wehub-resource-sync
142 lines
10 KiB
Python
Generated
142 lines
10 KiB
Python
Generated
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import google.protobuf
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from packaging.version import Version
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if Version(google.protobuf.__version__).major >= 5:
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# -*- coding: utf-8 -*-
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# Generated by the protocol buffer compiler. DO NOT EDIT!
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# source: prompt_optimization.proto
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# Protobuf Python Version: 5.26.0
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"""Generated protocol buffer code."""
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from google.protobuf import descriptor as _descriptor
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from google.protobuf import descriptor_pool as _descriptor_pool
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from google.protobuf import symbol_database as _symbol_database
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from google.protobuf.internal import builder as _builder
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# @@protoc_insertion_point(imports)
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_sym_db = _symbol_database.Default()
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from . import jobs_pb2 as jobs__pb2
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from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2
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DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x19prompt_optimization.proto\x12\x06mlflow\x1a\njobs.proto\x1a\x15scalapb/scalapb.proto\"6\n\x18PromptOptimizationJobTag\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t\"\x90\x01\n\x1bPromptOptimizationJobConfig\x12-\n\x0eoptimizer_type\x18\x01 \x01(\x0e\x32\x15.mlflow.OptimizerType\x12\x12\n\ndataset_id\x18\x02 \x01(\t\x12\x0f\n\x07scorers\x18\x03 \x03(\t\x12\x1d\n\x15optimizer_config_json\x18\x04 \x01(\t\"\xe1\x04\n\x15PromptOptimizationJob\x12\x0e\n\x06job_id\x18\x01 \x01(\t\x12\x0e\n\x06run_id\x18\x02 \x01(\t\x12\x1f\n\x05state\x18\x03 \x01(\x0b\x32\x10.mlflow.JobState\x12\x15\n\rexperiment_id\x18\x04 \x01(\t\x12\x19\n\x11source_prompt_uri\x18\x05 \x01(\t\x12\x1c\n\x14optimized_prompt_uri\x18\x06 \x01(\t\x12\x33\n\x06\x63onfig\x18\x07 \x01(\x0b\x32#.mlflow.PromptOptimizationJobConfig\x12\x1d\n\x15\x63reation_timestamp_ms\x18\x08 \x01(\x03\x12\x1f\n\x17\x63ompletion_timestamp_ms\x18\t \x01(\x03\x12.\n\x04tags\x18\n \x03(\x0b\x32 .mlflow.PromptOptimizationJobTag\x12Q\n\x13initial_eval_scores\x18\x0b \x03(\x0b\x32\x34.mlflow.PromptOptimizationJob.InitialEvalScoresEntry\x12M\n\x11\x66inal_eval_scores\x18\x0c \x03(\x0b\x32\x32.mlflow.PromptOptimizationJob.FinalEvalScoresEntry\x1a\x38\n\x16InitialEvalScoresEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\x01:\x02\x38\x01\x1a\x36\n\x14\x46inalEvalScoresEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\x01:\x02\x38\x01*g\n\rOptimizerType\x12\x1e\n\x1aOPTIMIZER_TYPE_UNSPECIFIED\x10\x00\x12\x17\n\x13OPTIMIZER_TYPE_GEPA\x10\x01\x12\x1d\n\x19OPTIMIZER_TYPE_METAPROMPT\x10\x02\x42\x1e\n\x14org.mlflow.api.proto\x90\x01\x01\xe2?\x02\x10\x01')
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_globals = globals()
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_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
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_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'prompt_optimization_pb2', _globals)
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if not _descriptor._USE_C_DESCRIPTORS:
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_globals['DESCRIPTOR']._loaded_options = None
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_globals['DESCRIPTOR']._serialized_options = b'\n\024org.mlflow.api.proto\220\001\001\342?\002\020\001'
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_globals['_PROMPTOPTIMIZATIONJOB_INITIALEVALSCORESENTRY']._loaded_options = None
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_globals['_PROMPTOPTIMIZATIONJOB_INITIALEVALSCORESENTRY']._serialized_options = b'8\001'
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_globals['_PROMPTOPTIMIZATIONJOB_FINALEVALSCORESENTRY']._loaded_options = None
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_globals['_PROMPTOPTIMIZATIONJOB_FINALEVALSCORESENTRY']._serialized_options = b'8\001'
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_globals['_OPTIMIZERTYPE']._serialized_start=887
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_globals['_OPTIMIZERTYPE']._serialized_end=990
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_globals['_PROMPTOPTIMIZATIONJOBTAG']._serialized_start=72
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_globals['_PROMPTOPTIMIZATIONJOBTAG']._serialized_end=126
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_globals['_PROMPTOPTIMIZATIONJOBCONFIG']._serialized_start=129
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_globals['_PROMPTOPTIMIZATIONJOBCONFIG']._serialized_end=273
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_globals['_PROMPTOPTIMIZATIONJOB']._serialized_start=276
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_globals['_PROMPTOPTIMIZATIONJOB']._serialized_end=885
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_globals['_PROMPTOPTIMIZATIONJOB_INITIALEVALSCORESENTRY']._serialized_start=773
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_globals['_PROMPTOPTIMIZATIONJOB_INITIALEVALSCORESENTRY']._serialized_end=829
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_globals['_PROMPTOPTIMIZATIONJOB_FINALEVALSCORESENTRY']._serialized_start=831
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_globals['_PROMPTOPTIMIZATIONJOB_FINALEVALSCORESENTRY']._serialized_end=885
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# @@protoc_insertion_point(module_scope)
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else:
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# -*- coding: utf-8 -*-
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# Generated by the protocol buffer compiler. DO NOT EDIT!
