from collections import defaultdict
import os
import json
import csv
csv.field_size_limit(100000000)

import datasets

_NAME="cv17_es_other_automatically_verified"
_VERSION="1.0.0"
_AUDIO_EXTENSIONS=".mp3"

_DESCRIPTION = """
Split called -other- of the Spanish Common Voice v17.0 that was automatically verified
using various ASR system.
"""

_CITATION = """
@misc{carlosmena2024cv17autoveri,
      title={Spanish Common Voice v17.0 Split Other Automatically Verified}, 
      author={Mena, Carlos},
      publisher={Barcelona Supercomputing Center}
      year={2024},
      url={https://huggingface.co/datasets/projecte-aina/cv17_es_other_automatically_verified},
}
"""

_HOMEPAGE = "https://huggingface.co/datasets/projecte-aina/cv17_es_other_automatically_verified"

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

_BASE_DATA_DIR = "corpus/"

_METADATA_OTHER   = os.path.join(_BASE_DATA_DIR,"files","other.tsv")

_TARS_REPO = os.path.join(_BASE_DATA_DIR,"files","tars_repo.paths")

class CV17EsOtherAutomaticallyVerifiedConfig(datasets.BuilderConfig):
    """BuilderConfig for The Spanish Common Voice v17.0 Split Other Automatically Verified"""

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

class CV17EsOtherAutomaticallyVerified(datasets.GeneratorBasedBuilder):
    """Spanish Common Voice v17.0 Split Other Automatically Verified"""

    VERSION = datasets.Version(_VERSION)
    BUILDER_CONFIGS = [
        CV17EsOtherAutomaticallyVerifiedConfig(
            name=_NAME,
            version=datasets.Version(_VERSION),
        )
    ]

    def _info(self):
        features = datasets.Features(
            {
                "audio": datasets.Audio(sampling_rate=16000),             
                "client_id": datasets.Value("string"),
                "path": datasets.Value("string"),
                "sentence_id": datasets.Value("string"),
                "sentence": datasets.Value("string"),
                "sentence_domain": datasets.Value("string"),
                "up_votes": datasets.Value("int32"),
                "down_votes": datasets.Value("int32"),
                "age": datasets.Value("string"),
                "gender": datasets.Value("string"),
                "accents": datasets.Value("string"),
                "variant": datasets.Value("string"),
                "locale": datasets.Value("string"),
                "segment": datasets.Value("string"),               
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):

        metadata_other=dl_manager.download_and_extract(_METADATA_OTHER)

        tars_repo=dl_manager.download_and_extract(_TARS_REPO)
       
        hash_tar_files=defaultdict(dict)

        with open(tars_repo,'r') as f:
            hash_tar_files['other']=[path.replace('\n','') for path in f]
                
        hash_meta_paths={"other":metadata_other}

        audio_paths = dl_manager.download(hash_tar_files)
        
        splits=["other"]
        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="other",
                gen_kwargs={
                    "audio_archives":[dl_manager.iter_archive(archive) for archive in audio_paths["other"]],
                    "local_extracted_archives_paths": local_extracted_audio_paths["other"],
                    "metadata_paths": hash_meta_paths["other"],
                }
            ),
        ]

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

        features = ["client_id","sentence_id","sentence","sentence_domain","up_votes",
                    "down_votes","age","gender", "accents","variant","locale","segment"]
        
        with open(metadata_paths) as f:
            metadata = {x["path"]: 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 =os.path.splitext(os.path.basename(audio_filename))[0]
                audio_id=audio_id+_AUDIO_EXTENSIONS
                path = os.path.join(local_extracted_archive_path, audio_filename) if local_extracted_archive_path else audio_filename
                
                try:                        
                        yield audio_id, {
                            "path": audio_id,
                            **{feature: metadata[audio_id][feature] for feature in features},
                            "audio": {"path": path, "bytes": audio_file.read()},
                }
                except:
                        continue