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from abc import abstractmethod
from typing import Dict, Union
from .dataclass import NonPositionalField
from .formats import Format
from .fusion import FixedFusion, WeightedFusion
from .operator import SourceOperator
from .standard import StandardRecipe
from .stream import MultiStream
from .system_prompts import SystemPrompt
class BaseBenchmark(SourceOperator):
format: Format = NonPositionalField(default=None)
num_demos: int = NonPositionalField(default=None)
system_prompt: SystemPrompt = NonPositionalField(default=None)
loader_limit: int = NonPositionalField(default=None)
@abstractmethod
def reset(self):
pass
class Benchmark(BaseBenchmark):
subsets: Dict[str, Union[StandardRecipe, BaseBenchmark]]
max_total_samples: int = None
max_samples_per_subset: int = None
def verify(self):
super().verify()
if (
self.max_total_samples is not None
and self.max_samples_per_subset is not None
):
raise ValueError("Set either max_total_samples or max_samples_per_subset")
def prepare_args(self):
self.subsets = dict(self.subsets)
def reset(self):
if (
self.format is not None
or self.num_demos is not None
or self.system_prompt is not None
or self.loader_limit is not None
):
for subset in self.subsets.values():
if self.num_demos is not None:
subset.num_demos = self.num_demos
if self.format is not None:
subset.format = self.format
if self.system_prompt is not None:
subset.system_prompt = self.system_prompt
if self.loader_limit is not None:
subset.loader_limit = self.loader_limit
subset.reset()
def prepare(self):
super().prepare()
self.reset()
def process(
self,
) -> MultiStream:
if self.max_total_samples is None:
operator = FixedFusion(
subsets=self.subsets,
max_instances_per_subset=self.max_samples_per_subset,
)
else:
operator = WeightedFusion(
subsets=self.subsets, max_total_samples=self.max_total_samples
)
return operator()
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