# 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.
"""Datasets loading script for wikitext_linked"""

import os

import datasets
import pyarrow as pa
import pyarrow.parquet as pq


logger = datasets.utils.logging.get_logger(__name__)


_CITATION = """\
@misc{merity2016pointer,
      title={Pointer Sentinel Mixture Models},
      author={Stephen Merity and Caiming Xiong and James Bradbury and Richard Socher},
      year={2016},
      eprint={1609.07843},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{nguyen2021trankit,
      title={Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing},
      author={Nguyen, Minh Van and Lai, Viet Dac and Veyseh, Amir Pouran Ben and Nguyen, Thien Huu},
      booktitle="Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
      year={2021}
}

@misc{entity-fishing,
    title = {entity-fishing},
    howpublished = {\\url{https://github.com/kermitt2/entity-fishing}},
    publisher = {GitHub},
    year = {2016--2022},
    archivePrefix = {swh},
    eprint = {1:dir:cb0ba3379413db12b0018b7c3af8d0d2d864139c}
}
"""

_DESCRIPTION = """\
 The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified
 Good and Featured articles on Wikipedia. Dependency Relations, POS, NER tags are marked with trankit and
 entities are linked with entity-fishing.
 The dataset is available under the Creative Commons Attribution-ShareAlike License.
"""

_HOMEPAGE = "https://github.com/GabrielKP/svo/"

_LICENSE = "Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)"


FEATURES = datasets.Features(
    {
        "text": datasets.Value("string"),
        "original_id": datasets.Value("int64"),
        "tok_span": datasets.Sequence(feature=datasets.Sequence(feature=datasets.Value("int64"))),
        "tok_upos": datasets.Sequence(feature=datasets.Value("string")),
        "tok_xpos": datasets.Sequence(feature=datasets.Value("string")),
        "tok_dephead": datasets.Sequence(feature=datasets.Value("int64")),
        "tok_deprel": datasets.Sequence(feature=datasets.Value("string")),
        "tok_lemma": datasets.Sequence(feature=datasets.Value("string")),
        "tok_ner": datasets.Sequence(feature=datasets.Value("string")),
        "ent_span": datasets.Sequence(feature=datasets.Sequence(feature=datasets.Value("int64"))),
        "ent_wikipedia_external_ref": datasets.Sequence(feature=datasets.Value("string")),
        "ent_ner": datasets.Sequence(feature=datasets.Value("string")),
        "ent_domains": datasets.Sequence(
            feature=datasets.Sequence(feature=datasets.Value("string"))
        ),
    }
)


class WikitextLinked(datasets.ArrowBasedBuilder):
    """wikitext_linked is an annotated and linked version from wikitext. Wikitext is a
    collection of over 100 million tokens extracted from the set of verified Good and
    Featured articles on Wikipedia.
    """

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="wikitext2",
            version=VERSION,
            description="The small version",
            data_dir="wikitext2",
        ),
        datasets.BuilderConfig(
            name="wikitext103",
            version=VERSION,
            description="The big version",
            data_dir="wikitext103",
        ),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            citation=_CITATION,
            license=_LICENSE,
            features=FEATURES,
            version=self.VERSION,
            homepage=_HOMEPAGE,
        )

    def _split_generators(self, dl_manager):
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(self.config.data_dir, "train.parquet"),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(self.config.data_dir, "validation.parquet"),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(self.config.data_dir, "test.parquet"),
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_tables(self, filepath):
        schema = pa.schema(FEATURES.type)
        with open(filepath, "rb") as f:
            parquet_file = pq.ParquetFile(f)
            try:
                for batch_idx, record_batch in enumerate(
                    parquet_file.iter_batches(batch_size=10000, columns=None)
                ):
                    pa_table = pa.Table.from_batches([record_batch])
                    pa_table = pa.Table.from_arrays(
                        [pa_table[field.name] for field in schema], schema=schema
                    )
                    # Uncomment for debugging (will print the Arrow table size and elements)
                    # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                    # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                    yield f"{batch_idx}", pa_table
            except ValueError as e:
                logger.error(f"Failed to read file '{filepath}' with error {type(e)}: {e}")
                raise