content: # Copyright (c) 2023 OpenGVLab
  # Copyright (c) 2025 Bytedance Ltd. and/or its affiliates.
  # SPDX-License-Identifier: MIT
  #
  # This file has been modified by ByteDance Ltd. and/or its affiliates. on 2025-05-20.
  #
  # Original file was released under MIT, with the full license text
  # available at https://github.com/OpenGVLab/InternVL/blob/main/LICENSE.
  #
  # This modified file is released under the same license.
  
  import argparse
  import itertools
  import json
  import os
  import random
  import re
  
  import torch
  from eval.vlm.utils import load_model_and_tokenizer, build_transform, process_conversation
  from PIL import Image
  from tqdm import tqdm
  
  ds_collections = {
      'pope': {
          'root': 'eval/vlm/data/pope/val2014',
          'question': 'eval/vlm/data/pope/llava_pope_test.jsonl',
          'metric': None,
          'max_new_tokens': 100,
          'min_new_tokens': 1,
      },
      'pope_cot': {
          'root': 'eval/vlm/data/pope/val2014',
          'question': 'eval/vlm/data/pope/llava_pope_test.jsonl',
          'metric': None,
          'max_new_tokens': 1000,
          'min_new_tokens': 1,
      }
  }
  
  
  COT_INSTRUCTION = (
      'Your task is to answer the question below. '
      "Give step by step reasoning before you answer, and when you're ready to answer, "
      "please use the format \"Final answer: ..\""
      '\n\n'
      'Question:'
      '\n\n'
      '{question}'
  )
  
  
  def extract_answer(text):
      match = re.search(r'(Final answer:|Answer:)\s*(.*)', text, re.IGNORECASE)
      if match:
          return match.group(2).strip()
      return text
  
  
  def collate_fn(batches):
      questions = [_['question'] for _ in batches]
      images = [_['images'] for _ in batches]
      conversations = [_['conversations'] for _ in batches]
      question_ids = [_['question_id'] for _ in batches]
      annotations = [_['annotation'] for _ in batches]
  
      return questions, images, conversations, question_ids, annotations
  
  
  class VQADataset(torch.utils.data.Dataset):
  
      def __init__(self, root, data, prompt):
          self.root = root
          self.data = open(data).readlines()
          self.prompt = prompt
  
      def __len__(self):
          return len(self.data)
  
      def __getitem__(self, idx):
          data = json.loads(self.data[idx].strip())
          image, question, question_id, annotation = data['image'], data[
              'text'], data['question_id'], data.get('answer', None)
  
          image = os.path.join(self.root, image)
          image = Image.open(image).convert('RGB')
          images = [image]
  
          llava_prompt = 'Answer the question using a single word or phrase.'
          assert llava_prompt in question
          question = question.replace(llava_prompt, self.prompt).strip()
  
          if args.cot:
              question = COT_INSTRUCTION.format(question=question)
          
          images, conversation = process_conversation(images, question)
  
          return {
              'question_id': question_id,
              'question': question,
              'images': images,
              'conversations': conversation,
              'annotation': annotation
          }
  
  
  class InferenceSampler(torch.utils.data.sampler.Sampler):
  
      def __init__(self, size):
          self._size = int(size)
          assert size > 0
          self._rank = torch.distributed.get_rank()
          self._world_size = torch.distributed.get_world_size()
          self._local_indices = self._get_local_indices(size, self._world_size, self._rank)
  
      @staticmethod
      def _get_local_indices(total_size, world_size, rank):
          shard_size = total_size // world_size
          left = total_size % world_size
          shard_sizes = [shard_size + int(r < left) for r in range(world_size)]
  
          begin = sum(shard_sizes[:rank])
          end = min(sum(shard_sizes[:rank + 1]), total_size)
          return range(begin, end)
  
      def __iter__(self):
          yield from self._local_indices
  
      def __len__(self):
          return len(self._local_indices)
  
  
  def evaluate_chat_model():
      prompt = '' if args.cot else 'Answer the question using a single word or phrase.'
      random.seed(args.seed)
  
      for ds_name in args.datasets:
          dataset = VQADataset(
              root=ds_collections[ds_name]['root'],
              data=ds_collections[ds_name]['question'],
              prompt=prompt,
          )
          dataloader = torch.utils.data.DataLoader(
              dataset=dataset,
              sampler=InferenceSampler(len(dataset)),
              batch_size=args.batch_size,
              num_workers=args.num_workers,
              pin_memory=True,
              drop_last=False,
              collate_fn=collate_fn,
          )
  
          outputs = []
          for _, (questions, images, conversations, question_ids, annotations) in tqdm(enumerate(dataloader)):
              pred = model.chat(
                  tokenizer, 
                  new_token_ids,
                  image_transform,
                  images=images[0], # batch=1
                  prompt=conversations[0], # batch=1
                  max_length=ds_collections[ds_name]['max_new_tokens'], # TODO: how to use ds_collections[ds_name]['min_new_tokens']
              )
              pred_orig = pred
              if args.cot:
                  pred = extract_answer(pred).strip()
              answers = [pred]
  
              for question_id, answer, annotation in zip(question_ids, answers, annotations):
                  outputs.append({
                      'question_id': question_id,
                      'text': pred,
                      'text_orig': pred_orig,
                      'metadata': {},
                  })
  
          torch.distributed.barrier()
  
          world_size = torch.distributed.get_world_size()
          merged_outputs = [None for _ in range(world_size)]
          torch.distributed.all_gather_object(merged_outputs, json.dumps(outputs))
  
          merged_outputs = [json.loads(_) for _ in merged_outputs]
          merged_outputs = [_ for _ in itertools.chain.from_iterable(merged_outputs)]
  
          if torch.distributed.get_rank() == 0:
              print(f'Evaluating {ds_name} ...')
              results_file = os.path.join(args.out_dir, 'results.json')
              json.dump(merged_outputs, open(results_file, 'w'))
              print('Results saved to {}'.format(results_file))
              cmd = 'python eval/vlm/eval/pope/eval_pope.py ' \
                    '--annotation-dir eval/vlm/data/pope/coco ' \
                    '--question-file eval/vlm/data/pope/llava_pope_test.jsonl ' \
                    f'--result-file {results_file} ' \
                    f'--out-dir {args.out_dir}'
              print(cmd)
              os.system(cmd)
  
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser()
      parser.add_argument('--datasets', type=str, default='pope')
      parser.add_argument('--batch-size', type=int, default=1)
      parser.add_argument('--num-workers', type=int, default=1)
      parser.add_argument('--out-dir', type=str, default='results')
      parser.add_argument('--seed', type=int, default=0)
      parser.add_argument('--model-path', type=str, default='hf/BAGEL-7B-MoT/')
      parser.add_argument('--cot', action='store_true')
      args = parser.parse_args()
  
      if not os.path.exists(args.out_dir):
          os.makedirs(args.out_dir, exist_ok=True)
  
      args.datasets = args.datasets.split(',')
      print('datasets:', args.datasets)
      assert args.batch_size == 1, 'Only batch size 1 is supported'
  
      torch.distributed.init_process_group(
          backend='nccl',
          world_size=int(os.getenv('WORLD_SIZE', '1')),
          rank=int(os.getenv('RANK', '0')),
      )
  
      torch.cuda.set_device(int(os.getenv('LOCAL_RANK', 0)))
  
      model, tokenizer, new_token_ids = load_model_and_tokenizer(args)
      image_transform = build_transform()
  
      total_params = sum(p.numel() for p in model.parameters()) / 1e9
      print(f'[test] total_params: {total_params}B')
  
      evaluate_chat_model()
  