"""Model loading, unloading, status, and Hub search."""

from __future__ import annotations

from collections.abc import Iterator

import gradio as gr
import torch
from huggingface_hub import HfApi

from api.auth import resolve_token
from api.serialize import error_payload
from miru_tracer.core.logging_config import get_logger
from miru_tracer.core.model_manager import ModelManager
from miru_tracer.core.session_manager import get_session_manager

logger = get_logger(__name__)

model_manager = ModelManager()

QUICK_MODEL_CHOICES = (
    "LiquidAI/LFM2.5-230M",
    "Qwen/Qwen3-0.6B",
    "Qwen/Qwen3-4B",
)


def memory_usage() -> str:
    if torch.cuda.is_available():
        allocated = torch.cuda.memory_allocated(0) / 1e9
        total = torch.cuda.get_device_properties(0).total_memory / 1e9
        percentage = (allocated / total) * 100 if total > 0 else 0
        return f"{allocated:.2f} GB / {total:.2f} GB ({percentage:.1f}%)"
    return "CPU mode (no GPU)"


def model_status() -> dict:
    """Current model state; also restores the Model view after a page load."""
    info = None
    if model_manager.is_loaded():
        model = model_manager.get_model()
        tokenizer = model_manager.get_tokenizer()
        info = {
            "model_name": model_manager.get_model_name(),
            "device": model_manager.get_device(),
            "vocab_size": len(tokenizer),
            "num_parameters_b": model.num_parameters() / 1e9,
        }
    return {
        "ok": True,
        "loaded": model_manager.is_loaded(),
        "model_name": model_manager.get_model_name(),
        "memory": memory_usage(),
        "cuda": torch.cuda.is_available(),
        "info": info,
        "quick_models": list(QUICK_MODEL_CHOICES),
    }


def load_model(
    model_name: str,
    quantization: str,
    trust_remote_code: bool,
    minimize_ram: bool,
    oauth_token: gr.OAuthToken | None = None,
) -> Iterator[dict]:
    """Load a model, streaming status so the user sees progress.

    Uses the signed-in user's OAuth token for gated/private models, falling
    back to the Space's HF_TOKEN secret / local login.
    """
    model_name = (model_name or "").strip()
    if not model_name:
        yield error_payload("Please enter a model name")
        return

    logger.info(
        f"Model load requested: {model_name} (quantization={quantization}, "
        f"trust_remote_code={trust_remote_code}, minimize_ram={minimize_ram})"
    )
    if trust_remote_code:
        logger.warning("trust_remote_code=True enabled (security risk)")

    progress_lines = [f"Loading model: {model_name}"]
    if quantization != "none":
        progress_lines.append(f"Quantization: {quantization}")
    if trust_remote_code:
        progress_lines.append("Warning: trust_remote_code=True")
    if minimize_ram:
        progress_lines.append("RAM optimization: enabled (slower loading)")
    progress_lines.append("")
    progress_lines.append(
        "Downloading/loading weights — this can take a while for large "
        "models. Please wait..."
    )
    yield {"ok": True, "done": False, "status": "\n".join(progress_lines)}

    try:
        _model, _tokenizer, _device, info = model_manager.load_model(
            model_name=model_name,
            quantization=quantization,
            trust_remote_code=bool(trust_remote_code),
            minimize_ram_usage=bool(minimize_ram),
            token=resolve_token(oauth_token),
        )
        success_lines = [
            "Model loaded successfully.",
            f"Device: {info['device_name']}",
            f"Vocabulary size: {info['vocab_size']:,}",
            f"Parameters: {info['num_parameters_b']:.2f}B",
        ]
        if info["device"] == "cuda":
            success_lines.append(f"VRAM: {info['vram_gb']:.2f} GB")
        if info.get("quantization_note"):
            success_lines.append(f"\n⚠️ {info['quantization_note']}")
        if info.get("is_vlm"):
            success_lines.append(f"\n⚠️ {info['vlm_warning']}")
        yield {
            "ok": True,
            "done": True,
            "status": "\n".join(success_lines),
            **model_status(),
        }
    except RuntimeError as e:
        # Concurrent load/unload in progress
        logger.warning(f"Load blocked: {e}")
        yield error_payload(str(e))
    except Exception as e:
        logger.error(f"Model load failed: {model_name} - {e}", exc_info=True)
        hint = ""
        if "gated" in str(e).lower() or "401" in str(e) or "403" in str(e):
            hint = (
                "\n\nThis looks like a gated or private model — sign in with "
                "Hugging Face (top right) with an account that has access."
            )
        yield error_payload(f"Error loading model:\n\n{e}{hint}")


def unload_model() -> dict:
    try:
        cleared_count = get_session_manager().clear_all_sessions()
        result = model_manager.unload_model()
        status = result["message"]
        if cleared_count:
            status += (
                f"\n\n{cleared_count} active Interactive session(s) were "
                "cleared.\nAny in-progress generation work has been lost."
            )
        if torch.cuda.is_available():
            status += "\n\nGPU memory has been freed."
        status += (
            "\n\nNote: any Generate-view run in progress is invalidated — "
            "start a new generation there."
        )
        logger.info("Model unload completed")
        return {"ok": True, "status": status, **model_status()}
    except RuntimeError as e:
        logger.warning(f"Unload blocked: {e}")
        return error_payload(str(e))


def search_models(query: str, oauth_token: gr.OAuthToken | None = None) -> dict:
    """Search the Hub for text-generation models (powers the loader autocomplete)."""
    query = (query or "").strip()
    if len(query) < 2:
        return {"ok": True, "results": []}
    try:
        api = HfApi(token=resolve_token(oauth_token))
        models = api.list_models(
            search=query,
            pipeline_tag="text-generation",
            sort="downloads",
            direction=-1,
            limit=12,
        )
        results = [
            {
                "id": m.id,
                "downloads": getattr(m, "downloads", None),
                "likes": getattr(m, "likes", None),
                "gated": bool(getattr(m, "gated", False)),
            }
            for m in models
        ]
        return {"ok": True, "results": results}
    except Exception as e:
        logger.warning(f"Model search failed: {e}")
        return {"ok": True, "results": [], "note": str(e)}