#   Copyright (c) 2020 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.
from __future__ import print_function
from abc import ABC

from fleetrec.reader.reader import Reader
from fleetrec.utils import envs


class TrainReader(Reader):
    def init(self):
        self.cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
        self.cont_max_ = [20, 600, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
        self.cont_diff_ = [20, 603, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
        self.hash_dim_ = envs.get_global_env("hyper_parameters.sparse_feature_number", None, "train.model")
        self.continuous_range_ = range(1, 14)
        self.categorical_range_ = range(14, 40)

    def generate_sample(self, line):
        """
        Read the data line by line and process it as a dictionary
        """

        def reader():
            """
            This function needs to be implemented by the user, based on data format
            """
            features = line.rstrip('\n').split('\t')

            dense_feature = []
            sparse_feature = []
            for idx in self.continuous_range_:
                if features[idx] == "":
                    dense_feature.append(0.0)
                else:
                    dense_feature.append(
                        (float(features[idx]) - self.cont_min_[idx - 1]) /
                        self.cont_diff_[idx - 1])

            for idx in self.categorical_range_:
                sparse_feature.append(
                    [hash(str(idx) + features[idx]) % self.hash_dim_])
            label = [int(features[0])]
            feature_name = ["dense_input"]
            for idx in self.categorical_range_:
                feature_name.append("C" + str(idx - 13))
            feature_name.append("label")
            yield zip(feature_name, [dense_feature] + sparse_feature + [label])

        return reader


class EvaluateReader(Reader, ABC):
    pass