TypeError: Couldn't cast array of type int64 to Sequence when using layoutlmv3
I am attempting to fine tune the layoutlmv3-base model and am running into issues when trying to use the model to encode.  Any help would be useful.Here is my code:from transformers import AutoProcessor

processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)

def convert_ner_tags_to_id(ner_tags):
  return [label2id[ner_tag] for ner_tag in ner_tags]

def prepare_dataset(annotations):
    images = annotations['image']
    boxes = annotations['bboxes']
    words = []
    for i in range (len(boxes)):
        words.append(" ")

    ner_tags = [convert_ner_tags_to_id(ner_tags) for ner_tags in annotations['ner_tags']]

    encoding = processor(images, words, boxes=boxes, word_labels=ner_tags, truncation=True, padding="max_length")

    return encoding

from datasets import Features, Sequence, ClassLabel, Value, Array2D, Array3D

features = Features({

    'pixel_values': Array3D(dtype="float32", shape=(3, 224, 224)),
    'input_ids': Sequence(feature=Value(dtype='int64')),
    'attention_mask': Sequence(Value(dtype='int64')),
    'bbox': Array2D(dtype="int64", shape=(512, 4)),
    'labels': Sequence(feature=Value(dtype='int64')),
})

train_dataset = layoutlm_ds["train"].map(
    prepare_dataset,
    batched=True,
    remove_columns=column_names,
    features=features,
)
eval_dataset = layoutlm_ds["test"].map(
    prepare_dataset,
    batched=True,
    remove_columns=column_names,
    features=features,
)And here is the error that I am getting:TypeError: Couldn't cast array of type
int64
to
Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)
File <command-1408067255887537>, line 19
      4 features = Features({
      5     # 'pixel_values': Array3D(dtype="float32", shape=(3, 224, 224)),
      6     # 'input_ids': Value("int64"),
   (...)
     16     'labels': Sequence(feature=Value(dtype='int64')),
     17 })
     18 # Prepare our train & eval dataset
---> 19 train_dataset = layoutlm_ds["train"].map(
     20     prepare_dataset,
     21     batched=True,
     22     remove_columns=column_names,
     23     features=features,
     24 )
     25 eval_dataset = layoutlm_ds["test"].map(
     26     prepare_dataset,
     27     batched=True,
     28     remove_columns=column_names,
     29     features=features,
     30 )
File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/datasets/arrow_dataset.py:602, in transmit_tasks.<locals>.wrapper(*args, **kwargs)
    600     self: "Dataset" = kwargs.pop("self")
    601 # apply actual function
--> 602 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
    603 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
    604 for dataset in datasets:
    605     # Remove task templates if a column mapping of the template is no longer valid
File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/datasets/arrow_dataset.py:567, in transmit_format.<locals>.wrapper(*args, **kwargs)
    560 self_format = {
    561     "type": self._format_type,
    562     "format_kwargs": self._format_kwargs,
    563     "columns": self._format_columns,
    564     "output_all_columns": self._output_all_columns,
    565 }
    566 # apply actual function
--> 567 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
    568 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
    569 # re-apply format to the output
File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/datasets/arrow_dataset.py:3161, in Dataset.map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)
   3155 if transformed_dataset is None:
   3156     with REDACTED_TOKEN(
   3157         unit=" examples",
   3158         total=pbar_total,
   3159         desc=desc or "Map",
   3160     ) as pbar:
-> 3161         for rank, done, content in Dataset._map_single(**dataset_kwargs):
   3162             if done:
   3163                 shards_done += 1
File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/datasets/arrow_dataset.py:3575, in Dataset._map_single(shard, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset)
   3573         writer.write_table(batch.to_arrow())
   3574     else:
-> 3575         writer.write_batch(batch)
   3576 num_examples_progress_update += num_examples_in_batch
   3577 if time.time() > _time + config.PBAR_REFRESH_TIME_INTERVAL:
File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/datasets/arrow_writer.py:568, in ArrowWriter.write_batch(self, batch_examples, writer_batch_size)
    566         col_try_type = try_features[col] if try_features is not None and col in try_features else None
    567         typed_sequence = OptimizedTypedSequence(col_values, type=col_type, try_type=col_try_type, col=col)
--> 568         arrays.append(pa.array(typed_sequence))
    569         inferred_features[col] = typed_sequence.get_inferred_type()
    570 schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema
File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/pyarrow/array.pxi:247, in pyarrow.lib.array()
File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/pyarrow/array.pxi:112, in pyarrow.lib._handle_arrow_array_protocol()
File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/datasets/arrow_writer.py:208, in TypedSequence.__arrow_array__(self, type)
    203     # otherwise we can finally use the user's type
    204     elif type is not None:
    205         # We use cast_array_to_feature to support casting to custom types like Audio and Image
    206         # Also, when trying type "string", we don't want to convert integers or floats to "string".
    207         # We only do it if trying_type is False - since this is what the user asks for.
--> 208         out = cast_array_to_feature(
    209             out, type, allow_primitive_to_str=not self.trying_type, allow_decimal_to_str=not self.trying_type
    210         )
    211     return out
    212 except (
    213     TypeError,
    214     pa.lib.ArrowInvalid,
    215     pa.lib.ArrowNotImplementedError,
    216 ) as e:  # handle type errors and overflows
    217     # Ignore ArrowNotImplementedError caused by trying type, otherwise re-raise
File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/datasets/table.py:1804, in _wrap_for_chunked_arrays.<locals>.wrapper(array, *args, **kwargs)
   1802     return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
   1803 else:
-> 1804     return func(array, *args, **kwargs)
File /local_disk0/.ephemeral_nfs/cluster_libraries/python/lib/python3.10/site-packages/datasets/table.py:2122, in cast_array_to_feature(array, feature, allow_primitive_to_str, allow_decimal_to_str)
   2115 elif not isinstance(feature, (Sequence, dict, list, tuple)):
   2116     return array_cast(
   2117         array,
   2118         feature(),
   2119         allow_primitive_to_str=allow_primitive_to_str,
   2120         allow_decimal_to_str=allow_decimal_to_str,
   2121     )
-> 2122 raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")