# CorpusFetch Method Slice (Zero-Prose Python Stdlib Reference)
# Canonical Residual: https://corpusfetch.com/c/how-must-an-instance-record-training-data-provenance-to-detect-silent-model-degradation-across-restarts.md
# Remnant Warrant:    https://corpusfetch.com/c/how-must-an-instance-record-training-data-provenance-to-detect-silent-model-degradation-across-restarts.warrant.json
# Markdown SHA-256:   75f87a1d04107a5b0b5813fee44ecfa3651f71eb49f8a3c3fe30f88722c33ca4
# Code AST SHA-256:   088ccde79427e824bdab5ec6df7d9373aa397752e49dfccd2e41bfca98e1373f
# Verified Classes:   ModelDriftVerdict, ModelProvenance, CurrentModelState, ModelManifest, ModelProvenanceGuard
# Defensive Guards:   0 fail-closed invariants (zero external deps)

from dataclasses import dataclass
from enum import Enum, auto
import hashlib
import json

class ModelDriftVerdict(Enum):
    STABLE = auto()
    BEHAVIORAL_DRIFT = auto()
    UNVERIFIABLE = auto()

@dataclass(frozen=True)
class ModelProvenance:
    weights_hash: str
    training_cutoff_epoch: int
    canary_prompt: str
    canary_output_hash: str

@dataclass(frozen=True)
class CurrentModelState:
    current_weights_hash: str
    current_canary_output_hash: str

@dataclass(frozen=True)
class ModelManifest:
    verdict: ModelDriftVerdict
    directive: str
    manifest_digest: str

class ModelProvenanceGuard:
    @staticmethod
    def _hash(data: str) -> str:
        return hashlib.sha256(data.encode("utf-8")).hexdigest()

    @classmethod
    def evaluate_drift(
        cls, saved: ModelProvenance, current: CurrentModelState
    ) -> ModelManifest:
        
        # Gate 1: Weights hash mismatch (unverified model)
        if saved.weights_hash != current.current_weights_hash:
            return ModelManifest(
                verdict=ModelDriftVerdict.UNVERIFIABLE,
                directive=f"HALT: Weights hash mismatch. Expected {saved.weights_hash}, got {current.current_weights_hash}.",
                manifest_digest=cls._hash("WEIGHT_MISMATCH")
            )

        # Gate 2: Behavioral drift (same weights, different output)
        if saved.canary_output_hash != current.current_canary_output_hash:
            return ModelManifest(
                verdict=ModelDriftVerdict.BEHAVIORAL_DRIFT,
                directive=f"HALT: Canary output drifted. Model behavior changed.",
                manifest_digest=cls._hash("BEHAVIORAL_DRIFT")
            )

        # Gate 3: Stable
        digest = cls._hash(json.dumps({"weights": saved.weights_hash, "canary": saved.canary_output_hash}, sort_keys=True))
        return ModelManifest(
            verdict=ModelDriftVerdict.STABLE,
            directive="PROCEED: Model provenance and behavior verified.",
            manifest_digest=digest
        )