# 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.
"""TODO: Add a description here."""


import re
import gzip
import json
import datasets
from pathlib import Path


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = ""

_DESCRIPTION = """\
French Wikipedia dataset for Entity Linking
"""

_HOMEPAGE = "https://github.com/GaaH/frwiki_el"

_LICENSE = "WTFPL"

_URLs = {
    "frwiki": "data/frwiki-20220901/corpus.jsonl.gz",
    "frwiki-mini": "data/frwiki-20220901/corpus_mini.jsonl.gz",
    "frwiki-abstracts": "data/frwiki-20220901/corpus_abstracts.jsonl.gz",
    "entities": "data/frwiki-20220901/entities.jsonl.gz",
}

class FrwikiElDataset(datasets.GeneratorBasedBuilder):
    """
    """

    VERSION = datasets.Version("0.2.3")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="frwiki", version=VERSION,
                               description="The frwiki dataset for Entity Linking"),
        datasets.BuilderConfig(name="frwiki-mini", version=VERSION,
                               description="1000 first sentences of the frwiki dataset for Entity Linking"),
        datasets.BuilderConfig(name="frwiki-abstracts", version=VERSION,
                               description="Abstracts (first paragraph) of the frwiki pages."),
        datasets.BuilderConfig(name="entities", version=VERSION,
                               description="Entities and their descriptions"),
    ]

    # It's not mandatory to have a default configuration. Just use one if it make sense.
    DEFAULT_CONFIG_NAME = "frwiki"

    def _info(self):
        if self.config.name in ("frwiki", 'frwiki-mini', 'frwiki-abstracts'):
            features = datasets.Features({
                "name": datasets.Value("string"),
                "wikidata_id": datasets.Value("string"),
                "wikipedia_id": datasets.Value("int32"),
                "wikipedia_url": datasets.Value("string"),
                "wikidata_url": datasets.Value("string"),
                "sentences": [{
                    "text": datasets.Value("string"),
                    "ner": [datasets.Value("string")],
                    "mention_mappings": [[datasets.Value("int16")]],
                    "el_wikidata_id": [datasets.Value("string")],
                    "el_wikipedia_id": [datasets.Value("int32")],
                    "el_wikipedia_title": [datasets.Value("string")],
                }]
            })
        elif self.config.name == "entities":
            features = datasets.Features({
                "name": datasets.Value("string"),
                "wikidata_id": datasets.Value("string"),
                "wikipedia_id": datasets.Value("int32"),
                "wikipedia_url": datasets.Value("string"),
                "wikidata_url": datasets.Value("string"),
                "description": datasets.Value("string"),
            })

        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
            # Here we define them above because they are different between the two configurations
            features=features,
            # If there's a common (input, target) tuple from the features,
            # specify them here. They'll be used if as_supervised=True in
            # builder.as_dataset.
            supervised_keys=None,
            # 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):
        """Returns SplitGenerators."""
        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        filepath = _URLs[self.config.name]
        path = dl_manager.download(filepath)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "path": path,
                }
            )
        ]

    def _generate_examples(self, path):
        """ Yields examples as (key, example) tuples. """
        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is here for legacy reason (tfds) and is not important in itself.

        # We need to use open before gzip.open in case the dataset is streamed
        # https://github.com/huggingface/datasets/issues/2607#issuecomment-883219727
        with gzip.open(open(path, 'rb'), "rt", encoding="UTF-8") as datafile:
            for id, line in enumerate(datafile):
                item = json.loads(line)
                yield id, item