import json
import os

from PIL import Image

import datasets

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 = """\
@article{Jaume2019FUNSDAD,
  title={FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents},
  author={Guillaume Jaume and H. K. Ekenel and J. Thiran},
  journal={2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)},
  year={2019},
  volume={2},
  pages={1-6}
}
"""

_DESCRIPTION = """\
https://guillaumejaume.github.io/FUNSD/
"""


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")),
                    #"words": datasets.Sequence(datasets.Value("string")),
                    "bboxes": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))),
                    "ner_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=["DATEISSUED","LOANTERM","PURPOSE","PRODUCT","PROPERTY","LOANAMOUNT","INTERESTRATE","MONTHLYPR","PREPENALTY","BALLOONPAYMENT","ESTMONTHLY","ESTAXES"]
                        )
                    ),
                    "image": datasets.features.Image(),
                    "image_path" : datasets.Value("string"),
                }
            ),
            supervised_keys=None,
            homepage="https://guillaumejaume.github.io/FUNSD/",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        downloaded_file = dl_manager.download_and_extract("/content/SLR1.zip") #"/content/SLR.zip"
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={"filepath": f"{downloaded_file}/dataset/training_data/"}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST, gen_kwargs={"filepath": f"{downloaded_file}/dataset/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 = []
            #words = []
            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 state in data: 
                for item in state["form"]:
                        labels=item['label']
                        word=item['text']
                        ner_tags.append(labels)
                        tokens.append(word)
                        #words.append(word)
                        bboxes.append(normalize_bbox(item['box'],size))
                
                    #cur_line_bboxes = []
                    #words, label = item["words"], item["label"]
                    #words = [w for w in words if w["text"].strip() != ""]
                    #if len(words) == 0:
                        #continue
                    #if label == "other":
                        #for w in words:
                           # tokens.append(w["text"])
                           # ner_tags.append("O")
                            #cur_line_bboxes.append(normalize_bbox(w["box"], size))
                   # else:
                        #tokens.append(words[0]["text"])
                        #ner_tags.append("B-" + label.upper())
                        #cur_line_bboxes.append(normalize_bbox(words[0]["box"], size))
                        #for w in words[1:]:
                            #tokens.append(w["text"])
                           # ner_tags.append("I-" + label.upper())
                            #cur_line_bboxes.append(normalize_bbox(w["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, "image_path":image_path}#"words":words,