"""Minimal Hugging Face adapter for Qwen decoder-only models."""

from __future__ import annotations

from typing import Any

import torch
from torch import nn


class QwenLensModel:
    def __init__(self, model: nn.Module, tokenizer: Any) -> None:
        self.model = model
        self.tokenizer = tokenizer
        self.decoder = model.model
        self.layers = self.decoder.layers
        self.n_layers = len(self.layers)
        self.d_model = model.config.hidden_size
        model.eval()
        for parameter in model.parameters():
            parameter.requires_grad_(False)

    @property
    def input_device(self) -> torch.device:
        return self.decoder.embed_tokens.weight.device

    def forward(self, input_ids: torch.Tensor) -> Any:
        return self.decoder(input_ids=input_ids, use_cache=False)

    def encode(self, text: str, max_length: int) -> torch.Tensor:
        encoded = self.tokenizer(
            text, return_tensors="pt", truncation=True, max_length=max_length,
            add_special_tokens=True,
        )
        return encoded.input_ids.to(self.input_device)


def load_qwen(model_path: str, *, device: str, dtype: torch.dtype) -> QwenLensModel:
    """Load a complete local checkpoint without network requests."""
    from transformers import AutoModelForCausalLM, AutoTokenizer

    tokenizer = AutoTokenizer.from_pretrained(model_path, local_files_only=True)
    model = AutoModelForCausalLM.from_pretrained(
        model_path, local_files_only=True, torch_dtype=dtype
    ).to(device)
    return QwenLensModel(model, tokenizer)