#   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.
"""
Distribute CTR model for test fleet api
"""

import shutil
import tempfile
import time

import paddle
import paddle.fluid as fluid
import os
import numpy as np

import ctr_dataset_reader
from test_dist_fleet_base import runtime_main, FleetDistRunnerBase

paddle.enable_static()

# Fix seed for test
fluid.default_startup_program().random_seed = 1
fluid.default_main_program().random_seed = 1


def fake_ctr_reader():

    def reader():
        for _ in range(1000):
            deep = np.random.random_integers(0, 1e5 - 1, size=16).tolist()
            wide = np.random.random_integers(0, 1e5 - 1, size=8).tolist()
            label = np.random.random_integers(0, 1, size=1).tolist()
            yield [deep, wide, label]

    return reader


class TestDistCTR2x2(FleetDistRunnerBase):
    """
    For test CTR model, using Fleet api
    """

    def net(self, args, is_train=True, batch_size=4, lr=0.01):
        """
        network definition

        Args:
            batch_size(int): the size of mini-batch for training
            lr(float): learning rate of training
        Returns:
            avg_cost: LoDTensor of cost.
        """
        dnn_input_dim, lr_input_dim = int(1e5), int(1e5)

        dnn_data = fluid.layers.data(name="dnn_data",
                                     shape=[-1, 1],
                                     dtype="int64",
                                     lod_level=1,
                                     append_batch_size=False)
        lr_data = fluid.layers.data(name="lr_data",
                                    shape=[-1, 1],
                                    dtype="int64",
                                    lod_level=1,
                                    append_batch_size=False)
        label = fluid.layers.data(name="click",
                                  shape=[-1, 1],
                                  dtype="int64",
                                  lod_level=0,
                                  append_batch_size=False)

        datas = [dnn_data, lr_data, label]

        if args.reader == "pyreader":
            if is_train:
                self.reader = fluid.io.PyReader(feed_list=datas,
                                                capacity=64,
                                                iterable=False,
                                                use_double_buffer=False)
            else:
                self.test_reader = fluid.io.PyReader(feed_list=datas,
                                                     capacity=64,
                                                     iterable=False,
                                                     use_double_buffer=False)


# build dnn model
        dnn_layer_dims = [128, 128, 64, 32, 1]
        dnn_embedding = fluid.layers.embedding(
            is_distributed=False,
            input=dnn_data,
            size=[dnn_input_dim, dnn_layer_dims[0]],
            param_attr=fluid.ParamAttr(
                name="deep_embedding",
                initializer=fluid.initializer.Constant(value=0.01)),
            is_sparse=True,
            padding_idx=0)
        dnn_pool = fluid.layers.sequence_pool(input=dnn_embedding,
                                              pool_type="sum")
        dnn_out = dnn_pool
        for i, dim in enumerate(dnn_layer_dims[1:]):
            fc = fluid.layers.fc(
                input=dnn_out,
                size=dim,
                act="relu",
                param_attr=fluid.ParamAttr(
                    initializer=fluid.initializer.Constant(value=0.01)),
                name='dnn-fc-%d' % i)
            dnn_out = fc

        # build lr model
        lr_embbding = fluid.layers.embedding(
            is_distributed=False,
            input=lr_data,
            size=[lr_input_dim, 1],
            param_attr=fluid.ParamAttr(
                name="wide_embedding",
                initializer=fluid.initializer.Constant(value=0.01)),
            is_sparse=True,
            padding_idx=0)
        lr_pool = fluid.layers.sequence_pool(input=lr_embbding, pool_type="sum")

        merge_layer = fluid.layers.concat(input=[dnn_out, lr_pool], axis=1)

        predict = fluid.layers.fc(input=merge_layer, size=2, act='softmax')
        acc = fluid.layers.accuracy(input=predict, label=label)

        auc_var, batch_auc_var, auc_states = fluid.layers.auc(input=predict,
                                                              label=label)

