# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""This is a data loader for the GenDocVQA Dataset."""


import csv
import json
import os
import ast
import pandas as pd

import datasets

_DESCRIPTION = """\
This dataset is dedicated to the non-extractive document visual question challenge GenDocVQA-2024.
"""

_URLS = {
    'img_tar': 'https://huggingface.co/datasets/lenagibee/GenDocVQA/resolve/main/archives/gendocvqa2024_imgs.tar.gz?download=true',
    'ocr_tar': 'https://huggingface.co/datasets/lenagibee/GenDocVQA/resolve/main/archives/gendocvqa2024_ocr.tar.gz?download=true',
    'annotations_tar': 'https://huggingface.co/datasets/lenagibee/GenDocVQA/resolve/main/archives/gendocvqa2024_annotations.tar.gz?download=true'
}

_LICENSE = "Other"

class GenDocVQA(datasets.GeneratorBasedBuilder):

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="default", version=VERSION, description="Whole dataset config"),
    ]

    DEFAULT_CONFIG_NAME = "default"

    def _info(self):
        features = datasets.Features(
            {
                "unique_id": datasets.Value("int64"),
                "image_path": datasets.Value("string"),
                "ocr": datasets.Sequence(
                    feature={
                        'text': datasets.Value("string"), 
                        'bbox': datasets.Sequence(datasets.Value("int64")),
                        'block_id': datasets.Value("int64"),
                        'text_id': datasets.Value("int64"),
                        'par_id': datasets.Value("int64"),
                        'line_id': datasets.Value("int64"),
                        'word_id': datasets.Value("int64")
                    }
                ),
                "question": datasets.Value("string"),
                "answer": datasets.Sequence(datasets.Value("string")),
                
            }
        )

        return datasets.DatasetInfo(
            features=features,
            description=_DESCRIPTION,
            license=_LICENSE
        )

    def _split_generators(self, dl_manager):
        imgs_dir = dl_manager.download_and_extract(_URLS["img_tar"])
        ocr_dir = dl_manager.download_and_extract(_URLS["ocr_tar"])
        annotations_dir = dl_manager.download_and_extract(_URLS["annotations_tar"])
        
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "annot_path": annotations_dir,
                    "imgs_dir": imgs_dir,
                    "ocr_dir": ocr_dir,
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "annot_path": annotations_dir,
                    "imgs_dir": imgs_dir,
                    "ocr_dir": ocr_dir,
                    "split": "dev",
                },
            )
        ]


    def _generate_examples(self, annot_path, imgs_dir, ocr_dir, split):
        df = pd.read_csv(os.path.join(annot_path, 'gendocvqa2024_annotations', f'{split}_v1.csv'))
        for _, row in df.iterrows():
            img_path = os.path.join(imgs_dir, 'gendocvqa2024_imgs', split, row['image_filename'])
            q_id = row['unique_id']
            ocr_path = os.path.join(ocr_dir,  'gendocvqa2024_ocr', split, row['ocr_filename'])
            question = row['question']
            answer = row['answer']
            with open(ocr_path, 'r') as f:
                ocr = json.load(f)
            ocr_list = []
            for item in ocr:
                ocr_dict = {
                    'block_id': item[0],
                    'text_id': item[1],
                    'par_id': item[2],
                    'line_id': item[3],
                    'word_id': item[4],
                    'bbox': item[5],
                    'text': item[6]
                }
                ocr_list.append(ocr_dict)
            if split != "test":
                answer = ast.literal_eval(answer)
            else:
                answer = []
            
            yield q_id, {
                "unique_id": q_id,
                "image_path": img_path,
                "ocr": ocr_list,
                "answer": answer,
                "question": question,

                    }
               


def read_image(img_path):
    with Image.open(img_path) as f:
        original_image = f.convert("RGB")
    return original_image