from typing import Optional, Tuple, Union

import torch
import torch.nn.functional as F
from torch import Tensor
from torch.nn import Parameter
from torch_sparse import SparseTensor, set_diag

from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.dense.linear import Linear
from torch_geometric.typing import NoneType  # noqa
from torch_geometric.typing import Adj, OptPairTensor, OptTensor, Size
from torch_geometric.utils import add_self_loops, remove_self_loops, softmax
from torch_geometric.nn.inits import glorot, zeros


'''
def group(xs: List[Tensor], aggr: Optional[str]) -> Optional[Tensor]:
    if len(xs) == 0:
        return None
    elif aggr is None:
        return torch.stack(xs, dim=1)
    elif len(xs) == 1:
        return xs[0]
    elif isinstance(xs[0], tuple):
        return xs
    else:
        out = torch.stack(xs, dim=0)
        out = getattr(torch, aggr)(out, dim=0)
        out = out[0] if isinstance(out, tuple) else out
        return out
'''



class GATConv(MessagePassing):
    def __init__(
        self,
        in_channels: Union[int, Tuple[int, int]],
        out_channels: int,
        heads: int = 1,
        concat: bool = True,
        negative_slope: float = 0.2,
        dropout: float = 0.0,
        add_self_loops: bool = True,
        edge_dim: Optional[int] = None,
        fill_value: Union[float, Tensor, str] = 'mean',
        bias: bool = True,
        sigmoid_gat: bool = False,
        temperature: float = 1,
        pheno_condition: bool = False,
        **kwargs,
    ):
        kwargs.setdefault('aggr', 'add')
        super().__init__(node_dim=0, **kwargs)

        self.in_channels = in_channels
        self.out_channels = out_channels
        self.heads = heads
        self.concat = concat
        self.negative_slope = negative_slope
        self.dropout = dropout
        self.add_self_loops = add_self_loops
        self.edge_dim = edge_dim
        self.fill_value = fill_value
        self.sigmoid_gat = sigmoid_gat
        self.temperature = temperature
        self.pheno_condition = pheno_condition
        
        if self.pheno_condition == 'ATT':
            self.lin_edge_ = Linear(self.out_channels, heads * out_channels, bias=False,
                                   weight_initializer='glorot')
            self.att_edge = Parameter(torch.Tensor(1, heads, out_channels))
            
        elif self.pheno_condition == 'MSG':
            self.pheno_mlp = Linear(edge_dim, heads * out_channels, bias=False,
                                   weight_initializer='glorot')
        
        # In case we are operating in bipartite graphs, we apply separate
        # transformations 'lin_src' and 'lin_dst' to source and target nodes:
        if isinstance(in_channels, int):
            self.lin_src = Linear(in_channels, heads * out_channels,
                                  bias=False, weight_initializer='glorot')
            self.lin_dst = self.lin_src
        else:
            self.lin_src = Linear(in_channels[0], heads * out_channels, False,
                                  weight_initializer='glorot')
            self.lin_dst = Linear(in_channels[1], heads * out_channels, False,
                                  weight_initializer='glorot')

        # The learnable parameters to compute attention coefficients:
        self.att_src = Parameter(torch.Tensor(1, heads, out_channels))
        self.att_dst = Parameter(torch.Tensor(1, heads, out_channels))

        if edge_dim is not None:
            self.lin_edge = Linear(edge_dim, heads * out_channels, bias=False,
                                   weight_initializer='glorot')
            self.att_edge = Parameter(torch.Tensor(1, heads, out_channels))
        else:
            self.lin_edge = None
            self.register_parameter('att_edge', None)

        if bias and concat:
            self.bias = Parameter(torch.Tensor(heads * out_channels))
        elif bias and not concat:
            self.bias = Parameter(torch.Tensor(out_channels))
        else:
            self.register_parameter('bias', None)

        self.reset_parameters()

    def reset_parameters(self):
        self.lin_src.reset_parameters()
        self.lin_dst.reset_parameters()
        if self.lin_edge is not None:
            self.lin_edge.reset_parameters()
        glorot(self.att_src)
        glorot(self.att_dst)
        glorot(self.att_edge)
        zeros(self.bias)

    def forward(self, x: Union[Tensor, OptPairTensor], edge_index: Adj,
                edge_attr: OptTensor = None, size: Size = None, pheno_emb = None,
                return_attention_weights=None, return_raw_attention_weights = None):
       
        
        if return_raw_attention_weights:
            self.return_raw_attention_weights = True
        else:
            self.return_raw_attention_weights = False
            
