from copy import deepcopy
from torch.nn.init import xavier_uniform_
import torch.nn.functional as F
from torch.nn import Parameter
from torch.nn.init import normal_
import torch.utils.checkpoint
from torch import Tensor, device
from TAAS_utils import *
from transformers.modeling_utils import ModuleUtilsMixin
from fairseq import utils
from fairseq.models import (
    FairseqEncoder,
    FairseqEncoderModel,
    register_model,
    register_model_architecture,
)
from fairseq.modules import (
    LayerNorm,
)
from fairseq.utils import safe_hasattr

def init_params(module, n_layers):
    if isinstance(module, nn.Linear):
        module.weight.data.normal_(mean=0.0, std=0.02 / math.sqrt(n_layers))
        if module.bias is not None:
            module.bias.data.zero_()
    if isinstance(module, nn.Embedding):
        module.weight.data.normal_(mean=0.0, std=0.02)


@torch.jit.script
def softmax_dropout(input, dropout_prob: float, is_training: bool):
    return F.dropout(F.softmax(input, -1), dropout_prob, is_training)


class SelfMultiheadAttention(nn.Module):
    def __init__(
            self,
            embed_dim,
            num_heads,
            dropout=0.0,
            bias=True,
            scaling_factor=1,
    ):
        super().__init__()
        self.embed_dim = embed_dim

        self.num_heads = num_heads
        self.dropout = dropout

        self.head_dim = embed_dim // num_heads
        assert (self.head_dim * num_heads == self.embed_dim), "embed_dim must be divisible by num_heads"
        self.scaling = (self.head_dim * scaling_factor) ** -0.5

        self.linear_q = nn.Linear(self.embed_dim, self.num_heads * self.head_dim)
        self.linear_k = nn.Linear(self.embed_dim, self.num_heads * self.head_dim)
        self.linear_v = nn.Linear(self.embed_dim, self.num_heads * self.head_dim)
        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=bias)

    def forward(
            self,
            query: Tensor,
            attn_bias: Tensor = None,
    ) -> Tensor:
        n_graph, n_node, embed_dim = query.size()
        # q, k, v = self.in_proj(query).chunk(3, dim=-1)

        _shape = (-1, n_graph * self.num_heads, self.head_dim)
        q = self.linear_q(query).contiguous().view(n_graph, -1, self.num_heads, self.head_dim).transpose(1, 2) * self.scaling
        k = self.linear_k(query).contiguous().view(n_graph, -1, self.num_heads, self.head_dim).transpose(1, 2)
        v = self.linear_v(query).contiguous().view(n_graph, -1, self.num_heads, self.head_dim).transpose(1, 2)

        attn_weights = torch.matmul(q, k.transpose(2, 3))
        attn_weights = attn_weights + attn_bias
        attn_probs = softmax_dropout(attn_weights, self.dropout, self.training)

        attn = torch.matmul(attn_probs, v)
        attn = attn.transpose(1, 2).contiguous().view(n_graph, -1, embed_dim)
        attn = self.out_proj(attn)
        return attn


class Graphormer3DEncoderLayer(nn.Module):
    """
    Implements a Graphormer-3D Encoder Layer.
    """

    def __init__(
            self,
            embedding_dim: int = 768,
            ffn_embedding_dim: int = 3072,
            num_attention_heads: int = 8,
            dropout: float = 0.1,
            attention_dropout: float = 0.1,
            activation_dropout: float = 0.1,
    ) -> None:
        super().__init__()

