# 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 torch
import torch.nn.functional as F
from fairseq.criterions import register_criterion
from fairseq.criterions.sentence_prediction import (
    SentencePredictionCriterion,
    SentencePredictionConfig,
)


@register_criterion("sentence_prediction_adapters", dataclass=SentencePredictionConfig)
class SentencePredictionCriterionAdapters(SentencePredictionCriterion):
    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
        """
        assert (
            hasattr(model, "classification_heads")
            and self.classification_head_name in model.classification_heads
        ), "model must provide sentence classification head for --criterion=sentence_prediction"

        if not hasattr(sample, "lang_id"):
            # If no language ID is given, we fall back to English
            lang_id = ["en_XX"] * sample["nsentences"]
        else:
            lang_id = sample["lang_id"]

        logits, _ = model(
            **sample["net_input"],
            features_only=True,
            classification_head_name=self.classification_head_name,
            lang_id=lang_id,
        )
        targets = model.get_targets(sample, [logits]).view(-1)
        sample_size = targets.numel()

        if not self.regression_target:
            lprobs = F.log_softmax(logits, dim=-1, dtype=torch.float32)
            loss = F.nll_loss(lprobs, targets, reduction="sum")
        else:
            logits = logits.view(-1).float()
            targets = targets.float()
            loss = F.mse_loss(logits, targets, reduction="sum")

        logging_output = {
            "loss": loss.data,
            "ntokens": sample["ntokens"],
            "nsentences": sample_size,
            "sample_size": sample_size,
        }
        if not self.regression_target:
            preds = logits.argmax(dim=1)
            logging_output["ncorrect"] = (preds == targets).sum()

        return loss, sample_size, logging_output