Repository: open-compass/opencompass
Issue: [Bug] 评测mbpp数据集时，infer过程报错TypeError: can only concatenate tuple (not "str") to tuple
Root cause: External Dependency Breakage

### Prerequisite

- [X] I have searched [Issues](https://github.com/open-compass/opencompass/issues/) and [Discussions](https://github.com/open-compass/opencompass/discussions) but cannot get the expected help.
- [X] The bug has not been fixed in the [latest version](https://github.com/open-compass/opencompass).

### Type

I'm evaluating with the officially supported tasks/models/datasets.

### Environment

1

### Reproduces the problem - code/configuration sample

1

### Reproduces the problem - command or script

1

### Reproduces the problem - error message

Traceback (most recent call last):
  File "/home/dengkaibiao/opencompass/opencompass/tasks/openicl_infer.py", line 153, in <module>
    inferencer.run()
  File "/home/dengkaibiao/opencompass/opencompass/tasks/openicl_infer.py", line 71, in run
    self.dataset = build_dataset_from_cfg(self.dataset_cfg)
  File "/home/dengkaibiao/anaconda3/lib/python3.10/site-packages/opencompass/utils/build.py", line 13, in build_dataset_from_cfg
    return LOAD_DATASET.build(dataset_cfg)
  File "/home/dengkaibiao/anaconda3/lib/python3.10/site-packages/mmengine/registry/registry.py", line 570, in build
    return self.build_func(cfg, *args, **kwargs, registry=self)
  File "/home/dengkaibiao/anaconda3/lib/python3.10/site-packages/mmengine/registry/build_functions.py", line 121, in build_from_cfg
    obj = obj_cls(**args)  # type: ignore
  File "/home/dengkaibiao/anaconda3/lib/python3.10/site-packages/opencompass/datasets/base.py", line 12, in __init__
    self.dataset = self.load(**kwargs)
  File "/home/dengkaibiao/anaconda3/lib/python3.10/site-packages/opencompass/datasets/mbpp.py", line 35, in load
    train = load_dataset('json', data_files=path,
  File "/home/dengkaibiao/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 2106, in load_dataset
    builder_instance = load_dataset_builder(
  File "/home/dengkaibiao/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 1792, in load_dataset_builder
    dataset_module = dataset_module_factory(
  File "/home/dengkaibiao/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 1410, in dataset_module_factory
    ).get_module()
  File "/home/dengkaibiao/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 947, in get_module
    data_files = DataFilesDict.from_patterns(
  File "/home/dengkaibiao/anaconda3/lib/python3.10/site-packages/datasets/data_files.py", line 671, in from_patterns
    DataFilesList.from_patterns(
  File "/home/dengkaibiao/anaconda3/lib/python3.10/site-packages/datasets/data_files.py", line 577, in from_patterns
    resolve_pattern(
  File "/home/dengkaibiao/anaconda3/lib/python3.10/site-packages/datasets/data_files.py", line 335, in resolve_pattern
    protocol_prefix = fs.protocol + "://" if fs.protocol != "file" else ""
TypeError: can only concatenate tuple (not "str") to tuple

### Other information

配置文件信息config/eval_deng.py：

from mmengine.config import read_base
from opencompass.models import OpenAI
from opencompass.partitioners import NaivePartitioner
from opencompass.runners import LocalRunner
from opencompass.tasks import OpenICLInferTask

with read_base():
    from .datasets.collections.chat_medium import datasets
    from .summarizers.medium import summarizer
    from .datasets.mbpp.mbpp_gen import mbpp_datasets

api_meta_template = dict(
    round=[
            dict(role='HUMAN', api_role='HUMAN'),
            dict(role='BOT', api_role='BOT', generate=True),
    ],
)

datasets = [*mbpp_datasets]

models = [
    dict(abbr='deng',
        type=OpenAI, path='deng',
        key='http://127.0.0.1:1000',  # The key will be obtained from $OPENAI_API_KEY, but you can write down your key here as well
        meta_template=api_meta_template,
        query_per_second=1,
        max_out_len=4096, max_seq_len=4096, batch_size=8),
]

infer = dict(
    partitioner=dict(type=NaivePartitioner),
    runner=dict(
        type=LocalRunner,
        max_num_workers=1,
        task=dict(type=OpenICLInferTask)),
)