 # Tencent is pleased to support the open source community by making Angel available.
 #
 # Copyright (C) 2017-2018 THL A29 Limited, a Tencent company. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in
 # compliance with the License. You may obtain a copy of the License at
 #
 # https://opensource.org/licenses/Apache-2.0
 #
 # Unless required by applicable law or agreed to in writing, software distributed under the License
 # is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
 # or implied. See the License for the specific language governing permissions and limitations under
 # the License.
 #
#!/usr/bin/env python

from __future__ import print_function

import argparse

import torch
import torch.nn.functional as F

from torch import Tensor
from typing import List


class DeepFM(torch.jit.ScriptModule):

    def __init__(self, input_dim=-1, n_fields=-1, embedding_dim=-1, fc_dims=[]):
        super(DeepFM, self).__init__()
        self.loss_fn = torch.nn.BCELoss()
        self.input_dim = input_dim
        self.n_fields = n_fields
        self.embedding_dim = embedding_dim
        self.mats = []
        if input_dim > 0 and embedding_dim > 0 and n_fields > 0 and fc_dims:
            self.bias = torch.nn.Parameter(torch.zeros(1, 1))
            self.weights = torch.nn.Parameter(torch.zeros(input_dim, 1))
            self.embedding = torch.nn.Parameter(torch.zeros(input_dim, embedding_dim))
            torch.nn.init.xavier_uniform_(self.weights)
            torch.nn.init.xavier_uniform_(self.embedding)
            dim = n_fields * embedding_dim
            for (index, fc_dim) in enumerate(fc_dims):
                self.mats.append(torch.nn.Parameter(torch.randn(dim, fc_dim)))
                self.mats.append(torch.nn.Parameter(torch.randn(1, 1)))
                torch.nn.init.xavier_uniform_(self.mats[index * 2])
                dim = fc_dim

            self.input_dim = torch.jit.Attribute(self.input_dim, int)
            self.n_fields = torch.jit.Attribute(self.n_fields, int)
            self.embedding_dim = torch.jit.Attribute(self.embedding_dim, int)
            self.mats = torch.jit.Attribute(self.mats, List[Tensor])

    @torch.jit.script_method
    def first_order(self, batch_size, index, values, bias, weights):
        # type: (int, Tensor, Tensor, Tensor, Tensor) -> Tensor
        size = batch_size
        srcs = weights.view(1, -1).mul(values.view(1, -1)).view(-1)
        output = torch.zeros(size, dtype=torch.float32)
        output.scatter_add_(0, index, srcs)
        first = output + bias
        return first

    @torch.jit.script_method
    def second_order(self, batch_size, index, values, embeddings):
        # type: (int, Tensor, Tensor, Tensor) -> Tensor
        k = embeddings.size(1)
        b = batch_size

        # t1: [k, n]
        t1 = embeddings.mul(values.view(-1, 1)).transpose_(0, 1)
        # t1: [k, b]
        t1_ = torch.zeros(k, b, dtype=torch.float32)

        for i in range(k):
            t1_[i].scatter_add_(0, index, t1[i])

        # t1: [k, b]
        t1 = t1_.pow(2)

        # t2: [k, n]
        t2 = embeddings.pow(2).mul(values.pow(2).view(-1, 1)).transpose_(0, 1)
        # t2: [k, b]
        t2_ = torch.zeros(k, b, dtype=torch.float32)
        for i in range(k):
            t2_[i].scatter_add_(0, index, t2[i])

        # t2: [k, b]
        t2 = t2_

        second = t1.sub(t2).transpose_(0, 1).sum(1).mul(0.5)
        return second

    @torch.jit.script_method
    def higher_order(self, batch_size, embeddings, mats):
        # type: (int, Tensor, List[Tensor]) -> Tensor
        b = batch_size
        output = embeddings.view(b, -1)

        for i in range(int(len(mats) / 2)):
            output = torch.relu(output.matmul(mats[i * 2]) + mats[i * 2 + 1])

        return output.view(-1)

    @torch.jit.script_method
    def forward_(self, batch_size, index, feats, values, bias, weights, embeddings, mats):
        # type: (int, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, List[Tensor]) -> Tensor

        first = self.first_order(batch_size, index, values, bias, weights)
        second = self.second_order(batch_size, index, values, embeddings)
        higher = self.higher_order(batch_size, embeddings, mats)

        return torch.sigmoid(first + second + higher)

    @torch.jit.script_method
    def forward(self, batch_size, index, feats, values):
        # type: (int, Tensor, Tensor, Tensor) -> Tensor
        batch_first = F.embedding(feats, self.weights)
        batch_second = F.embedding(feats, self.embedding)
        return self.forward_(batch_size, index, feats, values,
                             self.bias, batch_first, batch_second, self.mats)

    @torch.jit.script_method
    def loss(self, output, targets):
        return self.loss_fn(output, targets)

    @torch.jit.script_method
    def get_type(self):
        return "BIAS_WEIGHT_EMBEDDING_MATS"

    @torch.jit.script_method
    def get_name(self):
        return "DeepFM"


FLAGS = None


def main():
    dfm = DeepFM(FLAGS.input_dim, FLAGS.n_fields, FLAGS.embedding_dim, FLAGS.fc_dims)
    dfm.save("deepfm.pt")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.register("type", "bool", lambda v: v.lower() == "true")
    parser.add_argument(
        "--input_dim",
        type=int,
        default=-1,
        help="data input dim."
    )
    parser.add_argument(
        "--n_fields",
        type=int,
        default=-1,
        help="data num fields."
    )
    parser.add_argument(
        "--embedding_dim",
        type=int,
        default=-1,
        help="embedding dim."
    )
    parser.add_argument(
        "--fc_dims",
        nargs="+",
        type=int,
        default=-1,
        help="fc layers dim list."
    )
    FLAGS, unparsed = parser.parse_known_args()
    main()
