# coding=utf-8

import json
import os

import datasets

from PIL import Image
import numpy as np

logger = datasets.logging.get_logger(__name__)


_CITATION = """\

  title={FCD dataset},
 
}
"""

_DESCRIPTION = """\
#
"""

def load_image(image_path):
    image = Image.open(image_path).convert("RGB")
    w, h = image.size
    # resize image to 224x224
    image = image.resize((224, 224))
    image = np.asarray(image)
    image = image[:, :, ::-1] # flip color channels from RGB to BGR
    image = image.transpose(2, 0, 1) # move channels to first dimension
    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]),
    ]


class FCDConfig(datasets.BuilderConfig):
    """BuilderConfig for FCD"""

    def __init__(self, **kwargs):
        """BuilderConfig for FCD.

        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(FCDConfig, self).__init__(**kwargs)


class FCD(datasets.GeneratorBasedBuilder):
    """FCD dataset."""

    BUILDER_CONFIGS = [
        FCDConfig(name="FCD", version=datasets.Version("1.0.0"), description="FCD 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=["Borrower", "BorrowerValue", "City", "CityValue", "StateValue", "PUD", "PUDValueYear", "PUDValueMonth","PriorSaleHistory", "PriorSaleHistoryValue"]
                        )
                    ),
                    "image": datasets.Array3D(shape=(3, 224, 224), dtype="uint8"),
                }
            ),
            supervised_keys=None,
            homepage="#",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        downloaded_file = dl_manager.download_and_extract("/content/FCD/FCD/FCDdataset.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 _generate_examples(self, filepath):
        logger.info("⏳ Generating examples from = %s", filepath)
        ann_dir = os.path.join(filepath, "annotations/json_min")
        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", "jpg")
            image, size = load_image(image_path)
            for item in data:
                #words, label = item["worde"], ["borrower"]
                #words = [w for w in words if w["text"].strip() != ""]
                
                #if len(words) == 0:
                  continue
                #ner_tags.append(label)  
                  #if label == "other":
                   # for w in words:
                    #    tokens.append(w["text"])
                    #    ner_tags.append("O")
                     #   bboxes.append(normalize_bbox(w["box"], size))
               # else:
                #tokens.append(words[0]["text"])
                #ner_tags.append("B-" + label.upper())
                #bboxes.append(normalize_bbox(words[0]["box"], size))
                #for w in words[1:]:
                      #tokens.append(w["text"])
                      
                      #bboxes.append(normalize_bbox(w["box"], size))  
               
                 
               

            yield guid, {"id": str(guid), "tokens": tokens, "bboxes": bboxes, "ner_tags": ner_tags, "image": image}