Datasets:

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Error code:   StreamingRowsError
Exception:    TypeError
Message:      Couldn't cast array of type
struct<conditioning: struct<proprio_normalization: string, shared_pn1_current_state: bool, source: string>, decoder_ride: struct<action_tokens: int64, context: string, entry_layer: int64, language: string, native_block: string, self_attention: string, self_attention_seed: int64>, head: struct<blocks: int64, chunk: int64, type: string>, inference: string, initialization: struct<policy: string, seed: int64>, refine: struct<gate_init: string, language: string, layers: int64, tokens: string>, tensor_layout: string, training_objective: string, training_rng: string, variant: string>
to
{'action_dit': {'attn_head_dim': Value('int64'), 'eps': Value('float64'), 'ffn_dim': Value('int64'), 'freq_dim': Value('int64'), 'gradient_checkpointing': Value('bool'), 'hidden_dim': Value('int64'), 'initialization': {'backbone': Value('string'), 'random_modules': List(Value('string')), 'seed': Value('int64')}, 'max_action_tokens': Value('int64'), 'num_heads': Value('int64'), 'num_layers': Value('int64'), 'text_dim': Value('int64')}, 'conditioning': {'proprio_normalization': Value('string'), 'source': Value('string')}, 'flow_matching': {'infer_shift': Value('float64'), 'inference_seed': Value('int64'), 'initial_noise': Value('string'), 'integration': Value('string'), 'num_inference_steps': Value('int64'), 'num_train_timesteps': Value('int64'), 'scheduler_eps': Value('float64'), 'train_shift': Value('float64')}, 'tensor_layout': Value('string'), 'training_objective': Value('string'), 'training_rng': Value('string'), 'variant': Value('string')}
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2312, in cast_table_to_schema
                  cast_array_to_feature(
                  ~~~~~~~~~~~~~~~~~~~~~^
                      table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                      feature,
                      ^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ~~~~^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2158, in cast_array_to_feature
                  raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
              TypeError: Couldn't cast array of type
              struct<conditioning: struct<proprio_normalization: string, shared_pn1_current_state: bool, source: string>, decoder_ride: struct<action_tokens: int64, context: string, entry_layer: int64, language: string, native_block: string, self_attention: string, self_attention_seed: int64>, head: struct<blocks: int64, chunk: int64, type: string>, inference: string, initialization: struct<policy: string, seed: int64>, refine: struct<gate_init: string, language: string, layers: int64, tokens: string>, tensor_layout: string, training_objective: string, training_rng: string, variant: string>
              to
              {'action_dit': {'attn_head_dim': Value('int64'), 'eps': Value('float64'), 'ffn_dim': Value('int64'), 'freq_dim': Value('int64'), 'gradient_checkpointing': Value('bool'), 'hidden_dim': Value('int64'), 'initialization': {'backbone': Value('string'), 'random_modules': List(Value('string')), 'seed': Value('int64')}, 'max_action_tokens': Value('int64'), 'num_heads': Value('int64'), 'num_layers': Value('int64'), 'text_dim': Value('int64')}, 'conditioning': {'proprio_normalization': Value('string'), 'source': Value('string')}, 'flow_matching': {'infer_shift': Value('float64'), 'inference_seed': Value('int64'), 'initial_noise': Value('string'), 'integration': Value('string'), 'num_inference_steps': Value('int64'), 'num_train_timesteps': Value('int64'), 'scheduler_eps': Value('float64'), 'train_shift': Value('float64')}, 'tensor_layout': Value('string'), 'training_objective': Value('string'), 'training_rng': Value('string'), 'variant': Value('string')}