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 torch
  from .data_utils import CAT_SHORT2LONG, process_single_sample
  from datasets import concatenate_datasets, load_dataset
  from eval.vlm.utils import load_model_and_tokenizer, build_transform, process_conversation
  from PIL import Image
  from tqdm import tqdm
  
  ds_collections = {
      'MMMU_validation': {
          'root': 'MMMU/MMMU',
          'max_new_tokens': 10,
          'min_new_tokens': 1,
          'split': 'validation'
      },
      'MMMU_test': {
          'root': 'MMMU/MMMU',
          'max_new_tokens': 10,
          'min_new_tokens': 1,
          'split': 'test'
      },
      'MMMU_dev': {
          'root': 'MMMU/MMMU',
          'max_new_tokens': 10,
          'min_new_tokens': 1,
          'split': 'dev'
      },
  }
  
  
  def collate_fn(batches):
      questions = [_['question'] for _ in batches]
      images = [_['images'] for _ in batches]
      conversation = [_['conversation'] for _ in batches]
      answers = [_['answer'] for _ in batches]
      data_ids = [_['data_id'] for _ in batches]
      options = [_['option'] for _ in batches]
      return questions, images, conversation, answers, data_ids, options
  
  
  class MMMUDataset(torch.utils.data.Dataset):
  
      def __init__(self, root, split, prompt):
          # run for each subject
          sub_dataset_list = []
          for subject in tqdm(CAT_SHORT2LONG.values()):
              sub_dataset = load_dataset(root, subject, split=split, cache_dir=os.path.join(os.getcwd(), 'eval/vlm/data/MMMU/'))
              sub_dataset_list.append(sub_dataset)
  
          # merge all dataset
          self.data = concatenate_datasets(sub_dataset_list)
          self.prompt = prompt
  
      def __len__(self):
          return len(self.data)
  
      def __getitem__(self, idx):
  
          data = process_single_sample(self.data[idx])
          data_id = data['id']
          question = data['question'].strip()
          pil_images = data['image']
          question_type = data['question_type']
  
          choices = eval(data['options'])
          answer = data['answer'] if 'answer' in data else None
  
          choice_list = []
          options = {}
          multiple_choices = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M']
          for i, c in enumerate(choices):
              choice_list.append('{}. {}'.format(multiple_choices[i], c.strip()))
              options[multiple_choices[i]] = c.strip()
          choice_txt = '\n'.join(choice_list)
          images = []
          for idx, pil_image in enumerate(pil_images):
              if pil_image is not None:
                  if idx == 0:
                      pil_image = pil_image.resize((pil_image.width * 2, pil_image.height * 2), Image.BILINEAR)
                  images.append(pil_image)
  
          if len(choice_txt) > 0:
              question += '\n' + choice_txt
          question += '\n' + self.prompt[question_type]
          question = question.strip()
  
          # NOTE: Do not add <image> since <image 1> has been added
          # question = "<image>" * len(images) + "\n" + question
  
          images, conversation = process_conversation(images, question)
  
          return {
              'question': question,
              'images': images,
              'conversation': conversation,
              'answer': answer,
              'option': options,
              'data_id': data_id
          }
  
  
  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 post_process(pred, option):
      pred = pred.strip()
      option_candidate = list(option.keys())
      if len(pred) == 1:
          return pred
      elif len(pred) == 0:
          pred = "C"
      elif len(pred) != 1 and pred[0] in option_candidate:
          return pred[0]
      elif len(pred) != 1 and pred[0] not in option_candidate:
          for k, v in option.items():
              if v in pred:
                  return k
  
      return pred
  
  
  def evaluate_chat_model():
      prompt = {
          'multiple-choice': "Answer with the option's letter from the given choices directly.",
          'open': 'Answer the question using a single word or phrase.'
      }
      random.seed(args.seed)
  
      for ds_name in args.datasets:
          dataset = MMMUDataset(
              root=ds_collections[ds_name]['root'],
              split=ds_collections[ds_name]['split'],
              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, conversation, answers, data_ids, options) in tqdm(enumerate(dataloader)):
              pred = model.chat(
                  tokenizer, 
                  new_token_ids,
                  image_transform,
                  images=images[0], # batch=1
                  prompt=conversation[0], # batch=1
                  max_length=ds_collections[ds_name]['max_new_tokens'], # TODO: how to use ds_collections[ds_name]['min_new_tokens']
              )
              if len(options[0]) == 0:
                  preds = [pred]
              else:
                  preds = [post_process(pred, options[0])]
  
              for question, pred, answer, data_id in zip(questions, preds, answers, data_ids):
                  outputs.append({
                      'question': question,
                      'answer': pred,
                      'gt_answers': answer,
                      'data_id': data_id
                  })
  
          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} ...')
              output_path = os.path.join(args.out_dir, "prediction.json")
              outputs = {}
              for item in merged_outputs:
                  outputs[item['data_id']] = item['answer']
              with open(output_path, 'w') as f:
                  json.dump(outputs, f, indent=4)
              print('Results saved to {}'.format(output_path))
              if ds_collections[ds_name]['split'] == 'validation':
                  print('Evaluating ...')
                  cmd = f'python -m eval.vlm.eval.mmmu.main_eval_only ' \
                        f'--output_path {output_path} ' \
                        f'--answer_path eval/vlm/eval/mmmu/answer_dict_val.json ' \
                        f'--out-dir {args.out_dir}'
                  print(cmd)
                  os.system(cmd)
              output_path = os.path.join(args.out_dir, "results.jsonl")
              writer = open(output_path, 'w')
              for item in merged_outputs:
                  writer.write(json.dumps(item) + '\n')
              writer.close()
              print('Results saved to {}'.format(output_path))
  
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser()
      parser.add_argument('--datasets', type=str, default='MMMU_validation')
      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/')
      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()
  