import math

import torch

import torch.nn as nn

from torch.nn import functional as F

class MLP(nn.Module):

    """

    A simple Multi-Layer Perceptron (MLP) module consisting of two linear layers with a ReLU activation in between,

    followed by a dropout on the output.

    Attributes:

        fc1 (nn.Linear): The first fully-connected layer.

        act (nn.ReLU): ReLU activation function.

        fc2 (nn.Linear): The second fully-connected layer.

        droprateout (nn.Dropout): Dropout layer applied to the output.

    """

    def __init__(self, in_feat, hid_feat=None, out_feat=None, dropout=0.):

        """

        Initializes the MLP module.

        Args:

            in_feat (int): Number of input features.

            hid_feat (int, optional): Number of hidden features. Defaults to in_feat if not provided.

            out_feat (int, optional): Number of output features. Defaults to in_feat if not provided.

            dropout (float, optional): Dropout rate. Defaults to 0.

        """

        super().__init__()

        # Set hidden and output dimensions to input dimension if not specified

        if not hid_feat:

            hid_feat = in_feat

        if not out_feat:

            out_feat = in_feat

        self.fc1 = nn.Linear(in_feat, hid_feat)

        self.act = nn.ReLU()

        self.fc2 = nn.Linear(hid_feat, out_feat)

        self.droprateout = nn.Dropout(dropout)

    def forward(self, x):

        """

        Forward pass for the MLP.

        Args:

            x (torch.Tensor): Input tensor.

        Returns:

            torch.Tensor: Output tensor after applying the linear layers, activation, and dropout.

        """

        x = self.fc1(x)

        x = self.act(x)

        x = self.fc2(x)

        return self.droprateout(x)

class MHA(nn.Module):

    """

    Multi-Head Attention (MHA) module of the graph transformer with edge features incorporated into the attention computation.

    Attributes:

        heads (int): Number of attention heads.

        scale (float): Scaling factor for the attention scores.

        q, k, v (nn.Linear): Linear layers to project the node features into query, key, and value embeddings.

        e (nn.Linear): Linear layer to project the edge features.

        d_k (int): Dimension of each attention head.

        out_e (nn.Linear): Linear layer applied to the computed edge features.

        out_n (nn.Linear): Linear layer applied to the aggregated node features.

    """

    def __init__(self, dim, heads, attention_dropout=0.):

        """

        Initializes the Multi-Head Attention module.

        Args:

            dim (int): Dimensionality of the input features.

            heads (int): Number of attention heads.

            attention_dropout (float, optional): Dropout rate for attention (not used explicitly in this implementation).

        """

        super().__init__()

        # Ensure that dimension is divisible by the number of heads

        assert dim % heads == 0

        self.heads = heads

        self.scale = 1. / math.sqrt(dim)  # Scaling factor for attention

        # Linear layers for projecting node features

        self.q = nn.Linear(dim, dim)

        self.k = nn.Linear(dim, dim)

        self.v = nn.Linear(dim, dim)

        # Linear layer for projecting edge features

        self.e = nn.Linear(dim, dim)

        self.d_k = dim // heads  # Dimension per head

        # Linear layers for output transformations

        self.out_e = nn.Linear(dim, dim)

        self.out_n = nn.Linear(dim, dim)

    def forward(self, node, edge):

        """

        Forward pass for the Multi-Head Attention.

        Args:

            node (torch.Tensor): Node feature tensor of shape (batch, num_nodes, dim).

            edge (torch.Tensor): Edge feature tensor of shape (batch, num_nodes, num_nodes, dim).

        Returns:

            tuple: (updated node features, updated edge features)

        """

        b, n, c = node.shape

        # Compute query, key, and value embeddings and reshape for multi-head attention

        q_embed = self.q(node).view(b, n, self.heads, c // self.heads)

        k_embed = self.k(node).view(b, n, self.heads, c // self.heads)

        v_embed = self.v(node).view(b, n, self.heads, c // self.heads)

        # Compute edge embeddings

        e_embed = self.e(edge).view(b, n, n, self.heads, c // self.heads)

        # Adjust dimensions for broadcasting: add singleton dimensions to queries and keys

        q_embed = q_embed.unsqueeze(2)  # Shape: (b, n, 1, heads, c//heads)

        k_embed = k_embed.unsqueeze(1)  # Shape: (b, 1, n, heads, c//heads)

