# Copyright (c) 2021 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 os

from paddle.dataset.common import md5file
from paddle.utils.download import get_path_from_url

from ..utils.env import DATA_HOME
from .dataset import DatasetBuilder

__all__ = ["SeAbsa16"]


class SeAbsa16(DatasetBuilder):
    """
    SE-ABSA16_PHNS dataset for Aspect-level Sentiment Classification task.
    More information please refer to
    https://aistudio.baidu.com/aistudio/competition/detail/50/?isFromLuge=1.

    """

    BUILDER_CONFIGS = {
        # phns is short for phones.
        "phns": {
            "url": "https://bj.bcebos.com/paddlenlp/datasets/SE-ABSA16_PHNS.zip",
            "md5": "f5a62548f2fcf73892cacf2cdf159671",
            "splits": {
                "train": [
                    os.path.join("SE-ABSA16_PHNS", "train.tsv"),
                    "cb4f65aaee59fa76526a0c79b7c12689",
                    (0, 1, 2),
                    1,
                ],
                "test": [os.path.join("SE-ABSA16_PHNS", "test.tsv"), "7ad80f284e0eccc059ece3ce3d3a173f", (1, 2), 1],
            },
            "labels": ["0", "1"],
        },
        # came is short for cameras.
        "came": {
            "url": "https://bj.bcebos.com/paddlenlp/datasets/SE-ABSA16_CAME.zip",
            "md5": "3104e92217bbff80a1ed834230f1df51",
            "splits": {
                "train": [
                    os.path.join("SE-ABSA16_CAME", "train.tsv"),
                    "8c661c0e83bb34b66c6fbf039c7fae80",
                    (0, 1, 2),
                    1,
                ],
                "test": [os.path.join("SE-ABSA16_CAME", "test.tsv"), "8b80f77960be55adca1184d7a20501df", (1, 2), 1],
            },
            "labels": ["0", "1"],
        },
    }

    def _get_data(self, mode, **kwargs):
        """Downloads dataset."""
        builder_config = self.BUILDER_CONFIGS[self.name]
        default_root = os.path.join(DATA_HOME, f"SE-ABSA16_{self.name.upper()}")
        filename, data_hash, _, _ = builder_config["splits"][mode]
        fullname = os.path.join(default_root, filename)
        if not os.path.exists(fullname) or (data_hash and not md5file(fullname) == data_hash):
            url = builder_config["url"]
            md5 = builder_config["md5"]
            get_path_from_url(url, DATA_HOME, md5)

        return fullname

    def _read(self, filename, split):
        """Reads data"""
        _, _, field_indices, num_discard_samples = self.BUILDER_CONFIGS[self.name]["splits"][split]
        with open(filename, "r", encoding="utf-8") as f:
            for idx, line in enumerate(f):
                if idx < num_discard_samples:
                    continue
                line_stripped = line.strip().split("\t")
                if not line_stripped:
                    continue
                example = [line_stripped[indice] for indice in field_indices]
                if split == "test":
                    yield {"text": example[0], "text_pair": example[1]}
                else:
                    yield {"text": example[1], "text_pair": example[2], "label": example[0]}

    def get_labels(self):
        """
        Return labels of the SE_ABSA16.
        """
        return self.BUILDER_CONFIGS[self.name]["labels"]