"""
Most of the code here is a direct copy and paste from the fantastic tabnet
implementation here: https://github.com/dreamquark-ai/tabnet

Therefore, ALL CREDIT TO THE DREAMQUARK-AI TEAM
-----------------------------------------------

Here I simply adapted what I needed the TabNet to work within pytorch-widedeep
"""

import warnings

import numpy as np
import torch
import torch.nn.functional as F
from torch import nn

from pytorch_widedeep.wdtypes import *  # noqa: F403
from pytorch_widedeep.models.tabnet import sparsemax


def initialize_non_glu(module, input_dim: int, output_dim: int):
    gain_value = np.sqrt((input_dim + output_dim) / np.sqrt(4 * input_dim))
    torch.nn.init.xavier_normal_(module.weight, gain=gain_value)
    return


def initialize_glu(module, input_dim: int, output_dim: int):
    gain_value = np.sqrt((input_dim + output_dim) / np.sqrt(input_dim))
    torch.nn.init.xavier_normal_(module.weight, gain=gain_value)
    return


class GBN(torch.nn.Module):
    """
    Ghost Batch Normalization
    https://arxiv.org/abs/1705.08741
    """

    def __init__(
        self, input_dim: int, virtual_batch_size: int = 128, momentum: float = 0.01
    ):
        super(GBN, self).__init__()
        self.virtual_batch_size = virtual_batch_size
        self.bn = nn.BatchNorm1d(input_dim, momentum=momentum)

    def forward(self, X: Tensor) -> Tensor:
        chunks = X.chunk(int(np.ceil(X.shape[0] / self.virtual_batch_size)), 0)
        res = [self.bn(x_) for x_ in chunks]
        return torch.cat(res, dim=0)


class GLU_Layer(nn.Module):
    def __init__(
        self,
        input_dim: int,
        output_dim: int,
        dropout: float,
        fc: nn.Module = None,
        ghost_bn: bool = True,
        virtual_batch_size: int = 128,
        momentum: float = 0.02,
    ):
        super(GLU_Layer, self).__init__()

        if fc:
            self.fc = fc
        else:
            self.fc = nn.Linear(input_dim, 2 * output_dim, bias=False)
        initialize_glu(self.fc, input_dim, 2 * output_dim)

        if ghost_bn:
            self.bn: Union[GBN, nn.BatchNorm1d] = GBN(
                2 * output_dim, virtual_batch_size=virtual_batch_size, momentum=momentum
            )
        else:
            self.bn = nn.BatchNorm1d(2 * output_dim, momentum=momentum)

        self.dp = nn.Dropout(dropout)

    def forward(self, X: Tensor) -> Tensor:
        return self.dp(F.glu(self.bn(self.fc(X))))


class GLU_Block(nn.Module):
    def __init__(
        self,
        input_dim: int,
        output_dim: int,
        dropout: float,
        n_glu: int = 2,
        first: bool = False,
        shared_layers: nn.ModuleList = None,
        ghost_bn: bool = True,
        virtual_batch_size: int = 128,
        momentum: float = 0.02,
    ):
        super(GLU_Block, self).__init__()
        self.first = first

        if (shared_layers is not None) and (n_glu != len(shared_layers)):
            self.n_glu = len(shared_layers)
            warnings.warn(
                "If 'shared_layers' is nor None, 'n_glu' must be equal to the number of shared_layers."
                "Got n_glu = {} and n shared_layers = {}. 'n_glu' has been set to {}".format(
                    n_glu, len(shared_layers), len(shared_layers)
                ),
                UserWarning,
            )
        else:
            self.n_glu = n_glu

        glu_dim = [input_dim] + [output_dim] * self.n_glu
        self.glu_layers = nn.ModuleList()
        for i in range(self.n_glu):
            fc = shared_layers[i] if shared_layers else None
            self.glu_layers.append(
                GLU_Layer(
                    glu_dim[i],
                    glu_dim[i + 1],
                    dropout,
                    fc=fc,
                    ghost_bn=ghost_bn,
                    virtual_batch_size=virtual_batch_size,
                    momentum=momentum,
                )
            )

    def forward(self, X: Tensor) -> Tensor:
        scale = torch.sqrt(torch.FloatTensor([0.5]).to(X.device))

        if self.first:  # the first layer of the block has no scale multiplication
            x = self.glu_layers[0](X)
            layers_left = range(1, self.n_glu)
        else:
            x = nn.Identity()(X)
            layers_left = range(self.n_glu)

        for glu_id in layers_left:
            x = torch.add(x, self.glu_layers[glu_id](x)) * scale

        return x


class FeatTransformer(nn.Module):
    def __init__(
        self,
        input_dim: int,
        output_dim: int,
        dropout: float,
        shared_layers: nn.ModuleList,
        n_glu_step_dependent: int,
        ghost_bn=True,
        virtual_batch_size=128,
        momentum=0.02,
    ):
        super(FeatTransformer, self).__init__()

        params = {
            "ghost_bn": ghost_bn,
            "virtual_batch_size": virtual_batch_size,
            "momentum": momentum,
        }

        self.shared = GLU_Block(
            input_dim,
            output_dim,
            dropout,
            n_glu=len(shared_layers),
            first=True,
            shared_layers=shared_layers,
            **params
        )

        self.step_dependent = GLU_Block(
            output_dim,
            output_dim,
            dropout,
            n_glu=n_glu_step_dependent,
            first=False,
            **params
        )

    def forward(self, X: Tensor) -> Tensor:
        return self.step_dependent(self.shared(X))


