# 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.
# TODO: Address all TODOs and remove all explanatory comments

import datasets
import json
from typing import List
import pandas as pd
import csv

_LICENSE = "http://www.apache.org/licenses/LICENSE-2.0"
_HOMEPAGE='https://huggingface.co/datasets/THUIR/T2Ranking'

_DESCRIPTION = 'T2Ranking: A large-scale Chinese benchmark for passage retrieval.'
_CITATION = """
@article{sigir2023,
title={T2Ranking},
author={Qian Dong},
volume={2023},
number={2},
pages={99-110},
year={2022}
}
"""

_URLS_DICT = {
    "collection": "data/collection.tsv",
    "qrels.train": "data/qrels.train.tsv",
    "qrels.dev": "data/qrels.dev.tsv",
    "qrels.retrieval.train": "data/qrels.retrieval.train.tsv",
    "qrels.retrieval.dev": "data/qrels.retrieval.dev.tsv",
    "queries.train": "data/queries.train.tsv",
    "queries.test": "data/queries.test.tsv",
    "queries.dev": "data/queries.dev.tsv",
    "train.bm25.tsv": "data/train.bm25.tsv",
    "train.mined.tsv": "data/train.mined.tsv",
}

_FEATURES_DICT = {
    'collection': {
        "pid": datasets.Value("int64"),
        "text": datasets.Value("string"),
    },
    'qrels.train': {
        "qid": datasets.Value("int64"),
        "-": datasets.Value("int64"),
        "pid": datasets.Value("int64"),
        "rel": datasets.Value("int64"),
    },
    'qrels.retrieval.train': {
        "qid": datasets.Value("int64"),
        "pid": datasets.Value("int64"),
    },
    'qrels.dev': {
        "qid": datasets.Value("int64"),
        "-": datasets.Value("int64"),
        "pid": datasets.Value("int64"),
        "rel": datasets.Value("int64"),
    },
    'qrels.retrieval.dev': {
        "qid": datasets.Value("int64"),
        "pid": datasets.Value("int64"),
    },
    'queries.train': {
        "qid": datasets.Value("int64"),
        "text": datasets.Value("string"),
    },
    'queries.dev': {
        "qid": datasets.Value("int64"),
        "text": datasets.Value("string"),
    },
    'queries.test': {
        "qid": datasets.Value("int64"),
        "text": datasets.Value("string"),
    },
    "train.bm25.tsv": {
        "qid": datasets.Value("int64"),
        "pid": datasets.Value("int64"),
        "score": datasets.Value("float32"),
    },
    "train.mined.tsv": {
        "qid": datasets.Value("int64"),
        "pid": datasets.Value("int64"),
        "index": datasets.Value("int64"),
        "score": datasets.Value("float32"),
    },
}

class T2RankingConfig(datasets.BuilderConfig):
    """BuilderConfig for T2Ranking."""

    def __init__(self, splits, **kwargs):
        super().__init__(version=datasets.Version("1.0.0"), **kwargs)
        self.splits = splits


class T2Ranking(datasets.GeneratorBasedBuilder):
    """The T2Ranking benchmark."""

    BUILDER_CONFIGS = [
        T2RankingConfig(
            name="collection",
            splits=['train'],
        ),
        T2RankingConfig(
            name="qrels.train",
            splits=['train'],
        ),
        T2RankingConfig(
            name="qrels.dev",
            splits=['train'],
        ),
        T2RankingConfig(
            name="queries.train",
            splits=['train'],
        ),
        T2RankingConfig(
            name="queries.dev",
            splits=['train'],
        ),       
        T2RankingConfig(
            name="queries.test",
            splits=['train'],
        ),
        T2RankingConfig(
            name="qrels.retrieval.train",
            splits=['train'],
        ),
        T2RankingConfig(
            name="qrels.retrieval.dev",
            splits=['train'],
        ),
        T2RankingConfig(
            name="train.bm25.tsv",
            splits=['train'],
        ),
        T2RankingConfig(
            name="train.mined.tsv",
            splits=['train'],
        ),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(_FEATURES_DICT[self.config.name]),
            homepage=_HOMEPAGE,
            citation=_CITATION,
            license=_LICENSE,
        )

    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
        split_things = []
        for split_name in self.config.splits:
            # print('')
            split_data_path = _URLS_DICT[self.config.name]
            # print(split_data_path)
            filepath = dl_manager.download(split_data_path)
            # print(filepath)
            # print('')
            split_thing = datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": filepath,
                }
            )
            split_things.append(split_thing)
        return split_things

    def _generate_examples(self, filepath):
        # data = pd.read_csv(filepath, sep='\t', quoting=3)
        reader = csv.DictReader(open(filepath), delimiter='\t', quoting=csv.QUOTE_NONE)
        keys = _FEATURES_DICT[self.config.name].keys()
        idx = -1
        for data in reader:
            idx+=1
            yield idx, {key: data[key] for key in keys}
        # for idx in range(data.shape[0]):
        #     yield idx, {key: data[key][idx] for key in keys}