"""Skill allowed-tools is compatibility metadata, not a tool authority.

`allowed-tools` stays parsed, exported, and described, but it must never filter
model-visible schemas, refuse calls, or rewrite tool_search promotions. Agent
assembly/execution authorization (provider filtering + Guardrails) owns the
permission ceiling.

These regressions drive the real `build_middlewares` (lead) and
`build_subagent_runtime_middlewares` chains through `create_agent`, activate a
synthetic skill via a real slash command, and assert both model binding and
actual ToolNode execution on sync and async paths. The synthetic skill lives in
a real on-disk skills tree served through the configured storage provider, and
the block under test is taken structurally between the live
`SkillActivationMiddleware` and `DurableContextMiddleware` anchors, so
whatever middleware the builder actually installs between those anchors
reaches `create_agent`.
"""

from __future__ import annotations

import asyncio
from pathlib import Path
from types import SimpleNamespace
from typing import Any

import pytest
from langchain.agents import create_agent
from langchain_core.language_models.fake_chat_models import GenericFakeChatModel
from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
from langchain_core.tools import tool
from pydantic import Field

from deerflow.agents.lead_agent import agent as lead_agent_module
from deerflow.agents.middlewares.durable_context_middleware import DurableContextMiddleware
from deerflow.agents.middlewares.skill_activation_middleware import SkillActivationMiddleware
from deerflow.agents.middlewares.tool_error_handling_middleware import build_subagent_runtime_middlewares
from deerflow.agents.thread_state import ThreadState
from deerflow.authz.rbac import RbacAuthorizationProvider
from deerflow.config.app_config import AppConfig
from deerflow.config.authorization_config import AuthorizationConfig, AuthorizationProviderConfig
from deerflow.config.loop_detection_config import LoopDetectionConfig
from deerflow.config.model_config import ModelConfig
from deerflow.config.safety_finish_reason_config import SafetyFinishReasonConfig
from deerflow.config.sandbox_config import SandboxConfig
from deerflow.config.skills_config import SkillsConfig
from deerflow.config.subagents_config import SubagentsAppConfig
from deerflow.config.token_budget_config import TokenBudgetConfig

_PARSE_CALLS: list[str] = []
_WRITE_CALLS: list[str] = []


@tool
def parse_document(path: str) -> str:
    """Parse a document and return its text."""
    _PARSE_CALLS.append(path)
    return "parsed"


@tool
def write_file(path: str, content: str) -> str:
    """Write content to a file."""
    _WRITE_CALLS.append(path)
    return "written"


@tool
def read_file(path: str) -> str:
    """Read a file from the skills container."""
    return "file body"


class _RecordingModel(GenericFakeChatModel):
    bound_tool_names: list[list[str]] = Field(default_factory=list)

    def __init__(self, responses: list[AIMessage]):
        super().__init__(messages=iter(responses))

    def bind_tools(self, tools: Any, **kwargs: Any):
        self.bound_tool_names.append([getattr(candidate, "name", "") for candidate in tools])
        return self


class _NoopMiddleware:
    def __repr__(self) -> str:  # pragma: no cover - debug aid only
        return "<noop-test-middleware>"


def _make_app_config(skills_root: Path) -> AppConfig:
    return AppConfig(
        models=[
            ModelConfig(
                name="safe-model",
                display_name="safe-model",
                description=None,
                use="langchain_openai:ChatOpenAI",
                model="safe-model",
                supports_thinking=False,
                supports_vision=False,
            )
        ],
        sandbox=SandboxConfig(use="test"),
        loop_detection=LoopDetectionConfig(enabled=False),
        # The synthetic skills tree is served through the real configured
        # storage provider, so every middleware in the block under test
        # resolves skills through the common production seam.
        skills=SkillsConfig(path=str(skills_root)),
        # Keep the activation→durable-context block free of unrelated guards.
        safety_finish_reason=SafetyFinishReasonConfig(enabled=False),
        subagents=SubagentsAppConfig(token_budget=TokenBudgetConfig(enabled=False)),
    )


