"""CORD: A Consolidated Receipt Dataset for Post-OCR Parsing"""


import json
import os
from pathlib import Path
from typing import Any, Generator

import datasets
from PIL import Image

logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@article{park2019cord,
  title={CORD: A Consolidated Receipt Dataset for Post-OCR Parsing},
  author={Park, Seunghyun and Shin, Seung and Lee, Bado and Lee, Junyeop and Surh, Jaeheung and Seo, Minjoon and Lee, Hwalsuk}
  booktitle={Document Intelligence Workshop at Neural Information Processing Systems}
  year={2019}
}
"""

_DESCRIPTION = """\
CORD (Consolidated Receipt Dataset) with normalized bounding boxes.
"""

_URLS = [
    "https://drive.google.com/uc?id=1MqhTbcj-AHXOqYoeoh12aRUwIprzTJYI",
    "https://drive.google.com/uc?id=1wYdp5nC9LnHQZ2FcmOoC0eClyWvcuARU",
]

_LABELS = [
    "menu.cnt",
    "menu.discountprice",
    "menu.etc",
    "menu.itemsubtotal",
    "menu.nm",
    "menu.num",
    "menu.price",
    "menu.sub_cnt",
    "menu.sub_etc",
    "menu.sub_nm",
    "menu.sub_price",
    "menu.sub_unitprice",
    "menu.unitprice",
    "menu.vatyn",
    "sub_total.discount_price",
    "sub_total.etc",
    "sub_total.othersvc_price",
    "sub_total.service_price",
    "sub_total.subtotal_price",
    "sub_total.tax_price",
    "total.cashprice",
    "total.changeprice",
    "total.creditcardprice",
    "total.emoneyprice",
    "total.menuqty_cnt",
    "total.menutype_cnt",
    "total.total_etc",
    "total.total_price",
    "void_menu.nm",
    "void_menu.price",
]


def load_image(image_path: str) -> tuple:
    image = Image.open(image_path).convert("RGB")
    return image, image.size


def quad_to_bbox(quad: dict) -> list:
    return [
        quad["x3"],
        quad["y1"],
        quad["x1"],
        quad["y3"],
    ]


def normalize_bbox(bbox: list, width: int, height: int) -> list:
    return [
        int(1000 * (bbox[0] / width)),
        int(1000 * (bbox[1] / height)),
        int(1000 * (bbox[2] / width)),
        int(1000 * (bbox[3] / height)),
    ]


class CORDConfig(datasets.BuilderConfig):
    """BuilderConfig for CORD."""

    def __init__(self, **kwargs) -> None:
        """BuilderConfig for CORD.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(CORDConfig, self).__init__(**kwargs)


class CORD(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        CORDConfig(
            name="CORD",
            version=datasets.Version("1.0.0"),
            description="CORD (Consolidated Receipt Dataset)",
        ),
    ]

    def _info(self) -> datasets.DatasetInfo:
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "words": datasets.Sequence(datasets.Value("string")),
                    "bboxes": datasets.Sequence(
                        datasets.Sequence(datasets.Value("int64"))
                    ),
                    "labels": datasets.Sequence(
                        datasets.features.ClassLabel(names=_LABELS)
                    ),
                    "images": datasets.features.Image(),
                }
            ),
            citation=_CITATION,
            homepage="https://github.com/clovaai/cord/",
        )

    def _split_generators(self, dl_manager) -> list:
        base_dir_v1, base_dir_v2 = dl_manager.download_and_extract(_URLS)
        dest_dir = Path(base_dir_v1) / "CORD"

        for split_dir in ["train", "dev", "test"]:
            for type_dir in ["image", "json"]:
                if split_dir == "test" and type_dir == "json":
                    continue
                files = (Path(base_dir_v2) / "CORD" / split_dir / type_dir).iterdir()
                for f in files:
                    os.rename(f, dest_dir / split_dir / type_dir / f.name)

        return [
            datasets.SplitGenerator(
                name=str(datasets.Split.TRAIN), gen_kwargs={"filepath": dest_dir / "train"}
            ),
            datasets.SplitGenerator(
                name=str(datasets.Split.VALIDATION), gen_kwargs={"filepath": dest_dir / "dev"},
            ),
            datasets.SplitGenerator(
                name=str(datasets.Split.TEST), gen_kwargs={"filepath": dest_dir / "test"}
            ),
        ]

    def _generate_examples(self, **kwargs: Any) -> Generator:
        filepath = kwargs["filepath"]
        logger.info("generating examples from = %s", filepath)
        ann_dir = os.path.join(filepath, "json")
        img_dir = os.path.join(filepath, "image")

        for guid, file in enumerate(sorted(os.listdir(ann_dir))):
            WORDS, BBOXES, LABELS = [], [], []
            file_path = os.path.join(ann_dir, file)
            f = open(file_path)
            data = json.load(f)

            image_path = os.path.join(img_dir, file).replace("json", "png")
            image, (width, height) = load_image(image_path)

            for annotation in data["valid_line"]:
                label, words = annotation["category"], annotation["words"]
                for word in words:
                    bbox = normalize_bbox(
                        quad_to_bbox(word["quad"]), width=width, height=height
                    )
                    if min(bbox) >= 0 and max(bbox) <= 1000:
                        WORDS.append(word["text"])
                        BBOXES.append(bbox)
                        LABELS.append(label)

            yield guid, {
                "id": str(guid),
                "images": image,
                "words": WORDS,
                "bboxes": BBOXES,
                "labels": LABELS,
            }