# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2018 The HuggingFace Inc. team.
#
# 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 contextlib
import distutils.util
import json
import os
import random
import time

import numpy as np
import paddle
from datasets import load_dataset
from paddle.io import DataLoader

from paddlenlp.data import DataCollatorWithPadding
from paddlenlp.metrics.squad import compute_prediction, squad_evaluate
from paddlenlp.transformers import (
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    LinearDecayWithWarmup,
)
from paddlenlp.utils.log import logger


def parse_args():
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument(
        "--model_name_or_path",
        default=None,
        type=str,
        required=True,
        help="Path to pre-trained model or shortcut name of model.",
    )
    parser.add_argument(
        "--output_dir",
        default="best_cmrc_model",
        type=str,
        help="The output directory where the model predictions and checkpoints will be written.",
    )
    parser.add_argument(
        "--save_best_model", default=True, type=distutils.util.strtobool, help="Whether to save best model."
    )
    parser.add_argument(
        "--overwrite_cache",
        default=False,
        type=distutils.util.strtobool,
        help="Whether to overwrite cache for dataset.",
    )
    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("--batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.")
    parser.add_argument(
        "--num_proc",
        default=None,
        type=int,
        help="Max number of processes when generating cache. Already cached shards are loaded sequentially.",
    )
    parser.add_argument("--eval_batch_size", default=12, type=int, help="Batch size per GPU/CPU for training.")
    parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
    parser.add_argument("--weight_decay", default=0.01, type=float, help="Weight decay if we apply some.")
    parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
    parser.add_argument("--num_train_epochs", default=3, type=int, help="Total number of training epochs to perform.")
    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(
        "--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="Proportion of training steps to perform linear learning rate warmup for.",
    )
    parser.add_argument("--logging_steps", type=int, default=100, help="Log every X updates steps.")
    parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
    parser.add_argument(
        "--device", choices=["cpu", "gpu"], default="gpu", help="Select which device to train model, defaults to gpu."
    )
    parser.add_argument(
        "--doc_stride",
        type=int,
        default=128,
        help="When splitting up a long document into chunks, how much stride to take between chunks.",
    )
    parser.add_argument(
        "--n_best_size",
        type=int,
        default=20,
        help="The total number of n-best predictions to generate in the nbest_predictions.json output file.",
    )
    parser.add_argument("--max_query_length", type=int, default=64, help="Max query length.")
    parser.add_argument("--max_answer_length", type=int, default=50, help="Max answer length.")
    parser.add_argument(
        "--do_lower_case",
        action="store_false",
        help="Whether to lower case the input text. Should be True for uncased models and False for cased models.",
    )
    parser.add_argument("--verbose", action="store_true", help="Whether to output verbose log.")
    parser.add_argument("--do_train", action="store_true", help="Whether to train.")
    parser.add_argument("--do_predict", action="store_true", help="Whether to predict.")
    parser.add_argument(
        "--gradient_accumulation_steps",
        type=int,
        default=2,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )
    args = parser.parse_args()
    return args


def set_seed(args):
    random.seed(args.seed)
    np.random.seed(args.seed)
    paddle.seed(args.seed)


@paddle.no_grad()
def evaluate(model, raw_dataset, dataset, data_loader, args, do_eval=True):
    model.eval()

    all_start_logits = []
    all_end_logits = []
    tic_eval = time.time()
    for batch in data_loader:
        start_logits, end_logits = model(**batch)
        for idx in range(start_logits.shape[0]):
            if len(all_start_logits) % 1000 == 0 and len(all_start_logits):
                logger.info("Processing example: %d" % len(all_start_logits))
                logger.info("time per 1000: %s" % (time.time() - tic_eval))
                tic_eval = time.time()

            all_start_logits.append(start_logits.numpy()[idx])
            all_end_logits.append(end_logits.numpy()[idx])

    all_predictions, _, _ = compute_prediction(
        raw_dataset, dataset, (all_start_logits, all_end_logits), False, args.n_best_size, args.max_answer_length
    )

    if not os.path.exists(args.output_dir):
        os.makedirs(args.output_dir)
    if do_eval:
        filename = os.path.join(args.output_dir, "prediction_validation.json")
    else:
        filename = os.path.join(args.output_dir, "cmrc2018_predict.json")
    with open(filename, "w", encoding="utf-8") as writer:
        writer.write(json.dumps(all_predictions, ensure_ascii=False, indent=4) + "\n")
    if do_eval:
        res = squad_evaluate(
            examples=[raw_data for raw_data in raw_dataset], preds=all_predictions, is_whitespace_splited=False
        )
        model.train()
        return res["exact"], res["f1"]

    model.train()


class CrossEntropyLossForSQuAD(paddle.nn.Layer):
    def __init__(self):
        super(CrossEntropyLossForSQuAD, self).__init__()

    def forward(self, y, label):
        start_logits, end_logits = y
        start_position, end_position = label
        start_position = paddle.unsqueeze(start_position, axis=-1)
        end_position = paddle.unsqueeze(end_position, axis=-1)
        start_loss = paddle.nn.functional.cross_entropy(input=start_logits, label=start_position)
        end_loss = paddle.nn.functional.cross_entropy(input=end_logits, label=end_position)
        loss = (start_loss + end_loss) / 2
        return loss


