import csv
import os
import json

import datasets




_CITATION = """ TBD """

_DESCRIPTION = """\
ALORESB is a collection of african speech corpus for  ASR Task.
"""

_DL_URL_FORMAT = "audio/{name}"

class AloresbConfig(datasets.BuilderConfig):
    """BuilderConfig for aloresb"""
    def __init__(
        self, name, **kwargs
    ):
        """
        Args:
            name: name of the configuration
            **kwargs: keyword arguments forwarded to super.
        """
        super(AloresbConfig, self).__init__(
            version=datasets.Version("1.0.0", ""), name=name, **kwargs)
        self.data_root_url = _DL_URL_FORMAT.format(name=name)



class Aloresb(datasets.GeneratorBasedBuilder):
    """
    The Aloresb dataset
    """
    BUILDER_CONFIGS = [
        AloresbConfig(name="fongbe", description="Fongbe aloresb dataset"),
        AloresbConfig(name="hausa", description="Hausa aloresb dataset"),
        AloresbConfig(name="ahmaric", description="Ahmaric aloresb dataset"),
        AloresbConfig(name="wolof", description="Wolof aloresb dataset"),
        AloresbConfig(name="swahili", description="Swahili aloresb dataset"),
    ]
    
    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "file": datasets.Value("string"),
                    "text": datasets.Value("string"),
                    "audio_id": datasets.Value("string"),
                }
            ),
            supervised_keys=("file", "text"),
            task_templates=None,
        )
        
    def _split_generators(self, dl_manager):
        """
        Returns SplitGenerators.
        """
                
        if self.config.name in ["hausa", "wolof"]:
            transcripts = dl_manager.download({
                "train": self.config.data_root_url + "/train/transcripts.txt",
                "dev": self.config.data_root_url + "/dev/transcripts.txt",
                "test": self.config.data_root_url + "/test/transcripts.txt",
            })
            audio_filenames_paths = dl_manager.download({
            "train": self.config.data_root_url + "/train/audio_filenames.txt",
            "dev": self.config.data_root_url + "/dev/audio_filenames.txt",
            "test": self.config.data_root_url + "/test/audio_filenames.txt",
            })
        else:
            transcripts = dl_manager.download({
                "train": self.config.data_root_url + "/train/transcripts.txt",
                "test": self.config.data_root_url + "/test/transcripts.txt",
            })
            
            audio_filenames_paths = dl_manager.download({
                "train": self.config.data_root_url + "/train/audio_filenames.txt",
                "test": self.config.data_root_url + "/test/audio_filenames.txt",
            })
            
        
        
        audio_archives = {}
        for split in audio_filenames_paths:
            if os.path.exists(audio_filenames_paths[split]):
                with open(audio_filenames_paths[split], encoding="utf-8") as f:
                    audio_filenames = [line.strip() for line in f.readlines()]
                    audio_archives[split] = dl_manager.download([
                        self.config.data_root_url + "/" + split + "/audio/" + filename
                        for filename in audio_filenames
                    ])
        # (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
        local_extracted_archives = dl_manager.extract(audio_archives) if not dl_manager.is_streaming else {}
        
        train_splits = [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "transcript_path": transcripts["train"],
                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["train"]],
                    "local_extracted_archive": local_extracted_archives.get("train"),
                }
            ),
        ]
        if self.config.name in ["hausa", "wolof"]:
            return train_splits + [
                datasets.SplitGenerator(
                    name=datasets.Split.VALIDATION, gen_kwargs={
                        "transcript_path": transcripts["dev"],
                        "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["dev"]],
                        "local_extracted_archive": local_extracted_archives.get("dev"),
                    }
                ),
                datasets.SplitGenerator(
                    name=datasets.Split.TEST, gen_kwargs={
                        "transcript_path": transcripts["test"],
                        "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["test"]],
                        "local_extracted_archive": local_extracted_archives.get("test"),
                    }
                ),
                ]
        return train_splits + [
            datasets.SplitGenerator(
                name=datasets.Split.TEST, gen_kwargs={
                    "transcript_path": transcripts["test"],
                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["test"]],
                    "local_extracted_archive": local_extracted_archives.get("test"),
                }
            ),
        ]
        
    def _generate_examples(self, transcript_path, audio_archives, local_extracted_archive):
        """
        Generate examples as dicts.
        """
        transcripts = {}
        with open(transcript_path, encoding="utf-8") as f:
            for line in f:
                audio_id, text = line.strip().split("\t")
                transcripts[audio_id] = text
                
        for archive_idx, audio_archive in enumerate(audio_archives):
            for audio_filename, file in audio_archive:
                # get the audio_filename extension
                ext = os.path.splitext(audio_filename)[1]
                audio_id = audio_filename.split(ext)[0]
                audio_transcript = transcripts[audio_id]
                
                local_audio_file_path = os.path.join(
                    local_extracted_archive[archive_idx], audio_filename
                ) if local_extracted_archive else None
                yield audio_filename, {
                    "file": local_audio_file_path,
                    # "audio": {
                    #     "path": local_audio_file_path if local_audio_file_path else audio_filename,
                    #     "bytes": file.read()
                    # },
                    "text": audio_transcript,
                    "audio_id": audio_id
                }