class AttentiveTransformer(nn.Module):
    def __init__(
        self,
        input_dim: int,
        output_dim: int,
        mask_type: str = "sparsemax",
        ghost_bn=True,
        virtual_batch_size=128,
        momentum=0.02,
    ):

        super(AttentiveTransformer, self).__init__()
        self.fc = nn.Linear(input_dim, output_dim, bias=False)
        initialize_non_glu(self.fc, input_dim, output_dim)
        if ghost_bn:
            self.bn: Union[GBN, nn.BatchNorm1d] = GBN(
                output_dim, virtual_batch_size=virtual_batch_size, momentum=momentum
            )
        else:
            self.bn = nn.BatchNorm1d(output_dim, momentum=momentum)

        if mask_type == "sparsemax":
            self.mask: Union[Sparsemax, Entmax15] = sparsemax.Sparsemax(dim=-1)
        elif mask_type == "entmax":
            self.mask = sparsemax.Entmax15(dim=-1)
        else:
            raise NotImplementedError(
                "Please choose either 'sparsemax' or 'entmax' as masktype"
            )

    def forward(self, priors: Tensor, processed_feat: Tensor) -> Tensor:
        x = self.bn(self.fc(processed_feat))
        x = torch.mul(x, priors)
        return self.mask(x)


class TabNetEncoder(nn.Module):
    def __init__(
        self,
        input_dim: int,
        n_steps: int = 3,
        step_dim: int = 8,
        attn_dim: int = 8,
        dropout: float = 0.0,
        n_glu_step_dependent: int = 2,
        n_glu_shared: int = 2,
        ghost_bn: bool = True,
        virtual_batch_size: int = 128,
        momentum: float = 0.02,
        gamma: float = 1.3,
        epsilon: float = 1e-15,
        mask_type: str = "sparsemax",
    ):
        super(TabNetEncoder, self).__init__()

        self.input_dim = input_dim
        self.n_steps = n_steps
        self.step_dim = step_dim
        self.attn_dim = attn_dim
        self.gamma = gamma
        self.epsilon = epsilon

        self.initial_bn = nn.BatchNorm1d(input_dim, momentum=0.01)

        params = {
            "ghost_bn": ghost_bn,
            "virtual_batch_size": virtual_batch_size,
            "momentum": momentum,
        }

        shared_layers = nn.ModuleList()
        for i in range(n_glu_shared):
            if i == 0:
                shared_layers.append(
                    nn.Linear(input_dim, 2 * (step_dim + attn_dim), bias=False)
                )
            else:
                shared_layers.append(
                    nn.Linear(
                        step_dim + attn_dim, 2 * (step_dim + attn_dim), bias=False
                    )
                )

        self.initial_splitter = FeatTransformer(
            input_dim,
            step_dim + attn_dim,
            dropout,
            shared_layers,
            n_glu_step_dependent,
            **params
        )

        self.feat_transformers = nn.ModuleList()
        self.attn_transformers = nn.ModuleList()
        for step in range(n_steps):
            feat_transformer = FeatTransformer(
                input_dim,
                step_dim + attn_dim,
                dropout,
                shared_layers,
                n_glu_step_dependent,
                **params
            )
            attn_transformer = AttentiveTransformer(
                attn_dim, input_dim, mask_type, **params
            )
            self.feat_transformers.append(feat_transformer)
            self.attn_transformers.append(attn_transformer)

    def forward(self, X: Tensor) -> Tuple[List[Tensor], Tensor]:
        x = self.initial_bn(X)

        # P[n_step = 0] is initialized as all ones, 1^(B×D)
        prior = torch.ones(x.shape).to(x.device)

        # sparsity regularization
        M_loss = torch.FloatTensor([0.0]).to(x.device)

        # split block
        attn = self.initial_splitter(x)[:, self.step_dim :]

        steps_output = []
        for step in range(self.n_steps):
            # learnable mask: M[i] = sparsemax(prior[i − 1] · hi(a[i − 1]))
            # where hi = FC + BN
            M = self.attn_transformers[step](prior, attn)

            # update prior: P[i] = \prod_{i}^{j=1} (γ − M[j])
            prior = torch.mul(self.gamma - M, prior)

            # sparsity regularization
            M_loss += torch.mean(
                torch.sum(torch.mul(M, torch.log(M + self.epsilon)), dim=1)
            )

            # update attention and d_out
            masked_x = torch.mul(M, x)
            out = self.feat_transformers[step](masked_x)
            attn = out[:, self.step_dim :]
            d_out = nn.ReLU()(out[:, : self.step_dim])
            steps_output.append(d_out)

        M_loss /= self.n_steps  # type: ignore[has-type]

        return steps_output, M_loss

    def forward_masks(self, X: Tensor) -> Tuple[Tensor, Dict[int, Tensor]]:
        x = self.initial_bn(X)

        prior = torch.ones(x.shape).to(x.device)
        M_explain = torch.zeros(x.shape).to(x.device)
        attn = self.initial_splitter(x)[:, self.step_dim :]
        masks = {}

        for step in range(self.n_steps):
            M = self.attn_transformers[step](prior, attn)
            masks[step] = M

            prior = torch.mul(self.gamma - M, prior)

            masked_x = torch.mul(M, x)
            out = self.feat_transformers[step](masked_x)
            attn = out[:, self.step_dim :]
            # 'decision contribution' in the paper
            d_out = nn.ReLU()(out[:, : self.step_dim])

            # aggregate decision contribution
            agg_decision_contrib = torch.sum(d_out, dim=1)
            M_explain += torch.mul(M, agg_decision_contrib.unsqueeze(dim=1))

        return M_explain, masks
