class NumpyFormatter(TensorFormatter[Mapping, np.ndarray, Mapping]):
    def __init__(self, features=None, token_per_repo_id=None, **np_array_kwargs):
        super().__init__(features=features, token_per_repo_id=token_per_repo_id)
        self.np_array_kwargs = np_array_kwargs

    def _consolidate(self, column):
        if isinstance(column, list):
            if column and all(
                isinstance(x, np.ndarray) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column
            ):
                return np.stack(column)
            else:
                # don't use np.array(column, dtype=object)
                # since it fails in certain cases
                # see https://stackoverflow.com/q/51005699
                out = np.empty(len(column), dtype=object)
                out[:] = column
                return out
        return column

    def _tensorize(self, value):
        if isinstance(value, (str, bytes, type(None))):
            return value
        elif isinstance(value, (np.character, np.ndarray)) and np.issubdtype(value.dtype, np.character):
            return value
        elif isinstance(value, np.number):
            return value

        default_dtype = {}

        if isinstance(value, np.ndarray) and np.issubdtype(value.dtype, np.integer):
            default_dtype = {"dtype": np.int64}
        elif isinstance(value, np.ndarray) and np.issubdtype(value.dtype, np.floating):
            default_dtype = {"dtype": np.float32}

        if config.PIL_AVAILABLE and "PIL" in sys.modules:
            import PIL.Image

            if isinstance(value, PIL.Image.Image):
                return np.asarray(value, **self.np_array_kwargs)
        if config.DECORD_AVAILABLE and "decord" in sys.modules:
            # We need to import torch first, otherwise later it can cause issues
            # e.g. "RuntimeError: random_device could not be read"
            # when running `torch.tensor(value).share_memory_()`
            if config.TORCH_AVAILABLE:
                import torch  # noqa
            from decord import VideoReader

            if isinstance(value, VideoReader):
                value._hf_bridge_out = np.asarray
                return value

        return np.asarray(value, **{**default_dtype, **self.np_array_kwargs})

    def _recursive_tensorize(self, data_struct):
        # support for torch, tf, jax etc.
        if config.TORCH_AVAILABLE and "torch" in sys.modules:
            import torch

            if isinstance(data_struct, torch.Tensor):
                return self._tensorize(data_struct.detach().cpu().numpy()[()])
        if hasattr(data_struct, "__array__") and not isinstance(data_struct, (np.ndarray, np.character, np.number)):
            data_struct = data_struct.__array__()
        # support for nested types like struct of list of struct
        if isinstance(data_struct, np.ndarray):
            if data_struct.dtype == object:
                return self._consolidate([self.recursive_tensorize(substruct) for substruct in data_struct])
        if isinstance(data_struct, (list, tuple)):
            return self._consolidate([self.recursive_tensorize(substruct) for substruct in data_struct])
        return self._tensorize(data_struct)

    def recursive_tensorize(self, data_struct: dict):
        return map_nested(self._recursive_tensorize, data_struct, map_list=False)

    def format_row(self, pa_table: pa.Table) -> Mapping:
        row = self.numpy_arrow_extractor().extract_row(pa_table)
        row = self.python_features_decoder.decode_row(row)
        return self.recursive_tensorize(row)

    def format_column(self, pa_table: pa.Table) -> np.ndarray:
        column = self.numpy_arrow_extractor().extract_column(pa_table)
        column = self.python_features_decoder.decode_column(column, pa_table.column_names[0])
        column = self.recursive_tensorize(column)
        column = self._consolidate(column)
        return column

    def format_batch(self, pa_table: pa.Table) -> Mapping:
        batch = self.numpy_arrow_extractor().extract_batch(pa_table)
        batch = self.python_features_decoder.decode_batch(batch)
        batch = self.recursive_tensorize(batch)
        for column_name in batch:
            batch[column_name] = self._consolidate(batch[column_name])
        return batch
/Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/formatting/np_formatter.py