@contextlib.contextmanager
def main_process_first(desc="work"):
    if paddle.distributed.get_world_size() > 1:
        rank = paddle.distributed.get_rank()
        is_main_process = rank == 0
        main_process_desc = "main local process"

        try:
            if not is_main_process:
                # tell all replicas to wait
                logger.debug(f"{rank}: waiting for the {main_process_desc} to perform {desc}")
                paddle.distributed.barrier()
            yield
        finally:
            if is_main_process:
                # the wait is over
                logger.debug(f"{rank}: {main_process_desc} completed {desc}, releasing all replicas")
                paddle.distributed.barrier()
    else:
        yield


def run(args):
    if args.do_train:
        assert (
            args.batch_size % args.gradient_accumulation_steps == 0
        ), "Please make sure argument `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()
    rank = paddle.distributed.get_rank()

    tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)

    set_seed(args)

    train_examples, dev_examples, test_examples = load_dataset(
        "clue", "cmrc2018", split=["train", "validation", "test"], trust_remote_code=True
    )

    column_names = train_examples.column_names
    if rank == 0:
        if os.path.exists(args.model_name_or_path):
            logger.info("init checkpoint from %s" % args.model_name_or_path)

    model = AutoModelForQuestionAnswering.from_pretrained(args.model_name_or_path)

    if paddle.distributed.get_world_size() > 1:
        model = paddle.DataParallel(model)

    def prepare_train_features(examples):
        # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
        # in one example possible giving several features when a context is long, each of those features having a
        # context that overlaps a bit the context of the previous feature.
        # NOTE: Almost the same functionality as HuggingFace's prepare_train_features function. The main difference is
        # that HugggingFace uses ArrowTable as basic data structure, while we use list of dictionary instead.
        contexts = examples["context"]
        questions = examples["question"]

        tokenized_examples = tokenizer(questions, contexts, stride=args.doc_stride, max_seq_len=args.max_seq_length)

        # Since one example might give us several features if it has a long context, we need a map from a feature to
        # its corresponding example. This key gives us just that.
        sample_mapping = tokenized_examples.pop("overflow_to_sample")
        # The offset mappings will give us a map from token to character position in the original context. This will
        # help us compute the start_positions and end_positions.
        offset_mapping = tokenized_examples.pop("offset_mapping")

        # Let's label those examples!
        tokenized_examples["start_positions"] = []
        tokenized_examples["end_positions"] = []

        for i, offsets in enumerate(offset_mapping):
            # We will label impossible answers with the index of the CLS token.
            input_ids = tokenized_examples["input_ids"][i]
            cls_index = input_ids.index(tokenizer.cls_token_id)

            # Grab the sequence corresponding to that example (to know what is the context and what is the question).
            sequence_ids = tokenized_examples["token_type_ids"][i]

            # One example can give several spans, this is the index of the example containing this span of text.
            sample_index = sample_mapping[i]
            answers = examples["answers"][sample_index]
            # If no answers are given, set the cls_index as answer.
            if len(answers["answer_start"]) == 0:
                tokenized_examples["start_positions"].append(cls_index)
                tokenized_examples["end_positions"].append(cls_index)
            else:
                # Start/end character index of the answer in the text.
                start_char = answers["answer_start"][0]
                end_char = start_char + len(answers["text"][0])

                # Start token index of the current span in the text.
                token_start_index = 0
                while sequence_ids[token_start_index] != 1:
                    token_start_index += 1

                # End token index of the current span in the text.
                token_end_index = len(input_ids) - 1
                while sequence_ids[token_end_index] != 1:
                    token_end_index -= 1
                token_end_index -= 1

                # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).
                if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char):
                    tokenized_examples["start_positions"].append(cls_index)
                    tokenized_examples["end_positions"].append(cls_index)
                else:
                    # Otherwise move the token_start_index and token_end_index to the two ends of the answer.
                    # Note: we could go after the last offset if the answer is the last word (edge case).
                    while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:
                        token_start_index += 1
                    tokenized_examples["start_positions"].append(token_start_index - 1)
                    while offsets[token_end_index][1] >= end_char:
                        token_end_index -= 1
                    tokenized_examples["end_positions"].append(token_end_index + 1)

        return tokenized_examples

    def prepare_validation_features(examples):
        # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
        # in one example possible giving several features when a context is long, each of those features having a
        # context that overlaps a bit the context of the previous feature.
        # NOTE: Almost the same functionality as HuggingFace's prepare_train_features function. The main difference is
        # that HuggingFace uses ArrowTable as basic data structure, while we use list of dictionary instead.
        contexts = examples["context"]
        questions = examples["question"]

        tokenized_examples = tokenizer(
            questions, contexts, stride=args.doc_stride, max_seq_len=args.max_seq_length, return_attention_mask=True
        )

