"""
The implementation of some common-use modules related to RNN.
"""

# Created by Wenjie Du <wenjay.du@gmail.com>
# License: BSD-3-Clause

import math

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn.parameter import Parameter


class TemporalDecay(nn.Module):
    """The module used to generate the temporal decay factor gamma in the GRUD model.
    Please refer to the original paper :cite:`che2018GRUD` for more deinails.

    Attributes
    ----------
    W: tensor,
        The weights (parameters) of the module.
    b: tensor,
        The bias of the module.

    Parameters
    ----------
    input_size : int,
        the feature dimension of the input

    output_size : int,
        the feature dimension of the output

    diag : bool,
        whether to product the weight with an identity matrix before forward processing

    References
    ----------
    .. [1] `Che, Zhengping, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu.
        "Recurrent neural networks for multivariate time series with missing values."
        Scientific reports 8, no. 1 (2018): 6085.
        <https://www.nature.com/articles/s41598-018-24271-9.pdf>`_

    """

    def __init__(self, input_size: int, output_size: int, diag: bool = False):
        super().__init__()
        self.diag = diag
        self.W = Parameter(torch.Tensor(output_size, input_size))
        self.b = Parameter(torch.Tensor(output_size))

        if self.diag:
            assert input_size == output_size
            m = torch.eye(input_size, input_size)
            self.register_buffer("m", m)

        self._reset_parameters()

    def _reset_parameters(self) -> None:
        std_dev = 1.0 / math.sqrt(self.W.size(0))
        self.W.data.uniform_(-std_dev, std_dev)
        if self.b is not None:
            self.b.data.uniform_(-std_dev, std_dev)

    def forward(self, delta: torch.Tensor) -> torch.Tensor:
        """Forward processing of this NN module.

        Parameters
        ----------
        delta : tensor, shape [n_samples, n_steps, n_features]
            The time gaps.

        Returns
        -------
        gamma : tensor, of the same shape with parameter `delta`, values in (0,1]
            The temporal decay factor.
        """
        if self.diag:
            gamma = F.relu(F.linear(delta, self.W * Variable(self.m), self.b))
        else:
            gamma = F.relu(F.linear(delta, self.W, self.b))
        gamma = torch.exp(-gamma)
        return gamma
