"""
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.

---

This code borrows heavily from the DimeNet implementation as part of
pytorch-geometric: https://github.com/rusty1s/pytorch_geometric. License:

---

Copyright (c) 2020 Matthias Fey <matthias.fey@tu-dortmund.de>

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
"""

import torch
from torch import nn
from torch_geometric.nn import radius_graph
from torch_geometric.nn.acts import swish
from torch_geometric.nn.inits import glorot_orthogonal
from torch_geometric.nn.models.dimenet import (
    BesselBasisLayer,
    EmbeddingBlock,
    Envelope,
    ResidualLayer,
    SphericalBasisLayer,
)
from torch_geometric.utils import degree
from torch_scatter import scatter
from torch_sparse import SparseTensor

from ocpmodels.common.registry import registry
from ocpmodels.common.utils import (
    conditional_grad,
    get_pbc_distances,
    radius_graph_pbc,
)
#from ocpmodels.models.utils.pna import AGGREGATORS, SCALERS

try:
    import sympy as sym
except ImportError:
    sym = None


class InteractionPPBlock(torch.nn.Module):
    def __init__(
        self,
        hidden_channels,
        int_emb_size,
        basis_emb_size,
        num_spherical,
        num_radial,
        num_before_skip,
        num_after_skip,
        act=swish,
    ):
        super(InteractionPPBlock, self).__init__()
        self.act = act

        # Transformations of Bessel and spherical basis representations.
        self.lin_rbf1 = nn.Linear(num_radial, basis_emb_size, bias=False)
        self.lin_rbf2 = nn.Linear(basis_emb_size, hidden_channels, bias=False)
        self.lin_sbf1 = nn.Linear(
            num_spherical * num_radial, basis_emb_size, bias=False
        )
        self.lin_sbf2 = nn.Linear(basis_emb_size, int_emb_size, bias=False)

        # Dense transformations of input messages.
        self.lin_kj = nn.Linear(hidden_channels, hidden_channels)
        self.lin_ji = nn.Linear(hidden_channels, hidden_channels)

        # Embedding projections for interaction triplets.
        self.lin_down = nn.Linear(hidden_channels, int_emb_size, bias=False)
        self.lin_up = nn.Linear(int_emb_size, hidden_channels, bias=False)

        # Residual layers before and after skip connection.
        self.layers_before_skip = torch.nn.ModuleList(
            [
                ResidualLayer(hidden_channels, act)
                for _ in range(num_before_skip)
            ]
        )
        self.lin = nn.Linear(hidden_channels, hidden_channels)
        self.layers_after_skip = torch.nn.ModuleList(
            [
                ResidualLayer(hidden_channels, act)
                for _ in range(num_after_skip)
            ]
        )

        self.reset_parameters()

    def reset_parameters(self):
        glorot_orthogonal(self.lin_rbf1.weight, scale=2.0)
        glorot_orthogonal(self.lin_rbf2.weight, scale=2.0)
        glorot_orthogonal(self.lin_sbf1.weight, scale=2.0)
        glorot_orthogonal(self.lin_sbf2.weight, scale=2.0)

        glorot_orthogonal(self.lin_kj.weight, scale=2.0)
        self.lin_kj.bias.data.fill_(0)
        glorot_orthogonal(self.lin_ji.weight, scale=2.0)
        self.lin_ji.bias.data.fill_(0)

        glorot_orthogonal(self.lin_down.weight, scale=2.0)
        glorot_orthogonal(self.lin_up.weight, scale=2.0)

        for res_layer in self.layers_before_skip:
            res_layer.reset_parameters()
        glorot_orthogonal(self.lin.weight, scale=2.0)
        self.lin.bias.data.fill_(0)
        for res_layer in self.layers_after_skip:
            res_layer.reset_parameters()

    def forward(self, x, rbf, sbf, idx_kj, idx_ji):
        # Initial transformations.
        x_ji = self.act(self.lin_ji(x))
        x_kj = self.act(self.lin_kj(x))

