# 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.

import torch.nn as nn
import torch.nn.functional as F


class FairseqDecoder(nn.Module):
    """Base class for decoders."""

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

    def forward(self, prev_output_tokens, encoder_out):
        raise NotImplementedError

    def get_normalized_probs(self, net_output, log_probs, sample):
        """Get normalized probabilities (or log probs) from a net's output."""

        if hasattr(self, 'adaptive_softmax') and self.adaptive_softmax is not None:
            assert sample is not None and 'target' in sample
            out = self.adaptive_softmax.get_log_prob(net_output[0], sample['target'])
            return out.exp_() if not log_probs else out

        logits = net_output[0].float()
        if log_probs:
            return F.log_softmax(logits, dim=-1)
        else:
            return F.softmax(logits, dim=-1)

    def max_positions(self):
        """Maximum input length supported by the decoder."""
        raise NotImplementedError

    def upgrade_state_dict(self, state_dict):
        return state_dict
