#   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

from . import ps_pb2 as pslib


class Server(object):
    """
        A Server basic class.
    """

    def __init__(self):
        pass


class Worker(object):
    """
        A Worker basic class.
    """

    def __init__(self):
        pass


class DownpourServer(Server):
    """
        DownpourServer class is used to generate server program_desc
        Args:
            server: it is pslib.ServerParameter() 
        Examples:
            server = DownpourServer()
    """

    def __init__(self):
        self._server = pslib.ServerParameter()
        self._server.downpour_server_param.service_param.server_class = "DownpourBrpcPsServer"
        self._server.downpour_server_param.service_param.client_class = "DownpourBrpcPsClient"
        self._server.downpour_server_param.service_param.service_class = "DownpourPsService"
        self._server.downpour_server_param.service_param.start_server_port = 0
        self._server.downpour_server_param.service_param.server_thread_num = 12

    def add_sparse_table(self, table_id, strategy):
        """
        Args:
            table_id(int): id of sparse params table
            strategy(dict): the config dict.
        Returns:
            return None 
        """

        for table in self._server.downpour_server_param.downpour_table_param:
            if table.table_id == table_id:
                if table.type == pslib.PS_SPARSE_TABLE:
                    return
                else:
                    raise ValueError("expect table %s type=%s, but actual type=%s" \
                        %(table_id, pslib.PS_SPARSE_TABLE, table.type))
        if strategy is None:
            strategy = dict()
        table = self._server.downpour_server_param.downpour_table_param.add()
        table.table_id = table_id
        table.type = pslib.PS_SPARSE_TABLE

        support_sparse_key_list = ['sparse_table_class', 'sparse_compress_in_save', 'sparse_shard_num', \
                    'sparse_accessor_class', 'sparse_learning_rate', 'sparse_initial_g2sum', 'sparse_initial_range', \
                    'sparse_weight_bounds', 'sparse_embedx_dim', 'sparse_embedx_threshold', 'sparse_nonclk_coeff', \
                    'sparse_click_coeff', 'sparse_base_threshold', 'sparse_delta_threshold', 'sparse_delta_keep_days', \
                    'sparse_show_click_decay_rate', 'sparse_delete_threshold']

        for key in strategy:
            if key not in support_sparse_key_list:
                raise ValueError("strategy key '%s' not support" % (key))

        support_table_calss = ['DownpourSparseTable']
        if strategy.get('sparse_table_class') is not None:
            table_class = strategy.get('sparse_table_class')
            if table_class not in support_table_calss:
                raise ValueError(
                    "support sparse_table_class: [ 'DownpourSparseTable' ], \
                        but actual %s" % (table_class))
        else:
            table_class = 'DownpourSparseTable'

        table.table_class = table_class

        if table_class == 'DownpourSparseTable':
            table.compress_in_save = strategy.get('sparse_compress_in_save',
                                                  True)
            table.shard_num = strategy.get('sparse_shard_num', 1000)

            support_accessor_class = [
                'DownpourFeatureValueAccessor', 'DownpourCtrAccessor'
            ]
            if strategy.get('sparse_accessor_class') is not None:
                accessor_class = strategy.get('sparse_accessor_class')
                if accessor_class not in support_accessor_class:
                    raise ValueError(
                        "support sparse_accessor_class: ['DownpourFeatureValueAccessor', 'DownpourCtrAccessor'], \
                            but actual %s" % (accessor_class))
            else:
                accessor_class = 'DownpourCtrAccessor'

