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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 13 new columns ({'features', 'total_frames', 'fps', 'video_path', 'codebase_version', 'chunks_size', 'data_path', 'total_episodes', 'total_chunks', 'splits', 'total_tasks', 'total_videos', 'robot_type'}) and 2 missing columns ({'stats', 'episode_index'}).

This happened while the json dataset builder was generating data using

hf://datasets/Gukchan/dataset_251205_converted/meta/info.json (at revision 1af19f71e7755536a8fb66057c0c1e22daf9d175)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 714, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              codebase_version: string
              robot_type: string
              total_episodes: int64
              total_frames: int64
              total_tasks: int64
              total_videos: int64
              total_chunks: int64
              chunks_size: int64
              fps: int64
              splits: struct<>
              data_path: string
              video_path: string
              features: struct<action: struct<dtype: string, shape: list<item: int64>, names: list<item: string>>, observati (... 453 chars omitted)
                child 0, action: struct<dtype: string, shape: list<item: int64>, names: list<item: string>>
                    child 0, dtype: string
                    child 1, shape: list<item: int64>
                        child 0, item: int64
                    child 2, names: list<item: string>
                        child 0, item: string
                child 1, observation.state: struct<dtype: string, shape: list<item: int64>, names: list<item: string>>
                    child 0, dtype: string
                    child 1, shape: list<item: int64>
                        child 0, item: int64
                    child 2, names: list<item: string>
                        child 0, item: string
                child 2, timestamp: struct<dtype: string, shape: list<item: int64>, names: null>
                    child 0, dtype: string
                    child 1, shape: list<item: int64>
                        child 0, item: int64
                    child 2, names: null
                child 3, frame_index: struct<dtype: string, shape: list<item: int64>, names: null>
                    child 0, dtype: string
                    child 1, shape: list<item: int64>
                        child 0, item: int64
                    child 2, names: null
                child 4, episode_index: struct<dtype: string, shape: list<item: int64>, names: null>
                    child 0, dtype: string
                    child 1, shape: list<item: int64>
                        child 0, item: int64
                    child 2, names: null
                child 5, index: struct<dtype: string, shape: list<item: int64>, names: null>
                    child 0, dtype: string
                    child 1, shape: list<item: int64>
                        child 0, item: int64
                    child 2, names: null
                child 6, task_index: struct<dtype: string, shape: list<item: int64>, names: null>
                    child 0, dtype: string
                    child 1, shape: list<item: int64>
                        child 0, item: int64
                    child 2, names: null
              to
              {'episode_index': Value('int64'), 'stats': {'annotation.human.action.task_description': {'mean': Value('float64'), 'std': Value('float64'), 'min': Value('float64'), 'max': Value('float64')}, 'annotation.human.validity': {'mean': Value('float64'), 'std': Value('float64'), 'min': Value('float64'), 'max': Value('float64')}, 'next.reward': {'mean': Value('float64'), 'std': Value('float64'), 'min': Value('float64'), 'max': Value('float64')}}}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1339, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 972, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 894, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 970, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1702, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1833, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 13 new columns ({'features', 'total_frames', 'fps', 'video_path', 'codebase_version', 'chunks_size', 'data_path', 'total_episodes', 'total_chunks', 'splits', 'total_tasks', 'total_videos', 'robot_type'}) and 2 missing columns ({'stats', 'episode_index'}).
              
              This happened while the json dataset builder was generating data using
              
              hf://datasets/Gukchan/dataset_251205_converted/meta/info.json (at revision 1af19f71e7755536a8fb66057c0c1e22daf9d175)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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episode_index
int64
stats
dict
0
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
1
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
2
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
3
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
4
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
5
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
6
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
7
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
8
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
9
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
10
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
11
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
12
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
13
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
14
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
15
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
16
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
17
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
18
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
19
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
20
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
21
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
22
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
23
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
24
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
25
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
26
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
27
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
28
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
29
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
30
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
31
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
32
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
33
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
34
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
35
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
36
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
37
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
38
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
39
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
40
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
41
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
42
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
43
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
44
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
45
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
46
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
47
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
48
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
49
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
50
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
51
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
52
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
53
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
54
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
55
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
56
{ "annotation.human.action.task_description": { "mean": 0, "std": 0, "min": 0, "max": 0 }, "annotation.human.validity": { "mean": 1, "std": 0, "min": 1, "max": 1 }, "next.reward": { "mean": 1, "std": 0, "min": 1, "max": 1 } }
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