from collections import defaultdict
import os
import json
import csv

import datasets

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

_DESCRIPTION = """
The corpus \"RAVNURSSON FAROESE SPEECH AND TRANSCRIPTS\" (or RAVNURSSON Corpus for short) is a collection of speech recordings with transcriptions intended for Automatic Speech Recognition (ASR) applications in the language that is spoken at the Faroe Islands (Faroese). It was curated at the Reykjavík University (RU) in 2022.
"""

_CITATION = """
@misc{carlosmenaravnursson2022,
      title={Ravnursson Faroese Speech and Transcripts}, 
      author={Hernandez Mena, Carlos Daniel and Simonsen, Annika},
      year={2022},
      url={http://hdl.handle.net/20.500.12537/276},
}
"""

_HOMEPAGE = "http://hdl.handle.net/20.500.12537/276"

_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 RavnurssonAsrConfig(datasets.BuilderConfig):
    """BuilderConfig for Ravnursson Corpus"""

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

class RavnurssonAsr(datasets.GeneratorBasedBuilder):
    """Ravnursson Faroese Speech and Transcripts"""

    VERSION = datasets.Version(_VERSION)
    BUILDER_CONFIGS = [
        RavnurssonAsrConfig(
            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"),
                "dialect": 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","dialect"]
        
        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]
                audio_id =os.path.splitext(os.path.basename(audio_filename))[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()},
                }