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

logger = datasets.logging.get_logger(__name__)

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(100 * bbox[3] / size[1]),
    ]

def _get_drive_url(url):
    base_url = 'https://drive.google.com/uc?id='
    split_url = url.split("/")
    
    return base_url + split_url[5]

_URLS = [
    _get_drive_url("https://drive.google.com/file/d/1KdDBmGP96lFc7jv2Bf4eqrO121ST-TCh/"),
]

_CITATION = """\
@article{liharding-nguyen,
  title={CVDS: A Dataset for CV Form Understanding},
  author={MISA - employees},
  year={2022},
}
"""

_DESCRIPTION = """\
Dataset for key information extraction with cv form understanding
"""

class DatasetConfig(datasets.BuilderConfig):
    """BuilderConfig for CV Dataset"""
    def __init__(self, **kwargs):
        """BuilderConfig for CV Dataset.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(DatasetConfig, self).__init__(**kwargs)
        
class CVDS(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        DatasetConfig(name="CVDS", version=datasets.Version("1.0.0"), description="CV Dataset"),
    ]
    
    def _info(self):
        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"))),
                    "ner_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=['person_name', 'dob_key', 'dob_value', 'gender_key', 'gender_value', 'phonenumber_key', 'phonenumber_value', 'email_key', 'email_value', 'address_key', 'address_value', 'socical_address_value', 'education', 'education_name', 'education_time', 'experience', 'experience_name', 'experience_time', 'information', 'undefined']
                        )
                    ),
                    "image_path": datasets.Value("string"),
                }  
            ),
            supervised_keys=None,
            citation=_CITATION,
            homepage=""
        )
        
    def _split_generators(self, dl_manager):
        download_file = dl_manager.download_and_extract(_URLS)
        dest = Path(download_file[0])/"data1"
        
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={ "filepath": dest/"train.txt", "dest": dest }
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST, gen_kwargs={ "filepath": dest/"test.txt", "dest": dest}
            )
        ]
        
    def _generate_examples(self, file_path, dest):
        df = pd.read_csv(dest/"class_list.txt", delimiter="\s", header=None)
        id2label = dict(zip(df[0].tolist(), df[1].tolist()))
        
        logger.info("⏳ Generating examples from = %s", file_path)
        
        item_list = []
        with open(file_path, "r", encoding="utf8") as f:
            for line in f:
                item_list.append(line.rstrip('\n\r'))
        
        for guid, fname in enumerate(item_list):
            data = json.loads(fname)
            
            image_path = dest/data['file_name']
            image, size = load_image(image_path)
            
            bboxes = [[i["box"][6], i["box"][7], i["box"][2]. i["box"][3]] for i in data["annotations"]]
            word = [i['text'] for i in data["annotations"]]
            label = [id2label[i["label"]] for i in data["annotations"]]
            
            bboxes = [normalize_bbox(box, size) for box in bboxes]
            
            flag=0
            for i in bboxes:
                for j in i:
                    if j > 1000:
                        flag+=1
                        pass
            
            if flag > 0:
                print(image_path)
                
            yield guid, {"id": str(guid), "words": word, "bboxes": bboxes, "ner_tags": label, "image_path": image_path}