# SPDX-License-Identifier: EUPL-1.2
"""Lighteval custom model backend — routes inference through an Ollama
(or any OpenAI-compatible) server hosting GGUF models.

Pairs with mlx_lm_wrapper.py in the same directory. eval.py picks which
wrapper to point lighteval at based on the target's `type` field
(`mlx` → mlx_lm_wrapper.py, `gguf` → this file).

Usage (CLI, via eval.py):
    eval.py automatically points lighteval at this file when the target
    type is `gguf`.

Usage (lighteval direct):
    lighteval custom \\
        "hf.co/lthn/lemer:Q4_K_M" \\
        /path/to/gguf_wrapper.py \\
        "mmlu_pro" \\
        --max-samples 10

Endpoint configuration (env vars, both optional):
    LEM_OLLAMA_URL     default: http://localhost:11434/v1
    LEM_OLLAMA_API_KEY default: "ollama" (Ollama ignores the value)

config.model_name resolution:
    Passed straight through as the `model` parameter to the OpenAI chat
    completions endpoint. Use whatever string Ollama expects — for example:
        hf.co/lthn/lemer:Q4_K_M
        hf.co/lthn/lemmy:Q4_K_M
        gemma3:27b

    Ollama will lazy-pull models it doesn't have yet on first request.
    The wrapper sends a 1-token probe during __init__ to surface pull
    failures early rather than mid-eval.

Sampling policy (identical to mlx_lm_wrapper.py):
    Google-calibrated Gemma 4: temp=1.0, top_p=0.95, top_k=64. enable_thinking
    is signalled via `think: true` in the OpenAI extra_body — Ollama >= 0.3
    with a thinking-capable model will respect this; older versions ignore
    it, so our fork's LEK'd models still want the chat template to set the
    think-mode anchor server-side (which Ollama modelfiles can do).
"""

import os
import sys
from typing import List

from lighteval.models.abstract_model import LightevalModel
from lighteval.models.model_output import ModelResponse
from lighteval.tasks.requests import Doc
from lighteval.utils.cache_management import SampleCache


DEFAULT_TEMPERATURE = 1.0
DEFAULT_TOP_P = 0.95
DEFAULT_TOP_K = 64
DEFAULT_MAX_TOKENS = 4096  # CoT can run long with enable_thinking=True


class GGUFOllamaModel(LightevalModel):
    """Lighteval custom backend that runs inference via an OpenAI-compatible
    server (Ollama by default) hosting GGUF quants.

    The `config.model_name` field is used as the `model` parameter of the
    chat completions request — so it must be a string Ollama recognises,
    typically `hf.co/<org>/<repo>:<quant>` or a built-in `family:tag`.
    """

    def __init__(self, config) -> None:
        self.config = config
        self.model_name = config.model_name

        # The OpenAI-compat endpoint (chat/completions). Used for inference.
        self.base_url = os.environ.get("LEM_OLLAMA_URL", "http://localhost:11434/v1")
        # The Ollama-native endpoint (/api/pull, /api/tags). Used to ensure
        # the model is cached BEFORE we start inferring — Ollama's
        # /v1/chat/completions endpoint does NOT lazy-pull, it just 404s.
        self.ollama_native_url = os.environ.get(
            "LEM_OLLAMA_NATIVE_URL",
            self.base_url.replace("/v1", ""),
        )
        self.api_key = os.environ.get("LEM_OLLAMA_API_KEY", "ollama")

        try:
            from openai import OpenAI
        except ImportError as e:
            raise ImportError(
                "gguf_wrapper requires the `openai` package. Add it to PEP 723 "
                "dependencies in eval.py or install with `uv pip install openai`."
            ) from e

        print(f"[gguf_wrapper] Ollama OpenAI endpoint: {self.base_url}")
        print(f"[gguf_wrapper] Ollama native endpoint: {self.ollama_native_url}")
        print(f"[gguf_wrapper] model: {self.model_name}")
        self._client = OpenAI(base_url=self.base_url, api_key=self.api_key)

        # Step 1: ensure the model is in Ollama's cache — pull via /api/pull
        # if missing. Fails loudly if the pull itself errors (network, auth,
        # repo/tag doesn't exist on HF, disk full, etc.).
        self._ensure_cached()

        # Step 2: 1-token probe via chat/completions to confirm the model
        # actually serves. Raises on any failure — no more silent-empty.
        self._probe()

        self._cache = SampleCache(config)

    def _ensure_cached(self) -> None:
        """Make sure `self.model_name` is present in Ollama's local cache.

