#
#  Copyright 2019 The FATE 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.
#

from federatedml.model_selection.data_split.data_split import DataSplitter
from federatedml.util import LOGGER


class HomoDataSplitHost(DataSplitter):
    def __init__(self):
        super().__init__()

    def fit(self, data_inst):
        LOGGER.debug(f"Enter Hetero {self.role} Data Split fit")
        if self.need_run is False:
            return
        self.param_validator(data_inst)

        ids = self._get_ids(data_inst)
        y = self._get_y(data_inst)

        id_train, id_test_validate, y_train, y_test_validate = self._split(
            ids, y, test_size=self.test_size + self.validate_size, train_size=self.train_size)

        validate_size, test_size = DataSplitter.get_train_test_size(self.validate_size, self.test_size)
        id_validate, id_test, y_validate, y_test = self._split(id_test_validate, y_test_validate,
                                                               test_size=test_size, train_size=validate_size)
        LOGGER.info(f"Split ids obtained.")

        partitions = data_inst.partitions
        id_train_table = DataSplitter._parallelize_ids(id_train, partitions)
        id_validate_table = DataSplitter._parallelize_ids(id_validate, partitions)
        id_test_table = DataSplitter._parallelize_ids(id_test, partitions)

        train_data, validate_data, test_data = self.split_data(data_inst,
                                                               id_train_table,
                                                               id_validate_table,
                                                               id_test_table)
        LOGGER.info(f"Split data finished.")

        all_metas = {}

        all_metas = self.callback_count_info(id_train, id_validate, id_test, all_metas)
        if self.stratified:
            all_metas = self.callback_label_info(y_train, y_validate, y_test, all_metas)
        self.callback(all_metas)
        self.set_summary(all_metas)

        return [train_data, validate_data, test_data]


class HomoDataSplitGuest(DataSplitter):
    def __init__(self):
        super().__init__()

    def fit(self, data_inst):
        LOGGER.debug(f"Enter Hetero {self.role} Data Split fit")
        if self.need_run is False:
            return
        self.param_validator(data_inst)

        ids = self._get_ids(data_inst)
        y = self._get_y(data_inst)

        id_train, id_test_validate, y_train, y_test_validate = self._split(
            ids, y, test_size=self.test_size + self.validate_size, train_size=self.train_size)

        validate_size, test_size = DataSplitter.get_train_test_size(self.validate_size, self.test_size)
        id_validate, id_test, y_validate, y_test = self._split(id_test_validate, y_test_validate,
                                                               test_size=test_size, train_size=validate_size)
        LOGGER.info(f"Split ids obtained.")

        partitions = data_inst.partitions
        id_train_table = DataSplitter._parallelize_ids(id_train, partitions)
        id_validate_table = DataSplitter._parallelize_ids(id_validate, partitions)
        id_test_table = DataSplitter._parallelize_ids(id_test, partitions)

        train_data, validate_data, test_data = self.split_data(data_inst,
                                                               id_train_table,
                                                               id_validate_table,
                                                               id_test_table)
        LOGGER.info(f"Split data finished.")

        all_metas = {}

        all_metas = self.callback_count_info(id_train, id_validate, id_test, all_metas)
        if self.stratified:
            all_metas = self.callback_label_info(y_train, y_validate, y_test, all_metas)
        self.callback(all_metas)
        self.set_summary(all_metas)
        LOGGER.info(f"Callback given.")

        return [train_data, validate_data, test_data]