import csv
import datasets
from datasets import BuilderConfig, GeneratorBasedBuilder, DatasetInfo, SplitGenerator, Split
from pathlib import Path

_PROMPTS_PROSODIC_URLS = {
    "dev": "prosodic/validation.csv",
    "train": "prosodic/train.csv",
}

_PROMPTS_AUTOMATIC_URLS = {
    "dev": "automatic/validation.csv",
    "train": "automatic/train.csv",
}

_ARCHIVES_PROSODIC = {
    "dev": "prosodic/audios.tar.gz",
    "train": "prosodic/audios.tar.gz",
}

_ARCHIVES_AUTOMATIC = {
    "dev": "automatic/audios.tar.gz",
    "train": "automatic/audios.tar.gz",
}

_PATH_TO_CLIPS = {
    "dev": "",
    "train": "",
}

class NurcSPConfig(BuilderConfig):
    def __init__(self, prompts_type, **kwargs):
        super().__init__(**kwargs)
        self.prompts_type = prompts_type

class NurcSPDataset(GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        NurcSPConfig(name="automatic", description="Automatic audio prompts", prompts_type="automatic"),
        NurcSPConfig(name="prosodic", description="Prosodic audio prompts", prompts_type="prosodic"),
    ]

    def _info(self):
        if self.config.name == "prosodic":
            return DatasetInfo(
                features=datasets.Features(
                    {
                        "path": datasets.Value("string"),
                        "name": datasets.Value("string"),
                        "speaker": datasets.Value("string"),
                        "start_time": datasets.Value("string"),
                        "end_time": datasets.Value("string"),
                        "normalized_text": datasets.Value("string"),
                        "text": datasets.Value("string"),
                        "duration": datasets.Value("string"),
                        "type": datasets.Value("string"),
                        "year": datasets.Value("string"),
                        "gender": datasets.Value("string"),
                        "age_range": datasets.Value("string"),
                        "total_duration": datasets.Value("string"),
                        "quality": datasets.Value("string"),
                        "theme": datasets.Value("string"),
                        "audio": datasets.Audio(sampling_rate=16_000),
                    }
                )
            )
        elif self.config.name == "automatic":
            return DatasetInfo(
                features=datasets.Features(
                    {
                        "audio_name": datasets.Value("string"),
                        "file_path": datasets.Value("string"),
                        "text": datasets.Value("string"),
                        "start_time": datasets.Value("string"),
                        "end_time": datasets.Value("string"),
                        "duration": datasets.Value("string"),
                        "quality": datasets.Value("string"),
                        "speech_genre": datasets.Value("string"),
                        "speech_style": datasets.Value("string"),
                        "variety": datasets.Value("string"),
                        "accent": datasets.Value("string"),
                        "sex": datasets.Value("string"),
                        "age_range": datasets.Value("string"),
                        "num_speakers": datasets.Value("string"),
                        "speaker_id": datasets.Value("string"),
                        "audio": datasets.Audio(sampling_rate=16_000),
                    }
                )
            )

    def _split_generators(self, dl_manager):
        if self.config.prompts_type == "prosodic":
            prompts_urls = _PROMPTS_PROSODIC_URLS
            archive_link = _ARCHIVES_PROSODIC
        elif self.config.prompts_type == "automatic":
            prompts_urls = _PROMPTS_AUTOMATIC_URLS
            archive_link = _ARCHIVES_AUTOMATIC
        else:
            return

        prompts_path = dl_manager.download(prompts_urls)
        archive = dl_manager.download(archive_link)

        return [
            SplitGenerator(
                name=Split.VALIDATION,
                gen_kwargs={
                    "prompts_path": prompts_path["dev"],
                    "path_to_clips": _PATH_TO_CLIPS["dev"],
                    "audio_files": dl_manager.iter_archive(archive["dev"]),
                    "split_name": "validation"
                }
            ),
            SplitGenerator(
                name=Split.TRAIN,
                gen_kwargs={
                    "prompts_path": prompts_path["train"],
                    "path_to_clips": _PATH_TO_CLIPS["train"],
                    "audio_files": dl_manager.iter_archive(archive["train"]),
                    "split_name": "train"
                }
            ),
        ]

    def _generate_examples(self, prompts_path, path_to_clips, audio_files, split_name):
        examples = {}
        csv_paths = []

        with open(prompts_path, "r") as f:
            csv_reader = csv.DictReader(f)
            if self.config.prompts_type == "prosodic":
                for row in csv_reader:
                    file_path = Path(row['path']).as_posix()
                    examples[file_path] = {
                        "path": row['path'],
                        "name": row['name'],
                        "speaker": row['speaker'],
                        "start_time": row['start_time'],
                        "end_time": row['end_time'],
                        "normalized_text": row['normalized_text'],
                        "text": row['text'],
                        "duration": row['duration'],
                        "type": row['type'],
                        "year": row['year'],
                        "gender": row['gender'],
                        "age_range": row['age_range'],
                        "total_duration": row['total_duration'],
                        "quality": row['quality'],
                        "theme": row['theme'],
                    }
                    csv_paths.append(file_path)
            elif self.config.prompts_type == "automatic":
                for row in csv_reader:
                    file_path = Path(row['file_path']).as_posix()
                    examples[file_path] = {
                        "audio_name": row['audio_name'],
                        "file_path": row['file_path'],
                        "text": row['text'],
                        "start_time": row['start_time'],
                        "end_time": row['end_time'],
                        "duration": row['duration'],
                        "quality": row['quality'],
                        "speech_genre": row['speech_genre'],
                        "speech_style": row['speech_style'],
                        "variety": row['variety'],
                        "accent": row['accent'],
                        "sex": row['sex'],
                        "age_range": row['age_range'],
                        "num_speakers": row['num_speakers'],
                        "speaker_id": row['speaker_id'],
                    }
                    csv_paths.append(file_path)

        id_ = 0
        for path, f in audio_files:
            path = Path(path).as_posix()
            if path.startswith(path_to_clips) and path in examples:
                audio = {"path": path, "bytes": f.read()}
                yield id_, {**examples[path], "audio": audio}
                id_ += 1