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Adding some skip logic
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@ -610,6 +610,7 @@ if __name__ == '__main__':
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parser.add_argument('--pdf_profile', help='S3 configuration profile for accessing the raw pdf documents', default=None)
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parser.add_argument('--max_size_mb', type=int, default=250, help='Max file size in MB')
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parser.add_argument('--reindex', action='store_true', default=False, help='Reindex all of the page_results')
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parser.add_argument('--skip_build_queries', action='store_true', default=False, help='Skip generation of new pdf page queries for batch inferencing')
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args = parser.parse_args()
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if args.workspace_profile:
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@ -687,6 +688,9 @@ if __name__ == '__main__':
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if db.get_last_indexed_round() < current_round - 1:
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print(f"WARNING: No new batch inference results found, you need to run batch inference on {args.workspace}/inference_inputs/round_{current_round - 1}")
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potentially_done_pdfs = db.get_pdfs_by_status("pending")
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elif args.skip_build_queries:
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print(f"Skipping generating new batch inference files")
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potentially_done_pdfs = db.get_pdfs_by_status("pending")
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else:
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print(f"\nCreating batch inference files for new PDFs")
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pdf_list = list(db.get_pdfs_by_status("pending"))
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@ -696,7 +700,7 @@ if __name__ == '__main__':
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lines_written = 0
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new_inference_writer = BatchWriter(f"{args.workspace}/inference_inputs/round_{current_round}", args.max_size_mb)
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total_pdfs = len(pdf_list)
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max_pending = 5000
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max_pending = 300
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with tqdm(total=total_pdfs) as pbar:
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# Submit initial batch of futures
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