# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import os

from fairseq.data import (
    data_utils,
    Dictionary,
    AppendTokenDataset,
    DenoisingDataset,
    PrependTokenDataset,
    StripTokenDataset,
    TokenBlockDataset,
)

from fairseq.data.encoders.utils import get_whole_word_mask
from . import FairseqTask, register_task


@register_task('denoising')
class DenoisingTask(FairseqTask):
    """
    Denoising task for applying sequence to sequence denoising. (ie. BART)
    """

    @staticmethod
    def add_args(parser):
        """Add task-specific arguments to the parser."""
        parser.add_argument('data', help='path to data directory')
        parser.add_argument('--tokens-per-sample', default=512, type=int,
                            help='max number of total tokens over all segments'
                                 ' per sample for dataset')
        parser.add_argument('--raw-text', default=False, action='store_true',
                            help='load raw text dataset')
        parser.add_argument(
            '--sample-break-mode', default="complete_doc", type=str,
            help='mode for breaking sentence',
        )
        parser.add_argument(
            '--mask', default=0.0, type=float,
            help='fraction of words/subwords that will be masked',
        )
        parser.add_argument(
            '--mask-random', default=0.0, type=float,
            help='instead of using [MASK], use random token this often'
        )
        parser.add_argument(
            '--insert', default=0.0, type=float,
            help='insert this percentage of additional random tokens',
        )
        parser.add_argument(
            '--permute', default=0.0, type=float,
            help='take this proportion of subwords and permute them',
        )
        parser.add_argument(
            '--rotate', default=0.5, type=float,
            help='rotate this proportion of inputs',
        )
        parser.add_argument(
            '--poisson-lambda', default=3.0, type=float,
            help='randomly shuffle sentences for this proportion of inputs'
        )
        parser.add_argument(
            '--permute-sentences', default=0.0, type=float,
            help='shuffle this proportion of sentences in all inputs'
        )
        parser.add_argument(
            '--mask-length', default="subword", type=str,
            choices=['subword', 'word', 'span-possion'],
            help='mask length to choose'
        )
        parser.add_argument(
            '--replace-length', default=-1, type=int,
            help='when masking N tokens, replace with 0, 1, or N tokens (use -1 for N)'
        )

    def __init__(self, args, dictionary):
        super().__init__(args)
        self.dictionary = dictionary
        self.seed = args.seed

        # add mask token
        self.mask_idx = self.dictionary.add_symbol('<mask>')

    @classmethod
    def setup_task(cls, args, **kwargs):
        """Setup the task.
        """
        dictionary = Dictionary.load(os.path.join(args.data, 'dict.txt'))
        print('| dictionary: {} types'.format(len(dictionary)))
        if not hasattr(args, 'shuffle_instance'):
            args.shuffle_instance = False
        return cls(args, dictionary)

    def load_dataset(self, split, epoch=0, combine=False):
        """Load a given dataset split.

        Args:
            split (str): name of the split (e.g., train, valid, test)
        """

        paths = self.args.data.split(':')
        assert len(paths) > 0
        data_path = paths[epoch % len(paths)]
        split_path = os.path.join(data_path, split)

        dataset = data_utils.load_indexed_dataset(
            split_path,
            self.dictionary,
            self.args.dataset_impl,
            combine=combine,
        )
        if dataset is None:
            raise FileNotFoundError('Dataset not found: {} ({})'.format(split, split_path))

        dataset = StripTokenDataset(dataset, self.dictionary.eos())

        # create continuous blocks of tokens
        dataset = TokenBlockDataset(
                dataset,
                dataset.sizes,
                self.args.tokens_per_sample - 2,  # one less for <s> and one for </s>
                pad=self.dictionary.pad(),
                eos=self.dictionary.eos(),
                break_mode=self.args.sample_break_mode,
                document_sep_len=0
        )

        # prepend beginning-of-sentence token (<s>, equiv. to [CLS] in BERT)
        dataset = PrependTokenDataset(dataset, self.source_dictionary.bos())
        dataset = AppendTokenDataset(dataset, self.source_dictionary.eos())

        mask_whole_words = get_whole_word_mask(self.args, self.source_dictionary) \
            if self.args.mask_length != 'subword' else None

        self.datasets[split] = DenoisingDataset(
            dataset, dataset.sizes, self.dictionary, self.mask_idx,
            mask_whole_words, shuffle=self.args.shuffle_instance,
            seed=self.seed, args=self.args
        )
        print(
            "| Split: {0}, Loaded {1} samples of denoising_dataset".format(
                split,
                len(self.datasets[split]),
            )
        )

    def max_positions(self):
        """Return the max sentence length allowed by the task."""
        return (self.args.max_source_positions, self.args.max_target_positions)

    @property
    def source_dictionary(self):
        """Return the source :class:`~fairseq.data.Dictionary`."""
        return self.dictionary

    @property
    def target_dictionary(self):
        """Return the target :class:`~fairseq.data.Dictionary`."""
        return self.dictionary
