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#####
# imports for hf system:
#####
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 .common import __file__ as _
from .file_utils import __file__ as _
# from .fusion import __file__
from .generator_utils import __file__ as _
from .instructions import __file__ as _
from .loaders import __file__ as _
from .load import __file__ as _
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 .recipe import __file__ as _
from .register import __file__ as _
from .splitters import __file__ as _
from .split_utils 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 .schema import __file__ as _
# from .utilize import __file__ as _
# from .validate import __file__ as _
#############
from .stream import MultiStream, Stream
from .operator import SequntialOperator, SequntialOperatorInitilizer, MultiStreamOperator, StreamInitializerOperator
from .operators import (
ApplyValueOperatorsField,
ApplyStreamOperatorsField,
SplitByValue,
MergeStreams,
FlattenInstances,
)
import evaluate
import datasets
from datasets import (
Features,
Value,
Sequence,
)
from dataclasses import field
from typing import List, Union, Dict, Optional, Generator, Any, Iterable
class MultiStreamScoreMean(MultiStreamOperator):
def aggegate_results(self, multi_stream: MultiStream):
scores = []
for stream in multi_stream.values():
instance = stream.peak()
scores.append(instance["score"]["global"]["score"])
from statistics import mean
return mean(scores)
def spread_results(self, stream: Stream, score: float):
for instance in stream:
instance["score"]["global"]["groups_mean_score"] = score
yield instance
def process(self, multi_stream: MultiStream) -> MultiStream:
mean_score = self.aggegate_results(multi_stream)
result = {}
for stream_name, stream in multi_stream.items():
result[stream_name] = Stream(self.spread_results, gen_kwargs={"stream": stream, "score": mean_score})
return MultiStream(result)
class FromPredictionsAndOriginalData(StreamInitializerOperator):
def zip(self, predictions, references):
for prediction, original in zip(predictions, references):
yield {**original, "prediction": prediction}
def process(self, predictions: List[str], references: Iterable, split_name: str = "all") -> MultiStream:
return MultiStream(
{split_name: Stream(self.zip, gen_kwargs={"predictions": predictions, "references": references})}
)
from .schema import UNITXT_DATASET_SCHEMA
class MetricRecipe(SequntialOperatorInitilizer):
def prepare(self):
self.steps = [
FromPredictionsAndOriginalData(),
ApplyValueOperatorsField(
value_field="prediction", operators_field="processors", default_operators=["to_string"]
),
SplitByValue(["group"]),
ApplyStreamOperatorsField(
"metrics",
reversed=True,
),
MultiStreamScoreMean(),
MergeStreams(),
]
UNITXT_METRIC_SCHEMA = Features({"predictions": Value("string"), "references": dict(UNITXT_DATASET_SCHEMA)})
# @evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class Metric(evaluate.Metric):
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"):
recipe = MetricRecipe()
multi_stream = recipe(predictions=predictions, references=references, split_name=split_name)
if flatten:
operator = FlattenInstances()
multi_stream = operator(multi_stream)
stream = multi_stream[split_name]
return list(stream)
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