# 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: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""


import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://github.com/atenglens/taiwanese_english_translation"

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

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = "https://huggingface.co/datasets/atenglens/taiwanese_english_translation/tree/main"

class TaiwaneseEnglishTranslationConfig(datasets.BuilderConfig):
    """BuilderConfig for FLoRes."""

    def __init__(self, language_pair=(None, None), **kwargs):
        """BuilderConfig for TaiwaneseEnglishTranslation.
        Args:
            for the `datasets.features.text.TextEncoder` used for the features feature.
          language_pair: pair of languages that will be used for translation. Should
            contain 2-letter coded strings. First will be used at source and second
            as target in supervised mode. For example: ("se", "en").
          **kwargs: keyword arguments forwarded to super.
        """
        name = "%s%s" % (language_pair[0], language_pair[1])

        description = ("Translation dataset from %s to %s") % (language_pair[0], language_pair[1])
        super(TaiwaneseEnglishTranslationConfig, self).__init__(
            name=name,
            description=description,
            version=datasets.Version("1.1.0", ""),
            **kwargs,
        )

        self.language_pair = language_pair

# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class TaiwaneseEnglishTranslation(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("1.1.0")

    # 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 = [
        TaiwaneseEnglishTranslationConfig(
            language_pair=("tw", "en"),
        ),
    ]

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

    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        source, target = self.config.language_pair
        features = datasets.Features(
            {"translation": datasets.features.Translation(languages=self.config.language_pair)}
        )
        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=(source, target),
            # 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):
        # 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

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        urls = _URLS
        data_dir = dl_manager.download_and_extract(urls)
        
        path_tmpl = "{data_dir}/{split}.csv"

        files = {}
        for split in ("train", "valid", "test"):
            files[split] = {
                "file_path": path_tmpl.format(data_dir=data_dir, split=split),
            }

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs=files["train"]),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs=files["valid"]),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=files["test"]),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, file_path):
        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        """This function returns the examples in the raw (text) form."""
        with open(file_path, encoding="utf-8") as f:
            csv_reader = csv.reader(f, delimiter=',')
            source_sentences, target_sentences = [], []
            line_count = 1
            for row in csv_reader:
                source_sentences.append(row[0])
                target_sentences.append(row[1])
                line_count += 1
        source, target = self.config.language_pair
        for idx, (l1, l2) in enumerate(zip(source_sentences, target_sentences)):
            result = {"translation": {source: l1, target: l2}}
            # Make sure that both translations are non-empty.
            if all(result.values()):
                yield idx, result