# 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

import numpy as np
import torch

from fairseq import utils1 as utils
from fairseq.data import (
    data_utils,
    Dictionary,
    IdDataset,
    MonolingualDataset,
    NestedDictionaryDataset,
    NumelDataset,
    PadDataset,
    PrependTokenDataset,
    StripTokenDataset,
    TokenBlockDataset,
    TransformEosDataset,
    TruncateDataset,
    TruncatedDictionary,
)
from fairseq.tasks import FairseqTask, register_task


@register_task("language_modeling")
class LanguageModelingTask(FairseqTask):
    """
    Train a language model.

    Args:
        dictionary (~fairseq.data.Dictionary): the dictionary for the input of
            the language model
        output_dictionary (~fairseq.data.Dictionary): the dictionary for the
            output of the language model. In most cases it will be the same as
            *dictionary*, but could possibly be a more limited version of the
            dictionary (if ``--output-dictionary-size`` is used).
        targets (List[str]): list of the target types that the language model
            should predict.  Can be one of "self", "future", and "past".
            Defaults to "future".

    .. note::

        The language modeling task is compatible with :mod:`fairseq-train`,
        :mod:`fairseq-generate`, :mod:`fairseq-interactive` and
        :mod:`fairseq-eval-lm`.

    The language modeling task provides the following additional command-line
    arguments:

    .. argparse::
        :ref: fairseq.tasks.language_modeling_parser
        :prog:
    """

    @staticmethod
    def add_args(parser):
        """Add task-specific arguments to the parser."""
        # fmt: off
        parser.add_argument('data', help='path to data directory')
        parser.add_argument('--sample-break-mode', default='none',
                            choices=['none', 'complete', 'complete_doc', 'eos'],
                            help='If omitted or "none", fills each sample with tokens-per-sample '
                                 'tokens. If set to "complete", splits samples only at the end '
                                 'of sentence, but may include multiple sentences per sample. '
                                 '"complete_doc" is similar but respects doc boundaries. '
                                 'If set to "eos", includes only one sentence per sample.')
        parser.add_argument('--tokens-per-sample', default=1024, type=int,
                            help='max number of tokens per sample for LM dataset')
        parser.add_argument('--output-dictionary-size', default=-1, type=int,
                            help='limit the size of output dictionary')
        parser.add_argument('--self-target', action='store_true',
                            help='include self target')
        parser.add_argument('--future-target', action='store_true',
                            help='include future target')
        parser.add_argument('--past-target', action='store_true',
                            help='include past target')
        parser.add_argument('--add-bos-token', action='store_true',
                            help='prepend beginning of sentence token (<s>)')
        parser.add_argument('--max-target-positions', type=int, metavar='N',
                            help='max number of tokens in the target sequence')
        parser.add_argument('--truncate-sequence', action='store_true', default=False,
                            help='truncate sequences to --tokens-per-sample')
        # fmt: on

    def __init__(self, args, dictionary, output_dictionary=None, targets=None):
        super().__init__(args)
        self.dictionary = dictionary
        self.output_dictionary = output_dictionary or dictionary

        if targets is None:
            targets = ["future"]
        self.targets = targets

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

        Args:
            args (argparse.Namespace): parsed command-line arguments
        """
        dictionary = None
        output_dictionary = None
        if args.data:
            paths = args.data.split(":")
            assert len(paths) > 0
            dictionary = Dictionary.load(os.path.join(paths[0], "dict.txt"))
            print("| dictionary: {} types".format(len(dictionary)))
            output_dictionary = dictionary
            if args.output_dictionary_size >= 0:
                output_dictionary = TruncatedDictionary(
                    dictionary, args.output_dictionary_size
                )

        # upgrade old checkpoints
        if hasattr(args, "exclude_self_target"):
            args.self_target = not args.exclude_self_target

        targets = []
        if getattr(args, "self_target", False):
            targets.append("self")
        if getattr(args, "future_target", False):
            targets.append("future")
        if getattr(args, "past_target", False):
            targets.append("past")
        if len(targets) == 0:
            # standard language modeling
            targets = ["future"]

        return cls(args, dictionary, output_dictionary, targets=targets)

    def build_model(self, args):
        model = super().build_model(args)

        for target in self.targets:
            if target not in model.supported_targets:
                raise ValueError(
                    "Unsupported language modeling target: {}".format(target)
                )

        return model

    def load_dataset(self, split, epoch=0, combine=False, **kwargs):
        """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)
            )

        if self.args.truncate_sequence:
            dataset = TruncateDataset(dataset, self.args.tokens_per_sample)

        dataset = TokenBlockDataset(
            dataset,
            dataset.sizes,
            self.args.tokens_per_sample,
            pad=self.dictionary.pad(),
            eos=self.dictionary.eos(),
            break_mode=self.args.sample_break_mode,
            include_targets=True,
        )

        add_eos_for_other_targets = (
            self.args.sample_break_mode is not None
            and self.args.sample_break_mode != "none"
        )

        self.datasets[split] = MonolingualDataset(
            dataset,
            dataset.sizes,
            self.dictionary,
            self.output_dictionary,
            add_eos_for_other_targets=add_eos_for_other_targets,
            shuffle=True,
            targets=self.targets,
            add_bos_token=self.args.add_bos_token,
        )

    def build_dataset_for_inference(self, src_tokens, src_lengths, **kwargs):
        """
        Generate batches for inference. We prepend an eos token to src_tokens
        (or bos if `--add-bos-token` is set) and we append an eos to target.
        This is convenient both for generation with a prefix and LM scoring.
        """
        tgt_dataset = TokenBlockDataset(
            src_tokens,
            src_lengths,
            block_size=None,  # ignored for "eos" break mode
            pad=self.source_dictionary.pad(),
            eos=self.source_dictionary.eos(),
            break_mode="eos",
        )
        src_dataset = PrependTokenDataset(
            StripTokenDataset(
                tgt_dataset,
                # remove eos from (end of) target sequence
                self.source_dictionary.eos(),
            ),
            token=(
                self.source_dictionary.bos()
                if getattr(self.args, "add_bos_token", False)
                else self.source_dictionary.eos()
            ),
        )
        return NestedDictionaryDataset(
            {
                "id": IdDataset(),
                "net_input": {
                    "src_tokens": PadDataset(src_dataset, pad_idx=self.source_dictionary.pad(), left_pad=False),
                    "src_lengths": NumelDataset(src_dataset, reduce=False),
                },
                "target": PadDataset(tgt_dataset, pad_idx=self.source_dictionary.pad(), left_pad=False),
            },
            sizes=[np.array(src_lengths)],
        )

    def inference_step(self, generator, models, sample, prefix_tokens=None):
        with torch.no_grad():
            # Generation will always be conditioned on bos_token
            if getattr(self.args, "add_bos_token", False):
                bos_token = self.source_dictionary.bos()
            else:
                bos_token = self.source_dictionary.eos()

            # SequenceGenerator doesn't use src_tokens directly, we need to
            # pass the `prefix_tokens` argument instead
            if prefix_tokens is None and sample["net_input"]["src_tokens"].nelement():
                prefix_tokens = sample["net_input"]["src_tokens"]
                if prefix_tokens[:, 0].eq(bos_token).all():
                    prefix_tokens = prefix_tokens[:, 1:]

            return generator.generate(
                models, sample, prefix_tokens=prefix_tokens, bos_token=bos_token,
            )

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

    @property
    def target_dictionary(self):
        """Return the :class:`~fairseq.data.Dictionary` for the language
        model."""
        return self.output_dictionary