        cost = fluid.layers.cross_entropy(input=predict, label=label)
        avg_cost = paddle.mean(x=cost)

        self.feeds = datas
        self.train_file_path = ["fake1", "fake2"]
        self.avg_cost = avg_cost
        self.predict = predict

        return avg_cost

    def check_model_right(self, dirname):
        dirname = dirname + '/dnn_plugin/'
        model_filename = os.path.join(dirname, "__model__")

        with open(model_filename, "rb") as f:
            program_desc_str = f.read()

        program = fluid.Program.parse_from_string(program_desc_str)
        with open(os.path.join(dirname, "__model__.proto"), "w") as wn:
            wn.write(str(program))

    def do_distributed_testing(self, fleet):
        """
        do distributed
        """
        exe = self.get_executor()

        batch_size = 4
        test_reader = paddle.batch(fake_ctr_reader(), batch_size=batch_size)
        self.test_reader.decorate_sample_list_generator(test_reader)

        pass_start = time.time()
        batch_idx = 0

        self.test_reader.start()
        try:
            while True:
                batch_idx += 1
                loss_val = exe.run(program=paddle.static.default_main_program(),
                                   fetch_list=[self.avg_cost.name])
                loss_val = np.mean(loss_val)
                message = "TEST ---> batch_idx: {} loss: {}\n".format(
                    batch_idx, loss_val)
                fleet.util.print_on_rank(message, 0)
        except fluid.core.EOFException:
            self.test_reader.reset()

        pass_time = time.time() - pass_start
        message = "Distributed Test Succeed, Using Time {}\n".format(pass_time)
        fleet.util.print_on_rank(message, 0)

    def do_pyreader_training(self, fleet):
        """
        do training using dataset, using fetch handler to catch variable
        Args:
            fleet(Fleet api): the fleet object of Parameter Server, define distribute training role
        """
        exe = self.get_executor()
        exe.run(fluid.default_startup_program())
        fleet.init_worker()

        batch_size = 4
        train_reader = paddle.batch(fake_ctr_reader(), batch_size=batch_size)
        self.reader.decorate_sample_list_generator(train_reader)

        for epoch_id in range(1):
            self.reader.start()
            try:
                pass_start = time.time()
                while True:
                    loss_val = exe.run(program=fluid.default_main_program(),
                                       fetch_list=[self.avg_cost.name])
                    loss_val = np.mean(loss_val)
                    # TODO(randomly fail)
                    #   reduce_output = fleet.util.all_reduce(
                    #       np.array(loss_val), mode="sum")
                    #   loss_all_trainer = fleet.util.all_gather(float(loss_val))
                    #   loss_val = float(reduce_output) / len(loss_all_trainer)
                    message = "TRAIN ---> pass: {} loss: {}\n".format(
                        epoch_id, loss_val)
                    fleet.util.print_on_rank(message, 0)

                pass_time = time.time() - pass_start
            except fluid.core.EOFException:
                self.reader.reset()

        dirname = os.getenv("SAVE_DIRNAME", None)
        if dirname:
            fleet.save_persistables(exe, dirname=dirname)

        model_dir = tempfile.mkdtemp()
        fleet.save_inference_model(exe, model_dir,
                                   [feed.name for feed in self.feeds],
                                   self.avg_cost)
        if fleet.is_first_worker():
            self.check_model_right(model_dir)
        shutil.rmtree(model_dir)

    def do_dataset_training_queuedataset(self, fleet):
        train_file_list = ctr_dataset_reader.prepare_fake_data()

        exe = self.get_executor()
        exe.run(fluid.default_startup_program())
        fleet.init_worker()

        thread_num = 2
        batch_size = 128
        filelist = train_file_list

        # config dataset
        dataset = paddle.distributed.QueueDataset()
        pipe_command = 'python ctr_dataset_reader.py'

        dataset.init(batch_size=batch_size,
                     use_var=self.feeds,
                     pipe_command=pipe_command,
                     thread_num=thread_num)

