import jobs_pb2 as _jobs_pb2 from scalapb import scalapb_pb2 as _scalapb_pb2 from google.protobuf.internal import containers as _containers from google.protobuf.internal import enum_type_wrapper as _enum_type_wrapper from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from typing import ClassVar as _ClassVar, Iterable as _Iterable, Mapping as _Mapping, Optional as _Optional, Union as _Union DESCRIPTOR: _descriptor.FileDescriptor class OptimizerType(int, metaclass=_enum_type_wrapper.EnumTypeWrapper): __slots__ = () OPTIMIZER_TYPE_UNSPECIFIED: _ClassVar[OptimizerType] OPTIMIZER_TYPE_GEPA: _ClassVar[OptimizerType] OPTIMIZER_TYPE_METAPROMPT: _ClassVar[OptimizerType] OPTIMIZER_TYPE_UNSPECIFIED: OptimizerType OPTIMIZER_TYPE_GEPA: OptimizerType OPTIMIZER_TYPE_METAPROMPT: OptimizerType class PromptOptimizationJobTag(_message.Message): __slots__ = ("key", "value") KEY_FIELD_NUMBER: _ClassVar[int] VALUE_FIELD_NUMBER: _ClassVar[int] key: str value: str def __init__(self, key: _Optional[str] = ..., value: _Optional[str] = ...) -> None: ... class PromptOptimizationJobConfig(_message.Message): __slots__ = ("optimizer_type", "dataset_id", "scorers", "optimizer_config_json") OPTIMIZER_TYPE_FIELD_NUMBER: _ClassVar[int] DATASET_ID_FIELD_NUMBER: _ClassVar[int] SCORERS_FIELD_NUMBER: _ClassVar[int] OPTIMIZER_CONFIG_JSON_FIELD_NUMBER: _ClassVar[int] optimizer_type: OptimizerType dataset_id: str scorers: _containers.RepeatedScalarFieldContainer[str] optimizer_config_json: str def __init__(self, optimizer_type: _Optional[_Union[OptimizerType, str]] = ..., dataset_id: _Optional[str] = ..., scorers: _Optional[_Iterable[str]] = ..., optimizer_config_json: _Optional[str] = ...) -> None: ... class PromptOptimizationJob(_message.Message): __slots__ = ("job_id", "run_id", "state", "experiment_id", "source_prompt_uri", "optimized_prompt_uri", "config", "creation_timestamp_ms", "completion_timestamp_ms", "tags", "initial_eval_scores", "final_eval_scores") class InitialEvalScoresEntry(_message.Message): __slots__ = ("key", "value") KEY_FIELD_NUMBER: _ClassVar[int] VALUE_FIELD_NUMBER: _ClassVar[int] key: str value: float def __init__(self, key: _Optional[str] = ..., value: _Optional[float] = ...) -> None: ... class FinalEvalScoresEntry(_message.Message): __slots__ = ("key", "value") KEY_FIELD_NUMBER: _ClassVar[int] VALUE_FIELD_NUMBER: _ClassVar[int] key: str value: float def __init__(self, key: _Optional[str] = ..., value: _Optional[float] = ...) -> None: ... JOB_ID_FIELD_NUMBER: _ClassVar[int] RUN_ID_FIELD_NUMBER: _ClassVar[int] STATE_FIELD_NUMBER: _ClassVar[int] EXPERIMENT_ID_FIELD_NUMBER: _ClassVar[int] SOURCE_PROMPT_URI_FIELD_NUMBER: _ClassVar[int] OPTIMIZED_PROMPT_URI_FIELD_NUMBER: _ClassVar[int] CONFIG_FIELD_NUMBER: _ClassVar[int] CREATION_TIMESTAMP_MS_FIELD_NUMBER: _ClassVar[int] COMPLETION_TIMESTAMP_MS_FIELD_NUMBER: _ClassVar[int] TAGS_FIELD_NUMBER: _ClassVar[int] INITIAL_EVAL_SCORES_FIELD_NUMBER: _ClassVar[int] FINAL_EVAL_SCORES_FIELD_NUMBER: _ClassVar[int] job_id: str run_id: str state: _jobs_pb2.JobState experiment_id: str source_prompt_uri: str optimized_prompt_uri: str config: PromptOptimizationJobConfig creation_timestamp_ms: int completion_timestamp_ms: int tags: _containers.RepeatedCompositeFieldContainer[PromptOptimizationJobTag] initial_eval_scores: _containers.ScalarMap[str, float] final_eval_scores: _containers.ScalarMap[str, float] def __init__(self, job_id: _Optional[str] = ..., run_id: _Optional[str] = ..., state: _Optional[_Union[_jobs_pb2.JobState, _Mapping]] = ..., experiment_id: _Optional[str] = ..., source_prompt_uri: _Optional[str] = ..., optimized_prompt_uri: _Optional[str] = ..., config: _Optional[_Union[PromptOptimizationJobConfig, _Mapping]] = ..., creation_timestamp_ms: _Optional[int] = ..., completion_timestamp_ms: _Optional[int] = ..., tags: _Optional[_Iterable[_Union[PromptOptimizationJobTag, _Mapping]]] = ..., initial_eval_scores: _Optional[_Mapping[str, float]] = ..., final_eval_scores: _Optional[_Mapping[str, float]] = ...) -> None: ...