Repository: huggingface/datasets
File: src/datasets/arrow_writer.py (Python)

Code before:
            if self.trying_type:
                try:  # second chance
                    if isinstance(data, np.ndarray):
                        return numpy_to_pyarrow_listarray(data)
                    elif isinstance(data, list) and data and any(isinstance(value, np.ndarray) for value in data):
                        return list_of_np_array_to_pyarrow_listarray(data)
                    else:
                        trying_cast_to_python_objects = True
                        return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))
                except pa.lib.ArrowInvalid as e:
                    if "overflow" in str(e):
                        raise OverflowError(
                            f"There was an overflow with type {type_(data)}. Try to reduce writer_batch_size to have batches smaller than 2GB.\n({e})"
                        ) from None
                    elif self.trying_int_optimization and "not in range" in str(e):
                        optimized_int_pa_type_str = np.dtype(optimized_int_pa_type.to_pandas_dtype()).name
                        logger.info(
                            f"Failed to cast a sequence to {optimized_int_pa_type_str}. Falling back to int64."
                        )
                        return out
                    elif trying_cast_to_python_objects and "Could not convert" in str(e):
                        out = pa.array(
                            cast_to_python_objects(data, only_1d_for_numpy=True, optimize_list_casting=False)
                        )
                        if type is not None:
                            out = cast_array_to_feature(out, type, allow_primitive_to_str=True)
                        return out
                    else:
                        raise
            elif "overflow" in str(e):
                raise OverflowError(
                    f"There was an overflow with type {type_(data)}. Try to reduce writer_batch_size to have batches smaller than 2GB.\n({e})"
                ) from None
            elif self.trying_int_optimization and "not in range" in str(e):
                optimized_int_pa_type_str = np.dtype(optimized_int_pa_type.to_pandas_dtype()).name
                logger.info(f"Failed to cast a sequence to {optimized_int_pa_type_str}. Falling back to int64.")
                return out
            elif trying_cast_to_python_objects and "Could not convert" in str(e):
                out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True, optimize_list_casting=False))
                if type is not None:
                    out = cast_array_to_feature(out, type, allow_primitive_to_str=True)
                return out
            else:
                raise


class OptimizedTypedSequence(TypedSequence):
    def __init__(
        self,
        data,
        type: Optional[FeatureType] = None,

Review comment (suggestion):
Nit (to be explicit):

```suggestion
                            out = cast_array_to_feature(out, type, allow_primitive_to_str=True, allow_decimal_to_str=True)
```

Code after:
            if self.trying_type:
                try:  # second chance
                    if isinstance(data, np.ndarray):
                        return numpy_to_pyarrow_listarray(data)
                    elif isinstance(data, list) and data and any(isinstance(value, np.ndarray) for value in data):
                        return list_of_np_array_to_pyarrow_listarray(data)
                    else:
                        trying_cast_to_python_objects = True
                        return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))
                except pa.lib.ArrowInvalid as e:
                    if "overflow" in str(e):
                        raise OverflowError(
                            f"There was an overflow with type {type_(data)}. Try to reduce writer_batch_size to have batches smaller than 2GB.\n({e})"
                        ) from None
                    elif self.trying_int_optimization and "not in range" in str(e):
                        optimized_int_pa_type_str = np.dtype(optimized_int_pa_type.to_pandas_dtype()).name
                        logger.info(
                            f"Failed to cast a sequence to {optimized_int_pa_type_str}. Falling back to int64."
                        )
                        return out
                    elif trying_cast_to_python_objects and "Could not convert" in str(e):
                        out = pa.array(
                            cast_to_python_objects(data, only_1d_for_numpy=True, optimize_list_casting=False)
                        )
                        if type is not None:
                            out = cast_array_to_feature(
                                out, type, allow_primitive_to_str=True, allow_decimal_to_str=True
                            )
                        return out
                    else:
                        raise
            elif "overflow" in str(e):
                raise OverflowError(
                    f"There was an overflow with type {type_(data)}. Try to reduce writer_batch_size to have batches smaller than 2GB.\n({e})"
                ) from None
            elif self.trying_int_optimization and "not in range" in str(e):
                optimized_int_pa_type_str = np.dtype(optimized_int_pa_type.to_pandas_dtype()).name
                logger.info(f"Failed to cast a sequence to {optimized_int_pa_type_str}. Falling back to int64.")
                return out
            elif trying_cast_to_python_objects and "Could not convert" in str(e):
                out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True, optimize_list_casting=False))
                if type is not None:
                    out = cast_array_to_feature(out, type, allow_primitive_to_str=True, allow_decimal_to_str=True)
                return out
            else:
                raise


class OptimizedTypedSequence(TypedSequence):
    def __init__(
        self,