"""FastAPI server exposing the OpenSleuth environment over HTTP.

Two HTTP surfaces are served from this app:

* The legacy OpenSleuth contract (``/health``, ``/functions``, ``/tasks``,
  ``/reset``, ``/step``, ``/state/{episode_id}``, ``/probe_once``) used by the
  in-flight trainer and eval harness.
* The OpenEnv-conformant sub-app mounted at ``/openenv/*`` (added in v0.5.0
  for hackathon conformance) -- exposes ``/openenv/reset``, ``/openenv/step``,
  ``/openenv/state``, ``/openenv/health``, ``/openenv/metadata``,
  ``/openenv/schema``, and the canonical ``/openenv/ws`` WebSocket. See
  :mod:`opensleuth_env.openenv_adapter` and
  https://github.com/meta-pytorch/OpenEnv (v0.2.3).
"""

from __future__ import annotations

import logging
import random
from typing import Optional

from fastapi import FastAPI, HTTPException, Query

from opensleuth_env import (
    BLACK_BOX_FUNCTIONS,
    OpenSleuthEnv,
    ProbeAction,
    ResetRequest,
    StepRequest,
    StepResponse,
    SubmitAction,
    TaskCatalog,
)
from opensleuth_env.task_catalog import TaskResolutionError

logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
log = logging.getLogger("opensleuth.server")

app = FastAPI(title="OpenSleuth Env", version="0.5.0")
env = OpenSleuthEnv()


# ---------------------------------------------------------------------------
# OpenEnv conformance: mount an upstream-spec sub-app at /openenv.
# This is kept additive so the existing trainer (which talks to the bare
# /reset and /step routes above) is completely unaffected.
# ---------------------------------------------------------------------------

try:
    from openenv.core.env_server.http_server import HTTPEnvServer

    from opensleuth_env.openenv_adapter import (
        OPENENV_AVAILABLE,
        OpenSleuthAction,
        OpenSleuthEnvironment,
        OpenSleuthObservation,
    )

    if OPENENV_AVAILABLE:
        openenv_app = FastAPI(
            title="OpenSleuth (OpenEnv-conformant)",
            version="0.5.0",
            description=(
                "OpenEnv 0.2.x conformant surface for the OpenSleuth environment.\n\n"
                "See https://github.com/meta-pytorch/OpenEnv -- this sub-app implements"
                " the canonical reset/step/state/health/metadata/schema HTTP routes plus"
                " the /ws WebSocket session protocol."
            ),
        )
        _openenv_server = HTTPEnvServer(
            env=OpenSleuthEnvironment,
            action_cls=OpenSleuthAction,
            observation_cls=OpenSleuthObservation,
            max_concurrent_envs=8,
        )
        _openenv_server.register_routes(openenv_app)
        app.mount("/openenv", openenv_app)
        log.info("Mounted OpenEnv-conformant sub-app at /openenv (openenv-core %s)",
                 _openenv_server.__class__.__module__)
    else:  # pragma: no cover
        log.warning("openenv-core not importable; /openenv/* will be unavailable.")
except Exception as e:  # pragma: no cover - fail open so legacy routes keep working
    log.warning("Could not register OpenEnv sub-app: %s: %s", type(e).__name__, e)


@app.get("/health")
def health():
    return {
        "status": "ok",
        "episodes_tracked": len(env._states),  # noqa: SLF001
        "hub": env.catalog.hub_status(),
    }


@app.get("/functions")
def list_functions(
    difficulty: Optional[str] = Query(
        None,
        description="Optional filter: easy / medium / hard. Used by the trainer for curriculum scheduling.",
    ),
):
    # NOTE -- backwards compatibility: this endpoint deliberately keeps the
    # exact v0.3 shape (just the 9 builtin functions, with the original
    # field set), because the in-flight trainer queries it. The new "source"
    # field is additive. Open-ended / Hub tasks are exposed via /tasks.
    items = []
    for s in BLACK_BOX_FUNCTIONS.values():
        if difficulty is not None and getattr(s, "difficulty", None) != difficulty:
            continue
        items.append(
            {
                "name": s.name,
                "signature": s.signature,
                "description": s.description,
                "difficulty": getattr(s, "difficulty", None),
                "edge_case_count": len(getattr(s, "edge_cases", []) or []),
                "source": "builtin",
            }
        )
    return {"functions": items}


