import csv
import os
import datasets
import pandas as pd
from datasets import Split 

# Metadata

_DESCRIPTION = """\

Mumospee is a continuously growing, comprehensive, multilingual dataset across different modalities. 
This is the small version include no more 1000 rows.

"""

_LICENSE = "cc0-1.0"

_LANGUAGES = ["en", "bg", "de", "ar", "fr"] 

_TAGS = ["CoVoST", "GigaSpeech", "PeopleSpeech", "Librispeech", "LibriTTS", "Emilia", "MOSEL"]

_SPLITS = ["train", "validation", "test"]

# BuilderConfig class for your dataset
class MumospeeDatasetConfig(datasets.BuilderConfig):
    def __init__(self, name, download_audio=None, language=None, tag=None, **kwargs):
        super().__init__(**kwargs)
        self.name = name
        self.language = language
        self.tag = tag
        self.download_audio = download_audio
    

class MumospeeDataset(datasets.GeneratorBasedBuilder):

    VERSION = datasets.Version("1.0.0")

    # Define the available configurations (could be subsets like split or language)
    BUILDER_CONFIGS = [
        MumospeeDatasetConfig(
            version=datasets.Version("1.0.0"),
            description=_DESCRIPTION,
            name="train",
            download_audio=None,
            language=None,
            tag=None
            ),
        MumospeeDatasetConfig(
            version=datasets.Version("1.0.0"),
            description=_DESCRIPTION,
            name="test",
            download_audio=None,
            language=None,
            tag=None
            ),
        MumospeeDatasetConfig(
            version=datasets.Version("1.0.0"),
            description=_DESCRIPTION,
            name="validation",
            download_audio=None,
            language=None,
            tag=None
            )
        ]
    DEFAULT_CONFIG_NAME = "train"

    def _info(self):
        # Define the features of your dataset
        features = datasets.Features({
            "path": datasets.Value("string"),
            "url": datasets.Value("string"),
            "type": datasets.Value("string"),
            "duration": datasets.Value("string"),
            "language": datasets.Value("string"),
            "transcript": datasets.Value("string"),
            "tag": datasets.Value("string"),
            "split": datasets.Value("string"),
            "license": datasets.Value("string")
        })
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            license=_LICENSE,
        )
    
    def _adapt_args(self, arg, accepted_arg):
        """
        Adpat the input and make sure it outs as list 
        and all the elements within the list are accpeted.
        """

        if arg:
            if isinstance(arg, str):
                adapted_arg = [arg]
            else:
                adapted_arg = arg
            for aa in adapted_arg:
                if aa not in accepted_arg:
                    raise ValueError(f"Invalid input: '{aa}'. Accepted values are: {', '.join(accepted_arg)}.")
        else:
            adapted_arg = accepted_arg

        return adapted_arg



    def _split_generators(self, dl_manager):
        csv_path = dl_manager.download_and_extract("dataset.csv")

        if self.config.name==None:
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={"filepath": csv_path, "dl_manager": dl_manager}
                ),
                datasets.SplitGenerator(
                    name=datasets.Split.VALIDATION,
                    gen_kwargs={"filepath": csv_path, "dl_manager": dl_manager}
                ),
                datasets.SplitGenerator(
                    name=datasets.Split.TEST,
                    gen_kwargs={"filepath": csv_path, "dl_manager": dl_manager}
                ),
            ]

        else:
            return [
                datasets.SplitGenerator(
                    name = getattr(Split, self.config.name.upper()), 
                    gen_kwargs={"filepath": csv_path, "dl_manager": dl_manager}
                ),
            ]


    def _generate_examples(self, filepath, dl_manager):

        data = pd.read_csv(filepath)
        name = self.config.name
        language = self.config.language
        tag = self.config.tag
        download_audio = self.config.download_audio

        all_splits=[]
        # If split is None, generate examples for all splits
        if name is None:
            all_splits = _SPLITS
        else:
            all_splits = [name]

        print(f"Split input is {name}, so get split of {all_splits}.")

        # Split base on name split train, test, validation. 
        data_split = data[data["split"]==name]
        if data_split.empty:
            print(f"No data found for split='{name}'. Skipping this split.")
            return
        

        # Split based on tags.
        if tag is not None:
            tag_list = self._adapt_args(tag, _TAGS)
            data_split = data_split[data_split["tag"].isin(tag_list)]
        else:
            print(f"No specific tag provided, including all tags in split='{name}', language='{language or 'all'}'.")

        # split based on language.
        if language is not None:
            language_list = self._adapt_args(language, _LANGUAGES)
            data_split = data_split[data_split["language"].isin(language_list)]
        else:
            print(f"No specific language provided, including all languages in split='{name}', tag='{tag or 'all'}'.")

        if data_split.empty:
            print(f"No data found for split='{name}', language='{language}', tag='{tag}'. Skip this one.")
            return 

        # Generate examples
        for i, row in data_split.iterrows():
            # download the url file
            if download_audio:
                external_url = row["url"]
                dl_manager.download(external_url)
            yield i, {
                "path": row["path"],
                #"local_path": row["local_path"],
                "url": row["url"],
                "type": row["type"],
                "duration": float(row["duration"]),
                "language": row["language"],
                "transcript": row["transcript"],
                "tag": row["tag"],
                "split": row["split"],
                "license": row["license"]
            }