import os
import sys
from huggingface_hub import HfApi, create_repo

def load_naman_token():
    env_file = "/home/adminuser/.env"
    token = None
    if os.path.exists(env_file):
        with open(env_file, "r", encoding="utf-8") as f:
            for line in f:
                line = line.strip()
                if line.startswith("NAMAN_HF_TOKEN="):
                    _, val = line.split("=", 1)
                    val = val.strip().strip("'").strip('"')
                    if val:
                        token = val
                        break
    return token

def create_readme_card(repo_id):
    return f"""---
license: apache-2.0
tags:
- mamba
- codestral-mamba
- qwen2.5
- state-space-model
- benchmark
- evaluation
---

# MAMBA_7B: Codestral Mamba 7B Installation, Benchmarks & Architectural Research

This repository contains the complete codebase, benchmark suite, evaluation datasets, research documents, and visual PowerPoint presentations for **MAMBA_7B**.

Published to **namanadep** Hugging Face profile using `NAMAN_HF_TOKEN`.

---

## 📊 Summary Benchmark Metrics (Codestral Mamba 7B vs. Qwen 2.5 7B)

| Metric | Codestral Mamba 7B | Qwen 2.5 7B Instruct | Takeaway |
| :--- | :---: | :---: | :--- |
| **Architecture** | **Selective State Space Model (SSM S6)** | **Multi-Head Self-Attention Transformer** | Mamba eliminates $O(N^2)$ quadratic KV-cache memory scaling. |
| **Average Latency** | **4.28s** | **7.40s** | **42.2% faster completion** for Codestral Mamba. |
| **Generation Speed** | **194.8 t/s** | **193.3 t/s** | Identical throughput on NVIDIA H200 GPUs. |
| **Memory Footprint** | **Constant $O(1)$ Memory State** | $O(N)$ Growth | Fixed VRAM up to 256k long-context reasoning. |

---

## 📂 Repository Layout & Uploaded Artifacts

- `docs/CODESTRAL_MAMBA_7B_VS_QWEN_7B_COMPARISON.md`: 71 KB Exhaustive 10-Prompt Benchmark Report.
- `docs/CODESTRAL_MAMBA_7B_VS_QWEN_7B_COMPARISON.pptx`: 9-Slide Visual Benchmark Comparison Deck.
- `docs/MAMBA_7B_INSTALLATION_AND_ARCHITECTURE_GUIDE.pptx`: 8-Slide Hands-on Installation & Architecture Journey Deck.
- `docs/MAMBA_MODELS_RESEARCH_OLLAMA_HUGGINGFACE.md`: State Space Models Architectural Research Document.
- `data/mamba_vs_qwen_results.json`: Raw Evaluation JSON transcripts across 10 technical categories.
- `src/`: Complete Python benchmark test harness and slide generation scripts.
"""

def main():
    base_dir = "/home/adminuser/aiops_pocs/MAMBA_7B"
    print("Loading NAMAN_HF_TOKEN from /home/adminuser/.env...")
    token = load_naman_token()
    if not token:
        print("Error: Could not find NAMAN_HF_TOKEN in /home/adminuser/.env")
        sys.exit(1)
        
    api = HfApi(token=token)
    user_info = api.whoami()
    username = user_info.get("name", "namanadep")
    repo_id = f"{username}/MAMBA_7B"
    
    print(f"Authenticated as '{username}'. Creating repository: https://huggingface.co/{repo_id}...")
    try:
        create_repo(repo_id, token=token, exist_ok=True, repo_type="model")
        print(f"Repository ready: https://huggingface.co/{repo_id}")
    except Exception as e:
        print(f"Repo creation status: {e}")

    # Write README.md locally
    readme_path = os.path.join(base_dir, "README.md")
    with open(readme_path, "w", encoding="utf-8") as f:
        f.write(create_readme_card(repo_id))

    print(f"Uploading files from {base_dir} to Hugging Face repository https://huggingface.co/{repo_id}...")
    
    # 1. Upload README.md
    api.upload_file(
        path_or_fileobj=readme_path,
        path_in_repo="README.md",
        repo_id=repo_id,
        token=token
    )
    print("  -> Uploaded README.md")

    # 2. Upload Docs Folder
    docs_dir = os.path.join(base_dir, "docs")
    if os.path.exists(docs_dir):
        for fname in os.listdir(docs_dir):
            fpath = os.path.join(docs_dir, fname)
            if os.path.isfile(fpath):
                api.upload_file(
                    path_or_fileobj=fpath,
                    path_in_repo=f"docs/{fname}",
                    repo_id=repo_id,
                    token=token
                )
                print(f"  -> Uploaded docs/{fname}")

    # 3. Upload Src Folder
    src_dir = os.path.join(base_dir, "src")
    if os.path.exists(src_dir):
        for fname in os.listdir(src_dir):
            fpath = os.path.join(src_dir, fname)
            if os.path.isfile(fpath):
                api.upload_file(
                    path_or_fileobj=fpath,
                    path_in_repo=f"src/{fname}",
                    repo_id=repo_id,
                    token=token
                )
                print(f"  -> Uploaded src/{fname}")

    # 4. Upload Data Folder
    data_dir = os.path.join(base_dir, "data")
    if os.path.exists(data_dir):
        for fname in os.listdir(data_dir):
            fpath = os.path.join(data_dir, fname)
            if os.path.isfile(fpath):
                api.upload_file(
                    path_or_fileobj=fpath,
                    path_in_repo=f"data/{fname}",
                    repo_id=repo_id,
                    token=token
                )
                print(f"  -> Uploaded data/{fname}")

    print("==========================================================================")
    print(f" Successfully published MAMBA_7B to Hugging Face: https://huggingface.co/{repo_id} ")
    print("==========================================================================")

if __name__ == "__main__":
    main()