import datasets

_CITATION = """
@inproceedings{zhang2025adtec,
  title={{AdTEC}: A Unified Benchmark for Evaluating Text Quality in Search Engine Advertising},
  author={Peinan Zhang and Yusuke Sakai and Masato Mita and Hiroki Ouchi and Taro Watanabe},
  booktitle={Proceedings of the 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL)},
  year={2025},
  publisher={Association for Computational Linguistics},
  eprint={2408.05906},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2408.05906},
}
"""

_DESCRIPTION = """
AdTEC is a Japanese dataset designed to evaluate the quality of ad texts from multiple aspects, considering practical advertising operations. The dataset contains only Japanese text and covers the online advertisement (search engine advertising) domain. It consists of the following tasks:

1. Ad Acceptability: Given a text, predict the acceptance of overall quality with binary labels: acceptable/unacceptable.
2. Ad Consistency: Given a pair of ad text and landing page (LP) text, predict the consistency between the ad and LP text with binary labels: consistent/inconsistent.
3. Ad Similarity: Given a pair of ad texts, predict their similarity with a score ranging from 1 to 5.
4. A3 Recognition: Given a text, predict all possible aspects of advertising appeals (A3) as multi-labels. The comprehensive list of A3 can be found in Murakami et al., 2022.

Each task is provided in TSV format and split into train, dev, and test sets. The dataset was created by Peinan Zhang, Yusuke Sakai, Masato Mita, Hiroki Ouchi, and Taro Watanabe. For more details, see: https://arxiv.org/abs/2408.05906
"""


class AdtecConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)


class Adtec(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        AdtecConfig(
            name="ad-acceptability",
            description="Acceptability classification of ad titles (acceptable/unacceptable).",
            version=datasets.Version("1.0.0"),
        ),
        AdtecConfig(
            name="ad-consistency",
            description="Consistency classification between ad titles and landing page texts (consistent/inconsistent).",
            version=datasets.Version("1.0.0"),
        ),
        AdtecConfig(
            name="ad-similarity",
            description="Semantic similarity regression between pairs of ad texts (score: 1.0-5.0).",
            version=datasets.Version("1.0.0"),
        ),
        AdtecConfig(
            name="a3-recognition",
            description="Multi-label recognition of elements in ad titles (e.g., product features, offer, etc.).",
            version=datasets.Version("1.0.0"),
        ),
    ]

    def _info(self):
        if self.config.name == "ad-acceptability":
            features = datasets.Features(
                {
                    "label": datasets.ClassLabel(names=["acceptable", "unacceptable"]),
                    "title": datasets.Value("string"),
                }
            )
        elif self.config.name == "ad-consistency":
            features = datasets.Features(
                {
                    "label": datasets.ClassLabel(names=["consistent", "inconsistent"]),
                    "lp_text": datasets.Value("string"),
                    "title": datasets.Value("string"),
                }
            )
        elif self.config.name == "ad-similarity":
            features = datasets.Features(
                {
                    "text1": datasets.Value("string"),
                    "text2": datasets.Value("string"),
                    "score": datasets.Value("float32"),
                }
            )
        elif self.config.name == "a3-recognition":
            features = datasets.Features(
                {
                    "title": datasets.Value("string"),
                    "labels": datasets.Sequence(datasets.Value("string")),
                }
            )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        data_dir = "data/" + self.config.name
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"filepath": f"{data_dir}/train.tsv"},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"filepath": f"{data_dir}/valid.tsv"},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"filepath": f"{data_dir}/test.tsv"},
            ),
        ]

    def _generate_examples(self, filepath):
        if self.config.name == "ad-acceptability":
            # TSV: label, title
            with open(filepath, encoding="utf-8") as f:
                next(f)  # skip header
                for idx, line in enumerate(f):
                    label, title = line.strip().split("\t")
                    yield idx, {"label": label, "title": title}
        elif self.config.name == "ad-consistency":
            # TSV: label, lp_text, title
            with open(filepath, encoding="utf-8") as f:
                next(f)
                for idx, line in enumerate(f):
                    label, lp_text, title = line.strip().split("\t")
                    yield idx, {"label": label, "lp_text": lp_text, "title": title}
        elif self.config.name == "ad-similarity":
            # TSV: text1, text2, score
            with open(filepath, encoding="utf-8") as f:
                next(f)
                for idx, line in enumerate(f):
                    text1, text2, score = line.strip().split("\t")
                    yield idx, {"text1": text1, "text2": text2, "score": float(score)}
        elif self.config.name == "a3-recognition":
            # TSV: title, labels
            with open(filepath, encoding="utf-8") as f:
                next(f)
                for idx, line in enumerate(f):
                    title, labels = line.strip("\n ").split("\t")
                    label_list = labels.split("|")
                    yield idx, {"title": title, "labels": label_list}