Datasets:

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introvoyz041

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Error code:   StreamingRowsError
Exception:    TypeError
Message:      Couldn't cast array of type
struct<activation: string, base_keys: list<item: string>, boltzmann_dict: struct<boltz_basis: int64, final_act: string, head_pool: string, mol_basis: int64, num_heads: int64, type: string, equal_weights: bool, dropout_rate: double>, classifier: bool, cutoff: double, ext_feat_types: list<item: null>, extra_features: list<item: null>, gauss_embed: bool, grad_keys: list<item: null>, cp_input_layers: list<item: struct<name: string, param: struct<in_features: int64, out_features: int64>>>, schnet_input_layers: list<item: struct<name: string, param: struct<in_features: int64, out_features: int64>>>, model_type: string, mol_basis: int64, mol_fp_layers: list<item: null>, n_atom_basis: int64, n_bond_features: int64, n_bond_hidden: int64, n_convolutions: int64, n_filters: int64, n_gaussians: int64, output_layers: list<item: struct<name: string, param: struct<in_features: int64, out_features: int64>>>, readoutdict: struct<log_uniqueconfs: list<item: struct<name: string, param: struct<in_features: int64, out_features: int64, p: double>>>>, same_filters: bool, trainable_gauss: bool, use_mpnn: bool, dropout_rate: double>
to
{'activation': Value('string'), 'base_keys': List(Value('string')), 'boltzmann_dict': {'boltz_basis': Value('int64'), 'dropout_rate': Value('float64'), 'equal_weights': Value('bool'), 'final_act': Value('string'), 'head_pool': Value('string'), 'mol_basis': Value('int64'), 'num_heads': Value('int64'), 'type': Value('string')}, 'classifier': Value('bool'), 'cutoff': Value('float64'), 'dropout_rate': Value('float64'), 'ext_feat_types': List(Value('null')), 'extra_features': List(Value('null')), 'gauss_embed': Value('bool'), 'grad_keys': List(Value('null')), 'model_type': Value('string'), 'mol_basis': Value('int64'), 'mol_fp_layers': List(Value('null')), 'n_atom_basis': Value('int64'), 'n_atom_hidden': Value('int64'), 'n_bond_features': Value('int64'), 'n_bond_hidden': Value('int64'), 'n_convolutions': Value('int64'), 'n_filters': Value('int64'), 'n_gaussians': Value('int64'), 'readoutdict': {'log_uniqueconfs': List({'name': Value('string'), 'param': {'in_features': Value('int64'), 'out_features': Value('int64'), 'p': Value('float64')}})}, 'trainable_gauss': Value('bool'), 'use_mpnn': Value('bool')}
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                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 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 299, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2255, in cast_table_to_schema
                  cast_array_to_feature(
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1804, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2101, 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<activation: string, base_keys: list<item: string>, boltzmann_dict: struct<boltz_basis: int64, final_act: string, head_pool: string, mol_basis: int64, num_heads: int64, type: string, equal_weights: bool, dropout_rate: double>, classifier: bool, cutoff: double, ext_feat_types: list<item: null>, extra_features: list<item: null>, gauss_embed: bool, grad_keys: list<item: null>, cp_input_layers: list<item: struct<name: string, param: struct<in_features: int64, out_features: int64>>>, schnet_input_layers: list<item: struct<name: string, param: struct<in_features: int64, out_features: int64>>>, model_type: string, mol_basis: int64, mol_fp_layers: list<item: null>, n_atom_basis: int64, n_bond_features: int64, n_bond_hidden: int64, n_convolutions: int64, n_filters: int64, n_gaussians: int64, output_layers: list<item: struct<name: string, param: struct<in_features: int64, out_features: int64>>>, readoutdict: struct<log_uniqueconfs: list<item: struct<name: string, param: struct<in_features: int64, out_features: int64, p: double>>>>, same_filters: bool, trainable_gauss: bool, use_mpnn: bool, dropout_rate: double>
              to
              {'activation': Value('string'), 'base_keys': List(Value('string')), 'boltzmann_dict': {'boltz_basis': Value('int64'), 'dropout_rate': Value('float64'), 'equal_weights': Value('bool'), 'final_act': Value('string'), 'head_pool': Value('string'), 'mol_basis': Value('int64'), 'num_heads': Value('int64'), 'type': Value('string')}, 'classifier': Value('bool'), 'cutoff': Value('float64'), 'dropout_rate': Value('float64'), 'ext_feat_types': List(Value('null')), 'extra_features': List(Value('null')), 'gauss_embed': Value('bool'), 'grad_keys': List(Value('null')), 'model_type': Value('string'), 'mol_basis': Value('int64'), 'mol_fp_layers': List(Value('null')), 'n_atom_basis': Value('int64'), 'n_atom_hidden': Value('int64'), 'n_bond_features': Value('int64'), 'n_bond_hidden': Value('int64'), 'n_convolutions': Value('int64'), 'n_filters': Value('int64'), 'n_gaussians': Value('int64'), 'readoutdict': {'log_uniqueconfs': List({'name': Value('string'), 'param': {'in_features': Value('int64'), 'out_features': Value('int64'), 'p': Value('float64')}})}, 'trainable_gauss': Value('bool'), 'use_mpnn': Value('bool')}