import ast

import datasets as ds
import pandas as pd

_DESCRIPTION = """\
CAMERA (CyberAgent Multimodal Evaluation for Ad Text GeneRAtion) is the Japanese ad text generation dataset.
"""

_CITATION = """\
@misc{mita2024striking,
    title={Striking Gold in Advertising: Standardization and Exploration of Ad Text Generation},
    author={Masato Mita and Soichiro Murakami and Akihiko Kato and Peinan Zhang},
    year={2024},
    eprint={2309.12030},
    archivePrefix={arXiv},
    primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'}
}
"""

_HOMEPAGE = "https://github.com/CyberAgentAILab/camera"

_LICENSE = """\
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
"""

_URLS = {
    "without-lp-images": "https://storage.googleapis.com/camera-public/camera-v2.2-minimal.tar.gz",
    "with-lp-images": "https://storage.googleapis.com/camera-public/camera-v2.2.tar.gz",
}

_DESCRIPTION = {
    "without-lp-images": "The CAMERA dataset w/o LP images (ver.2.2.0)",
    "with-lp-images": "The CAMERA dataset w/ LP images (ver.2.2.0)",
}

_VERSION = ds.Version("2.2.0", "")


class CameraConfig(ds.BuilderConfig):
    def __init__(self, name: str, version: ds.Version = _VERSION, **kwargs):
        super().__init__(
            name=name,
            description=_DESCRIPTION[name],
            version=version,
            **kwargs,
        )


class CameraDataset(ds.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [CameraConfig(name="without-lp-images")]

    DEFAULT_CONFIG_NAME = "without-lp-images"

    def _info(self) -> ds.DatasetInfo:
        features = ds.Features(
            {
                "asset_id": ds.Value("int64"),
                "kw": ds.Value("string"),
                "lp_meta_description": ds.Value("string"),
                "title_org": ds.Value("string"),
                "title_ne1": ds.Value("string"),
                "title_ne2": ds.Value("string"),
                "title_ne3": ds.Value("string"),
                "domain": ds.Value("string"),
                "parsed_full_text_annotation": ds.Sequence(
                    {
                        "text": ds.Value("string"),
                        "xmax": ds.Value("int64"),
                        "xmin": ds.Value("int64"),
                        "ymax": ds.Value("int64"),
                        "ymin": ds.Value("int64"),
                    }
                ),
            }
        )

        if self.config.name == "with-lp-images":
            features["lp_image"] = ds.Image()

        return ds.DatasetInfo(
            description=_DESCRIPTION,
            citation=_CITATION,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            features=features,
        )

    def _split_generators(self, dl_manager: ds.DownloadManager):
        base_dir = dl_manager.download_and_extract(_URLS[self.config.name])
        lp_image_dir: str | None = None

        if self.config.name == "without-lp-images":
            data_dir = f"{base_dir}/camera-v2.2-minimal"
        elif self.config.name == "with-lp-images":
            data_dir = f"{base_dir}/camera-v2.2"
            lp_image_dir = f"{data_dir}/lp-screenshot"
        else:
            raise ValueError(f"Invalid config name: {self.config.name}")

        return [
            ds.SplitGenerator(
                name=ds.Split.TRAIN,
                gen_kwargs={
                    "file": f"{data_dir}/train.csv",
                    "lp_image_dir": lp_image_dir,
                },
            ),
            ds.SplitGenerator(
                name=ds.Split.VALIDATION,
                gen_kwargs={
                    "file": f"{data_dir}/dev.csv",
                    "lp_image_dir": lp_image_dir,
                },
            ),
            ds.SplitGenerator(
                name=ds.Split.TEST,
                gen_kwargs={
                    "file_name": f"{data_dir}/test.csv",
                    "lp_image_dir": lp_image_dir,
                },
            ),
        ]

    def _generate_examples(self, file: str, lp_image_dir: str | None = None):
        df = pd.read_csv(file)
        for i, data_dict in enumerate(df.to_dict("records")):
            asset_id = data_dict["asset_id"]
            example_dict = {
                "asset_id": asset_id,
                "kw": data_dict["kw"],
                "lp_meta_description": data_dict["lp_meta_description"],
                "title_org": data_dict["title_org"],
                "title_ne1": data_dict.get("title_ne1", ""),
                "title_ne2": data_dict.get("title_ne2", ""),
                "title_ne3": data_dict.get("title_ne3", ""),
                "domain": data_dict.get("domain", ""),
                "parsed_full_text_annotation": ast.literal_eval(
                    data_dict["parsed_full_text_annotation"]
                ),
            }

            if self.config.name == "with-lp-images" and lp_image_dir is not None:
                file_name = f"screen-1200-{asset_id}.png"
                example_dict["lp_image"] = f"{lp_image_dir}/{file_name}"

            yield i, example_dict