# coding=utf-8
# 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.
"""DUDE dataset loader"""

import os
import copy
import json
from pathlib import Path
from typing import List
import pdf2image
from tqdm import tqdm


import datasets


_CITATION = """
@inproceedings{dude2023icdar,
    title={ICDAR 2023 Challenge on Document UnderstanDing of Everything (DUDE)},
    author={Van Landeghem, Jordy  et . al.},
    booktitle={Proceedings of the ICDAR},
    year={2023}
}
"""

_DESCRIPTION = """\
DUDE requires models to reason and understand about document layouts in multi-page images/PDFs to answer questions about them.
Specifically, models need to incorporate a new modality of layout present in the images/PDFs and reason
over it to answer DUDE questions. DUDE Contains X questions and Y and ...
"""

_HOMEPAGE = "https://rrc.cvc.uab.es/?ch=23"

_LICENSE = "CC BY 4.0"

_SPLITS = ["sample"]  # ["train", "val", "test"]

_URLS = {}
for split in _SPLITS:
    _URLS[
        f"{split}_annotations"
    ] = f"https://huggingface.co/datasets/jordyvl/DUDE_loader/resolve/main/data/DUDE_{split}_dataset.json"
    _URLS[
        f"{split}_pdfs"
    ] = f"https://huggingface.co/datasets/jordyvl/DUDE_loader/resolve/main/data/DUDE_{split}_pdfs.tar.gz"
    _URLS[
        f"{split}_OCR"
    ] = f"https://huggingface.co/datasets/jordyvl/DUDE_loader/resolve/main/data/DUDE_{split}_OCR.tar.gz"


def batched_conversion(pdf_file):
    info = pdf2image.pdfinfo_from_path(pdf_file, userpw=None, poppler_path=None)
    maxPages = info["Pages"]

    logger.info(f"{pdf_file} has {str(maxPages)} pages")

    images = []

    for page in range(1, maxPages + 1, 10):
        images.extend(
            pdf2image.convert_from_path(
                pdf_file, dpi=200, first_page=page, last_page=min(page + 10 - 1, maxPages)
            )
        )
    return images


def open_pdf_binary(pdf_file):
    with open(pdf_file, "rb") as f:
        return f.read()


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

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="DUDE",
            version=datasets.Version("0.0.1"),
            description=_DESCRIPTION,
        )
    ]

    DEFAULT_CONFIG_NAME = "DUDE"

    def _info(self):

        features = datasets.Features(
            {
                "docId": datasets.Value("string"),
                "questionId": datasets.Value("string"),
                "question": datasets.Value("string"),
                "answers": datasets.Sequence(datasets.Value("string")),
                "answers_page_bounding_boxes": datasets.Sequence(
                    {
                        "left": datasets.Value("int32"),
                        "top": datasets.Value("int32"),
                        "width": datasets.Value("int32"),
                        "height": datasets.Value("int32"),
                        "page": datasets.Value("int32"),
                    }
                ),
                "answers_variants": datasets.Sequence(datasets.Value("string")),
                "answer_type": datasets.Value("string"),
                "data_split": datasets.Value("string"),
                "document": datasets.Value("binary"),
                "OCR": datasets.Value("binary"),
            }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(
        self, dl_manager: datasets.DownloadManager
    ) -> List[datasets.SplitGenerator]:

        splits = []
        for split in _SPLITS:
            annotations = {}
            if f"{split}_annotations" in _URLS:  # blind test set
                annotations = json.load(open(_URLS[f"{split}_annotations"], "r"))
            pdfs_archive_path = dl_manager.download(_URLS[f"{split}_pdfs"])
            pdfs_archive = dl_manager.iter_archive(pdfs_archive_path)
            OCR_archive_path = dl_manager.download(_URLS[f"{split}_OCR"])
            OCR_archive = dl_manager.iter_archive(OCR_archive_path)
            splits.append(
                datasets.SplitGenerator(
                    name=split,
                    gen_kwargs={
                        "pdfs_archive": pdfs_archive,
                        "OCR_archive": OCR_archive,
                        "annotations": annotations,
                        "split": split,
                    },
                )
            )
        return splits

    def _generate_examples(self, pdfs_archive, OCR_archive, annotations, split):
        def retrieve_doc(pdfs_archive, docid):
            for file_path, file_obj in pdfs_archive:
                path, ext = file_path.split(".")
                md5 = path.split("/")[-1]

                if md5 == docid:
                    # images = pdf2image.convert_from_bytes(file_obj.read())
                    return file_obj.read()  # binary

        def retrieve_OCR(OCR_archive, docid):
            for file_path, file_obj in OCR_archive:
                # /DUDE_sample_OCR/OCR/Amazon Textract/md5_{original,due}.json
                path, ext = file_path.split(".")
                filename = path.split("/")[-1]
                md5 = filename.split("_")[0]

                if md5 == docid and "original" in filename:
                    return json.loads(file_obj.read())  # binary

        question = self.info.features["question"]
        answers = self.info.features["answers"]

        extensions = {"pdf", "PDF"}

        for i, a in enumerate(annotations):
            a["data_split"] = split
            a["document"] = retrieve_doc(pdfs_archive, a["docId"])
            a["OCR"] = retrieve_OCR(OCR_archive, a["docId"])

            # FIXES for faulty generation
            #a.pop("answers_page_bounding_boxes")  # fix later            
            if a["answers_page_bounding_boxes"] in [ [], [[]] ]:
                a["answers_page_bounding_boxes"] = None
            else:
                if isinstance(a['answers_page_bounding_boxes'][0], list):
                    a["answers_page_bounding_boxes"] = a['answers_page_bounding_boxes'][0]                    
            yield i, a