        # Initialize parameters
        self.embedding_dim = embedding_dim
        self.num_attention_heads = num_attention_heads
        self.attention_dropout = attention_dropout

        self.dropout = dropout
        self.activation_dropout = activation_dropout

        self.self_attn = SelfMultiheadAttention(self.embedding_dim, num_attention_heads, dropout=attention_dropout)
        # layer norm associated with the self attention layer
        self.self_attn_layer_norm = nn.LayerNorm(self.embedding_dim)
        self.fc1 = nn.Linear(self.embedding_dim, ffn_embedding_dim)
        self.fc2 = nn.Linear(ffn_embedding_dim, self.embedding_dim)
        self.final_layer_norm = nn.LayerNorm(self.embedding_dim)

    def forward(self, x: Tensor, attn_bias: Tensor = None):
        residual = x
        x = self.self_attn_layer_norm(x)
        x = self.self_attn(query=x, attn_bias=attn_bias)
        x = F.dropout(x, p=self.dropout, training=self.training)
        x = residual + x

        residual = x
        x = self.final_layer_norm(x)
        x = F.gelu(self.fc1(x))
        x = F.dropout(x, p=self.activation_dropout, training=self.training)
        x = self.fc2(x)
        x = F.dropout(x, p=self.dropout, training=self.training)
        x = residual + x
        return x


from fairseq.models import (
    BaseFairseqModel,
    register_model,
    register_model_architecture,
)


class Graphormer3D(BaseFairseqModel):
    def __init__(self):
        super().__init__()
        self.atom_types = 64
        self.edge_types = 64 * 64
        self.embed_dim = 768
        self.layer_nums = 12
        self.ffn_embed_dim = 768
        self.blocks = 4
        self.attention_heads = 48
        self.input_dropout = 0.0
        self.dropout = 0.1
        self.attention_dropout = 0.1
        self.activation_dropout = 0.0
        self.node_loss_weight = 15
        self.min_node_loss_weight = 1
        self.eng_loss_weight = 1
        self.num_kernel = 128
        self.atom_encoder = nn.Embedding(self.atom_types, self.embed_dim, padding_idx=0)
        self.edge_embedding = nn.Embedding(32, self.attention_heads, padding_idx=0)
        self.input_dropout = nn.Dropout(0.1)
        self.layers = nn.ModuleList(
            [
                Graphormer3DEncoderLayer(
                    self.embed_dim,
                    self.ffn_embed_dim,
                    num_attention_heads=self.attention_heads,
                    dropout=self.dropout,
                    attention_dropout=self.attention_dropout,
                    activation_dropout=self.activation_dropout,
                )
                for _ in range(self.layer_nums)
            ]
        )
        self.atom_encoder = nn.Embedding(512 * 9 + 1, self.embed_dim, padding_idx=0)
        self.edge_encoder = nn.Embedding(512 * 3 + 1, self.attention_heads, padding_idx=0)
        self.edge_type = 'multi_hop'
        if self.edge_type == 'multi_hop':
            self.edge_dis_encoder = nn.Embedding(16 * self.attention_heads * self.attention_heads, 1)
        self.spatial_pos_encoder = nn.Embedding(512, self.attention_heads, padding_idx=0)
        self.in_degree_encoder = nn.Embedding(512, self.embed_dim, padding_idx=0)
        self.out_degree_encoder = nn.Embedding(512, self.embed_dim, padding_idx=0)
        self.node_position_ids_encoder = nn.Embedding(10, self.embed_dim, padding_idx=0)

        self.final_ln: Callable[[Tensor], Tensor] = nn.LayerNorm(self.embed_dim)

        self.engergy_proj: Callable[[Tensor], Tensor] = NonLinear(self.embed_dim, 1)
        self.energe_agg_factor: Callable[[Tensor], Tensor] = nn.Embedding(3, 1)
        nn.init.normal_(self.energe_agg_factor.weight, 0, 0.01)

        self.graph_token = nn.Embedding(1, 768)
        self.graph_token_virtual_distance = nn.Embedding(1, self.attention_heads)

