# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-FileCopyrightText: Copyright (c) 2021 NVIDIA CORPORATION & AFFILIATES
# SPDX-License-Identifier: MIT

import dgl
import numpy as np
import torch
import torch.nn as nn
from dgl import DGLGraph
from dgl.ops import edge_softmax
from torch import Tensor
from typing import Dict, Optional, Union

from ..fiber import Fiber
from .convolution import ConvSE3, ConvSE3FuseLevel
from .linear import LinearSE3
from ..utils import (
    degree_to_dim,
    aggregate_residual,
    unfuse_features,
)


class AttentionSE3(nn.Module):
    """Multi-headed sparse graph self-attention (SE(3)-equivariant)"""

    def __init__(self, num_heads: int, key_fiber: Fiber, value_fiber: Fiber):
        """
        :param num_heads:     Number of attention heads
        :param key_fiber:     Fiber for the keys (and also for the queries)
        :param value_fiber:   Fiber for the values
        """
        super().__init__()
        self.num_heads = num_heads
        self.key_fiber = key_fiber
        self.value_fiber = value_fiber

    def forward(
        self,
        value: Union[Tensor, Dict[str, Tensor]],  # edge features (may be fused)
        key: Union[Tensor, Dict[str, Tensor]],  # edge features (may be fused)
        query: Dict[str, Tensor],  # node features
        graph: DGLGraph,
    ):
        if isinstance(key, Tensor):
            # case where features of all types are fused
            key = key.reshape(key.shape[0], self.num_heads, -1)
            # need to reshape queries that way to keep the same layout as keys
            out = torch.cat([query[str(d)] for d in self.key_fiber.degrees], dim=-1)
            query = out.reshape(list(query.values())[0].shape[0], self.num_heads, -1)
        else:
            # features are not fused, need to fuse and reshape them
            key = self.key_fiber.to_attention_heads(key, self.num_heads)
            query = self.key_fiber.to_attention_heads(query, self.num_heads)

        # Compute attention weights (softmax of inner product between key and query)
        edge_weights = dgl.ops.e_dot_v(graph, key, query).squeeze(-1)
        edge_weights = edge_weights / np.sqrt(self.key_fiber.num_features)
        edge_weights = edge_softmax(graph, edge_weights)
        edge_weights = edge_weights[..., None, None]

        if isinstance(value, Tensor):
            # features of all types are fused
            v = value.view(value.shape[0], self.num_heads, -1, value.shape[-1])
            weights = edge_weights * v
            feat_out = dgl.ops.copy_e_sum(graph, weights)
            feat_out = feat_out.view(
                feat_out.shape[0], -1, feat_out.shape[-1]
            )  # merge heads
            out = unfuse_features(feat_out, self.value_fiber.degrees)
        else:
            out = {}
            for degree, channels in self.value_fiber:
                v = value[str(degree)].view(
                    -1,
                    self.num_heads,
                    channels // self.num_heads,
                    degree_to_dim(degree),
                )
                weights = edge_weights * v
                res = dgl.ops.copy_e_sum(graph, weights)
                out[str(degree)] = res.view(
                    -1, channels, degree_to_dim(degree)
                )  # merge heads

        return out


class AttentionBlockSE3(nn.Module):
    """Multi-headed sparse graph self-attention block with skip connection, linear projection (SE(3)-equivariant)"""

    def __init__(
        self,
        fiber_in: Fiber,
        fiber_out: Fiber,
        fiber_edge: Optional[Fiber] = None,
        num_heads: int = 4,
        channels_div: int = 2,
        mid_dim: int = 32,
        use_layer_norm: bool = False,
        nonlinearity: nn.Module = nn.ReLU(),
        max_degree: bool = 4,
        fuse_level: ConvSE3FuseLevel = ConvSE3FuseLevel.FULL,
        low_memory: bool = False,
        **kwargs
    ):
        """
        :param fiber_in:         Fiber describing the input features
        :param fiber_out:        Fiber describing the output features
        :param fiber_edge:       Fiber describing the edge features (node distances excluded)
        :param num_heads:        Number of attention heads
        :param channels_div:     Divide the channels by this integer for computing values
        :param use_layer_norm:   Apply layer normalization between MLP layers
        :param max_degree:       Maximum degree used in the bases computation
        :param fuse_level:       Maximum fuse level to use in TFN convolutions
        """
        super().__init__()
        if fiber_edge is None:
            fiber_edge = Fiber({})
        self.fiber_in = fiber_in
        # value_fiber has same structure as fiber_out but #channels divided by 'channels_div'
        value_fiber = Fiber(
            [(degree, channels // channels_div) for degree, channels in fiber_out]
        )
        # key_query_fiber has the same structure as fiber_out, but only degrees which are in in_fiber
        # (queries are merely projected, hence degrees have to match input)
        key_query_fiber = Fiber(
            [
                (fe.degree, fe.channels)
                for fe in value_fiber
                if fe.degree in fiber_in.degrees
            ]
        )

        self.to_key_value = ConvSE3(
            fiber_in,
            value_fiber + key_query_fiber,
            pool=False,
            fiber_edge=fiber_edge,
            use_layer_norm=use_layer_norm,
            nonlinearity=nonlinearity,
            max_degree=max_degree,
            mid_dim=mid_dim,
            fuse_level=fuse_level,
            allow_fused_output=True,
            low_memory=low_memory,
        )
        self.to_query = LinearSE3(fiber_in, key_query_fiber)
        self.attention = AttentionSE3(num_heads, key_query_fiber, value_fiber)
        self.project = LinearSE3(value_fiber + fiber_in, fiber_out)

    def forward(
        self,
        node_features: Dict[str, Tensor],
        edge_features: Dict[str, Tensor],
        graph: DGLGraph,
        basis: Dict[str, Tensor],
    ):
        fused_key_value = self.to_key_value(node_features, edge_features, graph, basis)
        key, value = self._get_key_value_from_fused(fused_key_value)

        query = self.to_query(node_features)

        z = self.attention(value, key, query, graph)
        z_concat = aggregate_residual(node_features, z, "cat")
        return self.project(z_concat)

    def _get_key_value_from_fused(self, fused_key_value):
        # Extract keys and queries features from fused features
        if isinstance(fused_key_value, Tensor):
            # Previous layer was a fully fused convolution
            value, key = torch.chunk(fused_key_value, chunks=2, dim=-2)
        else:
            key, value = {}, {}
            for degree, feat in fused_key_value.items():
                if int(degree) in self.fiber_in.degrees:
                    value[degree], key[degree] = torch.chunk(feat, chunks=2, dim=-2)
                else:
                    value[degree] = feat

        return key, value