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# source: prompt_optimization.proto
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"""Generated protocol buffer code."""
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from google.protobuf.internal import enum_type_wrapper
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from google.protobuf import descriptor as _descriptor
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from google.protobuf import descriptor_pool as _descriptor_pool
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from google.protobuf import message as _message
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from google.protobuf import reflection as _reflection
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from google.protobuf import symbol_database as _symbol_database
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# @@protoc_insertion_point(imports)
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_sym_db = _symbol_database.Default()
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from . import jobs_pb2 as jobs__pb2
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from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2
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DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x19prompt_optimization.proto\x12\x06mlflow\x1a\njobs.proto\x1a\x15scalapb/scalapb.proto\"6\n\x18PromptOptimizationJobTag\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t\"\x90\x01\n\x1bPromptOptimizationJobConfig\x12-\n\x0eoptimizer_type\x18\x01 \x01(\x0e\x32\x15.mlflow.OptimizerType\x12\x12\n\ndataset_id\x18\x02 \x01(\t\x12\x0f\n\x07scorers\x18\x03 \x03(\t\x12\x1d\n\x15optimizer_config_json\x18\x04 \x01(\t\"\xe1\x04\n\x15PromptOptimizationJob\x12\x0e\n\x06job_id\x18\x01 \x01(\t\x12\x0e\n\x06run_id\x18\x02 \x01(\t\x12\x1f\n\x05state\x18\x03 \x01(\x0b\x32\x10.mlflow.JobState\x12\x15\n\rexperiment_id\x18\x04 \x01(\t\x12\x19\n\x11source_prompt_uri\x18\x05 \x01(\t\x12\x1c\n\x14optimized_prompt_uri\x18\x06 \x01(\t\x12\x33\n\x06\x63onfig\x18\x07 \x01(\x0b\x32#.mlflow.PromptOptimizationJobConfig\x12\x1d\n\x15\x63reation_timestamp_ms\x18\x08 \x01(\x03\x12\x1f\n\x17\x63ompletion_timestamp_ms\x18\t \x01(\x03\x12.\n\x04tags\x18\n \x03(\x0b\x32 .mlflow.PromptOptimizationJobTag\x12Q\n\x13initial_eval_scores\x18\x0b \x03(\x0b\x32\x34.mlflow.PromptOptimizationJob.InitialEvalScoresEntry\x12M\n\x11\x66inal_eval_scores\x18\x0c \x03(\x0b\x32\x32.mlflow.PromptOptimizationJob.FinalEvalScoresEntry\x1a\x38\n\x16InitialEvalScoresEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\x01:\x02\x38\x01\x1a\x36\n\x14\x46inalEvalScoresEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\x01:\x02\x38\x01*g\n\rOptimizerType\x12\x1e\n\x1aOPTIMIZER_TYPE_UNSPECIFIED\x10\x00\x12\x17\n\x13OPTIMIZER_TYPE_GEPA\x10\x01\x12\x1d\n\x19OPTIMIZER_TYPE_METAPROMPT\x10\x02\x42\x1e\n\x14org.mlflow.api.proto\x90\x01\x01\xe2?\x02\x10\x01')
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_OPTIMIZERTYPE = DESCRIPTOR.enum_types_by_name['OptimizerType']
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OptimizerType = enum_type_wrapper.EnumTypeWrapper(_OPTIMIZERTYPE)
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OPTIMIZER_TYPE_UNSPECIFIED = 0
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OPTIMIZER_TYPE_GEPA = 1
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OPTIMIZER_TYPE_METAPROMPT = 2
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_PROMPTOPTIMIZATIONJOBTAG = DESCRIPTOR.message_types_by_name['PromptOptimizationJobTag']
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_PROMPTOPTIMIZATIONJOBCONFIG = DESCRIPTOR.message_types_by_name['PromptOptimizationJobConfig']
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_PROMPTOPTIMIZATIONJOB = DESCRIPTOR.message_types_by_name['PromptOptimizationJob']
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_PROMPTOPTIMIZATIONJOB_INITIALEVALSCORESENTRY = _PROMPTOPTIMIZATIONJOB.nested_types_by_name['InitialEvalScoresEntry']
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_PROMPTOPTIMIZATIONJOB_FINALEVALSCORESENTRY = _PROMPTOPTIMIZATIONJOB.nested_types_by_name['FinalEvalScoresEntry']
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PromptOptimizationJobTag = _reflection.GeneratedProtocolMessageType('PromptOptimizationJobTag', (_message.Message,), {
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'DESCRIPTOR' : _PROMPTOPTIMIZATIONJOBTAG,
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'__module__' : 'prompt_optimization_pb2'
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# @@protoc_insertion_point(class_scope:mlflow.PromptOptimizationJobTag)
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})
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_sym_db.RegisterMessage(PromptOptimizationJobTag)