        K = self.num_kernel

        self.gbf: Callable[[Tensor, Tensor], Tensor] = GaussianLayer(K, self.edge_types)
        self.bias_proj: Callable[[Tensor], Tensor] = NonLinear(K, self.attention_heads)
        self.edge_proj: Callable[[Tensor], Tensor] = nn.Linear(K, self.embed_dim)
        self.node_proc: Callable[[Tensor, Tensor, Tensor], Tensor] = NodeTaskHead(self.embed_dim, self.attention_heads)

    def forward(self, node_feature, spatial_pos, in_degree, out_degree, edge_type_matrix, edge_input, node_position_ids):
        """
        attn_bias：图中节点对之间的最短路径距离超过最短路径限制最大距离(spatial_pos_max)的位置为-inf，其余位置为0，形状为(n_graph, n_node+1, n_node+1)
        spatial_pos：图中节点对之间的最短路径长度，形状为(n_graph, n_node, n_node)
        x：图中节点的特征，形状为(n_graph, n_node, n_node_features)
        in_degree：图中节点的入度，形状为(n_graph, n_node)
        out_degree：图中节点的出度，形状为(n_graph, n_node)
        edge_input：图中节点对之间的最短路径(限制最短路径最大跳数为multi_hop_max_dist)上的边的特征，形状为(n_graph, n_node, n_node, multi_hop_max_dist, n_edge_features)
        attn_edge_type：图的边特征，形状为(n_graph, n_node, n_node, n_edge_features)
        :param batch_data:
        :return:
        """
        # attn_bias, spatial_pos, x = batch_data.attn_bias, batch_data.spatial_pos, batch_data.x
        # in_degree, out_degree = batch_data.in_degree, batch_data.out_degree
        # edge_input, attn_edge_type = batch_data.edge_input, batch_data.attn_edge_type
        # graph_attn_bias
        attn_edge_type = self.edge_embedding(edge_type_matrix)
        edge_input = self.edge_embedding(edge_input)#.mean(-2)
        # 添加虚拟节点表示全图特征表示，之后按照图中正常节点处理
        n_graph, n_node = node_feature.size()[:2]
        # graph_attn_bias = attn_bias.clone()
        # graph_attn_bias = graph_attn_bias.unsqueeze(1).repeat(1, self.attention_heads, 1, 1)  # [n_graph, n_head, n_node+1, n_node+1]

        # spatial pos
        # 空间编码,节点之间最短路径长度对应的可学习标量
        # [n_graph, n_node, n_node, n_head] -> [n_graph, n_head, n_node, n_node]
        spatial_pos_bias = self.spatial_pos_encoder(spatial_pos).permute(0, 3, 1, 2)
        # graph_attn_bias[:, :, 1:, 1:] = graph_attn_bias[:, :, 1:, 1:] + spatial_pos_bias
        # graph_attn_bias = spatial_pos_bias
        # reset spatial pos here
        # 所有节点都和虚拟节点直接有边相连，则所有节点和虚拟节点之间的最短路径长度为1
        # t = self.graph_token_virtual_distance.weight.view(1, self.attention_heads, 1)
        # graph_attn_bias[:, :, 1:, 0] = graph_attn_bias[:, :, 1:, 0] + t
        # graph_attn_bias[:, :, 0, :] = graph_attn_bias[:, :, 0, :] + t
        # edge feature
        # 每个节点对沿最短路径计算边特征和可学习嵌入点积的平均值，并作为偏置项添加到注意模块中
        if self.edge_type == 'multi_hop':
            spatial_pos_ = spatial_pos.clone()
            spatial_pos_[spatial_pos_ == 0] = 1  # set pad to 1
            # set 1 to 1, x > 1 to x - 1
            spatial_pos_ = torch.where(spatial_pos_ > 1, spatial_pos_ - 1, spatial_pos_)
            # if self.multi_hop_max_dist > 0:
            #     spatial_pos_ = spatial_pos_.clamp(0, self.multi_hop_max_dist)
            #     edge_input = edge_input[:, :, :, :self.multi_hop_max_dist, :]
            # [n_graph, n_node, n_node, max_dist, n_head]
            # edge_input = self.edge_encoder(edge_input).mean(-2)
            max_dist = edge_input.size(-2)
            edge_input_flat = edge_input.permute(3, 0, 1, 2, 4).reshape(max_dist, -1, self.attention_heads)
            edge_input_flat = torch.bmm(edge_input_flat, self.edge_dis_encoder.weight.reshape(-1, self.attention_heads, self.attention_heads)[:max_dist, :, :])
            edge_input = edge_input_flat.reshape(max_dist, n_graph, n_node, n_node, self.attention_heads).permute(1, 2, 3, 0, 4)
            edge_input = (edge_input.sum(-2) / (spatial_pos_.float().unsqueeze(-1))).permute(0, 3, 1, 2)
        else:
            # [n_graph, n_node, n_node, n_head] -> [n_graph, n_head, n_node, n_node]
            edge_input = self.edge_encoder(attn_edge_type).mean(-2).permute(0, 3, 1, 2)