            table.accessor.accessor_class = accessor_class

            if accessor_class == 'DownpourFeatureValueAccessor' or accessor_class == 'DownpourCtrAccessor':
                table.accessor.sparse_sgd_param.learning_rate = strategy.get(
                    'sparse_learning_rate', 0.05)
                table.accessor.sparse_sgd_param.initial_g2sum = strategy.get(
                    'sparse_initial_g2sum', 3)
                table.accessor.sparse_sgd_param.initial_range = strategy.get(
                    'sparse_initial_range', 1e-4)
                if strategy.get('sparse_weight_bounds') is None:
                    table.accessor.sparse_sgd_param.weight_bounds.extend(
                        [-10, 10])
                else:
                    table.accessor.sparse_sgd_param.weight_bounds.extend(
                        strategy.get('sparse_weight_bounds'))
                table.accessor.embedx_dim = strategy.get('sparse_embedx_dim', 8)
                table.accessor.embedx_threshold = strategy.get(
                    'sparse_embedx_threshold', 10)
                table.accessor.fea_dim = int(table.accessor.embedx_dim) + 3
                table.accessor.downpour_accessor_param.nonclk_coeff = strategy.get(
                    'sparse_nonclk_coeff', 0.1)
                table.accessor.downpour_accessor_param.click_coeff = strategy.get(
                    'sparse_click_coeff', 1)
                table.accessor.downpour_accessor_param.base_threshold = strategy.get(
                    'sparse_base_threshold', 1.5)
                table.accessor.downpour_accessor_param.delta_threshold = strategy.get(
                    'sparse_delta_threshold', 0.25)
                table.accessor.downpour_accessor_param.delta_keep_days = strategy.get(
                    'sparse_delta_keep_days', 16)
                table.accessor.downpour_accessor_param.delete_after_unseen_days = strategy.get(
                    'sparse_delete_after_unseen_days', 30)
                table.accessor.downpour_accessor_param.show_click_decay_rate = strategy.get(
                    'sparse_show_click_decay_rate', 0.98)
                table.accessor.downpour_accessor_param.delete_threshold = strategy.get(
                    'sparse_delete_threshold', 0.8)
                table1 = table.accessor.table_accessor_save_param.add()
                table1.param = 1
                table1.converter = "(scripts/xbox_compressor_mf.py | bin/xbox_pb_converter)"
                table1.deconverter = "(bin/xbox_pb_deconverter | scripts/xbox_decompressor_mf.awk)"
                table2 = table.accessor.table_accessor_save_param.add()
                table2.param = 2
                table2.converter = "(scripts/xbox_compressor_mf.py | bin/xbox_pb_converter)"
                table2.deconverter = "(bin/xbox_pb_deconverter | scripts/xbox_decompressor_mf.awk)"

    def add_dense_table(self, table_id, param_var, grad_var, strategy):
        """
        Args:
            table_id(int): id of sparse params table
            strategy(dict): the dense config dict.
        Returns:
            return None 
        """
        fea_dim = 0
        for param in filter(lambda x: x.name.find("embedding") == -1,
                            param_var):
            fea_dim += reduce(lambda x, y: x * y, param.shape, 1)

        for table in self._server.downpour_server_param.downpour_table_param:
            if table.table_id == table_id:
                if table.type == pslib.PS_DENSE_TABLE:
                    table.accessor.fea_dim = fea_dim
                    return
                else:
                    raise ValueError("expect table %s type=%s, but actual type=%s" \
                        %(table_id, pslib.PS_DENSE_TABLE, table.type))

        if strategy is None:
            strategy = dict()
        table = self._server.downpour_server_param.downpour_table_param.add()
        table.table_id = table_id
        support_dense_key_list = ['dense_table_class', 'dense_compress_in_save', 'dense_accessor_class', \
                'dense_optimizer', 'dense_learning_rate', 'dense_avg_decay', 'dense_ada_decay', \
                'dense_ada_epsilon', 'dense_mom_decay', 'dense_naive_lr']

        for key in strategy:
            if key not in support_dense_key_list:
                raise ValueError("strategy key '%s' not support" % (key))

        table.table_class = strategy.get('dense_table_class',
                                         "DownpourDenseTable")
        table.type = pslib.PS_DENSE_TABLE
        table.compress_in_save = strategy.get('dense_compress_in_save', True)
        table.accessor.accessor_class = strategy.get(
            'dense_accessor_class', "DownpourDenseValueAccessor")
        table.accessor.dense_sgd_param.name = strategy.get('dense_optimizer',
                                                           "adam")
        table.accessor.dense_sgd_param.adam.learning_rate = strategy.get(
            'dense_learning_rate', 5e-06)
        table.accessor.dense_sgd_param.adam.avg_decay_rate = strategy.get(
            'dense_avg_decay', 0.999993)
        table.accessor.dense_sgd_param.adam.ada_decay_rate = strategy.get(
            'dense_ada_decay', 0.9999)
        table.accessor.dense_sgd_param.adam.ada_epsilon = strategy.get(
            'dense_ada_epsilon', 1e-8)
        table.accessor.dense_sgd_param.adam.mom_decay_rate = strategy.get(
            'dense_mom_decay', 0.99)
        table.accessor.dense_sgd_param.naive.learning_rate = strategy.get(
            'dense_naive_lr', 0.0002)
        table.accessor.fea_dim = fea_dim

    def add_data_norm_table(self, table_id, learning_rate, param_var, grad_var,
                            strategy):
        """
        Args:
            table_id(int): id of datanorm table
            strategy(dict): the datanorm config dict.
        Returns:
            return None 
        """
        fea_dim = 0
        for param in filter(lambda x: x.name.find("embedding") == -1,
                            param_var):
            fea_dim += reduce(lambda x, y: x * y, param.shape, 1)

