from typing import Dict, Iterable, List import evaluate from .artifact import __file__ as _ from .blocks import __file__ as _ from .card import __file__ as _ from .catalog import __file__ as _ from .collections import __file__ as _ from .dataclass import __file__ as _ from .dataset_utils import __file__ as _ from .dict_utils import __file__ as _ from .eval_utils import __file__ as _ from .file_utils import __file__ as _ from .formats import __file__ as _ from .fusion import __file__ as _ from .generator_utils import __file__ as _ from .hf_utils import __file__ as _ from .instructions import __file__ as _ from .loaders import __file__ as _ from .logging_utils import __file__ as _ from .metric_utils import UNITXT_METRIC_SCHEMA from .metric_utils import __file__ as _ from .metric_utils import _compute from .metrics import __file__ as _ from .normalizers import __file__ as _ from .operator import __file__ as _ from .operators import __file__ as _ from .processors import __file__ as _ from .random_utils import __file__ as _ from .recipe import __file__ as _ from .register import __file__ as _ from .schema import __file__ as _ from .split_utils import __file__ as _ from .splitters import __file__ as _ from .standard import __file__ as _ from .stream import __file__ as _ from .task import __file__ as _ from .templates import __file__ as _ from .text_utils import __file__ as _ from .type_utils import __file__ as _ from .utils import __file__ as _ from .validate import __file__ as _ from .version import __file__ as _ # TODO: currently we have two classes with this name. metric.Metric and matrics.Metric... # @evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION) class Metric(evaluate.Metric): calc_confidence_intervals: bool = True def _info(self): return evaluate.MetricInfo( description="_DESCRIPTION", citation="_CITATION", # inputs_description=_KWARGS_DESCRIPTION, features=UNITXT_METRIC_SCHEMA, codebase_urls=["https://"], reference_urls=[ "https://", "https://", ], ) def _compute( self, predictions: List[str], references: Iterable, flatten: bool = False, split_name: str = "all", ): try: from unitxt.metric_utils import _compute as _compute_installed unitxt_installed = True except ImportError: unitxt_installed = False if unitxt_installed: return _compute_installed( predictions=predictions, references=references, flatten=flatten, split_name=split_name, calc_confidence_intervals=self.calc_confidence_intervals, ) return _compute( predictions=predictions, references=references, flatten=flatten, split_name=split_name, calc_confidence_intervals=self.calc_confidence_intervals, )