custom_tensorflow_keras_nlp/util/preprocessing.py [20:41]:
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def wait_for_file_stable(path: str, stable_secs: int=60, poll_secs: Optional[int]=None) -> bool:
    """Wait for a file to become stable (not recently modified) & return existence

    Returns False if file does not exist. Raises FileNotFoundError if file deleted during polling.

    When running through the two notebooks at the same time in parallel, this helps to minimize any
    errors caused by initiating multiple downloads/extractions/etc on the same file in parallel.
    """
    if not poll_secs:
        poll_secs = stable_secs / 4
    try:
        init_stat = os.stat(path)
    except FileNotFoundError:
        return False

    if (time.time() - init_stat.st_mtime) < stable_secs:
        print(f"Waiting for file to stabilize... {path}")
        while (time.time() - os.stat(path).st_mtime) < stable_secs:
            time.sleep(poll_secs)
        print("File ready")

    return True
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pytorch_alternatives/custom_pytorch_nlp/util/preprocessing.py [18:39]:
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def wait_for_file_stable(path: str, stable_secs: int=60, poll_secs: Optional[int]=None) -> bool:
    """Wait for a file to become stable (not recently modified) & return existence

    Returns False if file does not exist. Raises FileNotFoundError if file deleted during polling.

    When running through the two notebooks at the same time in parallel, this helps to minimize any
    errors caused by initiating multiple downloads/extractions/etc on the same file in parallel.
    """
    if not poll_secs:
        poll_secs = stable_secs / 4
    try:
        init_stat = os.stat(path)
    except FileNotFoundError:
        return False

    if (time.time() - init_stat.st_mtime) < stable_secs:
        print(f"Waiting for file to stabilize... {path}")
        while (time.time() - os.stat(path).st_mtime) < stable_secs:
            time.sleep(poll_secs)
        print("File ready")

    return True
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