        for table in self._server.downpour_server_param.downpour_table_param:
            if table.table_id == table_id:
                if table.type == pslib.PS_DENSE_TABLE:
                    table.accessor.fea_dim = fea_dim
                    return
                else:
                    raise ValueError("expect table %s type=%s, but actual type=%s" \
                        %(table_id, pslib.PS_DENSE_TABLE, table.type))
        if strategy is None:
            strategy = dict()

        support_datanorm_key_list = ['datanorm_table_class', 'datanorm_compress_in_save',\
                'datanorm_accessor_class', 'datanorm_operation', 'datanorm_decay_rate']

        for key in strategy:
            if key not in support_datanorm_key_list:
                raise ValueError("strategy key '%s' not support" % (key))

        table = self._server.downpour_server_param.downpour_table_param.add()
        table.table_id = table_id
        table.table_class = strategy.get('datanorm_table_class',
                                         "DownpourDenseDoubleTable")
        table.type = pslib.PS_DENSE_TABLE
        table.compress_in_save = strategy.get('datanorm_compress_in_save', True)
        table.accessor.accessor_class = strategy.get(
            'datanorm_accessor_class', "DownpourDenseValueDoubleAccessor")
        table.accessor.dense_sgd_param.name = strategy.get('datanorm_operation',
                                                           "summarydouble")
        table.accessor.dense_sgd_param.summary.summary_decay_rate = strategy.get(
            'datanorm_decay_rate', 0.999999)
        table.accessor.fea_dim = fea_dim

    def get_desc(self):
        """
        Return downpour server program_desc
        """
        return self._server


class DownpourWorker(Worker):
    """
        DownpourWorker class is used to generate worker program_desc
        Args:
            window (int): push params frequency
            worker: it is pslib.DownpourTrainerParameter 
        Examples:
            worker = DownpourWorker(1)
    """

    def __init__(self, window):
        self.window = window
        self._worker = pslib.DownpourTrainerParameter()

    def add_sparse_table(self, table_id, slot_key_vars, slot_value_vars):
        """
        Args:
            table_id(int): id of sparse params table
            slot_key_vars(string): slot key id 
            slot_value_var(string): slot key value after embedding
        Returns:
            return None 
        """
        for table in self._worker.sparse_table:
            if table.table_id == table_id:
                if [var.name for var in slot_key_vars
                    ] == self._worker.sparse_table[table_id].slot_key:
                    if [var.name for var in slot_value_vars
                        ] == self._worker.sparse_table[table_id].slot_value:
                        if [
                                var.name + "@GRAD" for var in slot_value_vars
                        ] == self._worker.sparse_table[table_id].slot_gradient:
                            return
                        else:
                            raise ValueError(
                                "sparse table %s slot_gradient error" %
                                table_id)

                    else:
                        raise ValueError("sparse table %s slot_value error" %
                                         table_id)
                else:
                    raise ValueError("sparse table %s slot_key error" %
                                     table_id)

        table = self._worker.sparse_table.add()
        table.table_id = table_id
        table.slot_key.extend([var.name for var in slot_key_vars])
        table.slot_value.extend([var.name for var in slot_value_vars])
        table.slot_gradient.extend(
            [var.name + "@GRAD" for var in slot_value_vars])

    def add_dense_table(self, table_id, learning_rate, param_vars, grad_vars,
                        dense_start_table_id):
        """
        Args:
            table_id(int): id of sparse params table
            learning_rate(float): the learning rate used to update parameters. \
                Can be a float value
            param_var(list): all dense param. it is a list.
            grad_var(list): all dense grad parm it is a list.
        Returns:
            return None 
        """
        for table in self._worker.dense_table:
            if table.table_id == table_id:
                if filter(lambda x: x.find("embedding") == -1, [p.name for p in param_vars]) ==\
                        self._worker.dense_table[table_id - dense_start_table_id].dense_variable_name:
                    if filter(lambda x: x.find("embedding") == -1, [g.name for g in grad_vars]) ==\
                        self._worker.dense_table[table_id - dense_start_table_id].dense_gradient_variable_name:
                        return
                    else:
                        raise ValueError(
                            "dense table %s dense_gradient_variable_name error"
                            % table_id)
                else:
                    raise ValueError(
                        "dense table %s dense_variable_name error" % table_id)

        table = self._worker.dense_table.add()
        table.table_id = table_id
        table.dense_variable_name.extend(
            filter(lambda x: x.find("embedding") == -1,
                   [p.name for p in param_vars]))
        table.dense_gradient_variable_name.extend(
            filter(lambda x: x.find("embedding") == -1,
                   [g.name for g in grad_vars]))

    def get_desc(self):
        """
        Return downpour worker program_desc
        """
        return self._worker