c923e7769b
Signed-off-by: Francisco Javier Arceo <4163062+franciscojavierarceo@users.noreply.github.com> Co-authored-by: Codex <noreply@openai.com> Co-authored-by: Misha Goin <mgoin64@gmail.com>
92 lines
2.7 KiB
Python
92 lines
2.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Translate Decisions questions and answers to and from label reads."""
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import json
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from .protocol import (
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ChoiceAnswer,
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ChoiceQuestion,
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DecisionAnswer,
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DecisionInputMessage,
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DecisionQuestion,
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PredicateAnswer,
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PredicateQuestion,
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ScoreAnswer,
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)
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from .question_types import (
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LABELS,
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Option,
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Question,
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StructuredDecisionError,
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argmax,
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)
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from .question_types import (
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ChoiceQuestion as LabelQuestion,
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)
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def make_read_question(
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index: int, question: DecisionQuestion, max_options: int = len(LABELS)
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) -> Question:
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if isinstance(question, PredicateQuestion):
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options = [Option("false"), Option("true")]
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elif isinstance(question, ChoiceQuestion):
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options = [
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Option(json.dumps(c.value, ensure_ascii=False), c.description)
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for c in question.choices
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]
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else:
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options = [Option(level.label, level.description) for level in question.levels]
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limit = min(max_options, len(LABELS))
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if len(options) > limit:
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raise StructuredDecisionError(
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f"This model supports at most {limit} choices per question"
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)
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return Question(
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id=str(index),
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type=LabelQuestion(),
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instructions=question.instructions,
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options=tuple(options),
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labels=LABELS[: len(options)],
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)
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def make_answer(
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question: DecisionQuestion, probs: list[float], label_mass: float
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) -> DecisionAnswer:
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if isinstance(question, PredicateQuestion):
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return PredicateAnswer(name=question.name, probability=probs[1])
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top = argmax(probs)
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confidence = probs[top] * label_mass
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if isinstance(question, ChoiceQuestion):
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return ChoiceAnswer(
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name=question.name,
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choice=question.choices[top].value,
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probabilities=[
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{"value": choice.value, "probability": probability}
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for choice, probability in zip(question.choices, probs)
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],
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confidence=confidence,
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)
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return ScoreAnswer(
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name=question.name,
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score=sum(i * probability for i, probability in enumerate(probs)),
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probabilities=[
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{"value": i, "label": level.label, "probability": probability}
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for i, (level, probability) in enumerate(zip(question.levels, probs))
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],
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confidence=confidence,
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)
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def input_text(input: str | list[DecisionInputMessage]) -> str:
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if isinstance(input, str):
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return input
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return "\n\n".join(
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message.content
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if isinstance(message.content, str)
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else "\n".join(part.text for part in message.content)
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for message in input
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)
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