import os
import csv
import datasets
from tqdm import tqdm  # لإظهار تقدم القراءة في ملفات metadata

_DESCRIPTION = "A speech dataset designed for automatic speech recognition (ASR), structured like Mozilla Common Voice."
_CITATION = "No citation available yet."

class MyDatasetConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super(MyDatasetConfig, self).__init__(**kwargs)

class MyDataset(datasets.GeneratorBasedBuilder):
    DEFAULT_WRITER_BATCH_SIZE = 1000  # مثل Common Voice

    BUILDER_CONFIGS = [
        MyDatasetConfig(
            name="default",
            version=datasets.Version("1.0.0"),
            description=_DESCRIPTION,
        ),
    ]

    def _info(self):
        features = datasets.Features({
            "client_id": datasets.Value("string"),
            "path": datasets.Value("string"),
            "audio": datasets.features.Audio(sampling_rate=16000),
            "text": datasets.Value("string"),
            "up_votes": datasets.Value("int64"),
            "down_votes": datasets.Value("int64"),
            "age": datasets.Value("string"),
            "gender": datasets.Value("string"),
            "accent": datasets.Value("string"),
            "locale": datasets.Value("string"),
            "segment": datasets.Value("string"),
            "variant": datasets.Value("string"),
        })

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            citation=_CITATION,
            version=self.config.version,
        )

    def _split_generators(self, dl_manager):
        data_dir = self.config.data_dir

        splits = ["train", "validation", "test"]
        split_generators = []

        for split in splits:
            audio_tar = os.path.join(data_dir, f"{split}_audio")
            metadata_csv = os.path.join(data_dir, f"{split}_metadata.csv")

            if os.path.exists(audio_tar) and os.path.exists(metadata_csv):
                split_generators.append(
                    datasets.SplitGenerator(
                        name=getattr(datasets.Split, split.upper()),
                        gen_kwargs={
                            "archives": dl_manager.iter_archive(audio_tar),
                            "metadata_path": metadata_csv,
                        },
                    )
                )

        return split_generators

    def _generate_examples(self, archives, metadata_path):
        # قراءة الميتاداتا في dict
        metadata = {}
        data_fields = list(self._info().features.keys())
        
        with open(metadata_path, encoding="utf-8-sig") as f:
            reader = csv.DictReader(f, delimiter=",", quoting=csv.QUOTE_NONE)
            reader.fieldnames = [name.strip().replace('"', '') for name in reader.fieldnames]

            for row in tqdm(reader, desc="Loading metadata..."):
                row = {k.replace('"', ''): v.replace('"', '') for k, v in row.items()}
                if not row["file_name"].endswith(".wav"):
                    row["file_name"] += ".wav"

                # تحويل accents إلى accent (كما في Common Voice 8.0)
                if "accents" in row:
                    row["accent"] = row["accents"]
                    del row["accents"]

                # ملء الحقول الناقصة بقيم فارغة أو افتراضية
                for field in data_fields:
                    if field not in row:
                        # للأعداد (up_votes, down_votes) نضع صفر، الباقي فراغ
                        if field in ["up_votes", "down_votes"]:
                            row[field] = 0
                        else:
                            row[field] = ""

                metadata[row["file_name"]] = row


        # قراءة ملفات الصوت من الأرشيف وتوليد الأمثلة
        for i, audio_archive in enumerate(archives):
            for path_in_tar, file_obj in audio_archive:
                _, filename = os.path.split(path_in_tar)

                if filename in metadata:
                    example = dict(metadata[filename])

                    # ضبط مسار الصوت (لتحميله محلياً أو عبر streaming)
                    example["audio"] = {"path": path_in_tar, "bytes": file_obj.read()}
                    example["path"] = path_in_tar

                    yield path_in_tar, example