import json
import os

from PIL import Image

import datasets
from datasets import load_dataset

def load_image(image_path):
    image = Image.open(image_path).convert("RGB")
    w, h = image.size
    return image, (w, h)

def normalize_bbox(bbox, size):
    return [
        int(1000 * bbox[0] / size[0]),
        int(1000 * bbox[1] / size[1]),
        int(1000 * bbox[2] / size[0]),
        int(1000 * bbox[3] / size[1]),
    ]

logger = datasets.logging.get_logger(__name__)


_CITATION = """\

}
"""

_DESCRIPTION = """\

"""


class FunsdConfig(datasets.BuilderConfig):
    """BuilderConfig for FUNSD"""

    def __init__(self, **kwargs):
        """BuilderConfig for FUNSD.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(FunsdConfig, self).__init__(**kwargs)


class Funsd(datasets.GeneratorBasedBuilder):
    """Conll2003 dataset."""

    BUILDER_CONFIGS = [
        FunsdConfig(name="funsd", version=datasets.Version("1.0.0"), description="FUNSD dataset"),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "bboxes": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))),
                    "ner_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names = [           "S-ANSWER_EXP",
                                                "S-ANSWER_FECHA_SERVICIO",
                                                "S-ANSWER_HORA_SERVICIO",
                                                "S-ANSWER_SALA",
                                                "B-ANSWER_NOMBRE1",
                                                "I-ANSWER_NOMBRE1",
                                                "E-ANSWER_NOMBRE1",
                                                "B-ANSWER_DIRECCION",
                                                "I-ANSWER_DIRECCION",
                                                "E-ANSWER_DIRECCION",
                                                "B-ANSWER_POBLACION",
                                                "I-ANSWER_POBLACION",
                                                "E-ANSWER_POBLACION",
                                                "S-ANSWER_DNI",
                                                "S-ANSWER_TELEFONO",
                                                "S-ANSWER_EDAD",
                                                "S-ANSWER_NACIMIENTO_DIF",
                                                "S-ANSWER_ESTADO_CIVIL_DIF",
                                                "S-ANSWER_FECHA_DEF",
                                                "B-ANSWER_LUGAR_DEF",
                                                "I-ANSWER_LUGAR_DEF",
                                                "E-ANSWER_LUGAR_DEF",
                                                "S-ANSWER_NATURAL_DE_DIF",
                                                "B-ANSWER_PADRES_DIF",
                                                "I-ANSWER_PADRES_DIF",
                                                "E-ANSWER_PADRES_DIF",
                                                "B-ANSWER_NOMBRE_TITULAR",
                                                "I-ANSWER_NOMBRE_TITULAR",
                                                "E-ANSWER_NOMBRE_TITULAR",
                                                "S-ANSWER_AUT_DNI_TITULAR",
                                                "B-ANSWER_DIRECCION_TITULAR",
                                                "I-ANSWER_DIRECCION_TITULAR",
                                                "E-ANSWER_DIRECCION_TITULAR",
                                                "B-ANSWER_POBLACION_TITULAR",
                                                "I-ANSWER_POBLACION_TITULAR",
                                                "E-ANSWER_POBLACION_TITULAR",
                                                "B-ANSWER_AUTORIZACION_TITULAR",
                                                "I-ANSWER_AUTORIZACION_TITULAR",
                                                "E-ANSWER_AUTORIZACION_TITULAR",
                                                "S-ANSWER_DNI_TITULAR",
                                                "S-ANSWER_HORA_DEFUNCION",
                                                "B-ANSWER_DESCRIPCION",
                                                "I-ANSWER_DESCRIPCION",
                                                "E-ANSWER_DESCRIPCION",
                                                "B-ANSWER_NOMBRE",
                                                "I-ANSWER_NOMBRE",
                                                "E-ANSWER_NOMBRE",
                                                "S-ANSWER_CANTIDAD",
                                                "S-ANSWER_IMPORTE"]
                        )
                    ),
                    "image": datasets.features.Image(),
                }
            ),
            supervised_keys=None,
            homepage="",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        downloaded_file = dl_manager.download_and_extract("https://huggingface.co/datasets/LauraExp/LILT2/resolve/main/Data.zip")
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={"filepath": f"{downloaded_file}/Data/training_data/"}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST, gen_kwargs={"filepath": f"{downloaded_file}/Data/testing_data/"}
            ),
        ]

    def get_line_bbox(self, bboxs):
        x = [bboxs[i][j] for i in range(len(bboxs)) for j in range(0, len(bboxs[i]), 2)]
        y = [bboxs[i][j] for i in range(len(bboxs)) for j in range(1, len(bboxs[i]), 2)]

        x0, y0, x1, y1 = min(x), min(y), max(x), max(y)

        assert x1 >= x0 and y1 >= y0
        bbox = [[x0, y0, x1, y1] for _ in range(len(bboxs))]
        return bbox

    def _generate_examples(self, filepath):
        logger.info("⏳ Generating examples from = %s", filepath)
        ann_dir = os.path.join(filepath, "annotations")
        img_dir = os.path.join(filepath, "images")
        for guid, file in enumerate(sorted(os.listdir(ann_dir))):
            tokens = []
            bboxes = []
            ner_tags = []

            file_path = os.path.join(ann_dir, file)
            with open(file_path, "r", encoding="utf8") as f:
                data = json.load(f)
            image_path = os.path.join(img_dir, file)
            image_path = image_path.replace("json", "png")
            image, size = load_image(image_path)
            for item in data["form"]:
                words_example, label = item["words"], item["label"]
                words_example = [w for w in words_example if w["text"].strip() != ""]
                if len(words_example) == 0:
                    continue
                if label == "other":
                    for w in words_example:
                        tokens.append(w["text"])
                        ner_tags.append("O")
                        bboxes.append(normalize_bbox(w["box"], size))
                else:
                    if len(words_example) == 1:
                        tokens.append(words_example[0]["text"])
                        ner_tags.append("S-" + label.upper())
                        bboxes.append(normalize_bbox(words_example[0]["box"], size))
                    else:
                        tokens.append(words_example[0]["text"])
                        ner_tags.append("B-" + label.upper())
                        bboxes.append(normalize_bbox(words_example[0]["box"], size))
                        for w in words_example[1:]:
                            tokens.append(w["text"])
                            ner_tags.append("I-" + label.upper())
                            bboxes.append(normalize_bbox(w["box"], size))
                        tokens.append(words_example[-1]["text"])
                        ner_tags.append("E-" + label.upper())
                        bboxes.append(normalize_bbox(words_example[-1]["box"], size))
                        cur_line_bboxes = self.get_line_bbox(cur_line_bboxes)
                        bboxes.extend(cur_line_bboxes)
            yield guid, {"id": str(guid), "tokens": tokens, "bboxes": bboxes, "ner_tags": ner_tags,
                         "image": image}