import csv
import os
from typing import Dict, List

import datasets

from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import (
    DEFAULT_SEACROWD_VIEW_NAME,
    DEFAULT_SOURCE_VIEW_NAME,
    Tasks,
)

_DATASETNAME = "su_id_asr"
_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
_UNIFIED_VIEW_NAME = DEFAULT_SEACROWD_VIEW_NAME

_LANGUAGES = ["sun"]
_LOCAL = False
_CITATION = """\
@inproceedings{sodimana18_sltu,
  author={Keshan Sodimana and Pasindu {De Silva} and Supheakmungkol Sarin and Oddur Kjartansson and Martin Jansche and Knot Pipatsrisawat and Linne Ha},
  title={{A Step-by-Step Process for Building TTS Voices Using Open Source Data and Frameworks for Bangla, Javanese, Khmer, Nepali, Sinhala, and Sundanese}},
  year=2018,
  booktitle={Proc. 6th Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU 2018)},
  pages={66--70},
  doi={10.21437/SLTU.2018-14}
}
"""

_DESCRIPTION = """\
Sundanese ASR training data set containing ~220K utterances.
This dataset was collected by Google in Indonesia.
"""

_HOMEPAGE = "https://indonlp.github.io/nusa-catalogue/card.html?su_id_asr"

_LICENSE = "Attribution-ShareAlike 4.0 International."

_URLs = {
    "su_id_asr_train": "https://univindonesia-my.sharepoint.com/:u:/g/personal/eduardus_tjitrahardja_office_ui_ac_id/EWvdH1Ne3oVPiHaIcPC4TSgBfPAIyvU8Y3EMvnyimVHmug?e=ZuFikN&download=1",
    "su_id_asr_dev": "https://univindonesia-my.sharepoint.com/:u:/g/personal/eduardus_tjitrahardja_office_ui_ac_id/EbKv5U95h_BBgyDOARciTY4BydUnB9i4-C8jGI8VeiHjiA?e=KA8Jbc&download=1",
    "su_id_asr_test": "https://univindonesia-my.sharepoint.com/:u:/g/personal/eduardus_tjitrahardja_office_ui_ac_id/EbKv5U95h_BBgyDOARciTY4BydUnB9i4-C8jGI8VeiHjiA?e=KA8Jbc&download=1",
}

_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]

_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"


class SuIdASR(datasets.GeneratorBasedBuilder):
    """su_id contains ~220K utterances for Sundanese ASR training data."""

    BUILDER_CONFIGS = [
        SEACrowdConfig(
            name="su_id_asr_source",
            version=datasets.Version(_SOURCE_VERSION),
            description="SU_ID_ASR source schema",
            schema="source",
            subset_id="su_id_asr",
        ),
        SEACrowdConfig(
            name="su_id_asr_seacrowd_sptext",
            version=datasets.Version(_SEACROWD_VERSION),
            description="SU_ID_ASR Nusantara schema",
            schema="seacrowd_sptext",
            subset_id="su_id_asr",
        ),
    ]

    DEFAULT_CONFIG_NAME = "su_id_asr_source"

    def _info(self):
        if self.config.schema == "source":
            features = datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "speaker_id": datasets.Value("string"),
                    "path": datasets.Value("string"),
                    "audio": datasets.Audio(sampling_rate=16_000),
                    "text": datasets.Value("string"),
                }
            )
        elif self.config.schema == "seacrowd_sptext":
            features = schemas.speech_text_features

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
            # task_templates=[
            #     datasets.AutomaticSpeechRecognition(
            #         audio_column="audio", transcription_column="text"
            #     )
            # ],
        )

    def _split_generators(
        self, dl_manager: datasets.DownloadManager
    ) -> List[datasets.SplitGenerator]:
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": dl_manager.download_and_extract(
                        _URLs["su_id_asr_train"]
                    )
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": dl_manager.download_and_extract(_URLs["su_id_asr_dev"])
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": dl_manager.download_and_extract(_URLs["su_id_asr_test"])
                },
            ),
        ]

    def _generate_examples(self, filepath: str):

        if self.config.schema == "source" or self.config.schema == "seacrowd_sptext":

            tsv_file = os.path.join(filepath, "asr_sundanese", "utt_spk_text.tsv")

            with open(tsv_file, "r") as file:
                tsv_file = csv.reader(file, delimiter="\t")

                for line in tsv_file:
                    audio_id, speaker_id, transcription_text = line[0], line[1], line[2]

                    wav_path = os.path.join(
                        filepath,
                        "asr_sundanese",
                        "data",
                        "{}".format(audio_id[:2]),
                        "{}.flac".format(audio_id),
                    )

                    if os.path.exists(wav_path):
                        if self.config.schema == "source":
                            ex = {
                                "id": audio_id,
                                "speaker_id": speaker_id,
                                "path": wav_path,
                                "audio": wav_path,
                                "text": transcription_text,
                            }
                            yield audio_id, ex
                        elif self.config.schema == "seacrowd_sptext":
                            ex = {
                                "id": audio_id,
                                "speaker_id": speaker_id,
                                "path": wav_path,
                                "audio": wav_path,
                                "text": transcription_text,
                                "metadata": {
                                    "speaker_age": None,
                                    "speaker_gender": None,
                                },
                            }
                            yield audio_id, ex

        else:
            raise ValueError(f"Invalid config: {self.config.name}")