        # Transformation via Bessel basis.
        rbf = self.lin_rbf1(rbf)
        rbf = self.lin_rbf2(rbf)
        x_kj = x_kj * rbf

        # Down-project embeddings and generate interaction triplet embeddings.
        x_kj = self.act(self.lin_down(x_kj))

        # Transform via 2D spherical basis.
        sbf = self.lin_sbf1(sbf)
        sbf = self.lin_sbf2(sbf)
        x_kj = x_kj[idx_kj] * sbf

        # Aggregate interactions and up-project embeddings.
        x_kj = scatter(x_kj, idx_ji, dim=0, dim_size=x.size(0))
        x_kj = self.act(self.lin_up(x_kj))

        h = x_ji + x_kj
        for layer in self.layers_before_skip:
            h = layer(h)
        h = self.act(self.lin(h)) + x
        for layer in self.layers_after_skip:
            h = layer(h)

        return h


class OutputPPBlock(torch.nn.Module):
    def __init__(
        self,
        num_radial,
        hidden_channels,
        out_emb_channels,
        out_channels,
        num_layers,
        act,
        aggregators,
        scalers,
    ):
        super(OutputPPBlock, self).__init__()
        self.act = act
        self.pna = False
        if aggregators and scalers:
            self.pna = True
            self.aggregators = [AGGREGATORS[aggr] for aggr in aggregators]
            self.scalers = [SCALERS[scale] for scale in scalers]
            # average degree computed from data
            self.avg_deg = {"lin": 33.53, "log": 3.47}
            self.aggr_down = nn.Linear(
                len(aggregators) * len(scalers) * hidden_channels,
                out_emb_channels,
                bias=True,
            )
        else:
            self.lin_up = nn.Linear(
                hidden_channels, out_emb_channels, bias=True
            )

        self.lin_rbf = nn.Linear(num_radial, hidden_channels, bias=False)
        self.lins = torch.nn.ModuleList()
        for _ in range(num_layers):
            self.lins.append(nn.Linear(out_emb_channels, out_emb_channels))
        self.lin = nn.Linear(out_emb_channels, out_channels, bias=False)

        self.reset_parameters()

    def reset_parameters(self):
        glorot_orthogonal(self.lin_rbf.weight, scale=2.0)
        if not self.pna:
            glorot_orthogonal(self.lin_up.weight, scale=2.0)
        for lin in self.lins:
            glorot_orthogonal(lin.weight, scale=2.0)
            lin.bias.data.fill_(0)
        self.lin.weight.data.fill_(0)

    def forward(self, x, rbf, i, num_nodes=None):
        x = self.lin_rbf(rbf) * x
        if self.pna:
            outs = [
                aggr(x, i, dim_size=num_nodes) for aggr in self.aggregators
            ]
            out = torch.cat(outs, dim=-1)
            deg = degree(i, num_nodes, dtype=x.dtype).view(-1, 1)
            outs = [scaler(out, deg, self.avg_deg) for scaler in self.scalers]
            x = torch.cat(outs, dim=-1)
            x = self.aggr_down(x)
        else:
            x = scatter(x, i, dim=0, dim_size=num_nodes)
            x = self.lin_up(x)
        for lin in self.lins:
            x = self.act(lin(x))
        return self.lin(x)


class DimeNetPlusPlus(torch.nn.Module):
    r"""DimeNet++ implementation based on https://github.com/klicperajo/dimenet.

    Args:
        hidden_channels (int): Hidden embedding size.
        out_channels (int): Size of each output sample.
        num_blocks (int): Number of building blocks.
        int_emb_size (int): Embedding size used for interaction triplets
        basis_emb_size (int): Embedding size used in the basis transformation
        out_emb_channels(int): Embedding size used for atoms in the output block
        num_spherical (int): Number of spherical harmonics.
        num_radial (int): Number of radial basis functions.
        cutoff: (float, optional): Cutoff distance for interatomic
            interactions. (default: :obj:`5.0`)
        envelope_exponent (int, optional): Shape of the smooth cutoff.
            (default: :obj:`5`)
        num_before_skip: (int, optional): Number of residual layers in the
            interaction blocks before the skip connection. (default: :obj:`1`)
        num_after_skip: (int, optional): Number of residual layers in the
            interaction blocks after the skip connection. (default: :obj:`2`)
        num_output_layers: (int, optional): Number of linear layers for the
            output blocks. (default: :obj:`3`)
        act: (function, optional): The activation funtion.
            (default: :obj:`swish`)
    """

