# Copyright (c) 2018 PaddlePaddle Authors. 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
#
#     http://www.apache.org/licenses/LICENSE-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.
"""
neural network for word2vec
"""

from __future__ import print_function

import math
import numpy as np

import paddle.fluid as fluid


def skip_gram_word2vec(dict_size,
                       word_frequencys,
                       embedding_size,
                       max_code_length=None,
                       with_hsigmoid=False,
                       with_nce=True,
                       is_sparse=False):
    def nce_layer(input, label, embedding_size, num_total_classes,
                  num_neg_samples, sampler, word_frequencys, sample_weight):

        w_param_name = "nce_w"
        b_param_name = "nce_b"
        w_param = fluid.default_main_program().global_block().create_parameter(
            shape=[num_total_classes, embedding_size],
            dtype='float32',
            name=w_param_name)
        b_param = fluid.default_main_program().global_block().create_parameter(
            shape=[num_total_classes, 1], dtype='float32', name=b_param_name)

        cost = fluid.layers.nce(input=input,
                                label=label,
                                num_total_classes=num_total_classes,
                                sampler=sampler,
                                custom_dist=word_frequencys,
                                sample_weight=sample_weight,
                                param_attr=fluid.ParamAttr(name=w_param_name),
                                bias_attr=fluid.ParamAttr(name=b_param_name),
                                num_neg_samples=num_neg_samples,
                                is_sparse=is_sparse)

        return cost

    def hsigmoid_layer(input, label, path_table, path_code, non_leaf_num,
                       is_sparse):
        if non_leaf_num is None:
            non_leaf_num = dict_size

        cost = fluid.layers.hsigmoid(
            input=input,
            label=label,
            num_classes=non_leaf_num,
            path_table=path_table,
            path_code=path_code,
            is_custom=True,
            is_sparse=is_sparse)

        return cost

    datas = []

    input_word = fluid.layers.data(name="input_word", shape=[1], dtype='int64')
    predict_word = fluid.layers.data(
        name='predict_word', shape=[1], dtype='int64')
    datas.append(input_word)
    datas.append(predict_word)

    if with_hsigmoid:
        path_table = fluid.layers.data(
            name='path_table',
            shape=[max_code_length if max_code_length else 40],
            dtype='int64')
        path_code = fluid.layers.data(
            name='path_code',
            shape=[max_code_length if max_code_length else 40],
            dtype='int64')
        datas.append(path_table)
        datas.append(path_code)

    py_reader = fluid.layers.create_py_reader_by_data(
        capacity=64, feed_list=datas, name='py_reader', use_double_buffer=True)

    words = fluid.layers.read_file(py_reader)
    target_emb = fluid.layers.embedding(
        input=words[0],
        is_sparse=is_sparse,
        size=[dict_size, embedding_size],
        param_attr=fluid.ParamAttr(
            name='embeding',
            initializer=fluid.initializer.Normal(scale=1 /
                                                 math.sqrt(dict_size))))
    context_emb = fluid.layers.embedding(
        input=words[1],
        is_sparse=is_sparse,
        size=[dict_size, embedding_size],
        param_attr=fluid.ParamAttr(
            name='embeding',
            initializer=fluid.initializer.Normal(scale=1 /
                                                 math.sqrt(dict_size))))
    cost, cost_nce, cost_hs = None, None, None

    if with_nce:
        cost_nce = nce_layer(target_emb, words[1], embedding_size, dict_size, 5,
                             "uniform", word_frequencys, None)
        cost = cost_nce
    if with_hsigmoid:
        cost_hs = hsigmoid_layer(context_emb, words[0], words[2], words[3],
                                 dict_size, is_sparse)
        cost = cost_hs
    if with_nce and with_hsigmoid:
        cost = fluid.layers.elementwise_add(cost_nce, cost_hs)

    avg_cost = fluid.layers.reduce_mean(cost)

    return avg_cost, py_reader