        # Since one example might give us several features if it has a long context, we need a map from a feature to
        # its corresponding example. This key gives us just that.
        sample_mapping = tokenized_examples.pop("overflow_to_sample")

        # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
        # corresponding example_id and we will store the offset mappings.
        tokenized_examples["example_id"] = []

        for i in range(len(tokenized_examples["input_ids"])):
            # Grab the sequence corresponding to that example (to know what is the context and what is the question).
            sequence_ids = tokenized_examples["token_type_ids"][i]
            context_index = 1

            # One example can give several spans, this is the index of the example containing this span of text.
            sample_index = sample_mapping[i]
            tokenized_examples["example_id"].append(examples["id"][sample_index])

            # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
            # position is part of the context or not.
            tokenized_examples["offset_mapping"][i] = [
                (o if sequence_ids[k] == context_index and k != len(sequence_ids) - 1 else None)
                for k, o in enumerate(tokenized_examples["offset_mapping"][i])
            ]

        return tokenized_examples

    if args.do_train:
        args.batch_size = int(args.batch_size / args.gradient_accumulation_steps)

        with main_process_first(desc="train dataset map pre-processing"):
            train_ds = train_examples.map(
                prepare_train_features,
                batched=True,
                remove_columns=column_names,
                load_from_cache_file=not args.overwrite_cache,
                num_proc=args.num_proc,
                desc="Running tokenizer on train dataset",
            )
        train_batch_sampler = paddle.io.DistributedBatchSampler(train_ds, batch_size=args.batch_size, shuffle=True)

        batchify_fn = DataCollatorWithPadding(tokenizer)
        train_data_loader = DataLoader(
            dataset=train_ds, batch_sampler=train_batch_sampler, collate_fn=batchify_fn, return_list=True
        )

        with main_process_first(desc="evaluate dataset map pre-processing"):
            dev_ds = dev_examples.map(
                prepare_validation_features,
                batched=True,
                remove_columns=column_names,
                num_proc=args.num_proc,
                load_from_cache_file=args.overwrite_cache,
                desc="Running tokenizer on validation dataset",
            )
        dev_ds_for_model = dev_ds.remove_columns(["example_id", "offset_mapping", "attention_mask"])
        dev_batch_sampler = paddle.io.BatchSampler(dev_ds, batch_size=args.eval_batch_size, shuffle=False)

        dev_data_loader = DataLoader(
            dataset=dev_ds_for_model, batch_sampler=dev_batch_sampler, collate_fn=batchify_fn, return_list=True
        )

        num_training_steps = (
            int(args.max_steps / args.gradient_accumulation_steps)
            if args.max_steps >= 0
            else int(len(train_data_loader) * args.num_train_epochs / args.gradient_accumulation_steps)
        )

        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,
            epsilon=args.adam_epsilon,
            parameters=model.parameters(),
            weight_decay=args.weight_decay,
            apply_decay_param_fun=lambda x: x in decay_params,
        )
        criterion = CrossEntropyLossForSQuAD()
        best_res = (0.0, 0.0)
        global_step = 0
        tic_train = time.time()
        for epoch in range(args.num_train_epochs):
            for step, batch in enumerate(train_data_loader):
                start_positions = batch.pop("start_positions")
                end_positions = batch.pop("end_positions")
                logits = model(**batch)
                loss = criterion(logits, (start_positions, end_positions))
                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, loss: %f, speed: %.2f step/s"
                            % (
                                global_step,
                                num_training_steps,
                                epoch,
                                step + 1,
                                loss,
                                args.logging_steps / (time.time() - tic_train),
                            )
                        )
                        tic_train = time.time()
                    if global_step >= num_training_steps:
                        break
            if global_step > num_training_steps:
                break
            em, f1 = evaluate(model, dev_examples, dev_ds, dev_data_loader, args)
            if paddle.distributed.get_rank() == 0 and em > best_res[0]:
                best_res = (em, f1)
                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:
                break
        logger.info("best_result: %.2f/%.2f" % (best_res[0], best_res[1]))

    if args.do_predict and rank == 0:
        test_ds = test_examples.map(
            prepare_validation_features, batched=True, remove_columns=column_names, num_proc=args.num_proc
        )
        test_ds_for_model = test_ds.remove_columns(["example_id", "offset_mapping", "attention_mask"])
        test_batch_sampler = paddle.io.BatchSampler(test_ds_for_model, batch_size=args.eval_batch_size, shuffle=False)

        batchify_fn = DataCollatorWithPadding(tokenizer)
        test_data_loader = DataLoader(
            dataset=test_ds_for_model, batch_sampler=test_batch_sampler, collate_fn=batchify_fn, return_list=True
        )

        evaluate(model, test_examples, test_ds, test_data_loader, args, do_eval=False)


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)
    run(args)
