# 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 as nn
from typing import Dict, List, NamedTuple, Optional
from torch import Tensor

EncoderOut = NamedTuple(
    "EncoderOut",
    [
        ("encoder_out", Tensor),  # T x B x C
        ("encoder_padding_mask", Optional[Tensor]),  # B x T
        ("encoder_embedding", Optional[Tensor]),  # B x T x C
        ("encoder_states", Optional[List[Tensor]]),  # List[T x B x C]
        ("src_tokens", Optional[Tensor]),  # B x T
        ("src_lengths", Optional[Tensor]),  # B x 1
        ("tgt_k", Optional[Tensor]),  # 3T(k) x B x C
        ("tgt_v", Optional[Tensor]),  # 3T(k) x B x C
        ("tgt_k_padding_mask", Optional[Tensor]),  # B x 3T(k)
        ("tgt_v_embed", Optional[Tensor]),  # 3T(v) x B x C
        ("tgt_v_padding_mask", Optional[Tensor]),  # B x 3T(v)
    ],
)


class FairseqEncoder(nn.Module):
    """Base class for encoders."""

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

    def forward(self, src_tokens, src_lengths=None, **kwargs):
        """
        Args:
            src_tokens (LongTensor): tokens in the source language of shape
                `(batch, src_len)`
            src_lengths (LongTensor): lengths of each source sentence of shape
                `(batch)`
        """
        raise NotImplementedError

    def forward_torchscript(self, net_input: Dict[str, Tensor]):
        """A TorchScript-compatible version of forward.

        Encoders which use additional arguments may want to override
        this method for TorchScript compatibility.
        """
        if torch.jit.is_scripting():
            return self.forward(
                src_tokens=net_input["src_tokens"],
                src_lengths=net_input["src_lengths"],
            )
        else:
            return self.forward_non_torchscript(net_input)

    @torch.jit.unused
    def forward_non_torchscript(self, net_input: Dict[str, Tensor]):
        encoder_input = {
            k: v
            for k, v in net_input.items()
            if k != "prev_output_tokens"
        }
        return self.forward(**encoder_input)

    def reorder_encoder_out(self, encoder_out, new_order):
        """
        Reorder encoder output according to `new_order`.

        Args:
            encoder_out: output from the ``forward()`` method
            new_order (LongTensor): desired order

        Returns:
            `encoder_out` rearranged according to `new_order`
        """
        raise NotImplementedError

    def max_positions(self):
        """Maximum input length supported by the encoder."""
        return 1e6  # an arbitrary large number

    def upgrade_state_dict(self, state_dict):
        """Upgrade a (possibly old) state dict for new versions of fairseq."""
        return state_dict

    def set_num_updates(self, num_updates):
        """State from trainer to pass along to model at every update."""

        def _apply(m):
            if hasattr(m, 'set_num_updates') and m != self:
                m.set_num_updates(num_updates)
        self.apply(_apply)
