# 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 numpy as np
import torch

from fairseq.data import Dictionary, FairseqDataset
from fairseq.tasks import FairseqTask, register_task


@register_task('dummy_masked_lm')
class DummyMaskedLMTask(FairseqTask):

    @staticmethod
    def add_args(parser):
        """Add task-specific arguments to the parser."""
        parser.add_argument('--dict-size', default=50000, type=int)
        parser.add_argument('--dataset-size', default=100000, type=int)
        parser.add_argument('--tokens-per-sample', default=512, type=int,
                            help='max number of total tokens over all segments '
                                 'per sample for BERT dataset')

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

        # add mask token
        self.mask_idx = dictionary.add_symbol('<mask>')
        assert len(dictionary) % 8 == 0

        mask_idx = 0
        pad_idx = 1
        seq = torch.arange(args.tokens_per_sample) + pad_idx + 1
        mask = torch.arange(2, args.tokens_per_sample, 7)  # ~15%
        src = seq.clone()
        src[mask] = mask_idx
        tgt = torch.full_like(seq, pad_idx)
        tgt[mask] = seq[mask]

        self.dummy_src = src
        self.dummy_tgt = tgt

    @classmethod
    def setup_task(cls, args, **kwargs):
        """Setup the task. """
        dictionary = Dictionary()
        for i in range(args.dict_size):
            dictionary.add_symbol('word{}'.format(i))
        print('| dictionary: {} types'.format(len(dictionary)))

        return cls(args, dictionary)

    def load_dataset(self, split, epoch=1, combine=False, **kwargs):
        """Load a given dataset split.
        Args:
            split (str): name of the split (e.g., train, valid, test)
        """
        bsz = self.args.max_sentences
        self.datasets[split] = DummyDataset(
            {
                'id': 1,
                'net_input': {
                    'src_tokens': torch.stack([self.dummy_src for _ in range(bsz)]),
                    'src_lengths': torch.full((bsz, ), self.args.tokens_per_sample),
                },
                'target': torch.stack([self.dummy_tgt for _ in range(bsz)]),
                'nsentences': bsz,
                'ntokens': bsz * self.args.tokens_per_sample,
            },
            num_items=self.args.dataset_size,
            item_size=self.args.tokens_per_sample,
        )

    @property
    def source_dictionary(self):
        return self.dictionary

    @property
    def target_dictionary(self):
        return self.dictionary


class DummyDataset(FairseqDataset):

    def __init__(self, batch, num_items, item_size):
        super().__init__()
        self.batch = batch
        self.num_items = num_items
        self.item_size = item_size

    def __getitem__(self, index):
        return index

    def __len__(self):
        return self.num_items

    def collater(self, samples):
        return self.batch

    @property
    def sizes(self):
        return np.array([self.item_size] * self.num_items)

    def num_tokens(self, index):
        return self.item_size

    def size(self, index):
        return self.item_size

    def ordered_indices(self):
        return np.arange(self.num_items)

    @property
    def supports_prefetch(self):
        return False
