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from __future__ import annotations
from typing import Dict, List, Literal, Optional
from pydantic import BaseModel, Field
class Hypothesis(BaseModel):
bug_type: str = Field(..., description="Current suspected bug type")
affected_file: str = Field(..., description="Current suspected file")
confidence: float = Field(..., ge=0.0, le=1.0)
class InvestigationAction(BaseModel):
action: Literal[
"reveal_file",
"extend_loss_curve",
"extend_gpu_profile",
"reveal_log_chunk",
"run_diagnostic",
]
target: Optional[str] = None
class FinalDiagnosis(BaseModel):
bug_type: str
affected_file: str
line_range: List[int]
fix_strategy: str
confidence: float = Field(..., ge=0.0, le=1.0)
class PyTorchDebugAction(BaseModel):
current_hypothesis: Hypothesis
investigation_action: Optional[InvestigationAction] = None
commit_diagnosis: bool = False
final_diagnosis: Optional[FinalDiagnosis] = None
class HypothesisRecord(BaseModel):
step: int
hypothesis: Hypothesis
quality: float
class PyTorchDebugObservation(BaseModel):
scenario_id: str
task_id: str
revealed_files: Dict[str, str]
available_files: List[str]
loss_curve_window: List[Dict]
gpu_profile_window: List[Dict]
training_log_tail: str
diagnostic_report: Optional[str] = None
step_num: int
steps_remaining: int
investigation_budget: int
hypothesis_history: List[HypothesisRecord]
last_feedback: str
class PyTorchDebugState(BaseModel):
scenario_id: str
task_id: str
max_steps: int
current_step: int
revealed_files: List[str]
remaining_files: List[str]
diagnostic_revealed: bool = False
done: bool
final_score: float = 0.0
class PyTorchDebugReward(BaseModel):
value: float = Field(..., ge=0.0, le=1.0)
components: Dict[str, float]
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