| from functools import lru_cache |
| from typing import Any, Dict, List, Union |
|
|
| from datasets import DatasetDict |
|
|
| from .artifact import fetch_artifact |
| from .dataset_utils import get_dataset_artifact |
| from .logging_utils import get_logger |
| from .metric_utils import _compute |
| from .operator import SourceOperator |
|
|
| logger = get_logger() |
|
|
|
|
| def load(source: Union[SourceOperator, str]) -> DatasetDict: |
| assert isinstance( |
| source, (SourceOperator, str) |
| ), "source must be a SourceOperator or a string" |
| if isinstance(source, str): |
| source, _ = fetch_artifact(source) |
| return source().to_dataset() |
|
|
|
|
| def load_dataset(dataset_query: str) -> DatasetDict: |
| dataset_query = dataset_query.replace("sys_prompt", "instruction") |
| dataset_stream = get_dataset_artifact(dataset_query) |
| return dataset_stream().to_dataset() |
|
|
|
|
| def evaluate(predictions, data) -> List[Dict[str, Any]]: |
| return _compute(predictions=predictions, references=data) |
|
|
|
|
| @lru_cache |
| def _get_produce_with_cache(recipe_query): |
| return get_dataset_artifact(recipe_query).produce |
|
|
|
|
| def produce(instance_or_instances, recipe_query): |
| is_list = isinstance(instance_or_instances, list) |
| if not is_list: |
| instance_or_instances = [instance_or_instances] |
| result = _get_produce_with_cache(recipe_query)(instance_or_instances) |
| if not is_list: |
| result = result[0] |
| return result |
|
|
