# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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

import datasets
from datasets.tasks import TextClassification


_CITATION = """\
        @author tianjie
        fdRE
        Chinese
}
"""

_DESCRIPTION = """\
     fdRE是一个中文的轴承故障诊断领域的关系抽取数据集
     该数据集主要包含正向从属、反向从属以及无关三类标签
"""

_URL = "https://huggingface.co/datasets/leonadase/fdRE/resolve/main/fdRE.zip"


class SemEval2010Task8(datasets.GeneratorBasedBuilder):
    """The SemEval-2010 Task 8 focuses on Multi-way classification of semantic relations between pairs of nominals.
    The task was designed to compare different approaches to semantic relation classification
    and to provide a standard testbed for future research."""

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=datasets.Features(
                {
                    "sentence": datasets.Value("string"),
                    "relation": datasets.ClassLabel(
                        names=[
                            "Part_Of(E1,E2)",
                            "Part_Of(E2,E1)",
                            "Other",
                        ]
                    ),
                }
            ),
            # If there's a common (input, target) tuple from the features,
            # specify them here. They'll be used if as_supervised=True in
            # builder.as_dataset.
            supervised_keys=datasets.info.SupervisedKeysData(input="sentence", output="relation"),
            # Homepage of the dataset for documentation
            citation=_CITATION,
            task_templates=[TextClassification(text_column="sentence", label_column="relation")],
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        # dl_manager is a datasets.download.DownloadManager that can be used to
        # download and extract URLs
        dl_dir = dl_manager.download_and_extract(_URL)
        # data_dir = os.path.join(dl_dir, "fdRE")
        data_dir = dl_dir
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "train.txt"),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "test.txt"),
                },
            ),
        ]

    def _generate_examples(self, filepath):
        """Yields examples."""
        with open(filepath, encoding="utf-8") as file:
            lines = file.readlines()
            num_lines_per_sample = 4

            for i in range(0, len(lines), num_lines_per_sample):
                idx = int(lines[i].split("\t")[0])
                sentence = lines[i].split("\t")[1][1:-2]  # remove " at the start and "\n at the end
                relation = lines[i + 1][:-1]  # remove \n at the end
                yield idx, {
                    "sentence": sentence,
                    "relation": relation,
                }