        # Compute  attention scores

        attn = q_embed * k_embed

        attn = attn / math.sqrt(self.d_k)

        attn = attn * (e_embed + 1) * e_embed   # Modulated attention incorporating edge features

        edge_out = self.out_e(attn.flatten(3))  # Flatten last dimension for linear layer

        # Apply softmax over the node dimension to obtain normalized attention weights

        attn = F.softmax(attn, dim=2)

        v_embed = v_embed.unsqueeze(1)  # Adjust dimensions to broadcast: (b, 1, n, heads, c//heads)

        v_embed = attn * v_embed

        v_embed = v_embed.sum(dim=2).flatten(2)

        node_out = self.out_n(v_embed)

        return node_out, edge_out

class Encoder_Block(nn.Module):

    """

    Transformer encoder block that integrates node and edge features.

    Consists of:

        - A multi-head attention layer with edge modulation.

        - Two MLP layers, each with residual connections and layer normalization.

    Attributes:

        ln1, ln3, ln4, ln5, ln6 (nn.LayerNorm): Layer normalization modules.

        attn (MHA): Multi-head attention module.

        mlp, mlp2 (MLP): MLP modules for further transformation of node and edge features.

    """

    def __init__(self, dim, heads, act, mlp_ratio=4, drop_rate=0.):

        """

        Initializes the encoder block.

        Args:

            dim (int): Dimensionality of the input features.

            heads (int): Number of attention heads.

            act (callable): Activation function (not explicitly used in this block, but provided for potential extensions).

            mlp_ratio (int, optional): Ratio to determine the hidden layer size in the MLP. Defaults to 4.

            drop_rate (float, optional): Dropout rate applied in the MLPs. Defaults to 0.

        """

        super().__init__()

        self.ln1 = nn.LayerNorm(dim)

        self.attn = MHA(dim, heads, drop_rate)

        self.ln3 = nn.LayerNorm(dim)

        self.ln4 = nn.LayerNorm(dim)

        self.mlp = MLP(dim, dim * mlp_ratio, dim, dropout=drop_rate)

        self.mlp2 = MLP(dim, dim * mlp_ratio, dim, dropout=drop_rate)

        self.ln5 = nn.LayerNorm(dim)

        self.ln6 = nn.LayerNorm(dim)

    def forward(self, x, y):

        """

        Forward pass of the encoder block.

        Args:

            x (torch.Tensor): Node feature tensor.

            y (torch.Tensor): Edge feature tensor.

        Returns:

            tuple: (updated node features, updated edge features)

        """

        x1 = self.ln1(x)

        x2, y1 = self.attn(x1, y)

        x2 = x1 + x2

        y2 = y + y1

        x2 = self.ln3(x2)

        y2 = self.ln4(y2)

        x = self.ln5(x2 + self.mlp(x2))

        y = self.ln6(y2 + self.mlp2(y2))

        return x, y

class TransformerEncoder(nn.Module):

    """

    Transformer Encoder composed of a sequence of encoder blocks.

    Attributes:

        Encoder_Blocks (nn.ModuleList): A list of Encoder_Block modules stacked sequentially.

    """

    def __init__(self, dim, depth, heads, act, mlp_ratio=4, drop_rate=0.1):

        """

        Initializes the Transformer Encoder.

        Args:

            dim (int): Dimensionality of the input features.

            depth (int): Number of encoder blocks to stack.

            heads (int): Number of attention heads in each block.

            act (callable): Activation function (passed to encoder blocks for potential use).

            mlp_ratio (int, optional): Ratio for determining the hidden layer size in MLP modules. Defaults to 4.

            drop_rate (float, optional): Dropout rate for the MLPs within each block. Defaults to 0.1.

        """

        super().__init__()

        self.Encoder_Blocks = nn.ModuleList([

            Encoder_Block(dim, heads, act, mlp_ratio, drop_rate)

            for _ in range(depth)

        ])

    def forward(self, x, y):

        """

        Forward pass of the Transformer Encoder.

        Args:

            x (torch.Tensor): Node feature tensor.

            y (torch.Tensor): Edge feature tensor.

        Returns:

            tuple: (final node features, final edge features) after processing through all encoder blocks.

        """

        for block in self.Encoder_Blocks:

            x, y = block(x, y)

        return x, y