import json
import os
import ast
from pathlib import Path
import datasets
from PIL import Image
import pandas as pd

logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@article{,
  title={},
  author={},
  journal={},
  year={},
  volume={}
}
"""
_DESCRIPTION = """\
This is a sample dataset for training layoutlmv3 model on custom annotated data.
"""

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]),
    ]


_URLS = []

'''Edit your working directory folder path here if required. 
If this file is in the same folder as the "layoutlmv3" folder keep it as it is.
'''
data_path = r'./' 

class DatasetConfig(datasets.BuilderConfig):
    """BuilderConfig for InvoiceExtraction Dataset"""
    def __init__(self, **kwargs):
        """BuilderConfig for InvoiceExtraction Dataset.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(DatasetConfig, self).__init__(**kwargs)


class InvoiceExtraction(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        DatasetConfig(name="InvoiceExtraction", version=datasets.Version("1.0.0"), description="InvoiceExtraction 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 = ['num_facture','date_facture','fournisseur','client','mat_client','mat_fournisseur','tva','pourcentage_tva','remise','pourcentage_remise','timbre','fodec','ttc','devise','net_ht'] #Enter the list of labels that you have here.
                            )
                    ),
                    "image_path": datasets.Value("string"),
                    "image": datasets.features.Image()
                }
            ),
            supervised_keys=None,
            citation=_CITATION,
            homepage="",
        )




    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        """Uses local files located with data_dir"""
        dest = os.path.join(data_path, 'layoutlmv3')

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(dest, "train.txt"), "dest": dest}
            ),            
            datasets.SplitGenerator(
                name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(dest, "test.txt"), "dest": dest}
            ),
        ]

    def _generate_examples(self, filepath, dest):

        df = pd.read_csv(os.path.join(dest, 'class_list.txt'), delimiter=',', header=None)
        id2labels = dict(zip(df[0].tolist(), df[1].tolist()))


        logger.info("⏳ Generating examples from = %s", filepath)

        item_list = []
        with open(filepath, 'r', encoding='utf-8', errors='ignore') as f:
            for line in f:
                item_list.append(line.rstrip('\n\r'))
        print(item_list)
        for guid, fname in enumerate(item_list):
            print(fname)
            data = ast.literal_eval(fname)
            image_path = os.path.join(dest, data['file_name'])
            image, size = load_image(image_path)
            boxes = data['bboxes']

            text = data['tokens']
            label = data['ner_tags']
            
            #print(boxes)
            #for i in boxes:
            #  print(i)
            boxes = [normalize_bbox(box, size) for box in boxes]
            flag=0
            #print(image_path)
            for i in boxes:
              #print(i)
              for j in i:
                if j>1000:
                  flag+=1
                  #print(j)
                  pass
            if flag>0: print(image_path)
 
            yield guid, {"id": str(guid), "tokens": text, "bboxes": boxes, "ner_tags": label, "image_path": image_path, "image": image}