def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height,
                              image_size):
    best_ratio_diff = float('inf')
    best_ratio = (1, 1)
    area = width * height
    for ratio in target_ratios:
        target_aspect_ratio = ratio[0] / ratio[1]
        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
        if ratio_diff < best_ratio_diff:
            best_ratio_diff = ratio_diff
            best_ratio = ratio
        elif ratio_diff == best_ratio_diff:
            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
                best_ratio = ratio
    return best_ratio

def dynamic_preprocess(image,
                       min_num=1,
                       max_num=6,
                       image_size=448,
                       use_thumbnail=False):
    orig_width, orig_height = image.size
    aspect_ratio = orig_width / orig_height

    target_ratios = {(i, j)
                     for n in range(min_num, max_num + 1)
                     for i in range(1, n + 1) for j in range(1, n + 1)
                     if i * j <= max_num and i * j >= min_num}
    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])

    target_aspect_ratio = find_closest_aspect_ratio(aspect_ratio,
                                                    target_ratios, orig_width,
                                                    orig_height, image_size)

    target_width = image_size * target_aspect_ratio[0]
    target_height = image_size * target_aspect_ratio[1]
    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]

    resized_img = image.resize((target_width, target_height))
    processed_images = []
    for i in range(blocks):
        box = ((i % (target_width // image_size)) * image_size,
               (i // (target_width // image_size)) * image_size,
               ((i % (target_width // image_size)) + 1) * image_size,
               ((i // (target_width // image_size)) + 1) * image_size)
        split_img = resized_img.crop(box)
        processed_images.append(split_img)
    assert len(processed_images) == blocks
    if use_thumbnail and len(processed_images) != 1:
        thumbnail_img = image.resize((image_size, image_size))
        processed_images.append(thumbnail_img)
    return processed_images



import re
def find_seg_indices(text):
    all_seg_indices = [m.start() for m in re.finditer(r'\[SEG\]', text)]
    answer_spans = [(m.start(), m.end()) for m in re.finditer(r'<answer>.*?</answer>', text, re.DOTALL)]
    if len(answer_spans) == 0:
        return [], []
    if len(answer_spans) > 1:
        print(f"Warning: There should be only one <answer> tag in the text. {text}")
    answer_span = answer_spans[0]
    start, end = answer_span
    
    seg_indices_in_reason = []
    seg_indices_in_answer = []
    for idx, seg_ind in enumerate(all_seg_indices):
        if start <= seg_ind < end:
            seg_indices_in_answer.append(idx)
        elif seg_ind < start:
            seg_indices_in_reason.append(idx)
        else:
            continue
    return seg_indices_in_reason, seg_indices_in_answer