    url = "https://github.com/klicperajo/dimenet/raw/master/pretrained"

    def __init__(
        self,
        hidden_channels,
        out_channels,
        num_blocks,
        int_emb_size,
        basis_emb_size,
        out_emb_channels,
        num_spherical,
        num_radial,
        cutoff=5.0,
        envelope_exponent=5,
        num_before_skip=1,
        num_after_skip=2,
        num_output_layers=3,
        act=swish,
        aggregators=None,
        scalers=None,
    ):
        super(DimeNetPlusPlus, self).__init__()

        self.cutoff = cutoff

        if sym is None:
            raise ImportError("Package `sympy` could not be found.")

        self.num_blocks = num_blocks

        self.rbf = BesselBasisLayer(num_radial, cutoff, envelope_exponent)
        self.sbf = SphericalBasisLayer(
            num_spherical, num_radial, cutoff, envelope_exponent
        )

        self.emb = EmbeddingBlock(num_radial, hidden_channels, act)

        self.output_blocks = torch.nn.ModuleList(
            [
                OutputPPBlock(
                    num_radial,
                    hidden_channels,
                    out_emb_channels,
                    out_channels,
                    num_output_layers,
                    act,
                    aggregators,
                    scalers,
                )
                for _ in range(num_blocks + 1)
            ]
        )

        self.interaction_blocks = torch.nn.ModuleList(
            [
                InteractionPPBlock(
                    hidden_channels,
                    int_emb_size,
                    basis_emb_size,
                    num_spherical,
                    num_radial,
                    num_before_skip,
                    num_after_skip,
                    act,
                )
                for _ in range(num_blocks)
            ]
        )

        self.reset_parameters()

    def reset_parameters(self):
        self.rbf.reset_parameters()
        self.emb.reset_parameters()
        for out in self.output_blocks:
            out.reset_parameters()
        for interaction in self.interaction_blocks:
            interaction.reset_parameters()

    def triplets(self, edge_index, num_nodes):
        row, col = edge_index  # j->i

        value = torch.arange(row.size(0), device=row.device)
        adj_t = SparseTensor(
            row=col, col=row, value=value, sparse_sizes=(num_nodes, num_nodes)
        )
        adj_t_row = adj_t[row]
        num_triplets = adj_t_row.set_value(None).sum(dim=1).to(torch.long)

        # Node indices (k->j->i) for triplets.
        idx_i = col.repeat_interleave(num_triplets)
        idx_j = row.repeat_interleave(num_triplets)
        idx_k = adj_t_row.storage.col()
        mask = idx_i != idx_k  # Remove i == k triplets.
        idx_i, idx_j, idx_k = idx_i[mask], idx_j[mask], idx_k[mask]

        # Edge indices (k-j, j->i) for triplets.
        idx_kj = adj_t_row.storage.value()[mask]
        idx_ji = adj_t_row.storage.row()[mask]

        return col, row, idx_i, idx_j, idx_k, idx_kj, idx_ji

    def forward(self, z, pos, batch=None):
        """"""
        raise NotImplementedError