        # graph_attn_bias[:, :, 1:, 1:] = graph_attn_bias[:, :, 1:, 1:] + edge_input
        graph_attn_bias = spatial_pos_bias + edge_input
        # graph_attn_bias = graph_attn_bias + attn_bias.unsqueeze(1)  # reset
        # graph_attn_bias = graph_attn_bias.contiguous().view(-1, 6, 6)
        # node feauture + graph token
        # node_feature = x # self.atom_encoder(x).sum(dim=-2)  # [n_graph, n_node, n_hidden]
        # if self.flag and perturb is not None:
        #     node_feature += perturb

        node_position_embedding = self.node_position_ids_encoder(node_position_ids)
        node_position_embedding = node_position_embedding.contiguous().view(n_graph, n_node, self.embed_dim)
        # print(node_position_embedding.shape)
        # 根据节点的入度、出度为每个节点分配两个实值嵌入向量，添加到节点特征中作为输入
        node_feature = node_feature + self.in_degree_encoder(in_degree) + \
                       self.out_degree_encoder(out_degree) + node_position_embedding
        # print(node_feature.shape)
        # graph_token_feature = self.graph_token.weight.unsqueeze(0).repeat(n_graph, 1, 1)
        # graph_node_feature = torch.cat([graph_token_feature, node_feature], dim=1)

        # transfomrer encoder
        output = self.input_dropout(node_feature)#.permute(1, 0, 2)
        for enc_layer in self.layers:
            output = enc_layer(output, graph_attn_bias)
        output = self.final_ln(output)

        # output part
        # 整个图的表示是最后一层虚拟节点的特征
        # if self.dataset_name == 'PCQM4M-LSC':
        #     # get whole graph rep
        #     output = self.out_proj(output[:, 0, :])
        # else:
        #     output = self.downstream_out_proj(output[:, 0, :])
        # print(output.shape)
        return output


@torch.jit.script
def gaussian(x, mean, std):
    pi = 3.14159
    a = (2 * pi) ** 0.5
    return torch.exp(-0.5 * (((x - mean) / std) ** 2)) / (a * std)


class GaussianLayer(nn.Module):
    def __init__(self, K=128, edge_types=1024):
        super().__init__()
        self.K = K
        self.means = nn.Embedding(1, K)
        self.stds = nn.Embedding(1, K)
        self.mul = nn.Embedding(edge_types, 1)
        self.bias = nn.Embedding(edge_types, 1)
        nn.init.uniform_(self.means.weight, 0, 3)
        nn.init.uniform_(self.stds.weight, 0, 3)
        nn.init.constant_(self.bias.weight, 0)
        nn.init.constant_(self.mul.weight, 1)

    def forward(self, x, edge_types):
        mul = self.mul(edge_types)
        bias = self.bias(edge_types)
        x = mul * x.unsqueeze(-1) + bias
        x = x.expand(-1, -1, -1, self.K)
        mean = self.means.weight.float().view(-1)
        std = self.stds.weight.float().view(-1).abs() + 1e-5
        return gaussian(x.float(), mean, std).type_as(self.means.weight)


