# coding=utf-8
# Copyright 2022 The PolyAI and 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.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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import csv
import os

import datasets

logger = datasets.logging.get_logger(__name__)


""" MInDS-14 Dataset"""

_CITATION = """\
@article{gerz2021multilingual,
  title={Multilingual and cross-lingual intent detection from spoken data},
  author={Gerz, Daniela and Su, Pei-Hao and Kusztos, Razvan and Mondal, Avishek and Lis, Michal and Singhal, Eshan and Mrk{\v{s}}i{\'c}, Nikola and Wen, Tsung-Hsien and Vuli{\'c}, Ivan},
  journal={arXiv preprint arXiv:2104.08524},
  year={2021}
}
"""

_DESCRIPTION = """\
MINDS-14 is training and evaluation resource for intent
detection task with spoken data. It covers 14
intents extracted from a commercial system
in the e-banking domain, associated with spoken examples in 14 diverse language varieties.
"""

_ALL_CONFIGS = sorted([
    "cs-CZ", "de-DE", "en-AU", "en-GB", "en-US", "es-ES", "fr-FR", "it-IT", "ko-KR", "nl-NL", "pl-PL", "pt-PT", "ru-RU", "zh-CN"
])


_DESCRIPTION = "MINDS-14 is a dataset for the intent detection task with spoken data. It covers 14 intents extracted from a commercial system in the e-banking domain, associated with spoken examples in 14 diverse language varieties."

_HOMEPAGE_URL = "https://arxiv.org/abs/2104.08524"

_DATA_URL = "data/MInDS-14.zip"


class Minds14Config(datasets.BuilderConfig):
    """BuilderConfig for xtreme-s"""

    def __init__(
        self, name, description, homepage, data_url
    ):
        super(Minds14Config, self).__init__(
            name=self.name,
            version=datasets.Version("1.0.0", ""),
            description=self.description,
        )
        self.name = name
        self.description = description
        self.homepage = homepage
        self.data_url = data_url


def _build_config(name):
    return Minds14Config(
        name=name,
        description=_DESCRIPTION,
        homepage=_HOMEPAGE_URL,
        data_url=_DATA_URL,
    )


class Minds14(datasets.GeneratorBasedBuilder):

    DEFAULT_WRITER_BATCH_SIZE = 1000
    BUILDER_CONFIGS = [_build_config(name) for name in _ALL_CONFIGS + ["all"]]

    def _info(self):
        task_templates = None
        langs = _ALL_CONFIGS
        features = datasets.Features(
            {
                "path": datasets.Value("string"),
                "audio": datasets.Audio(sampling_rate=8_000),
                "transcription": datasets.Value("string"),
                "english_transcription": datasets.Value("string"),
                "intent_class": datasets.ClassLabel(
                    names=[
                        "abroad",
                        "address",
                        "app_error",
                        "atm_limit",
                        "balance",
                        "business_loan",
                        "card_issues",
                        "cash_deposit",
                        "direct_debit",
                        "freeze",
                        "high_value_payment",
                        "joint_account",
                        "latest_transactions",
                        "pay_bill",
                    ]
                ),
                "lang_id": datasets.ClassLabel(names=langs),
            }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=("audio", "transcription"),
            homepage=self.config.homepage,
            citation=_CITATION,
            task_templates=task_templates,
        )

    def _split_generators(self, dl_manager):
        langs = (
            _ALL_CONFIGS
            if self.config.name == "all"
            else [self.config.name]
        )

        archive_path = dl_manager.download_and_extract(self.config.data_url)
        audio_path = dl_manager.extract(
            os.path.join(archive_path, "MInDS-14", "audio.zip")
        )
        text_path = dl_manager.extract(
            os.path.join(archive_path, "MInDS-14", "text.zip")
        )

        text_path = {l: os.path.join(text_path, f"{l}.csv") for l in langs}

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "audio_path": audio_path,
                    "text_paths": text_path,
                },
            )
        ]


    def _generate_examples(self, audio_path, text_paths):
        key = 0
        for lang in text_paths.keys():
            text_path = text_paths[lang]
            with open(text_path, encoding="utf-8") as csv_file:
                csv_reader = csv.reader(csv_file, delimiter=",", skipinitialspace=True)
                next(csv_reader)
                for row in csv_reader:
                    file_path, transcription, english_transcription, intent_class = row

                    file_path = os.path.join(audio_path, *file_path.split("/"))
                    yield key, {
                        "path": file_path,
                        "audio": file_path,
                        "transcription": transcription,
                        "english_transcription": english_transcription,
                        "intent_class": intent_class.lower(),
                        "lang_id": _ALL_CONFIGS.index(lang),
                    }
                    key += 1