import tensorflow as tf

            dst_paths.append(dst_path)
    
    print("skipping %d files that already exist" % skipped)
            
    global total
    total = len(src_paths)
    
    print("processing %d files" % total)

    global start
    start = time.time()



    if a.workers == 1:
        with tf.Session() as sess:
            for src_path, dst_path in zip(src_paths, dst_paths):
                process(src_path, dst_path)
                complete()
    else:
        queue = tf.train.input_producer(zip(src_paths, dst_paths), shuffle=False, num_epochs=1)
        dequeue_op = queue.dequeue()

        def worker(coord):
            with sess.as_default():
                while not coord.should_stop():
                    try:
                        src_path, dst_path = sess.run(dequeue_op)
                    except tf.errors.OutOfRangeError:
                        coord.request_stop()
