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chore: enable pyupgrade fixes

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Schema reflection code needed a minor adjustment to handle UnionTypes
and collections.abc.AsyncIterator. (Both are preferred for latest Python
releases.)

Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
This commit is contained in:
Ihar Hrachyshka 2025-03-26 18:33:23 -04:00
parent ffe3d0b2cd
commit 1deb95f922
319 changed files with 2843 additions and 3033 deletions
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19
llama_stack/apis/eval/eval.py
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@ -4,10 +4,9 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Any, Dict, List, Literal, Optional, Protocol, Union
from typing import Annotated, Any, Literal, Protocol
from pydantic import BaseModel, Field
from typing_extensions import Annotated
from llama_stack.apis.agents import AgentConfig
from llama_stack.apis.common.job_types import Job
@ -29,7 +28,7 @@ class ModelCandidate(BaseModel):
type: Literal["model"] = "model"
model: str
sampling_params: SamplingParams
system_message: Optional[SystemMessage] = None
system_message: SystemMessage | None = None
@json_schema_type
@ -43,7 +42,7 @@ class AgentCandidate(BaseModel):
config: AgentConfig
EvalCandidate = Annotated[Union[ModelCandidate, AgentCandidate], Field(discriminator="type")]
EvalCandidate = Annotated[ModelCandidate | AgentCandidate, Field(discriminator="type")]
register_schema(EvalCandidate, name="EvalCandidate")
@ -57,11 +56,11 @@ class BenchmarkConfig(BaseModel):
"""
eval_candidate: EvalCandidate
scoring_params: Dict[str, ScoringFnParams] = Field(
scoring_params: dict[str, ScoringFnParams] = Field(
description="Map between scoring function id and parameters for each scoring function you want to run",
default_factory=dict,
)
num_examples: Optional[int] = Field(
num_examples: int | None = Field(
description="Number of examples to evaluate (useful for testing), if not provided, all examples in the dataset will be evaluated",
default=None,
)
@ -76,9 +75,9 @@ class EvaluateResponse(BaseModel):
:param scores: The scores from the evaluation.
"""
generations: List[Dict[str, Any]]
generations: list[dict[str, Any]]
# each key in the dict is a scoring function name
scores: Dict[str, ScoringResult]
scores: dict[str, ScoringResult]
class Eval(Protocol):
@ -101,8 +100,8 @@ class Eval(Protocol):
async def evaluate_rows(
self,
benchmark_id: str,
input_rows: List[Dict[str, Any]],
scoring_functions: List[str],
input_rows: list[dict[str, Any]],
scoring_functions: list[str],
benchmark_config: BenchmarkConfig,
) -> EvaluateResponse:
"""Evaluate a list of rows on a benchmark.