from collections import defaultdict
import os
import json
import csv

import datasets

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

_DESCRIPTION = """
The Raddrómur Corpus is intended for the speech recognition field and it is made out of radio podcasts mostly taken from RÚV (ruv.is). Such podcasts were selected because they contained a text script that matches with certain fidelity what is said during the show. After automatic segmentation of the episodes, the transcriptions were inferred using the scripts along with a forced alignment technique.
"""

_CITATION = """
@misc{carlosmenaraddromur2022,
      title={Raddrómur Icelandic Speech 22.09}, 
      author={Hernández Mena, Carlos Daniel and Hedström, Staffan and Þórhallsdóttir, Ragnheiður and Fong, Judy Y. and Gunnarsson, Þorsteinn Daði and Sigurðardóttir, Helga Svala and Þorsteinsdóttir, Helga Lára and Guðnason, Jón},
      year={2022},
      url={http://hdl.handle.net/20.500.12537/286},
}
"""

_HOMEPAGE = "http://hdl.handle.net/20.500.12537/286"

_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")

_TARS_TRAIN = os.path.join(_BASE_DATA_DIR,"files","tars_train.paths")

class RaddromurAsrConfig(datasets.BuilderConfig):
    """BuilderConfig for Raddrómur Corpus"""

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

class RaddromurAsr(datasets.GeneratorBasedBuilder):
    """Raddrómur Icelandic Speech 22.09"""

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

    def _info(self):
        features = datasets.Features(
            {
                "audio_id": datasets.Value("string"),
                "audio": datasets.Audio(sampling_rate=16000),
                "podcast_id": datasets.Value("string"),
                "segment_num": datasets.Value("int32"),
                "start_time": datasets.Value("string"),
                "duration": datasets.Value("float32"),
                "mafia_score": 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)
 
        tars_train=dl_manager.download_and_extract(_TARS_TRAIN)

        hash_tar_files=defaultdict(dict)
        with open(tars_train,'r') as f:
            hash_tar_files['train']=[path.replace('\n','') for path in f]
    
        hash_meta_paths={"train":metadata_train}
        audio_paths = dl_manager.download(hash_tar_files)
        
        splits=["train"]
        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"],
                }
            ),
        ]

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

        features = ["podcast_id","segment_num","start_time","duration","mafia_score","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]
                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()},
                }