        H, C = self.heads, self.out_channels

        # We first transform the input node features. If a tuple is passed, we
        # transform source and target node features via separate weights:
        if isinstance(x, Tensor):
            assert x.dim() == 2, "Static graphs not supported in 'GATConv'"
            x_src = x_dst = self.lin_src(x).view(-1, H, C)
        else:  # Tuple of source and target node features:
            x_src, x_dst = x
            assert x_src.dim() == 2, "Static graphs not supported in 'GATConv'"
            x_src = self.lin_src(x_src).view(-1, H, C)
            if x_dst is not None:
                x_dst = self.lin_dst(x_dst).view(-1, H, C)

        x = (x_src, x_dst)

        # Next, we compute node-level attention coefficients, both for source
        # and target nodes (if present):
        alpha_src = (x_src * self.att_src).sum(dim=-1)
        alpha_dst = None if x_dst is None else (x_dst * self.att_dst).sum(-1)
        alpha = (alpha_src, alpha_dst)

        if self.add_self_loops:
            if isinstance(edge_index, Tensor):
                # We only want to add self-loops for nodes that appear both as
                # source and target nodes:
                num_nodes = x_src.size(0)
                if x_dst is not None:
                    num_nodes = min(num_nodes, x_dst.size(0))
                num_nodes = min(size) if size is not None else num_nodes
                edge_index, edge_attr = remove_self_loops(
                    edge_index, edge_attr)
                edge_index, edge_attr = add_self_loops(
                    edge_index, edge_attr, fill_value=self.fill_value,
                    num_nodes=num_nodes)
            elif isinstance(edge_index, SparseTensor):
                if self.edge_dim is None:
                    edge_index = set_diag(edge_index)
                else:
                    raise NotImplementedError(
                        "The usage of 'edge_attr' and 'add_self_loops' "
                        "simultaneously is currently not yet supported for "
                        "'edge_index' in a 'SparseTensor' form")

        # edge_updater_type: (alpha: OptPairTensor, edge_attr: OptTensor)
        alpha = self.edge_updater(edge_index, alpha=alpha, edge_attr=edge_attr)
        
        #if self.return_raw_attention_weights:
        #    return 0, (edge_index, alpha)
        # propagate_type: (x: OptPairTensor, alpha: Tensor)
        out = self.propagate(edge_index, x=x, alpha=alpha, size=size)

        if self.concat:
            out = out.view(-1, self.heads * self.out_channels)
        else:
            out = out.mean(dim=1)

        if self.bias is not None:
            out = out + self.bias

        if isinstance(return_attention_weights, bool):
            if isinstance(edge_index, Tensor):
                return out, (edge_index, alpha)
            elif isinstance(edge_index, SparseTensor):
                return out, edge_index.set_value(alpha, layout='coo')
        else:
            return out

    def edge_update(self, alpha_j: Tensor, alpha_i: OptTensor,
                    edge_attr: OptTensor, index: Tensor, ptr: OptTensor,
                    size_i: Optional[int]) -> Tensor:
        # Given edge-level attention coefficients for source and target nodes,
        # we simply need to sum them up to "emulate" concatenation:
        alpha = alpha_j if alpha_i is None else alpha_j + alpha_i

        if edge_attr is not None and self.lin_edge is not None:
            if edge_attr.dim() == 1:
                edge_attr = edge_attr.view(-1, 1)
            #print(edge_attr)
            #print(edge_attr.shape)
            edge_attr = self.lin_edge(edge_attr)
            edge_attr = edge_attr.view(-1, self.heads, self.out_channels)
            alpha_edge = (edge_attr * self.att_edge).sum(dim=-1)
            alpha = alpha + alpha_edge

        alpha = F.leaky_relu(alpha, self.negative_slope)        
            
        if self.sigmoid_gat:
            alpha = torch.sigmoid(alpha/self.temperature)
        else:
            if not self.return_raw_attention_weights:
                alpha = softmax(alpha/self.temperature, index, ptr, size_i)
        alpha = F.dropout(alpha, p=self.dropout, training=self.training)
        return alpha

    def message(self, x_j: Tensor, alpha: Tensor) -> Tensor:
        return alpha.unsqueeze(-1) * x_j

    def __repr__(self) -> str:
        return (f'{self.__class__.__name__}({self.in_channels}, '
                f'{self.out_channels}, heads={self.heads})')