def _make_skill(skills_root: Path, name: str, allowed_tools: list[str] | None) -> None:
    """Write one real custom skill into the per-test skills tree."""
    skill_dir = skills_root / "custom" / name
    skill_dir.mkdir(parents=True, exist_ok=True)
    lines = [
        "---",
        f"name: {name}",
        "description: Demo skill for tool authority regressions",
        "license: MIT",
    ]
    if allowed_tools is None:
        pass
    elif not allowed_tools:
        # An actual empty YAML sequence, not a null `allowed-tools:` key.
        lines.append("allowed-tools: []")
    else:
        lines.append("allowed-tools:")
        lines.extend(f"  - {entry}" for entry in allowed_tools)
    lines.extend(["---", "", "# Demo", "", "Use the parser."])
    skill_file = skill_dir / "SKILL.md"
    skill_file.write_text("\n".join(lines), encoding="utf-8")


def _lead_middlewares(monkeypatch, app_config: AppConfig) -> list:
    monkeypatch.setattr(lead_agent_module, "build_lead_runtime_middlewares", lambda *, app_config, lazy_init=True: [])
    monkeypatch.setattr(lead_agent_module, "_create_summarization_middleware", lambda **_kwargs: None)
    monkeypatch.setattr(lead_agent_module, "_create_todo_list_middleware", lambda is_plan_mode: None)
    monkeypatch.setattr(lead_agent_module, "TitleMiddleware", lambda *, app_config, extensions: _NoopMiddleware())
    monkeypatch.setattr(lead_agent_module, "MemoryMiddleware", lambda *args, **kwargs: _NoopMiddleware())
    return lead_agent_module.build_middlewares(
        {"configurable": {"is_plan_mode": False, "subagent_enabled": False}},
        model_name="safe-model",
        app_config=app_config,
    )


def _subagent_middlewares(app_config: AppConfig) -> list:
    return build_subagent_runtime_middlewares(app_config=app_config, model_name="safe-model")


def _skill_authority_slice(middlewares: list) -> list:
    """The contiguous skill block the builder installs, taken structurally.

    The block starts at the live ``SkillActivationMiddleware`` anchor and ends
    just before the live ``DurableContextMiddleware`` anchor — both types
    exist in every real builder output. Whatever middleware the builder
    actually places between those anchors is included positionally, so the
    block stays meaningful against any assembly without recognizing class
    names that a later revision may delete. Adjacent placement on the current
    chain is pinned separately by
    ``test_build_middlewares_orders_skill_activation_before_durable_context``.
    """
    start = next(i for i, middleware in enumerate(middlewares) if isinstance(middleware, SkillActivationMiddleware))
    end = next(i for i in range(start + 1, len(middlewares)) if isinstance(middlewares[i], DurableContextMiddleware))
    return middlewares[start:end]


def _scripted_model() -> _RecordingModel:
    return _RecordingModel(
        [
            AIMessage(
                content="",
                tool_calls=[
                    {
                        "name": "parse_document",
                        "args": {"path": "report.pdf"},
                        "id": "parse-call",
                        "type": "tool_call",
                    }
                ],
            ),
            AIMessage(content="done"),
        ]
    )


class _Journal:
    def __init__(self) -> None:
        self.middleware_calls: list[dict] = []
        self.skill_usages: list[Any] = []

    def record_middleware(self, *args, **kwargs):
        self.middleware_calls.append(kwargs)

    def record_skill_usage(self, *args, **kwargs):
        self.skill_usages.append(args[0] if args else kwargs)


def _invoke(graph, *, asynchronous: bool) -> tuple[dict, _Journal]:
    journal = _Journal()
    graph_input = {"messages": [HumanMessage(content="/demo-skill parse the report", id="msg-1")]}
    context = {"__run_journal": journal}
    config = {"recursion_limit": 20}
    if asynchronous:
        result = asyncio.run(graph.ainvoke(graph_input, context=context, config=config))
    else:
        result = graph.invoke(graph_input, context=context, config=config)
    return result, journal


@pytest.mark.parametrize("builder", ["lead", "subagent"])
@pytest.mark.parametrize("asynchronous", [False, True])
@pytest.mark.parametrize(
    "allowed_tools",
    [["write_file"], [], None],
    ids=["restrictive", "empty", "omitted"],
)
def test_active_skill_allowed_tools_does_not_remove_authorized_tools(monkeypatch, tmp_path, builder, asynchronous, allowed_tools):
    """A slash-activated skill keeps every already-authorized tool bound and executable.

