# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import torch
import torch.nn as nn


class RNNStateEncoder(nn.Module):
    def __init__(
        self,
        input_size: int,
        hidden_size: int,
        num_layers: int = 1,
        rnn_type: str = "GRU",
    ):
        r"""An RNN for encoding the state in RL.

        Supports masking the hidden state during various timesteps in the forward lass

        Args:
            input_size: The input size of the RNN
            hidden_size: The hidden size
            num_layers: The number of recurrent layers
            rnn_type: The RNN cell type.  Must be GRU or LSTM
        """

        super().__init__()
        self._num_recurrent_layers = num_layers
        self._rnn_type = rnn_type

        self.rnn = getattr(nn, rnn_type)(
            input_size=input_size,
            hidden_size=hidden_size,
            num_layers=num_layers,
        )

        self.layer_init()

    def layer_init(self):
        for name, param in self.rnn.named_parameters():
            if "weight" in name:
                nn.init.orthogonal_(param)
            elif "bias" in name:
                nn.init.constant_(param, 0)

    @property
    def num_recurrent_layers(self):
        return self._num_recurrent_layers * (
            2 if "LSTM" in self._rnn_type else 1
        )

    def _pack_hidden(self, hidden_states):
        if "LSTM" in self._rnn_type:
            hidden_states = torch.cat(
                [hidden_states[0], hidden_states[1]], dim=0
            )

        return hidden_states

    def _unpack_hidden(self, hidden_states):
        if "LSTM" in self._rnn_type:
            hidden_states = (
                hidden_states[0 : self._num_recurrent_layers],
                hidden_states[self._num_recurrent_layers :],
            )

        return hidden_states

    def _mask_hidden(self, hidden_states, masks):
        if isinstance(hidden_states, tuple):
            hidden_states = tuple(v * masks for v in hidden_states)
        else:
            hidden_states = masks * hidden_states

        return hidden_states

    def single_forward(self, x, hidden_states, masks):
        r"""Forward for a non-sequence input
        """
        hidden_states = self._unpack_hidden(hidden_states)
        x, hidden_states = self.rnn(
            x.unsqueeze(0),
            self._mask_hidden(hidden_states, masks.unsqueeze(0)),
        )
        x = x.squeeze(0)
        hidden_states = self._pack_hidden(hidden_states)
        return x, hidden_states

    def seq_forward(self, x, hidden_states, masks):
        r"""Forward for a sequence of length T

        Args:
            x: (T, N, -1) Tensor that has been flattened to (T * N, -1)
            hidden_states: The starting hidden state.
            masks: The masks to be applied to hidden state at every timestep.
                A (T, N) tensor flatten to (T * N)
        """
        # x is a (T, N, -1) tensor flattened to (T * N, -1)
        n = hidden_states.size(1)
        t = int(x.size(0) / n)

        # unflatten
        x = x.view(t, n, x.size(1))
        masks = masks.view(t, n)

        # steps in sequence which have zero for any agent. Assume t=0 has
        # a zero in it.
        has_zeros = (masks[1:] == 0.0).any(dim=-1).nonzero().squeeze().cpu()

        # +1 to correct the masks[1:]
        if has_zeros.dim() == 0:
            has_zeros = [has_zeros.item() + 1]  # handle scalar
        else:
            has_zeros = (has_zeros + 1).numpy().tolist()

        # add t=0 and t=T to the list
        has_zeros = [0] + has_zeros + [t]

        hidden_states = self._unpack_hidden(hidden_states)
        outputs = []
        for i in range(len(has_zeros) - 1):
            # process steps that don't have any zeros in masks together
            start_idx = has_zeros[i]
            end_idx = has_zeros[i + 1]

            rnn_scores, hidden_states = self.rnn(
                x[start_idx:end_idx],
                self._mask_hidden(
                    hidden_states, masks[start_idx].view(1, -1, 1)
                ),
            )

            outputs.append(rnn_scores)

        # x is a (T, N, -1) tensor
        x = torch.cat(outputs, dim=0)
        x = x.view(t * n, -1)  # flatten

        hidden_states = self._pack_hidden(hidden_states)
        return x, hidden_states

    def forward(self, x, hidden_states, masks):
        if x.size(0) == hidden_states.size(1):
            return self.single_forward(x, hidden_states, masks)
        else:
            return self.seq_forward(x, hidden_states, masks)