@app.get("/tasks")
def list_tasks(
    source: str = Query(
        "all",
        description="Filter by source: 'builtin', 'hub', or 'all' (default).",
    ),
    difficulty: Optional[str] = Query(None, description="Optional curriculum filter."),
):
    src = source.lower()
    if src == "builtin":
        tasks = env.catalog.list_builtin()
    elif src == "hub":
        tasks = env.catalog.list_hub()
    elif src == "all":
        tasks = env.catalog.list_all()
    else:
        raise HTTPException(
            status_code=400, detail="source must be one of: builtin, hub, all"
        )
    if difficulty is not None:
        tasks = [t for t in tasks if t.get("difficulty") == difficulty]
    return {
        "tasks": tasks,
        "count": len(tasks),
        "hub": env.catalog.hub_status(),
    }


@app.post("/reset")
def reset(req: ResetRequest):
    # Validation: legacy callers pass only target_name; open-ended callers
    # pass target_code + target_function_name. At least one of those paths
    # must be populated.
    if not req.target_name and not req.target_code:
        raise HTTPException(
            status_code=400,
            detail="Either 'target_name' or ('target_code' + 'target_function_name') must be set.",
        )
    if req.target_code and not req.target_function_name:
        raise HTTPException(
            status_code=400,
            detail="'target_function_name' is required when 'target_code' is provided.",
        )
    try:
        obs = env.reset(
            target_name=req.target_name,
            seed=req.seed,
            max_steps=req.max_steps,
            target_code=req.target_code,
            target_function_name=req.target_function_name,
            edge_cases=req.edge_cases,
            fuzz_spec=req.fuzz_spec,
        )
    except ValueError as e:
        raise HTTPException(status_code=400, detail=str(e)) from e
    return obs


@app.post("/step", response_model=StepResponse)
def step(req: StepRequest):
    try:
        return env.step(req.episode_id, req.action)
    except KeyError as e:
        raise HTTPException(status_code=404, detail=str(e)) from e


@app.get("/state/{episode_id}")
def get_state(episode_id: str):
    state = env.get_state(episode_id)
    if not state:
        raise HTTPException(status_code=404, detail=f"Unknown episode_id {episode_id!r}")
    return state


@app.post("/probe_once")
def probe_once(target_name: str, input_repr: str):
    obs = env.reset(target_name=target_name)
    resp = env.step(obs.episode_id, ProbeAction(input_repr=input_repr))
    return resp


@app.get("/tasks/{name}/sample_inputs")
def sample_inputs(
    name: str,
    n: int = Query(8, ge=1, le=64, description="How many inputs to draw."),
    seed: int = Query(0, description="Deterministic seed for the fuzzer."),
):
    """Return ``n`` Python-literal `repr` strings drawn from the task's
    auto-fuzzer (or hand-written fuzzer for builtins).

    Used by the trainer to build in-context probe pools without having to
    duplicate the auto-fuzzer logic on the trainer side. Each returned
    string is `ast.literal_eval`-safe and can be POSTed straight back to
    `/step` as a `ProbeAction.input_repr`.
    """
    try:
        spec = env.catalog.resolve(target_name=name)
    except TaskResolutionError as e:
        raise HTTPException(status_code=404, detail=str(e)) from e
    rng = random.Random(seed)
    try:
        raw_inputs = spec.fuzzer(rng, n)
    except Exception as e:  # noqa: BLE001
        raise HTTPException(
            status_code=500,
            detail=f"fuzzer for {name!r} failed: {type(e).__name__}: {e}",
        ) from e
    return {
        "name": name,
        "n": n,
        "seed": seed,
        "unpack_args": bool(getattr(spec, "unpack_args", False)),
        "inputs": [repr(x) for x in raw_inputs],
    }