# CorpusFetch Method Slice (Zero-Prose Python Stdlib Reference)
# Canonical Residual: https://corpusfetch.com/c/how-must-an-instance-distinguish-stochastic-non-determinism-from-genuine-model-drift.md
# Remnant Warrant:    https://corpusfetch.com/c/how-must-an-instance-distinguish-stochastic-non-determinism-from-genuine-model-drift.warrant.json
# Markdown SHA-256:   c778c845d368def95b04c8c6cd52fa7aa4a31936ec53430bd2b2e361e5954793
# Code AST SHA-256:   a8d82ba9a4de061664c399d8520c7e60e5b64c7fd33f5deb302c4ac9c76df460
# Verified Classes:   DriftVerdict, DriftTestState, DriftPolicy, DriftManifest, StochasticDriftGuard
# Defensive Guards:   0 fail-closed invariants (zero external deps)

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

class DriftVerdict(Enum):
    DETERMINISTIC_STABLE = auto()
    STOCHASTIC_VARIANCE = auto()
    GENUINE_DRIFT = auto()
    INSUFFICIENT_SAMPLES = auto()

@dataclass(frozen=True)
class DriftTestState:
    baseline_distribution_hash: str
    observed_samples_count: int
    required_sample_size: int
    distribution_divergence_score: float
    drift_threshold: float
    model_is_deterministic: bool

@dataclass(frozen=True)
class DriftPolicy:
    min_samples_for_test: int

@dataclass(frozen=True)
class DriftManifest:
    verdict: DriftVerdict
    directive: str
    manifest_digest: str

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

    @classmethod
    def evaluate_drift(
        cls, state: DriftTestState, policy: DriftPolicy
    ) -> DriftManifest:
        
        # Gate 1: Deterministic model
        if state.model_is_deterministic:
            if state.distribution_divergence_score > 0.0:
                return DriftManifest(
                    verdict=DriftVerdict.GENUINE_DRIFT,
                    directive="HALT: Deterministic model produced different output. Definite drift.",
                    manifest_digest=cls._hash(f"DET_DRIFT:{state.baseline_distribution_hash}")
                )
            digest = cls._hash(json.dumps({
                "baseline": state.baseline_distribution_hash,
                "divergence": "0.0"
            }, sort_keys=True))
            return DriftManifest(
                verdict=DriftVerdict.DETERMINISTIC_STABLE,
                directive="PROCEED: Deterministic model stable.",
                manifest_digest=digest
            )

        # Gate 2: Insufficient samples
        if state.observed_samples_count < policy.min_samples_for_test:
            return DriftManifest(
                verdict=DriftVerdict.INSUFFICIENT_SAMPLES,
                directive=f"WAIT: {state.observed_samples_count}/{policy.min_samples_for_test} samples. Collect more before testing.",
                manifest_digest=cls._hash(f"INSUFFICIENT:{state.observed_samples_count}")
            )

        # Gate 3: Divergence threshold
        if state.distribution_divergence_score > state.drift_threshold:
            digest = cls._hash(json.dumps({
                "baseline": state.baseline_distribution_hash,
                "divergence": f"{state.distribution_divergence_score:.4f}",
                "threshold": f"{state.drift_threshold:.4f}",
                "samples": str(state.observed_samples_count)
            }, sort_keys=True))
            return DriftManifest(
                verdict=DriftVerdict.GENUINE_DRIFT,
                directive=f"HALT: Divergence {state.distribution_divergence_score:.4f} > {state.drift_threshold:.4f}. Genuine drift.",
                manifest_digest=digest
            )

        # Gate 4: Within stochastic variance
        digest = cls._hash(json.dumps({
            "baseline": state.baseline_distribution_hash,
            "divergence": f"{state.distribution_divergence_score:.4f}",
            "threshold": f"{state.drift_threshold:.4f}",
            "samples": str(state.observed_samples_count)
        }, sort_keys=True))
        
        return DriftManifest(
            verdict=DriftVerdict.STOCHASTIC_VARIANCE,
            directive=f"PROCEED: Divergence {state.distribution_divergence_score:.4f} within threshold {state.drift_threshold:.4f}.",
            manifest_digest=digest
        )