# 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.

import sys


class CriteoDataset(object):
    def setup(self, sparse_feature_dim):
        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_ = sparse_feature_dim
        # here, training data are lines with line_index < train_idx_
        self.train_idx_ = 41256555
        self.continuous_range_ = range(1, 14)
        self.categorical_range_ = range(14, 40)

    def _process_line(self, line):
        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_])

        return dense_feature, sparse_feature, [int(features[0])]

    def infer_reader(self, filelist, batch, buf_size):
        def local_iter():
            for fname in filelist:
                with open(fname.strip(), "r") as fin:
                    for line in fin:
                        dense_feature, sparse_feature, label = self._process_line(
                            line)
                        #yield dense_feature, sparse_feature, label
                        yield [dense_feature] + sparse_feature + [label]

        import paddle
        batch_iter = paddle.batch(
            paddle.reader.shuffle(
                local_iter, buf_size=buf_size),
            batch_size=batch)
        return batch_iter

    def generate_sample(self, line):
        def data_iter():
            dense_feature, sparse_feature, label = self._process_line(line)
            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 data_iter


if __name__ == "__main__":
    criteo_dataset = CriteoDataset()
    criteo_dataset.setup(int(sys.argv[1]))
    criteo_dataset.run_from_stdin()