@registry.register_model("dimenetplusplus")
class DimeNetPlusPlusWrap(DimeNetPlusPlus):
    def __init__(
        self,
        num_atoms,
        bond_feat_dim,  # not used
        num_targets,
        use_pbc=True,
        regress_forces=True,
        regress_position=False,
        hidden_channels=128,
        num_blocks=4,
        int_emb_size=64,
        basis_emb_size=8,
        out_emb_channels=256,
        num_spherical=7,
        num_radial=6,
        otf_graph=False,
        cutoff=10.0,
        envelope_exponent=5,
        num_before_skip=1,
        num_after_skip=2,
        num_output_layers=3,
        aggregators=None,
        scalers=None,
    ):
        self.regress_position = regress_position

        if self.regress_position:
            self.num_targets = 3
        else:
            self.num_targets = num_targets

        self.regress_forces = regress_forces
        self.use_pbc = use_pbc
        self.cutoff = cutoff
        self.otf_graph = otf_graph

        super(DimeNetPlusPlusWrap, self).__init__(
            hidden_channels=hidden_channels,
            out_channels=self.num_targets,
            num_blocks=num_blocks,
            int_emb_size=int_emb_size,
            basis_emb_size=basis_emb_size,
            out_emb_channels=out_emb_channels,
            num_spherical=num_spherical,
            num_radial=num_radial,
            cutoff=cutoff,
            envelope_exponent=envelope_exponent,
            num_before_skip=num_before_skip,
            num_after_skip=num_after_skip,
            num_output_layers=num_output_layers,
            aggregators=aggregators,
            scalers=scalers,
        )

    @conditional_grad(torch.enable_grad())
    def _forward(self, data):
        pos = data.pos
        batch = data.batch

        if self.otf_graph:
            edge_index, cell_offsets, neighbors = radius_graph_pbc(
                data, self.cutoff, 50, data.pos.device
            )
            data.edge_index = edge_index
            data.cell_offsets = cell_offsets
            data.neighbors = neighbors

        if self.use_pbc:
            out = get_pbc_distances(
                pos,
                data.edge_index,
                data.cell,
                data.cell_offsets,
                data.neighbors,
                return_offsets=True,
            )

            edge_index = out["edge_index"]
            dist = out["distances"]
            offsets = out["offsets"]

            j, i = edge_index
        else:
            edge_index = radius_graph(pos, r=self.cutoff, batch=batch)
            j, i = edge_index
            dist = (pos[i] - pos[j]).pow(2).sum(dim=-1).sqrt()

        _, _, idx_i, idx_j, idx_k, idx_kj, idx_ji = self.triplets(
            edge_index, num_nodes=data.atomic_numbers.size(0)
        )

        # Calculate angles.
        pos_i = pos[idx_i].detach()
        pos_j = pos[idx_j].detach()
        if self.use_pbc:
            pos_ji, pos_kj = (
                pos[idx_j].detach() - pos_i + offsets[idx_ji],
                pos[idx_k].detach() - pos_j + offsets[idx_kj],
            )
        else:
            pos_ji, pos_kj = (
                pos[idx_j].detach() - pos_i,
                pos[idx_k].detach() - pos_j,
            )

        a = (pos_ji * pos_kj).sum(dim=-1)
        b = torch.cross(pos_ji, pos_kj).norm(dim=-1)
        angle = torch.atan2(b, a)

        rbf = self.rbf(dist)
        sbf = self.sbf(dist, angle, idx_kj)

        # Embedding block.
        x = self.emb(data.atomic_numbers.long(), rbf, i, j)
        P = self.output_blocks[0](x, rbf, i, num_nodes=pos.size(0))

        # Interaction blocks.
        for interaction_block, output_block in zip(
            self.interaction_blocks, self.output_blocks[1:]
        ):
            x = interaction_block(x, rbf, sbf, idx_kj, idx_ji)
            P += output_block(x, rbf, i, num_nodes=pos.size(0))

        energy = (
            P.sum(dim=0)
            if data.batch is None
            else scatter(P, data.batch, dim=0)
        )

        return P, energy

    def forward(self, data):
        if self.regress_forces:
            data.pos.requires_grad_(True)

        P, energy = self._forward(data)

        if self.regress_forces:
            forces = -1 * (
                torch.autograd.grad(
                    energy,
                    data.pos,
                    grad_outputs=torch.ones_like(energy),
                    create_graph=True,
                )[0]
            )
            return energy, forces
        elif self.regress_position:
            return P
        else:
            return energy

    @property
    def num_params(self):
        return sum(p.numel() for p in self.parameters())
