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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,
)
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