        dataset.set_filelist(filelist)

        for epoch_id in range(1):
            pass_start = time.time()
            dataset.set_filelist(filelist)
            exe.train_from_dataset(program=fluid.default_main_program(),
                                   dataset=dataset,
                                   fetch_list=[self.avg_cost],
                                   fetch_info=["cost"],
                                   print_period=2,
                                   debug=int(os.getenv("Debug", "0")))
            pass_time = time.time() - pass_start

        if os.getenv("SAVE_MODEL") == "1":
            model_dir = tempfile.mkdtemp()
            fleet.save_inference_model(exe, model_dir,
                                       [feed.name for feed in self.feeds],
                                       self.avg_cost)
            if fleet.is_first_worker():
                self.check_model_right(model_dir)
            shutil.rmtree(model_dir)

        dirname = os.getenv("SAVE_DIRNAME", None)
        if dirname:
            fleet.save_persistables(exe, dirname=dirname)

    def do_dataset_training(self, fleet):
        train_file_list = ctr_dataset_reader.prepare_fake_data()

        exe = self.get_executor()
        exe.run(fluid.default_startup_program())
        fleet.init_worker()

        thread_num = 2
        batch_size = 128
        filelist = train_file_list

        # config dataset
        dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
        dataset.set_use_var(self.feeds)
        dataset.set_batch_size(128)
        dataset.set_thread(2)
        dataset.set_filelist(filelist)
        dataset.set_pipe_command('python ctr_dataset_reader.py')
        dataset.load_into_memory()

        dataset.global_shuffle(fleet, 12)  ##TODO: thread configure
        shuffle_data_size = dataset.get_shuffle_data_size(fleet)
        local_data_size = dataset.get_shuffle_data_size()
        data_size_list = fleet.util.all_gather(local_data_size)
        print('after global_shuffle data_size_list: ', data_size_list)
        print('after global_shuffle data_size: ', shuffle_data_size)

        for epoch_id in range(1):
            pass_start = time.time()
            exe.train_from_dataset(program=fluid.default_main_program(),
                                   dataset=dataset,
                                   fetch_list=[self.avg_cost],
                                   fetch_info=["cost"],
                                   print_period=2,
                                   debug=int(os.getenv("Debug", "0")))
            pass_time = time.time() - pass_start
        dataset.release_memory()

        if os.getenv("SAVE_MODEL") == "1":
            model_dir = tempfile.mkdtemp()
            fleet.save_inference_model(exe, model_dir,
                                       [feed.name for feed in self.feeds],
                                       self.avg_cost)
            fleet.load_inference_model(model_dir, mode=0)
            if fleet.is_first_worker():
                self.check_model_right(model_dir)
            shutil.rmtree(model_dir)

        dirname = os.getenv("SAVE_DIRNAME", None)
        if dirname:
            fleet.save_persistables(exe, dirname=dirname)
            fleet.load_model(dirname, mode=0)

        cache_dirname = os.getenv("SAVE_CACHE_DIRNAME", None)
        if cache_dirname:
            fleet.save_cache_model(cache_dirname)

        dense_param_dirname = os.getenv("SAVE_DENSE_PARAM_DIRNAME", None)
        if dense_param_dirname:
            fleet.save_dense_params(exe, dense_param_dirname,
                                    fluid.global_scope(),
                                    fluid.default_main_program())

        save_one_table_dirname = os.getenv("SAVE_ONE_TABLE_DIRNAME", None)
        if save_one_table_dirname:
            fleet.save_one_table(0, save_one_table_dirname, 0)
            fleet.load_one_table(0, save_one_table_dirname, 0)

        patch_dirname = os.getenv("SAVE_PATCH_DIRNAME", None)
        if patch_dirname:
            fleet.save_persistables(exe, patch_dirname, None, 5)
            fleet.check_save_pre_patch_done()

if __name__ == "__main__":
    runtime_main(TestDistCTR2x2)
