# CorpusFetch Method Slice (Zero-Prose Python Stdlib Reference)
# Canonical Residual: https://corpusfetch.com/c/what-happens-when-an-instance-of-the-litmus-safety-benchmark-harness-receives-an-adversarial-tool-schema-whose-injection-payload-lives-entirely-inside.md
# Remnant Warrant:    https://corpusfetch.com/c/what-happens-when-an-instance-of-the-litmus-safety-benchmark-harness-receives-an-adversarial-tool-schema-whose-injection-payload-lives-entirely-inside.warrant.json
# Markdown SHA-256:   7c6704d2e62be807a3096414c460c83cbbd34995e6318756973d36bbd452ca14
# Code AST SHA-256:   2e238c16f5d5b0fa965e28b5a0fd6fd14e7e044b3a304dcee513a5dbd1585356
# Verified Classes:   LITMUSAttributionError
# Defensive Guards:   4 fail-closed invariants (zero external deps)

import json
from typing import Any


class LITMUSAttributionError(RuntimeError):
    """Raised when a flagged LITMUS case cannot be attributed to a single
    injection channel by ablation, or when the declared injection field is
    not the field whose neutralization removes the divergence."""


ANNOTATION_KEYWORDS = ("default", "examples", "const", "enum")


def _neutralize_annotation(schema: dict[str, Any]) -> dict[str, Any]:
    """Return a copy of the schema with all annotation keywords removed."""
    out = json.loads(json.dumps(schema))
    for prop in out.get("properties", {}).values():
        for kw in ANNOTATION_KEYWORDS:
            prop.pop(kw, None)
    return out


def _neutralize_field(schema: dict[str, Any], field: str) -> dict[str, Any]:
    """Return a copy of the schema with a single named field blanked."""
    out = json.loads(json.dumps(schema))
    if field == "description":
        out["description"] = ""
        for prop in out.get("properties", {}).values():
            prop.pop("description", None)
    else:
        for prop in out.get("properties", {}).values():
            prop.pop(field, None)
    return out


def attribute_case(
    case: dict[str, Any],
    run_agent: "callable",
    baseline_trace: list[dict[str, Any]],
) -> str:
    """Determine which injection channel caused a flagged divergence.

    Returns one of: 'description', 'annotation', 'unattributed'.
    Raises LITMUSAttributionError if the declared field and the observed
    channel disagree, or if neither ablation removes the divergence.
    """
    declared = case.get("injection_field", "description")
    schema = case["tool_schema"]

    full_trace = run_agent(schema)
    if full_trace == baseline_trace:
        raise LITMUSAttributionError(
            f"case {case.get('id')} flagged but no divergence from baseline"
        )

    no_declared = run_agent(_neutralize_field(schema, declared))
    no_annotation = run_agent(_neutralize_annotation(schema))

    declared_removes = no_declared == baseline_trace
    annotation_removes = no_annotation == baseline_trace

    if annotation_removes and not declared_removes:
        if declared != "annotation":
            raise LITMUSAttributionError(
                f"case {case.get('id')}: declared injection_field={declared!r} "
                f"but divergence is removed only by neutralizing annotation "
                f"keywords ({ANNOTATION_KEYWORDS}); relabel before aggregating"
            )
        return "annotation"
    if declared_removes and not annotation_removes:
        return declared
    if declared_removes and annotation_removes:
        raise LITMUSAttributionError(
            f"case {case.get('id')}: both ablations remove divergence; "
            f"channel is confounded and the case is not single-channel"
        )
    raise LITMUSAttributionError(
        f"case {case.get('id')}: neither ablation removes divergence; "
        f"divergence is not attributable to the tool schema"
    )