# Copyright (c) 2020 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 json
import math
import os
import random
import time
from functools import partial

import numpy as np
import paddle
from args import parse_args
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 (
    BertForQuestionAnswering,
    BertTokenizer,
    ErnieForQuestionAnswering,
    ErnieTokenizer,
    FunnelForQuestionAnswering,
    FunnelTokenizer,
    LinearDecayWithWarmup,
)

MODEL_CLASSES = {
    "bert": (BertForQuestionAnswering, BertTokenizer),
    "ernie": (ErnieForQuestionAnswering, ErnieTokenizer),
    "funnel": (FunnelForQuestionAnswering, FunnelTokenizer),
}


def prepare_train_features(examples, tokenizer, args):
    # Some of the questions have lots of whitespace on the left, which is not useful and will make the
    # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
    # left whitespace
    contexts = examples["context"]
    questions = examples["question"]

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


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


@paddle.no_grad()
def evaluate(model, data_loader, raw_dataset, features, args):
    model.eval()

    all_start_logits = []
    all_end_logits = []
    tic_eval = time.time()

    for batch in data_loader:
        start_logits_tensor, end_logits_tensor = model(
            batch["input_ids"], token_type_ids=batch["token_type_ids"], attention_mask=batch["attention_mask"]
        )

        for idx in range(start_logits_tensor.shape[0]):
            if len(all_start_logits) % 1000 == 0 and len(all_start_logits):
                print("Processing example: %d" % len(all_start_logits))
                print("time per 1000:", time.time() - tic_eval)
                tic_eval = time.time()

            all_start_logits.append(start_logits_tensor.numpy()[idx])
            all_end_logits.append(end_logits_tensor.numpy()[idx])

    all_predictions, all_nbest_json, scores_diff_json = compute_prediction(
        raw_dataset,
        features,
        (all_start_logits, all_end_logits),
        args.version_2_with_negative,
        args.n_best_size,
        args.max_answer_length,
        args.null_score_diff_threshold,
    )

    # Can also write all_nbest_json and scores_diff_json files if needed
    with open("prediction.json", "w", encoding="utf-8") as writer:
        writer.write(json.dumps(all_predictions, ensure_ascii=False, indent=4) + "\n")

    squad_evaluate(examples=[raw_data for raw_data in raw_dataset], preds=all_predictions, na_probs=scores_diff_json)

    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


def run(args):
    paddle.set_device(args.device)
    if paddle.distributed.get_world_size() > 1:
        paddle.distributed.init_parallel_env()
    rank = paddle.distributed.get_rank()
    args.model_type = args.model_type.lower()
    model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
    tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)

    if args.version_2_with_negative:
        train_examples = load_dataset("squad_v2", split="train", trust_remote_code=True)
        dev_examples = load_dataset("squad_v2", split="validation", trust_remote_code=True)
    else:
        train_examples = load_dataset("squad", split="train", trust_remote_code=True)
        dev_examples = load_dataset("squad", split="validation", trust_remote_code=True)
    set_seed(args)
    if rank == 0:
        if os.path.exists(args.model_name_or_path):
            print("init checkpoint from %s" % args.model_name_or_path)

    model = model_class.from_pretrained(args.model_name_or_path)
    column_names = train_examples.column_names
    if paddle.distributed.get_world_size() > 1:
        model = paddle.DataParallel(model)

    if args.do_train:
        train_ds = train_examples.map(
            partial(prepare_train_features, tokenizer=tokenizer, args=args),
            batched=True,
            remove_columns=column_names,
            num_proc=1,
        )
        train_batch_sampler = paddle.io.DistributedBatchSampler(train_ds, batch_size=args.batch_size, shuffle=True)
        train_batchify_fn = DataCollatorWithPadding(tokenizer)

        train_data_loader = DataLoader(
            dataset=train_ds, batch_sampler=train_batch_sampler, collate_fn=train_batchify_fn, return_list=True
        )

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

        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,
            epsilon=args.adam_epsilon,
            parameters=model.parameters(),
            weight_decay=args.weight_decay,
            apply_decay_param_fun=lambda x: x in decay_params,
        )
        criterion = CrossEntropyLossForSQuAD()

        if args.use_amp:
            scaler = paddle.amp.GradScaler(init_loss_scaling=args.scale_loss)

        global_step = 0
        tic_train = time.time()

        for epoch in range(num_train_epochs):
            for step, batch in enumerate(train_data_loader):
                global_step += 1
                if args.use_amp:
                    with paddle.amp.auto_cast(args.use_amp, custom_white_list=["layer_norm", "softmax", "gelu"]):
                        logits = model(
                            input_ids=batch["input_ids"],
                            token_type_ids=batch["token_type_ids"],
                            attention_mask=batch["attention_mask"],
                        )
                        loss = criterion(logits, (batch["start_positions"], batch["end_positions"]))
                    scaler.scale(loss).backward()
                    scaler.minimize(optimizer, loss)
                else:
                    logits = model(
                        input_ids=batch["input_ids"],
                        token_type_ids=batch["token_type_ids"],
                        attention_mask=batch["attention_mask"],
                    )
                    loss = criterion(logits, (batch["start_positions"], batch["end_positions"]))
                    loss.backward()
                    optimizer.step()
                lr_scheduler.step()
                optimizer.clear_grad()

                if global_step % args.logging_steps == 0:
                    print(
                        "global step %d, epoch: %d, batch: %d, loss: %f, speed: %.2f step/s"
                        % (global_step, epoch + 1, step + 1, loss, args.logging_steps / (time.time() - tic_train))
                    )
                    tic_train = time.time()

                if global_step % args.save_steps == 0 or global_step == num_training_steps:
                    if rank == 0:
                        output_dir = os.path.join(args.output_dir, "model_%d" % global_step)
                        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)
                        print("Saving checkpoint to:", output_dir)
                    if global_step == num_training_steps:
                        break

    if args.do_predict and rank == 0:
        dev_ds = dev_examples.map(
            partial(prepare_validation_features, tokenizer=tokenizer, args=args),
            batched=True,
            remove_columns=column_names,
            num_proc=1,
        )
        dev_batch_sampler = paddle.io.BatchSampler(dev_ds, batch_size=args.batch_size, shuffle=False)
        dev_ds_for_model = dev_ds.remove_columns(["example_id", "offset_mapping"])
        dev_batchify_fn = DataCollatorWithPadding(tokenizer)

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

        evaluate(model, dev_data_loader, dev_examples, dev_ds, args)


if __name__ == "__main__":
    args = parse_args()
    run(args)
