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#                                                           #
#   This file was created by: Alberto Palomo Alonso         #
# Universidad de Alcalá - Escuela Politécnica Superior      #
#                                                           #
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"""
Wikipedia ZIM extraction and segmentation script.

Main workflow:
1) Ask the user for a ZIM path and an identifier.
2) Extract articles using `WikipediaExtractor`.
3) Convert the extracted list to a Hugging Face `datasets.Dataset`.
4) Post-process the dataset with `wiki_to_seg` (segmentation).
5) Save the resulting dataset to disk and reload it.

Notes:
- This script assumes `src.WikipediaExtractor` and `src.wiki_to_seg` are available.
- Output is saved under `./wikipedia-es-<identifier>`.
"""

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#                       IMPORT STATEMENTS                   #
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import logging
import datasets
from src import WikipediaExtractor, wiki_to_seg


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#                        FUNCTION DEF                       #
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def setup_logger() -> logging.Logger:
    """
    Set up the logger for debugging.

    Creates a module-level logger configured at DEBUG level with a StreamHandler.

    Returns:
        logging.Logger: Configured logger instance.

    Notes:
        If this function is called multiple times in the same process, it may attach
        multiple handlers to the same logger. If that is undesirable in your runtime,
        consider checking `logger.handlers` before adding a new handler.
    """
    logger = logging.getLogger(__name__)
    logger.setLevel(logging.DEBUG)

    handler = logging.StreamHandler()
    handler.setLevel(logging.DEBUG)

    formatter = logging.Formatter(
        '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
    )
    handler.setFormatter(formatter)

    logger.addHandler(handler)
    logger.debug('Debugging WikipediaExtractor')
    return logger


def extract(
        zim_path: str,
        relation_recursion: int = 0,
        n_trials: int = 30_000
) -> datasets.Dataset:
    """
    Extract Wikipedia articles from a ZIM file and return a Hugging Face Dataset.

    Args:
        zim_path (str):
            Path to the Wikipedia ZIM file.
        relation_recursion (int, optional):
            Recursion depth for relation/link exploration (as implemented by
            `WikipediaExtractor`). Defaults to 0.
        n_trials (int, optional):
            Trial/iteration budget for extraction (as implemented by the extractor).
            Defaults to 30_000.

    Returns:
        datasets.Dataset:
            A Hugging Face Dataset built from the extracted articles list.

    Raises:
        Any exception raised by `WikipediaExtractor` or `datasets.Dataset.from_list`.
    """
    extractor = WikipediaExtractor(
        zim_path,
        encoding='utf-8',
        logger=setup_logger()
    )

    articles, _ = extractor.get_database(
        relation_recursion=relation_recursion,
        n_trials=n_trials,
        from_cnt=0
    )

    hf_ds = datasets.Dataset.from_list(articles)
    return hf_ds


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#                           MAIN                            #
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if __name__ == '__main__':
    """
    Script entry point.

    Prompts for user inputs, runs extraction + segmentation, saves the dataset to disk,
    and reloads it at the end.

    Inputs:
        - Wikipedia (zim file) path
        - Wikipedia identifier (e.g., B000)

    Side effects:
        - Creates `./wikipedia-es-<identifier>` containing the saved dataset.
        - Reloads the dataset from disk into the `dataset` variable.
    """
    # Ask user for input data:
    z_path = input("Wikipedia (zim file) path: ")
    identifier = input("Wikipedia (Wikipedia identifier, e.g. B000): ")

    # Pathing:
    path_to_disk = rf'./wikipedia-es-{identifier}'
    # Extract:
    hf_pre_dataset = extract(z_path)
    # Post-processing:
    segmentation_dataset = wiki_to_seg(hf_pre_dataset, 50)
    # Save the dataset:
    segmentation_dataset.save_to_disk(path_to_disk)
    # Load the dataset:
    dataset = datasets.load_from_disk(path_to_disk)

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#                        END OF FILE                        #
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