# 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.
"""ParsiNLU Persian reading comprehension task"""

from __future__ import absolute_import, division, print_function

import csv
import json

import datasets


logger = datasets.logging.get_logger(__name__)

_CITATION = """\
@article{huggingface:dataset,
    title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian},
    authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others},
    year={2020}
    journal = {arXiv e-prints},
    eprint = {2012.06154},    
}
"""

# You can copy an official description
_DESCRIPTION = """\
A Persian translation dataset (English -> Persian).     
"""

_HOMEPAGE = "https://github.com/persiannlp/parsinlu/"

_LICENSE = "CC BY-NC-SA 4.0"

_URL = "https://media.githubusercontent.com/media/persiannlp/parsinlu/master/data/translation/translation_combined_en_fa/"
_URLs = {
    "train": _URL + "train.tsv",
    "dev": _URL + "dev.tsv",
    "test": _URL + "test.tsv",
}


class ParsinluReadingComprehension(datasets.GeneratorBasedBuilder):
    """ParsiNLU Persian reading comprehension task."""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="parsinlu-repo", version=VERSION, description="ParsiNLU repository: translation"
        ),
    ]

    def _info(self):
        features = datasets.Features(
            {
                "source": datasets.Value("string"),
                "targets": datasets.features.Sequence(
                    datasets.Value("string")
                ),
                "category": 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
            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,
            # 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):
        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": data_dir["train"],
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={"filepath": data_dir["test"], "split": "test"},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir["dev"],
                    "split": "dev",
                },
            ),
        ]

    def _generate_examples(self, filepath, split):
        logger.info("generating examples from = %s", filepath)

        print(filepath)
        with open(filepath) as f:
            for id_, row in enumerate(f.readlines()):
                try:
                    if id_ == 0:
                        continue
                    row = row.split("\t")

                    if len(row) < 3:
                        print(f"* Ignoring the following line since it doesn't have three columns: {row}")
                        continue
                    source = row[0].replace("\t", "").replace("\n", "")
                    targets = row[1].replace("\t", "").replace("\n", "").split('///')
                    category = row[2].replace("\t", "").replace("\n", "")
                    yield id_, {
                        'source': source,
                        'targets': targets,
                        'category': category,
                    }
                except:
                    print(" * skipping . . . ")