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PromptOptimizationJobConfig = _reflection.GeneratedProtocolMessageType('PromptOptimizationJobConfig', (_message.Message,), {
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'DESCRIPTOR' : _PROMPTOPTIMIZATIONJOBCONFIG,
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'__module__' : 'prompt_optimization_pb2'
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# @@protoc_insertion_point(class_scope:mlflow.PromptOptimizationJobConfig)
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})
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_sym_db.RegisterMessage(PromptOptimizationJobConfig)
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PromptOptimizationJob = _reflection.GeneratedProtocolMessageType('PromptOptimizationJob', (_message.Message,), {
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'InitialEvalScoresEntry' : _reflection.GeneratedProtocolMessageType('InitialEvalScoresEntry', (_message.Message,), {
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'DESCRIPTOR' : _PROMPTOPTIMIZATIONJOB_INITIALEVALSCORESENTRY,
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'__module__' : 'prompt_optimization_pb2'
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# @@protoc_insertion_point(class_scope:mlflow.PromptOptimizationJob.InitialEvalScoresEntry)
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})
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,
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'FinalEvalScoresEntry' : _reflection.GeneratedProtocolMessageType('FinalEvalScoresEntry', (_message.Message,), {
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'DESCRIPTOR' : _PROMPTOPTIMIZATIONJOB_FINALEVALSCORESENTRY,
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'__module__' : 'prompt_optimization_pb2'
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# @@protoc_insertion_point(class_scope:mlflow.PromptOptimizationJob.FinalEvalScoresEntry)
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})
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,
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'DESCRIPTOR' : _PROMPTOPTIMIZATIONJOB,
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'__module__' : 'prompt_optimization_pb2'
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# @@protoc_insertion_point(class_scope:mlflow.PromptOptimizationJob)
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})
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_sym_db.RegisterMessage(PromptOptimizationJob)
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_sym_db.RegisterMessage(PromptOptimizationJob.InitialEvalScoresEntry)
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_sym_db.RegisterMessage(PromptOptimizationJob.FinalEvalScoresEntry)
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if _descriptor._USE_C_DESCRIPTORS == False:
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DESCRIPTOR._options = None
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DESCRIPTOR._serialized_options = b'\n\024org.mlflow.api.proto\220\001\001\342?\002\020\001'
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_PROMPTOPTIMIZATIONJOB_INITIALEVALSCORESENTRY._options = None
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_PROMPTOPTIMIZATIONJOB_INITIALEVALSCORESENTRY._serialized_options = b'8\001'
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_PROMPTOPTIMIZATIONJOB_FINALEVALSCORESENTRY._options = None
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_PROMPTOPTIMIZATIONJOB_FINALEVALSCORESENTRY._serialized_options = b'8\001'
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_OPTIMIZERTYPE._serialized_start=887
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_OPTIMIZERTYPE._serialized_end=990
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_PROMPTOPTIMIZATIONJOBTAG._serialized_start=72
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_PROMPTOPTIMIZATIONJOBTAG._serialized_end=126
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_PROMPTOPTIMIZATIONJOBCONFIG._serialized_start=129
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_PROMPTOPTIMIZATIONJOBCONFIG._serialized_end=273
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_PROMPTOPTIMIZATIONJOB._serialized_start=276
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_PROMPTOPTIMIZATIONJOB._serialized_end=885
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_PROMPTOPTIMIZATIONJOB_INITIALEVALSCORESENTRY._serialized_start=773
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_PROMPTOPTIMIZATIONJOB_INITIALEVALSCORESENTRY._serialized_end=829
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_PROMPTOPTIMIZATIONJOB_FINALEVALSCORESENTRY._serialized_start=831
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_PROMPTOPTIMIZATIONJOB_FINALEVALSCORESENTRY._serialized_end=885
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# @@protoc_insertion_point(module_scope)
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