# Copyright (c) 2019 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.

import paddle.fluid as fluid
import numpy as np


def simple_fc_net_with_inputs(img, label, class_num=10):
    hidden = img
    for _ in range(4):
        hidden = fluid.layers.fc(
            hidden,
            size=200,
            act='relu',
            bias_attr=fluid.ParamAttr(
                initializer=fluid.initializer.Constant(value=1.0)))
    prediction = fluid.layers.fc(hidden, size=class_num, act='softmax')
    loss = fluid.layers.cross_entropy(input=prediction, label=label)
    loss = fluid.layers.mean(loss)
    return loss


def simple_fc_net(use_feed=None):
    img = fluid.layers.data(name='image', shape=[784], dtype='float32')
    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
    return simple_fc_net_with_inputs(img, label, class_num=10)


def batchnorm_fc_with_inputs(img, label, class_num=10):
    hidden = img
    for _ in range(2):
        hidden = fluid.layers.fc(
            hidden,
            size=200,
            act='relu',
            bias_attr=fluid.ParamAttr(
                initializer=fluid.initializer.Constant(value=1.0)))

        hidden = fluid.layers.batch_norm(input=hidden)

    prediction = fluid.layers.fc(hidden, size=class_num, act='softmax')
    loss = fluid.layers.cross_entropy(input=prediction, label=label)
    loss = fluid.layers.mean(loss)
    return loss


def fc_with_batchnorm(use_feed=None):
    img = fluid.layers.data(name='image', shape=[784], dtype='float32')
    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
    return batchnorm_fc_with_inputs(img, label, class_num=10)


def bow_net(use_feed,
            dict_dim,
            is_sparse=False,
            emb_dim=128,
            hid_dim=128,
            hid_dim2=96,
            class_dim=2):
    """
    BOW net
    This model is from https://github.com/PaddlePaddle/models:
    fluid/PaddleNLP/text_classification/nets.py
    """
    data = fluid.layers.data(
        name="words", shape=[1], dtype="int64", lod_level=1)
    label = fluid.layers.data(name="label", shape=[1], dtype="int64")
    emb = fluid.layers.embedding(
        input=data, is_sparse=is_sparse, size=[dict_dim, emb_dim])
    bow = fluid.layers.sequence_pool(input=emb, pool_type='sum')
    bow_tanh = fluid.layers.tanh(bow)
    fc_1 = fluid.layers.fc(input=bow_tanh, size=hid_dim, act="tanh")
    fc_2 = fluid.layers.fc(input=fc_1, size=hid_dim2, act="tanh")
    prediction = fluid.layers.fc(input=[fc_2], size=class_dim, act="softmax")
    cost = fluid.layers.cross_entropy(input=prediction, label=label)
    avg_cost = fluid.layers.mean(x=cost)

    return avg_cost


def init_data(batch_size=32, img_shape=[784], label_range=9):
    np.random.seed(5)
    assert isinstance(img_shape, list)
    input_shape = [batch_size] + img_shape
    img = np.random.random(size=input_shape).astype(np.float32)
    label = np.array(
        [np.random.randint(0, label_range) for _ in range(batch_size)]).reshape(
            (-1, 1)).astype("int64")
    return img, label
