from collections import defaultdict
import os
import json
import csv

import datasets

_NAME="malromur_asr"
_VERSION="1.0.0"
_AUDIO_EXTENSIONS=".flac"

_DESCRIPTION = """
The Málrómur corpus is an open source corpus of Icelandic voice samples.
"""

_CITATION = """
@inproceedings{steingrimsson2017malromur,
  title={Málrómur: A manually verified corpus of recorded Icelandic speech},
  author={Steingrímsson, Steinþór and Guðnason, Jón and Helgadóttir, Sigrún and Rögnvaldsson, Eiríkur},
  booktitle={Proceedings of the 21st Nordic Conference on Computational Linguistics},
  pages={237--240},
  year={2017}
}
"""

_HOMEPAGE = "https://clarin.is/en/resources/malromur/"

_LICENSE = "CC-BY-4.0, See https://creativecommons.org/licenses/by/4.0/"

_BASE_DATA_DIR = "corpus/"
_METADATA_TRAIN =  os.path.join(_BASE_DATA_DIR,"files","metadata_train.tsv")
_METADATA_TEST  =  os.path.join(_BASE_DATA_DIR,"files", "metadata_test.tsv")
_METADATA_DEV   =  os.path.join(_BASE_DATA_DIR,"files",  "metadata_dev.tsv")

_TARS_TRAIN = os.path.join(_BASE_DATA_DIR,"files","tars_train.paths")
_TARS_TEST  = os.path.join(_BASE_DATA_DIR,"files", "tars_test.paths")
_TARS_DEV   = os.path.join(_BASE_DATA_DIR,"files",  "tars_dev.paths")

class MalromurAsrConfig(datasets.BuilderConfig):
    """BuilderConfig for The Málrómur Corpus"""

    def __init__(self, name, **kwargs):
        name=_NAME
        super().__init__(name=name, **kwargs)

class MalromurAsr(datasets.GeneratorBasedBuilder):
    """The Málrómur Corpus"""

    VERSION = datasets.Version(_VERSION)
    BUILDER_CONFIGS = [
        MalromurAsrConfig(
            name=_NAME,
            version=datasets.Version(_VERSION),
        )
    ]

    def _info(self):
        features = datasets.Features(
            {
                "audio_id": datasets.Value("string"),
                "audio": datasets.Audio(sampling_rate=16000),
                "speaker_id": datasets.Value("string"),
                "gender": datasets.Value("string"),
                "age": datasets.Value("string"),
                "duration": datasets.Value("float32"),
                "normalized_text": datasets.Value("string"),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
    
        metadata_train=dl_manager.download_and_extract(_METADATA_TRAIN)
        metadata_test=dl_manager.download_and_extract(_METADATA_TEST)
        metadata_dev=dl_manager.download_and_extract(_METADATA_DEV)   
        
        tars_train=dl_manager.download_and_extract(_TARS_TRAIN)
        tars_test=dl_manager.download_and_extract(_TARS_TEST)
        tars_dev=dl_manager.download_and_extract(_TARS_DEV)
        
        hash_tar_files=defaultdict(dict)
        with open(tars_train,'r') as f:
            hash_tar_files['train']=[path.replace('\n','') for path in f]

        with open(tars_test,'r') as f:
            hash_tar_files['test']=[path.replace('\n','') for path in f]
            
        with open(tars_dev,'r') as f:
            hash_tar_files['dev']=[path.replace('\n','') for path in f]
    
        hash_meta_paths={"train":metadata_train,"test":metadata_test,"dev":metadata_dev}
        audio_paths = dl_manager.download(hash_tar_files)
        
        splits=["train","dev","test"]
        local_extracted_audio_paths = (
            dl_manager.extract(audio_paths) if not dl_manager.is_streaming else
            {
                split:[None] * len(audio_paths[split]) for split in splits
            }
        )                                                                                                            
                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "audio_archives":[dl_manager.iter_archive(archive) for archive in audio_paths["train"]],
                    "local_extracted_archives_paths": local_extracted_audio_paths["train"],
                    "metadata_paths": hash_meta_paths["train"],
                }
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["dev"]],
                    "local_extracted_archives_paths": local_extracted_audio_paths["dev"],
                    "metadata_paths": hash_meta_paths["dev"],
                }
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["test"]],
                    "local_extracted_archives_paths": local_extracted_audio_paths["test"],
                    "metadata_paths": hash_meta_paths["test"],
                }
            ),
        ]

    def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths):

        features = ["speaker_id","gender","age","duration","normalized_text"]
        
        with open(metadata_paths) as f:
            metadata = {x["audio_id"]: x for x in csv.DictReader(f, delimiter="\t")}

        for audio_archive, local_extracted_archive_path in zip(audio_archives, local_extracted_archives_paths):
            for audio_filename, audio_file in audio_archive:
                audio_id = audio_filename.split(os.sep)[-1].split(_AUDIO_EXTENSIONS)[0]
                path = os.path.join(local_extracted_archive_path, audio_filename) if local_extracted_archive_path else audio_filename
                                        
                yield audio_id, {
                    "audio_id": audio_id,
                    **{feature: metadata[audio_id][feature] for feature in features},
                    "audio": {"path": path, "bytes": audio_file.read()},
                }