    allowed-tools is compatibility metadata: restrictive, empty, or omitted
    declarations must not narrow the Agent's tool set on the lead or Subagent
    assembly, sync or async. The restrictive case lists only `write_file` while
    the scripted model executes `parse_document`: an Agent-authorized tool
    OMITTED by the restrictive metadata must still run.
    """
    _make_skill(tmp_path, "demo-skill", allowed_tools)
    app_config = _make_app_config(tmp_path)
    if builder == "lead":
        middlewares = _lead_middlewares(monkeypatch, app_config)
    else:
        middlewares = _subagent_middlewares(app_config)
    slice_ = _skill_authority_slice(middlewares)
    assert slice_, "builder must install SkillActivationMiddleware"

    model = _scripted_model()
    graph = create_agent(
        model=model,
        tools=[read_file, parse_document, write_file],
        middleware=slice_,
        state_schema=ThreadState,
    )

    _PARSE_CALLS.clear()
    result, journal = _invoke(graph, asynchronous=asynchronous)

    assert {"read_file", "parse_document", "write_file"} <= set(model.bound_tool_names[0])
    assert _PARSE_CALLS == ["report.pdf"]
    assert _WRITE_CALLS == []
    assert result["messages"][-1].content == "done"
    # The real slash activation still ran: usage was recorded for the skill.
    assert [usage.get("name") for usage in journal.skill_usages if isinstance(usage, dict)] == ["demo-skill"]


def test_allowed_tools_metadata_cannot_grant_rbac_denied_tool(monkeypatch, tmp_path):
    """Declaring a tool in allowed-tools never bypasses genuine Agent authorization.

    Control test: RBAC denies `write_file` for the guest role. A skill whose
    allowed-tools lists `write_file` must not make it executable; the
    Guardrail-backed authorization middleware still refuses the call.
    """
    from deerflow.agents.middlewares.tool_error_handling_middleware import build_lead_runtime_middlewares

    provider = RbacAuthorizationProvider(roles={"guest": {"tools": {"allow": ["read_file", "parse_document"]}}})
    _make_skill(tmp_path, "demo-skill", ["write_file"])
    app_config = _make_app_config(tmp_path)
    app_config.authorization = AuthorizationConfig(
        enabled=True,
        provider=AuthorizationProviderConfig(use="unused:Provider"),
    )

    monkeypatch.setattr(lead_agent_module, "_create_summarization_middleware", lambda **_kwargs: None)
    monkeypatch.setattr(lead_agent_module, "_create_todo_list_middleware", lambda is_plan_mode: None)
    monkeypatch.setattr(lead_agent_module, "TitleMiddleware", lambda *, app_config, extensions: _NoopMiddleware())
    monkeypatch.setattr(lead_agent_module, "MemoryMiddleware", lambda *args, **kwargs: _NoopMiddleware())

    runtime_middlewares = build_lead_runtime_middlewares(
        app_config=app_config,
        authorization_provider=provider,
    )
    full_chain = _lead_middlewares(monkeypatch, app_config)
    slice_ = _skill_authority_slice(full_chain)

    model = _RecordingModel(
        [
            AIMessage(
                content="",
                tool_calls=[
                    {
                        "name": "write_file",
                        "args": {"path": "out.txt", "content": "x"},
                        "id": "denied-call",
                        "type": "tool_call",
                    }
                ],
            ),
            AIMessage(content="done"),
        ]
    )
    graph = create_agent(
        model=model,
        tools=[read_file, parse_document, write_file],
        middleware=[*runtime_middlewares, *slice_],
        state_schema=ThreadState,
    )

    _WRITE_CALLS.clear()
    journal = SimpleNamespace(
        record_middleware=lambda *args, **kwargs: None,
        record_skill_usage=lambda *args, **kwargs: None,
    )
    result = graph.invoke(
        {"messages": [HumanMessage(content="/demo-skill write the report", id="msg-1")]},
        context={"__run_journal": journal, "user_role": "guest", "thread_id": "t-authority-test"},
        config={"recursion_limit": 20, "configurable": {"thread_id": "t-authority-test"}},
    )

    assert _WRITE_CALLS == []
    blocked = [message for message in result["messages"] if isinstance(message, ToolMessage) and message.tool_call_id == "denied-call"]
    assert len(blocked) == 1
    assert blocked[0].status == "error"
    # The denial must be visible in the ToolMessage content itself, not only
    # via the status error flag.
    assert "authz.denied" in blocked[0].content