"""Meta Model API (Muse Spark) provider profile — https://api.meta.ai/v1.

Bundled from albertodepaola/hermes-meta-provider; rides entirely on
ProviderProfile hooks (zero core edits). The reasoning dial is emitted as a
top-level ``reasoning_effort`` kwarg — not ``extra_body.reasoning``, whose
emission is gated by a core host allowlist a third-party plugin must not edit.
"""

from __future__ import annotations

import os
from typing import Any

from agent.reasoning_effort import META_AI_EFFORTS, clamp_effort
from providers import register_provider
from providers.base import ProviderProfile


class MetaAIProfile(ProviderProfile):
    """Meta Model API — top-level reasoning_effort, self-contained."""

    def build_api_kwargs_extras(
        self,
        *,
        reasoning_config: dict | None = None,
        supports_reasoning: bool = False,  # noqa: ARG002 — we self-gate below
        **context: Any,
    ) -> tuple[dict[str, Any], dict[str, Any]]:
        """Ignores the core ``supports_reasoning`` gate (host-allowlist driven);
        Muse Spark always accepts ``reasoning_effort``.

        Muse 400s on ``none``: disabled/"none" -> ``minimal`` (closest to off);
        unset/bespoke levels -> ``medium``.
        """
        rc = reasoning_config or {}
        effort = str(rc.get("effort") or "").strip().lower()
        if rc.get("enabled") is False or effort == "none":
            mapped = "minimal"
        else:
            clamped = clamp_effort(effort, META_AI_EFFORTS)
            mapped = clamped if clamped in META_AI_EFFORTS else "medium"
        return {}, {"reasoning_effort": mapped}


meta_ai = MetaAIProfile(
    name="meta-ai",
    aliases=("meta", "muse", "muse-spark", "model-api", "msl"),
    display_name="Meta Model API",
    description="Meta Muse Spark family (Meta Superintelligence Labs)",
    signup_url="https://developer.meta.com/ai/",
    # MODEL_API_KEY is Meta's documented env var; the aliases are conveniences.
    env_vars=("MODEL_API_KEY", "META_API_KEY", "META_MODEL_API_KEY", "META_BASE_URL"),
    base_url=os.getenv("META_BASE_URL", "").strip() or "https://api.meta.ai/v1",
    auth_type="api_key",
    # Responses API engages Muse prompt caching (0 cached tokens on
    # chat/completions vs 93-99% hits on /v1/responses); the chat-completions
    # hook above still covers custom non-api.meta.ai base URLs.
    api_mode="codex_responses",
    supports_vision=True,
    default_aux_model="muse-spark-1.2-contributor",
    # Muse spends completion budget on hidden reasoning first; low caps can
    # finish with empty content.
    default_max_tokens=16384,
    fallback_models=("muse-spark-1.2", "muse-spark-1.2-contributor"),
)

register_provider(meta_ai)