# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.

from fairseq.data import data_utils, FairseqDataset, iterators
import torch


class FairseqTask(object):
    """
    Tasks store dictionaries and provide helpers for loading/iterating over
    Datasets, initializing the Model/Criterion and calculating the loss.
    """

    @staticmethod
    def add_args(parser):
        """Add task-specific arguments to the parser."""
        pass

    def __init__(self, args):
        self.args = args
        self.datasets = {}

    @classmethod
    def setup_task(cls, args, **kwargs):
        """Setup the task (e.g., load dictionaries).

        Args:
            args (argparse.Namespace): parsed command-line arguments
        """
        return cls(args)

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

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

    def dataset(self, split):
        """
        Return a loaded dataset split.

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

        Returns:
            a :class:`~fairseq.data.FairseqDataset` corresponding to *split*
        """
        from fairseq.data import FairseqDataset
        if split not in self.datasets:
            raise KeyError('Dataset not loaded: ' + split)
        if not isinstance(self.datasets[split], FairseqDataset):
            raise TypeError('Datasets are expected to be of type FairseqDataset')
        return self.datasets[split]

    def get_batch_iterator(
        self, dataset, max_tokens=None, max_sentences=None, max_positions=None,
        ignore_invalid_inputs=False, required_batch_size_multiple=1,
        seed=1, num_shards=1, shard_id=0, num_workers=0,
    ):
        """
        Get an iterator that yields batches of data from the given dataset.

        Args:
            dataset (~fairseq.data.FairseqDataset): dataset to batch
            max_tokens (int, optional): max number of tokens in each batch
                (default: None).
            max_sentences (int, optional): max number of sentences in each
                batch (default: None).
            max_positions (optional): max sentence length supported by the
                model (default: None).
            ignore_invalid_inputs (bool, optional): don't raise Exception for
                sentences that are too long (default: False).
            required_batch_size_multiple (int, optional): require batch size to
                be a multiple of N (default: 1).
            seed (int, optional): seed for random number generator for
                reproducibility (default: 1).
            num_shards (int, optional): shard the data iterator into N
                shards (default: 1).
            shard_id (int, optional): which shard of the data iterator to
                return (default: 0).
            num_workers (int, optional): how many subprocesses to use for data
                loading. 0 means the data will be loaded in the main process
                (default: 0).

        Returns:
            ~fairseq.iterators.EpochBatchIterator: a batched iterator over the
                given dataset split
        """
        assert isinstance(dataset, FairseqDataset)

        # get indices ordered by example size
        with data_utils.numpy_seed(seed):
            indices = dataset.ordered_indices()

        # filter examples that are too large
        indices = data_utils.filter_by_size(
            indices, dataset.size, max_positions, raise_exception=(not ignore_invalid_inputs),
        )

        # create mini-batches with given size constraints
        batch_sampler = data_utils.batch_by_size(
            indices, dataset.num_tokens, max_tokens=max_tokens, max_sentences=max_sentences,
            required_batch_size_multiple=required_batch_size_multiple,
        )

        # return a reusable, sharded iterator
        return iterators.EpochBatchIterator(
            dataset=dataset,
            collate_fn=dataset.collater,
            batch_sampler=batch_sampler,
            seed=seed,
            num_shards=num_shards,
            shard_id=shard_id,
            num_workers=num_workers,
        )

    def build_model(self, args):
        """
        Build the :class:`~fairseq.models.BaseFairseqModel` instance for this
        task.

        Args:
            args (argparse.Namespace): parsed command-line arguments

        Returns:
            a :class:`~fairseq.models.BaseFairseqModel` instance
        """
        from fairseq import models
        return models.build_model(args, self)

    def build_criterion(self, args):
        """
        Build the :class:`~fairseq.criterions.FairseqCriterion` instance for
        this task.

        Args:
            args (argparse.Namespace): parsed command-line arguments

        Returns:
            a :class:`~fairseq.criterions.FairseqCriterion` instance
        """
        from fairseq import criterions
        return criterions.build_criterion(args, self)

    def train_step(self, sample, model, criterion, optimizer, ignore_grad=False):
        """
        Do forward and backward, and return the loss as computed by *criterion*
        for the given *model* and *sample*.

        Args:
            sample (dict): the mini-batch. The format is defined by the
                :class:`~fairseq.data.FairseqDataset`.
            model (~fairseq.models.BaseFairseqModel): the model
            criterion (~fairseq.criterions.FairseqCriterion): the criterion
            optimizer (~fairseq.optim.FairseqOptimizer): the optimizer
            ignore_grad (bool): multiply loss by 0 if this is set to True

        Returns:
            tuple:
                - the loss
                - the sample size, which is used as the denominator for the
                  gradient
                - logging outputs to display while training
        """
        model.train()

        loss, sample_size, logging_output = criterion(model, sample)
        if ignore_grad:
            loss *= 0
        optimizer.backward(loss)
        return loss, sample_size, logging_output

    def valid_step(self, sample, model, criterion):
        model.eval()
        with torch.no_grad():
            loss, sample_size, logging_output = criterion(model, sample)
        return loss, sample_size, logging_output

    def init_logging_output(self, sample):
        return {
            'ntokens': sample['ntokens'] if sample is not None else 0,
            'nsentences': sample['target'].size(0) if sample is not None else 0,
        }

    def grad_denom(self, sample_sizes, criterion):
        return criterion.__class__.grad_denom(sample_sizes)

    def aggregate_logging_outputs(self, logging_outputs, criterion):
        return criterion._aggregate_logging_outputs(logging_outputs)

    def max_positions(self):
        """Return the max input length allowed by the task."""
        return None

    @property
    def source_dictionary(self):
        """Return the source :class:`~fairseq.data.Dictionary` (if applicable
        for this task)."""
        raise NotImplementedError

    @property
    def target_dictionary(self):
        """Return the target :class:`~fairseq.data.Dictionary` (if applicable
        for this task)."""
        raise NotImplementedError
