# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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.
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# limitations under the License.

# Lint as: python3
"""The FTRACE benchmark."""


import json
import os
import textwrap

import datasets

_FTRACE_CITATION = """\
"""

_FTRACE_DESCRIPTION = """\
    Factual Tracing Dataset that contains queries and abstracts, and their corresponding ground truth.
"""

_FTRACE_ABSTRACTS_DESCRIPTION = """\
    Abstracts based on TREx dataset.
"""

_FTRACE_ABSTRACTS_LICENSE = """\
    Creative Commons Attribution-ShareAlike 4.0 International License.
see https://creativecommons.org/licenses/by-sa/4.0/"""

_FTRACE_ABSTRACTS_CITATION = """\
    @inproceedings{elsahar2018t,
  title={T-rex: A large scale alignment of natural language with knowledge base triples},
  author={Elsahar, Hady and Vougiouklis, Pavlos and Remaci, Arslen and Gravier, Christophe and Hare, Jonathon and Laforest, Frederique and Simperl, Elena},
  booktitle={Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  year={2018}
}"""

_FTRACE_QUERIES_DESCRIPTION = """\
    Queries based on LAMA dataset.
"""

_FTRACE_QUERIES_CITATION = """\
    @inproceedings{petroni2019language,
  title={Language Models as Knowledge Bases?},
  author={F. Petroni, T. Rockt{\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel},
  booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019},
  year={2019}
}"""

FTRACE_QUERIES_LICENSE = """\
    The Creative Commons Attribution-Noncommercial 4.0 International License.
see https://github.com/facebookresearch/LAMA/blob/master/LICENSE"""


class FTRACEConfig(datasets.BuilderConfig):
    """BuilderConfig for FTRACE."""

    def __init__(
        self,
        features,
        data_url,
        citation,
        license,
        url,
        **kwargs,
    ):
        """BuilderConfig for FTRACE.
        Args:
          features: `list[string]`, list of the features that will appear in the
            feature dict. Should not include "label".
          data_url: `string`, url to download the zip file from.
          citation: `string`, citation for the data set.
          url: `string`, url for information about the data set.
          **kwargs: keyword arguments forwarded to super.
        """
        # Version history:
        # 0.0.2: Initial version.
        super(FTRACEConfig, self).__init__(
            version=datasets.Version("0.0.2"), **kwargs
        )
        self.features = features
        self.data_url = data_url
        self.citation = citation
        self.license = license
        self.url = url


class FTRACE(datasets.GeneratorBasedBuilder):
    """The SuperFTRACE benchmark."""

    BUILDER_CONFIGS = [
        FTRACEConfig(
            name="abstracts",
            description=_FTRACE_ABSTRACTS_DESCRIPTION,
            features=[
                "inputs_pretokenized",
                "targets_pretokenized",
                "masked_uri",
                "masked_type",
                "facts",
                "id",
                "example_uris",
                "page_uri",
            ],
            data_url="https://people.csail.mit.edu/akyurek/ftrace/abstracts.zip",
            citation=textwrap.dedent(_FTRACE_ABSTRACTS_CITATION),
            license=_FTRACE_ABSTRACTS_LICENSE,
            url="https://hadyelsahar.github.io/t-rex/",
        ),
        FTRACEConfig(
            name="queries",
            description=_FTRACE_QUERIES_DESCRIPTION,
            features=[
                "inputs_pretokenized",
                "targets_pretokenized",
                "uuid",
                "obj_uri",
                "sub_uri",
                "predicate_id",
                "sub_surface",
                "obj_surface",
            ],
            data_url="https://people.csail.mit.edu/akyurek/ftrace/queries.zip",
            citation=textwrap.dedent(_FTRACE_QUERIES_CITATION),
            license=FTRACE_QUERIES_LICENSE,
            url="https://github.com/facebookresearch/LAMA",
        ),
    ]

    def _info(self):
        features = {
            feature: datasets.Value("string")
            for feature in self.config.features
        }

        return datasets.DatasetInfo(
            description=_FTRACE_DESCRIPTION + self.config.description,
            features=datasets.Features(features),
            homepage=self.config.url,
            citation=self.config.citation + "\n" + _FTRACE_CITATION,
        )

    def _split_generators(self, dl_manager):
        dl_dir = dl_manager.download_and_extract(self.config.data_url) or ""
        task_name = _get_task_name_from_data_url(self.config.data_url)
        dl_dir = os.path.join(dl_dir, task_name)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "train.jsonl"),
                    "split": "train",
                },
            ),
        ]

    def _generate_examples(self, data_file, split):
        with open(data_file, encoding="utf-8") as f:
            for idx, line in enumerate(f):
                row = json.loads(line)
                yield idx, row


def _get_task_name_from_data_url(data_url):
    if "queries" in data_url:
        return "queries"
    elif "abstracts" in data_url:
        return "abstracts"

    return "queries"