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chore: enable pyupgrade fixes (#1806)

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# What does this PR do?

The goal of this PR is code base modernization.

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

Note to reviewers: almost all changes here are automatically generated
by pyupgrade. Some additional unused imports were cleaned up. The only
change worth of note can be found under `docs/openapi_generator` and
`llama_stack/strong_typing/schema.py` where reflection code was updated
to deal with "newer" types.

Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
This commit is contained in:
Ihar Hrachyshka 2025-05-01 17:23:50 -04:00 • committed by GitHub
parent ffe3d0b2cd
commit 9e6561a1ec
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319 changed files with 2843 additions and 3033 deletions
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20
llama_stack/apis/scoring/scoring.py
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@ -4,7 +4,7 @@
# 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, Optional, Protocol, runtime_checkable
from typing import Any, Protocol, runtime_checkable
from pydantic import BaseModel
@ -12,7 +12,7 @@ from llama_stack.apis.scoring_functions import ScoringFn, ScoringFnParams
from llama_stack.schema_utils import json_schema_type, webmethod
# mapping of metric to value
ScoringResultRow = Dict[str, Any]
ScoringResultRow = dict[str, Any]
@json_schema_type
@ -24,15 +24,15 @@ class ScoringResult(BaseModel):
:param aggregated_results: Map of metric name to aggregated value
"""
score_rows: List[ScoringResultRow]
score_rows: list[ScoringResultRow]
# aggregated metrics to value
aggregated_results: Dict[str, Any]
aggregated_results: dict[str, Any]
@json_schema_type
class ScoreBatchResponse(BaseModel):
dataset_id: Optional[str] = None
results: Dict[str, ScoringResult]
dataset_id: str | None = None
results: dict[str, ScoringResult]
@json_schema_type
@ -44,7 +44,7 @@ class ScoreResponse(BaseModel):
"""
# each key in the dict is a scoring function name
results: Dict[str, ScoringResult]
results: dict[str, ScoringResult]
class ScoringFunctionStore(Protocol):
@ -59,15 +59,15 @@ class Scoring(Protocol):
async def score_batch(
self,
dataset_id: str,
scoring_functions: Dict[str, Optional[ScoringFnParams]],
scoring_functions: dict[str, ScoringFnParams | None],
save_results_dataset: bool = False,
) -> ScoreBatchResponse: ...
@webmethod(route="/scoring/score", method="POST")
async def score(
self,
input_rows: List[Dict[str, Any]],
scoring_functions: Dict[str, Optional[ScoringFnParams]],
input_rows: list[dict[str, Any]],
scoring_functions: dict[str, ScoringFnParams | None],
) -> ScoreResponse:
"""Score a list of rows.