class RBF(nn.Module):
    def __init__(self, K, edge_types):
        super().__init__()
        self.K = K
        self.means = nn.parameter.Parameter(torch.empty(K))
        self.temps = nn.parameter.Parameter(torch.empty(K))
        self.mul: Callable[..., Tensor] = nn.Embedding(edge_types, 1)
        self.bias: Callable[..., Tensor] = nn.Embedding(edge_types, 1)
        nn.init.uniform_(self.means, 0, 3)
        nn.init.uniform_(self.temps, 0.1, 10)
        nn.init.constant_(self.bias.weight, 0)
        nn.init.constant_(self.mul.weight, 1)

    def forward(self, x: Tensor, edge_types):
        mul = self.mul(edge_types)
        bias = self.bias(edge_types)
        x = mul * x.unsqueeze(-1) + bias
        mean = self.means.float()
        temp = self.temps.float().abs()
        return ((x - mean).square() * (-temp)).exp().type_as(self.means)


class NonLinear(nn.Module):
    def __init__(self, input, output_size, hidden=None):
        super(NonLinear, self).__init__()
        if hidden is None:
            hidden = input
        self.layer1 = nn.Linear(input, hidden)
        self.layer2 = nn.Linear(hidden, output_size)

    def forward(self, x):
        x = F.gelu(self.layer1(x))
        x = self.layer2(x)
        return x


class NodeTaskHead(nn.Module):
    def __init__(
            self,
            embed_dim: int,
            num_heads: int,
    ):
        super().__init__()
        self.embed_dim = embed_dim
        self.q_proj: Callable[[Tensor], Tensor] = nn.Linear(embed_dim, embed_dim)
        self.k_proj: Callable[[Tensor], Tensor] = nn.Linear(embed_dim, embed_dim)
        self.v_proj: Callable[[Tensor], Tensor] = nn.Linear(embed_dim, embed_dim)
        self.num_heads = num_heads
        self.scaling = (embed_dim // num_heads) ** -0.5
        self.force_proj1: Callable[[Tensor], Tensor] = nn.Linear(embed_dim, 1)
        self.force_proj2: Callable[[Tensor], Tensor] = nn.Linear(embed_dim, 1)
        self.force_proj3: Callable[[Tensor], Tensor] = nn.Linear(embed_dim, 1)

    def forward(
            self,
            query: Tensor,
            attn_bias: Tensor,
            delta_pos: Tensor,
    ) -> Tensor:
        bsz, n_node, _ = query.size()
        q = (self.q_proj(query).view(bsz, n_node, self.num_heads, -1).transpose(1, 2) * self.scaling)
        k = self.k_proj(query).view(bsz, n_node, self.num_heads, -1).transpose(1, 2)
        v = self.v_proj(query).view(bsz, n_node, self.num_heads, -1).transpose(1, 2)
        attn = q @ k.transpose(-1, -2)  # [bsz, head, n, n]
        attn_probs = softmax_dropout(attn.view(-1, n_node, n_node) + attn_bias, 0.1, self.training).view(bsz, self.num_heads, n_node, n_node)
        rot_attn_probs = attn_probs.unsqueeze(-1) * delta_pos.unsqueeze(1).type_as(attn_probs)  # [bsz, head, n, n, 3]
        rot_attn_probs = rot_attn_probs.permute(0, 1, 4, 2, 3)
        x = rot_attn_probs @ v.unsqueeze(2)  # [bsz, head , 3, n, d]
        x = x.permute(0, 3, 2, 1, 4).contiguous().view(bsz, n_node, 3, -1)
        f1 = self.force_proj1(x[:, :, 0, :]).view(bsz, n_node, 1)
        f2 = self.force_proj2(x[:, :, 1, :]).view(bsz, n_node, 1)
        f3 = self.force_proj3(x[:, :, 2, :]).view(bsz, n_node, 1)
        cur_force = torch.cat([f1, f2, f3], dim=-1).float()
        return cur_force