import os
import tarfile
import datasets
import pandas as pd
from typing import Dict, List
import io
from tqdm import tqdm
import csv
import os

# TODO: Optionally update the description for your specific dataset
_DESCRIPTION = """
This dataset consists of about 400 hours of audio extracted from various Filimo videos in the Persian language.
Note: This dataset contains raw, unvalidated transcriptions. Users are advised to:
1. Perform their own quality assessment
2. Create their own train/validation/test splits based on their specific needs
3. Validate a subset of the data if needed for their use case
"""

# TODO: Update with your repository information or desired citation method
_CITATION = """
Use this repo info/link for citation: https://huggingface.co/datasets/msghol/filimo-farsi
"""

_LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/" # Or your chosen license

# TODO: Update HOMEPAGE to your dataset's Hugging Face page
_HOMEPAGE = "https://huggingface.co/datasets/msghol/filimo-farsi"

# TODO: Update BASE_URL to point to your dataset repository
_BASE_URL = "https://huggingface.co/datasets/msghol/filimo-farsi/resolve/main/"

_AUDIO_URL = _BASE_URL + "data/unvalidated_{shard_idx:03d}.tar" # This will now use your updated _BASE_URL

# TODO: Consider renaming the class for clarity, e.g., FilimoFarsiASR
class FilimoASRDataset(datasets.GeneratorBasedBuilder): # Or class FilimoFarsiASR(datasets.GeneratorBasedBuilder):

    DEFAULT_WRITER_BATCH_SIZE = 1000

    VERSION = datasets.Version("1.0.0") # Update if desired

    def _info(self):
        return datasets.DatasetInfo(
            features=datasets.Features({
                "audio": datasets.Audio(sampling_rate=16_000),  # Adjust sampling rate if your audio is different
                "text": datasets.Value("string"),
                "file_name": datasets.Value("string"),
            }),
            supervised_keys=None,
            license=_LICENSE,
            citation=_CITATION,
            version=self.VERSION,
            description=_DESCRIPTION,
            homepage=_HOMEPAGE, # Added homepage here
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""

        # TODO: Adjust the range if you have a different number of tar shards
        # For example, if you have 10 shards (unvalidated_001.tar to unvalidated_010.tar):
        # archive_paths = [_AUDIO_URL.format(shard_idx=i) for i in range(1, 11)]
        archive_paths = [_AUDIO_URL.format(shard_idx=i) for i in range(1, 34)] # Default is 33 shards

        local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}

        return [
            datasets.SplitGenerator(
                name="unvalidated", # You can rename this split if needed, e.g., datasets.Split.TRAIN
                gen_kwargs={
                    "local_extracted_archive_paths": local_extracted_archive_paths,
                    "archives": [dl_manager.iter_archive(path) for path in archive_paths],
                    "meta_path": _BASE_URL + "unvalidated.csv", # This will now use your updated _BASE_URL
                },
            ),
        ]

    def _generate_examples(self, local_extracted_archive_paths, archives, meta_path):
        """Yields examples."""
        data_fields = list(self._info().features.keys())
        metadata = {}
        # For remote CSV file, dl_manager.download_and_extract may be more robust
        # However, Hugging Face often handles direct HTTPS links for text files well.
        # If issues arise, consider: downloaded_meta_path = dl_manager.download_and_extract(meta_path)
        downloaded_meta_path = dl_manager.download(meta_path) # Download the csv file
        with open(downloaded_meta_path, encoding="utf-8") as f: # Open the downloaded file
            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
            for row in tqdm(reader, desc="Reading metadata..."):
                if not row["file_name"].endswith(".mp3"):
                    row["file_name"] += ".mp3"
                if "sentence" in row:
                    row['text'] = row['sentence']
                    # del row['sentence'] # Keep sentence column if you want it in final dataset
                else: # Ensure 'text' field exists if 'sentence' is not present
                    row['text'] = row.get('text', '')

                # Ensure all defined features have at least a default value
                for field in data_fields:
                    if field not in row:
                        row[field] = ""

                metadata[row["file_name"]] = row

        for i, audio_archive in enumerate(archives):
            for path, file_obj in audio_archive: # Changed 'file' to 'file_obj' to avoid conflict with os.path.file
                _, filename = os.path.split(path)
                if filename in metadata:
                    result = dict(metadata[filename])
                    # set the audio feature and the path to the extracted file
                    # If streaming, path is the original path in archive, file_obj is a file-like object
                    # If not streaming (local_extracted_archive_paths is populated), path needs to be joined
                    if local_extracted_archive_paths:
                        full_path_to_audio = os.path.join(local_extracted_archive_paths[i], path)
                        result["audio"] = {"path": full_path_to_audio, "bytes": file_obj.read()}
                        result["file_name"] = full_path_to_audio # Or keep original filename: filename
                    else: # Streaming case
                        result["audio"] = {"path": path, "bytes": file_obj.read()} # path is relative path in archive
                        result["file_name"] = filename # Or path if you prefer relative path from archive root

                    yield path, result # Path can be used as a unique key
                # else:
                    # print(f"Warning: File {filename} from archive not found in metadata.") # Optional: for debugging