Function parallel_by_sections

PosterAgent/deoverflow_parallel.py:251–342

Main entry point to parallelize processing across sections. Returns the merged logs and token counters after processing all sections in parallel.

(
    sections,
    content,
    outline,
    style_logs,
    critic_logs,
    actor_logs,
    img_logs,
    slide_width,
    slide_height,
    name_to_hierarchy,
    critic_template,
    actor_template,
    critic_agent,
    actor_agent,
    neg_img,
    pos_img,
    MAX_ATTEMPTS,
    documentation,
    total_input_token,
    total_output_token,
    max_workers=4
)

    }

def parallel_by_sections(
    sections,
    content,
    outline,
    style_logs,
    critic_logs,
    actor_logs,
    img_logs,
    slide_width,
    slide_height,
    name_to_hierarchy,
    critic_template,
    actor_template,
    critic_agent,
    actor_agent,
    neg_img,
    pos_img,
    MAX_ATTEMPTS,
    documentation,
    total_input_token,
    total_output_token,
    max_workers=4
):
    """
    Main entry point to parallelize processing across sections.

    Returns the merged logs and token counters after processing all sections in parallel.
    """
    # Because we’ll be modifying dictionaries (like style_logs, etc.),
    # it can be safer to create a copy for the workers, then merge results
    # after. (Below is a simple approach—depending on your scale, consider
    # explicit concurrency controls or a database-backed approach.)
    
    # Summaries from each future
    results = []

    # We’ll store fresh copies for each section to avoid concurrency collisions
    # on dictionary updates. If the data is large, you might want a more
    # sophisticated synchronization or partition approach rather than naive copies.
    with ThreadPoolExecutor(max_workers=max_workers) as executor:
        futures = []
        
        for section_name in sections:
            # Make shallow copies or deep copies of logs
            _style_logs = copy.deepcopy(style_logs)
            _critic_logs = copy.deepcopy(critic_logs)
            _actor_logs = copy.deepcopy(actor_logs)
            _img_logs = copy.deepcopy(img_logs)
            
            futures.append(executor.submit(
                process_section,
                section_name,
                content,
                outline,
                sections,
                _style_logs,
                _critic_logs,
                _actor_logs,