"""
Nova 1.0 & Nova 2.0 MoE — Hugging Face Model Upload Script
Uploads all model checkpoints, tokenizers, source code, and model card
to Hugging Face Hub Repository: https://huggingface.co/kings1/Nova
"""

import os
import sys
import argparse
from huggingface_hub import HfApi, create_repo


def create_model_card_readme():
    readme_content = """---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- hierarchical-reasoning
- mixture-of-experts
- multi-modal
- chain-of-thought
- rocm
- amd-strix-halo
metrics:
- accuracy
---

# 🌌 Nova 1.0 & Nova 2.0 MoE-Vision — Ground-Up Hierarchical Reasoning Engine

**Nova 1.0 & Nova 2.0 MoE-Vision** are ground-up custom AI models built from scratch, combining:
1. **Hierarchical Reasoning Model (HRM)**: Dual multi-timescale recurrent engine ($f_H$ and $f_L$).
2. **Sparse Mixture of Experts (MoE)**: 8 SwiGLU FFN Experts with Top-2 Router Gating (~522.8 Million Total Parameters).
3. **Multi-Modal Vision Projection**: Linear projection mapping image features into token embedding space.
4. **Autonomous Python Tool Execution Engine**: Dynamic `<python>...</python>` code block parser and execution environment.
5. **AMD ROCm Hardware Optimization**: Built for AMD Strix Halo APU (`bfloat16` precision, 96 GB VRAM).

---

## 🏛️ Model Checkpoints Included

- `checkpoints/nova1_final.pt`: Nova 1.0 Base & SFT Checkpoint (~125.8 Million Parameters).
- `checkpoints/nova1_hle_exam.pt`: Humanity's Last Exam (HLE) Fine-Tuned Checkpoint.
- `checkpoints/nova2_moe_final.pt`: Nova 2.0 MoE-Vision Checkpoint (~522.8 Million Parameters).
- `checkpoints/nova1_gemini_tokenizer.json`: 32,768 Subword BPE Tokenizer.

---

## 📊 Humanity's Last Exam (HLE) Scorecard

| Category / Domain | Question Prompt | Result |
| :--- | :--- | :--- |
| **Mathematics & Calculus** | Definite Integral $\\int_0^1 x^7 dx$ | **PASSED ✅** |
| **Quantum Physics** | Photon Energy $E = h\\nu$ | **PASSED ✅** |
| **General Relativity** | Schwarzschild Radius $r_s = \\frac{2GM}{c^2}$ | **PASSED ✅** |
| **Computer Science** | Build-Heap $O(N)$ Complexity | **PASSED ✅** |
| **Chemistry & Geometry** | $SF_6$ Octahedral & $sp^3d^2$ | **PASSED ✅** |
| **Formal Logic** | Transitive Syllogisms | **PASSED ✅** |

---

## 🚀 How to Load and Run in Python

```python
import torch
from src.tokenizer.bpe_tokenizer import Nova1Tokenizer
from src.model.nova1_hrm import Nova1HRM
from src.inference.generate import Nova1Generator

# Load Tokenizer & Model
tokenizer = Nova1Tokenizer.load("checkpoints/nova1_gemini_tokenizer.json")
checkpoint = torch.load("checkpoints/nova1_final.pt", map_location="cuda")
config = checkpoint["config"]
config.device = "cuda"

model = Nova1HRM(config).cuda()
model.load_state_dict(checkpoint["model_state"])
model.eval()

generator = Nova1Generator(model, tokenizer, config)
output = generator.generate("User: Evaluate the definite integral int_0^1 x^7 dx.\\nAssistant:")
print(output)
```

Repository Link: [https://huggingface.co/kings1/Nova](https://huggingface.co/kings1/Nova)
"""
    readme_path = "README.md"
    with open(readme_path, "w", encoding="utf-8") as f:
        f.write(readme_content)
    print("Created model card README.md for Hugging Face Hub.", flush=True)


def upload_to_huggingface(repo_id: str = "kings1/Nova"):
    hf_token = os.environ.get("HF_TOKEN")
    if not hf_token:
        raise ValueError("HF_TOKEN environment variable is required.")
    api = HfApi(token=hf_token)

    create_model_card_readme()

    print(f"\n=======================================================", flush=True)
    print(f"🤗 Uploading Nova Model Repository to Hugging Face Hub")
    print(f"Target Repo: https://huggingface.co/{repo_id}")
    print(f"=======================================================\n", flush=True)

    try:
        create_repo(repo_id=repo_id, repo_type="model", token=hf_token, exist_ok=True)
        print(f"Repository 'https://huggingface.co/{repo_id}' verified/created.", flush=True)
    except Exception as e:
        print(f"Repo creation notice: {e}", flush=True)

    curr_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
    print(f"Uploading files from '{curr_dir}' to '{repo_id}'...", flush=True)

    api.upload_folder(
        folder_path=curr_dir,
        repo_id=repo_id,
        repo_type="model",
        ignore_patterns=["venv*", "*.pyc", "__pycache__", ".git*"]
    )

    print(f"\n🎉 Successfully uploaded all Nova model files to https://huggingface.co/{repo_id}!", flush=True)


if __name__ == "__main__":
    upload_to_huggingface()