zeros(len(label2id))
            # One hot Encoding
            for label in similar_text.strip().split(","):
                text2similar[text][label2id[label]] = 1
    # Convert predicted labels into one hot encoding
    pred_labels = {}
    with open(recall_result_file, "r", encoding="utf-8") as f:
        for index, line in enumerate(f):
            text_arr = line.rstrip().split("\t")
            text, labels, cosine_sim = text_arr
            # One hot Encoding
            if text not in pred_labels:
                pred_labels[text] = np.zeros(len(label2id))
            if float(cosine_sim) > args.threshold:
                for label in labels.split(","):
                    pred_labels[text][label2id[label]] = float(cosine_sim)

        for text, probs in pred_labels.items():
            metric.update(probs, text2similar[text])
        micro_f1_score, macro_f1_score = metric.accumulate()
    return macro_f1_score


def do_train():
    paddle.set_device(args.device)
    rank = paddle.distributed.get_rank()
    if paddle.distributed.get_world_size() > 1:
        paddle.distributed.init_parallel_env()
    set_seed(args.seed)
    train_ds = load_dataset(read_text_pair, data_path=args.train_set_file, lazy=False)
    pretrained_model = AutoModel.from_pretrained(args.model_name_or_path)
    tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
    trans_func = partial(convert_example, tokenizer=tokenizer, max_seq_length=args.max_seq_length)
    batchify_fn = lambda samples, fn=Tuple(
        Pad(axis=0, pad_val=tokenizer.pad_token_id, dtype="int64"),  # query_input
        Pad(axis=0, pad_val=tokenizer.pad_token_type_id, dtype="int64"),  # query_segment
        Pad(axis=0, pad_val=tokenizer.pad_token_id, dtype="int64"),  # title_input
        Pad(axis=0, pad_val=tokenizer.pad_token_type_id, dtype="int64"),  # title_segment
    ): [data for data in fn(samples)]
    train_data_loader = create_dataloader(
        train_ds, mode="train", batch_size=args.batch_size, batchify_fn=batchify_fn, trans_fn=trans_func
    )
    model = SemanticIndexBatchNeg(
        pretrained_model, margin=args.margin, scale=args.scale, output_emb_size=args.output_emb_size
    )
    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)
        print("warmup from:{}".format(args.init_from_ckpt))
    model = paddle.DataParallel(model)
    batchify_fn_dev = lambda samples, fn=Tuple(
        Pad(axis=0, pad_val=tokenizer.pad_token_id, dtype="int64"),  # text_input
        Pad(axis=0, pad_val=tokenizer.pad_token_type_id, dtype="int64"),  # text_segment
    ): [data for data in fn(samples)]
    if args.evaluate:
        eval_func = partial(convert_example, tokenizer=tokenizer, max_seq_length=args.max_seq_length)
        id2corpus = gen_id2corpus(args.corpus_file)
        label2id = label2ids(args.corpus_file)
        # conver_example function's input must be dict
        corpus_list = [{idx: text} for idx, text in id2corpus.items()]
        corpus_ds = MapDataset(corpus_list)
        corpus_data_loader = create_dataloader(
            corpus_ds, mode="predict", batch_size=args.batch_size, batchify_fn=batchify_fn_dev, trans_fn=eval_func
        )
        query_func = partial(convert_example, tokenizer=tokenizer, max_seq_length=args.max_seq_length)
        text_list, _ = gen_text_file(args.similar_text_pair_file)
        query_ds = MapDataset(text_list)
        query_data_loader = create_dataloader(
            query_ds, mode="predict", batch_size=args.batch_size, batchify_fn=batchify_fn_dev, trans_fn=query_func
        )
        if not os.path.exists(args.recall_result_dir):
            os.mkdir(args.recall_result_dir)
        recall_result_file = os.path.join(args.recall_result_dir, args.recall_result_file)
    num_training_steps = len(train_data_loader) * args.epochs
    lr_scheduler = LinearDecayWithWarmup(args.learning_rate, num_training_steps, args.warmup_proportion)
    # 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,
        parameters=model.parameters(),
        weight_decay=args.weight_decay,
        apply_decay_param_fun=lambda x: x in decay_params,
        grad_clip=nn.ClipGradByNorm(clip_norm=1.0),
    )
    global_step = 0
    best_score = 0.0
    tic_train = time.time()
    for epoch in range(1, args.epochs + 1):
        for step, batch in enumerate(train_data_loader, start=1):
            query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids = batch
            loss = model(
                query_input_ids=query_input_ids,
                title_input_ids=title_input_ids,
                query_token_type_ids=query_token_type_ids,
                title_token_type_ids=title_token_type_ids,
            )
            global_step += 1
            if global_step % args.log_steps == 0 and rank == 0:
                print(
                    "global step %d, epoch: %d, batch: %d, loss: %.5f, speed: %.2f step/s"
                    % (global_step, epoch, step, loss, 10 / (time.time() - tic_train))
                )
                tic_train = time.time()
            loss.backward()
            optimizer.step()
            lr_scheduler.step()
            optimizer.clear_grad()
            if not args.evaluate and rank == 0:
                if global_step % args.save_steps == 0 and rank == 0:
                    save_dir = os.path.join(args.save_dir, "model_%d" % global_step)
                    if not os.path.exists(save_dir):
                        os.makedirs(save_dir)
                    save_param_path = os.path.join(save_dir, "model_state.pdparams")
                    paddle.save(model.state_dict(), save_param_path)
                    tokenizer.save_pretrained(save_dir)
        if args.evaluate and rank == 0:
            print("evaluating")
            macro_f1_score = evaluate(
                model, corpus_data_loader, query_data_loader, recall_result_file, text_list, id2corpus, label2id
            )
            if macro_f1_score > best_score:
                best_score = macro_f1_score
                save_dir = os.path.join(args.save_dir, "model_best")
                if not os.path.exists(save_dir):
                    os.makedirs(save_dir)
                save_param_path = os.path.join(save_dir, "model_state.pdparams")
                paddle.save(model.state_dict(), save_param_path)
                tokenizer.save_pretrained(save_dir)
                with open(os.path.join(save_dir, "train_result.txt"), "a", encoding="utf-8") as fp:
                    fp.write("epoch=%d, global_step: %d, Macro f1: %s\n" % (epoch, global_step, macro_f1_score))


if __name__ == "__main__":
    do_train()
