# 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.
"""ReadingBank is a benchmark dataset for reading order detection built with weak supervision from WORD documents, which contains 500K document images with a wide range of document types as well as the corresponding reading order information."""


from pathlib import Path
import pandas as pd
import datasets


_CITATION = """\
@misc{wang2021layoutreader,
      title={LayoutReader: Pre-training of Text and Layout for Reading Order Detection}, 
      author={Zilong Wang and Yiheng Xu and Lei Cui and Jingbo Shang and Furu Wei},
      year={2021},
      eprint={2108.11591},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
"""

_DESCRIPTION = """\
ReadingBank is a benchmark dataset for reading order detection built with weak supervision from WORD documents, which contains 500K document images with a wide range of document types as well as the corresponding reading order information.
"""

_HOMEPAGE = "https://github.com/doc-analysis/ReadingBank"

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

_URLS = {
    "dataset": "https://layoutlm.blob.core.windows.net/readingbank/dataset/ReadingBank.zip",
}

def parse_files(files):
    layout_text = {}

    for i in [1,2,3,4,6,7]:
        layout_text[f'm{i}'] = {}

    for file in files:
        stem = file.stem
        shard = stem.split('-')[-1]
        if 'text' in stem:
            layout_text[shard]['text']=file
        elif 'layout' in stem:
            layout_text[shard]['layout']=file
            
    return layout_text

def get_dataframe(files,split):
    df_list = []
    for shard in files.keys():
        df_list.append(pd.read_json(files[shard][split],lines=True))
    df = pd.concat(df_list)
    df.reset_index(inplace=True,drop=True)
    return df

class ReadingBank(datasets.GeneratorBasedBuilder):
    """ReadingBank is a benchmark dataset for reading order detection built with weak supervision from WORD documents, which contains 500K document images with a wide range of document types as well as the corresponding reading order information."""

    VERSION = datasets.Version("1.1.0")

    def _info(self):
        features = datasets.Features(
            {
                "src": datasets.Value("string"),
                "tgt": datasets.Value("string"),
                "bleu": datasets.Value("float"),
                "tgt_index": datasets.Sequence(datasets.Value("int16")),
                "original_filename": datasets.Value("string"),
                "filename": datasets.Value("string"),
                "page_idx": datasets.Value("int16"),
                "src_layout": datasets.Sequence(datasets.Sequence(datasets.Value("int16"))),
                "tgt_layout": datasets.Sequence(datasets.Sequence(datasets.Value("int16"))),
                # These are the features of your dataset like images, labels ...
            }
        )
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):

        urls = _URLS["dataset"]
        data_dir = dl_manager.download_and_extract(urls)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": parse_files(list(Path(f'{data_dir}/train/').glob('*'))),
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": parse_files(list(Path(f'{data_dir}/dev/').glob('*'))),
                    "split": "dev",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": parse_files(list(Path(f'{data_dir}/test/').glob('*'))),
                    "split": "test"
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):

        print('\nCreating dataframes.. please wait..')
        text_df = get_dataframe(filepath,'text')
        layout_df = get_dataframe(filepath,'layout')
        layout_df.rename(columns={'src':'src_layout',
                                  'tgt':'tgt_layout'},inplace=True)

        df = text_df.merge(layout_df,left_index=True,right_index=True)
        print('Dataframes created..\n')
        yield from enumerate(df.to_dict(orient='records'))