import csv
import os
from typing import Dict, List

import datasets

from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import (DEFAULT_SEACROWD_VIEW_NAME,
                                       DEFAULT_SOURCE_VIEW_NAME, Tasks)

_DATASETNAME = "su_id_asr"
_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
_UNIFIED_VIEW_NAME = DEFAULT_SEACROWD_VIEW_NAME

_LANGUAGES = ["sun"]
_LOCAL = False
_CITATION = """\
@inproceedings{sodimana18_sltu,
  author={Keshan Sodimana and Pasindu {De Silva} and Supheakmungkol Sarin and Oddur Kjartansson and Martin Jansche and Knot Pipatsrisawat and Linne Ha},
  title={{A Step-by-Step Process for Building TTS Voices Using Open Source Data and Frameworks for Bangla, Javanese, Khmer, Nepali, Sinhala, and Sundanese}},
  year=2018,
  booktitle={Proc. 6th Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU 2018)},
  pages={66--70},
  doi={10.21437/SLTU.2018-14}
}
"""

_DESCRIPTION = """\
Sundanese ASR training data set containing ~220K utterances.
This dataset was collected by Google in Indonesia.
"""

_HOMEPAGE = "https://indonlp.github.io/nusa-catalogue/card.html?su_id_asr"
_LICENSE = "Attribution-ShareAlike 4.0 International."

_URLs = {
    "su_id_asr_train": "https://univindonesia-my.sharepoint.com/personal/patrick_samuel_office_ui_ac_id/_layouts/15/download.aspx?share=ESbYerhrepxPsggILmK8hZwB9ywXeZzLX7fF885Yo9F7JA",
    "su_id_asr_dev": "https://univindonesia-my.sharepoint.com/personal/patrick_samuel_office_ui_ac_id/_layouts/15/download.aspx?share=EdmZ2KYglRBJrKacGRklGD4BEcZXqY6txIrEhj2csx3I3g",
    "su_id_asr_test": "https://univindonesia-my.sharepoint.com/personal/patrick_samuel_office_ui_ac_id/_layouts/15/download.aspx?share=ET_Yu0vwbk9Mu-2vg68mSnkBJ-CnY1DOBjm8GVjGLKFZxQ",
}

_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"


class SuIdASR(datasets.GeneratorBasedBuilder):
    def _info(self):
        features = datasets.Features({
            "id": datasets.Value("string"),
            "speaker_id": datasets.Value("string"),
            "audio": datasets.Audio(sampling_rate=16_000),
            "text": datasets.Value("string"),
        })
        return datasets.DatasetInfo(features=features)

    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
        print("Downloading the dataset...")

        # Download the files
        train_file = dl_manager.download(_URLs["su_id_asr_train"])
        dev_file = dl_manager.download(_URLs["su_id_asr_dev"])
        test_file = dl_manager.download(_URLs["su_id_asr_test"])

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"filepath": train_file},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"filepath": dev_file},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"filepath": test_file},
            ),
        ]

    def _generate_examples(self, filepath: str):
        """Yields examples as (key, example) tuples."""
        try:
            with open(filepath, "r") as f:
                tsv_reader = csv.reader(f, delimiter="\t")
                header = next(tsv_reader)  # Skip header if present
                print(f"Header: {header}")  # Print the header for debugging
                
                for line in tsv_reader:
                    print(f"Line: {line}")  # Print each line to check contents
                    if len(line) < 3:  # Ensure there are enough columns
                        print("Skipping line, not enough columns:", line)
                        continue
                    
                    audio_id, sp_id, text = line[0], line[1], line[2]
                    wav_path = os.path.join(os.path.dirname(filepath), f"{audio_id}.flac")

                    # Check if the audio file exists
                    if os.path.exists(wav_path):
                        yield audio_id, {
                            "id": audio_id,
                            "speaker_id": sp_id,
                            "audio": wav_path,
                            "text": text,
                        }
                    else:
                        print(f"Audio file does not exist: {wav_path}. Skipping this entry.")

            print(f"Completed loading data from {filepath}.")

        except FileNotFoundError:
            print(f"Error: The file {filepath} was not found. Please check the path.")
        except Exception as e:
            print(f"An error occurred: {e}")

# Load the dataset
try:
    dataset = datasets.load_dataset(__name__)  # Use the current module name
    print("Dataset loaded successfully.")
    print(dataset)

except Exception as e:
    print(f"An error occurred while loading the dataset: {e}")