# 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 inspect
from typing import Any, Dict, List

from fairseq import metrics, utils
from fairseq.dataclass import FairseqDataclass
from fairseq.dataclass.utils import gen_parser_from_dataclass
from torch.nn.modules.loss import _Loss


class FairseqCriterion(_Loss):
    def __init__(self, task):
        super().__init__()
        self.task = task
        if hasattr(task, "target_dictionary"):
            tgt_dict = task.target_dictionary
            self.padding_idx = tgt_dict.pad() if tgt_dict is not None else -100

    @classmethod
    def add_args(cls, parser):
        """Add criterion-specific arguments to the parser."""
        dc = getattr(cls, "__dataclass", None)
        if dc is not None:
            gen_parser_from_dataclass(parser, dc())

    @classmethod
    def build_criterion(cls, cfg: FairseqDataclass, task):
        """Construct a criterion from command-line args."""
        # arguments in the __init__.
        init_args = {}
        for p in inspect.signature(cls).parameters.values():
            if (
                p.kind == p.POSITIONAL_ONLY
                or p.kind == p.VAR_POSITIONAL
                or p.kind == p.VAR_KEYWORD
            ):
                # we haven't implemented inference for these argument types,
                # but PRs welcome :)
                raise NotImplementedError("{} not supported".format(p.kind))

            assert p.kind in {p.POSITIONAL_OR_KEYWORD, p.KEYWORD_ONLY}

            if p.name == "task":
                init_args["task"] = task
            elif p.name == "cfg":
                init_args["cfg"] = cfg
            elif hasattr(cfg, p.name):
                init_args[p.name] = getattr(cfg, p.name)
            elif p.default != p.empty:
                pass  # we'll use the default value
            else:
                raise NotImplementedError(
                    "Unable to infer Criterion arguments, please implement "
                    "{}.build_criterion".format(cls.__name__)
                )
        return cls(**init_args)

    def forward(self, model, sample, reduce=True):
        """Compute the loss for the given sample.

        Returns a tuple with three elements:
        1) the loss
        2) the sample size, which is used as the denominator for the gradient
        3) logging outputs to display while training
        """
        raise NotImplementedError

    @staticmethod
    def aggregate_logging_outputs(
        logging_outputs: List[Dict[str, Any]]
    ) -> Dict[str, Any]:
        """Aggregate logging outputs from data parallel training."""
        utils.deprecation_warning(
            "The aggregate_logging_outputs API is deprecated. "
            "Please use the reduce_metrics API instead."
        )
        raise NotImplementedError

    @classmethod
    def reduce_metrics(cls, logging_outputs: List[Dict[str, Any]]) -> None:
        """Aggregate logging outputs from data parallel training."""
        utils.deprecation_warning(
            "Criterions should implement the reduce_metrics API. "
            "Falling back to deprecated aggregate_logging_outputs API."
        )
        agg_logging_outputs = cls.aggregate_logging_outputs(logging_outputs)
        for k, v in agg_logging_outputs.items():
            if k in {"nsentences", "ntokens", "sample_size"}:
                continue
            metrics.log_scalar(k, v)

    @staticmethod
    def logging_outputs_can_be_summed() -> bool:
        """
        Whether the logging outputs returned by `forward` can be summed
        across workers prior to calling `reduce_metrics`. Setting this
        to True will improves distributed training speed.
        """
        return False