        Ollama's /v1/chat/completions doesn't lazy-pull — it 404s on unknown
        models. So we explicitly call /api/pull first. If the model is
        already cached this is a no-op (Ollama responds immediately).
        """
        import urllib.request
        import urllib.error
        import json as _json

        # Check cached models via /api/tags — cheap fast call.
        try:
            with urllib.request.urlopen(f"{self.ollama_native_url}/api/tags", timeout=10) as r:
                tags = _json.loads(r.read().decode())
                cached = {m.get("name") or m.get("model") for m in tags.get("models", [])}
                if self.model_name in cached:
                    print(f"[gguf_wrapper] already cached: {self.model_name}")
                    return
        except Exception as e:
            print(f"[gguf_wrapper] /api/tags check failed ({type(e).__name__}: {e}), "
                  f"attempting pull anyway", file=sys.stderr)

        # Not cached — pull via /api/pull. This is a streaming endpoint;
        # we read the chunks and watch for a final success/error.
        print(f"[gguf_wrapper] pulling {self.model_name} (not cached)...")
        req = urllib.request.Request(
            f"{self.ollama_native_url}/api/pull",
            data=_json.dumps({"name": self.model_name, "stream": True}).encode(),
            headers={"Content-Type": "application/json"},
        )
        try:
            with urllib.request.urlopen(req, timeout=3600) as resp:
                last_status = None
                for line in resp:
                    try:
                        evt = _json.loads(line)
                    except _json.JSONDecodeError:
                        continue
                    if "error" in evt:
                        raise RuntimeError(
                            f"Ollama pull failed: {evt['error']}"
                        )
                    status = evt.get("status")
                    if status and status != last_status:
                        print(f"[gguf_wrapper]   {status}")
                        last_status = status
        except urllib.error.HTTPError as e:
            raise RuntimeError(
                f"Ollama /api/pull returned HTTP {e.code}: {e.read().decode()[:200]}"
            ) from e
        print(f"[gguf_wrapper] pull complete: {self.model_name}")

    def _probe(self) -> None:
        """1-token sanity check. Raises on any failure — no silent empty results.

        If this wrapper is going to produce canon rows, the model MUST be
        able to generate text. A failing probe means something upstream is
        broken (Ollama down, model missing, chat template borked) and the
        right response is to halt the run, not write empty ?-rows.
        """
        try:
            resp = self._client.chat.completions.create(
                model=self.model_name,
                messages=[{"role": "user", "content": "ping"}],
                max_tokens=1,
                temperature=0.0,
            )
        except Exception as e:
            raise RuntimeError(
                f"[gguf_wrapper] probe FAILED for {self.model_name}: "
                f"{type(e).__name__}: {e}"
            ) from e
        content = resp.choices[0].message.content or ""
        if not content.strip():
            raise RuntimeError(
                f"[gguf_wrapper] probe returned empty content for {self.model_name} "
                f"— model is reachable but not generating. Check chat template / quant integrity."
            )
        print(f"[gguf_wrapper] probe OK ({len(content)} chars returned)")

    @property
    def tokenizer(self):
        # OpenAI-compatible endpoints don't expose a tokenizer. Lighteval's
        # generative path doesn't call this when the metric is extractive
        # regex-based (our case), so it's safe to return None and let
        # something downstream complain if it actually needs one.
        return None

    def tok_encode(self, text: str):
        # Not available via the OpenAI API. Return an empty list so
        # lighteval's length-accounting code doesn't crash.
        return []

    @property
    def add_special_tokens(self) -> bool:
        return False

    @property
    def max_length(self) -> int:
        # Gemma 4 E2B/E4B: 128K; 26B MoE / 31B: 256K. Generous default.
        return 131072

    def greedy_until(self, requests: List[Doc]) -> List[ModelResponse]:
        """Generate text responses via Ollama chat completions.

        Despite the name, this is sampling (not greedy) — matches
        mlx_lm_wrapper. Each request can ask for num_samples > 1; we send
        one request per sample so the server's PRNG produces independent
        outputs.
        """
        results: List[ModelResponse] = []

        for r in requests:
            max_tokens = r.generation_size or DEFAULT_MAX_TOKENS
            n_samples = getattr(r, "num_samples", 1) or 1

            messages = [{"role": "user", "content": r.query}]

            samples: List[str] = []
            for i in range(n_samples):
                try:
                    resp = self._client.chat.completions.create(
                        model=self.model_name,
                        messages=messages,
                        max_tokens=max_tokens,
                        temperature=DEFAULT_TEMPERATURE,
                        top_p=DEFAULT_TOP_P,
                        # top_k and think flags aren't in OpenAI's standard
                        # chat schema — pass through extra_body so Ollama
                        # can consume them. Ollama >= 0.3 respects `think`
                        # for thinking-capable models; earlier versions
                        # ignore unknown extras silently.
                        extra_body={
                            "top_k": DEFAULT_TOP_K,
                            "think": True,
                        },
                    )
                    text = resp.choices[0].message.content or ""
                except Exception as e:
                    print(f"[gguf_wrapper] sample {i + 1}/{n_samples} failed: "
                          f"{type(e).__name__}: {e}", file=sys.stderr)
                    text = ""
                samples.append(text)

            results.append(ModelResponse(
                text=samples,
                input_tokens=[],
                output_tokens=[[] for _ in samples],
                reasonings=[None for _ in samples],
                logprobs=[],
                argmax_logits_eq_gold=[],
            ))

        return results

    def loglikelihood(self, requests):
        raise NotImplementedError(
            "gguf_wrapper does not implement loglikelihood. "
            "Ollama's OpenAI endpoint doesn't expose per-token logprobs "
            "suitable for multiple-choice loglikelihood scoring. Use a "
            "generative task variant with num_samples + maj_at_k."
        )

    def loglikelihood_rolling(self, requests):
        raise NotImplementedError(
            "gguf_wrapper does not implement loglikelihood_rolling. "
            "Perplexity-based metrics are not in our eval set."
        )