Caching examples at: '/home/user/app/gradio_cached_examples/21'
/home/user/.local/lib/python3.8/site-packages/realtabformer/realtabformer.py:76: UserWarning: The device=cuda is not available, using device=cpu instead.
  warnings.warn(f"The device={device} is not available, using device={_device} instead.")
/home/user/.local/lib/python3.8/site-packages/realtabformer/realtabformer.py:566: UserWarning: Duplicate rate (0.0) in the data is zero. The `qt_interval` will be set                     to qt_interval_unique=100.
  warnings.warn(
Computing the sensitivity threshold...
Using parallel computation!!!
/home/user/.local/lib/python3.8/site-packages/realtabformer/realtabformer.py:593: UserWarning: qt_interval adjusted from 100 to 6...
  warnings.warn(

Bootstrap round:   0%|          | 0/10 [00:00<?, ?it/s]
Bootstrap round:  80%|████████  | 8/10 [00:06<00:01,  1.27it/s]
Bootstrap round: 100%|██████████| 10/10 [00:06<00:00,  1.59it/s]
Sensitivity threshold summary:
count    10.000000
mean      0.001138
std       0.010237
min      -0.012831
25%      -0.006415
50%      -0.000529
75%       0.007672
max       0.021825
dtype: float64
Sensitivity threshold: 0.015753968253968235 qt_max: 0.05

Map:   0%|          | 0/768 [00:00<?, ? examples/s]
Map:  47%|████▋     | 363/768 [00:00<00:00, 3581.70 examples/s]
Map: 100%|██████████| 768/768 [00:00<00:00, 3477.86 examples/s]
                                                               
Traceback (most recent call last):
  File "app.py", line 110, in <module>
    examples = gr.Examples(examples=[['diabetes.arff',5], ["titanic.csv", 15]],inputs = [data_input_u,num_samples], outputs = [data_output], cache_examples = True, fn = generate_data)
  File "/home/user/.local/lib/python3.8/site-packages/gradio/helpers.py", line 69, in create_examples
    utils.synchronize_async(examples_obj.create)
  File "/home/user/.local/lib/python3.8/site-packages/gradio/utils.py", line 420, in synchronize_async
    return fsspec.asyn.sync(fsspec.asyn.get_loop(), func, *args, **kwargs)
  File "/home/user/.local/lib/python3.8/site-packages/fsspec/asyn.py", line 100, in sync
    raise return_result
  File "/home/user/.local/lib/python3.8/site-packages/fsspec/asyn.py", line 55, in _runner
    result[0] = await coro
  File "/home/user/.local/lib/python3.8/site-packages/gradio/helpers.py", line 273, in create
    await self.cache()
  File "/home/user/.local/lib/python3.8/site-packages/gradio/helpers.py", line 308, in cache
    prediction = await Context.root_block.process_api(
  File "/home/user/.local/lib/python3.8/site-packages/gradio/blocks.py", line 1017, in process_api
    result = await self.call_function(
  File "/home/user/.local/lib/python3.8/site-packages/gradio/blocks.py", line 835, in call_function
    prediction = await anyio.to_thread.run_sync(
  File "/home/user/.local/lib/python3.8/site-packages/anyio/to_thread.py", line 33, in run_sync
    return await get_asynclib().run_sync_in_worker_thread(
  File "/home/user/.local/lib/python3.8/site-packages/anyio/_backends/_asyncio.py", line 877, in run_sync_in_worker_thread
    return await future
  File "/home/user/.local/lib/python3.8/site-packages/anyio/_backends/_asyncio.py", line 807, in run
    result = context.run(func, *args)
  File "app.py", line 19, in generate_data
    rtf_model.fit(df, num_bootstrap=10) # Default is 500
  File "/home/user/.local/lib/python3.8/site-packages/realtabformer/realtabformer.py", line 454, in fit
    trainer = self._train_with_sensitivity(
  File "/home/user/.local/lib/python3.8/site-packages/realtabformer/realtabformer.py", line 693, in _train_with_sensitivity
    trainer = self._fit_tabular(
  File "/home/user/.local/lib/python3.8/site-packages/realtabformer/realtabformer.py", line 1088, in _fit_tabular
    return self._build_tabular_trainer(
  File "/home/user/.local/lib/python3.8/site-packages/realtabformer/realtabformer.py", line 1138, in _build_tabular_trainer
    args=TrainingArguments(**training_args_kwargs),
  File "<string>", line 111, in __init__
  File "/home/user/.local/lib/python3.8/site-packages/transformers/training_args.py", line 1333, in __post_init__
    and (self.device.type != "cuda")
  File "/home/user/.local/lib/python3.8/site-packages/transformers/training_args.py", line 1697, in device
    return self._setup_devices
  File "/home/user/.local/lib/python3.8/site-packages/transformers/utils/generic.py", line 54, in __get__
    cached = self.fget(obj)
  File "/home/user/.local/lib/python3.8/site-packages/transformers/training_args.py", line 1613, in _setup_devices
    raise ImportError(
ImportError: Using the `Trainer` with `PyTorch` requires `accelerate`: Run `pip install --upgrade accelerate`