#!/usr/bin/env python
"""
Submit Unsloth VLM fine-tuning job to HF Jobs.

This script submits a training job using the Unsloth Docker image with UV script execution.
Simplifies the process of running iconclass-vlm-sft.py on cloud GPUs.
"""

import os
from huggingface_hub import HfApi
from dotenv import load_dotenv

load_dotenv()  # Load environment variables from .env file if present


# =============================================================================
# CONFIGURATION
# =============================================================================

# Model and dataset configuration
BASE_MODEL = "Qwen/Qwen3-VL-8B-Instruct"
DATASET = "davanstrien/iconclass-vlm-sft"
OUTPUT_MODEL = "davanstrien/Qwen3-VL-8B-iconclass-vlm"

# Training hyperparameters
BATCH_SIZE = 2
GRADIENT_ACCUMULATION = 8
MAX_STEPS = None  # Set to None to use full dataset (1 epoch)
NUM_EPOCHS = 1.0  # Only used if MAX_STEPS is None
LEARNING_RATE = 2e-5

# LoRA configuration
LORA_R = 16
LORA_ALPHA = 32
LORA_DROPOUT = 0.1

# Training infrastructure
GPU_FLAVOR = "a100-large"  # Options: a100-large, a100, a10g-large
TIMEOUT = "12h"  # Adjust based on dataset size

# Script location
SCRIPT_URL = "https://huggingface.co/datasets/uv-scripts/training/raw/main/iconclass-vlm-sft.py"
# For local testing, you can also use a local path:
# SCRIPT_PATH = "/path/to/iconclass-vlm-sft.py"

# Optional: Calculate max_steps for full dataset
if MAX_STEPS is None:
    from datasets import load_dataset

    print("Calculating max_steps for full dataset...")
    dataset = load_dataset(DATASET, split="train")
    steps_per_epoch = len(dataset) // (BATCH_SIZE * GRADIENT_ACCUMULATION)
    MAX_STEPS = int(steps_per_epoch * NUM_EPOCHS)
    print(f"Dataset size: {len(dataset):,} samples")
    print(f"Steps per epoch: {steps_per_epoch:,}")
    print(f"Total steps ({NUM_EPOCHS} epoch(s)): {MAX_STEPS:,}")
    print()


# =============================================================================
# SUBMISSION FUNCTION
# =============================================================================


def submit_training_job():
    """Submit VLM training job using HF Jobs with Unsloth Docker image."""

    # Verify HF token is available
    HF_TOKEN = os.environ.get("HF_TOKEN")
    if not HF_TOKEN:
        print("⚠️  HF_TOKEN not found in environment")
        print("Please set: export HF_TOKEN=your_token_here")
        print("Or add it to a .env file in this directory")
        return

    api = HfApi(token=HF_TOKEN)

    # Build the script arguments
    script_args = [
        "--base-model",
        BASE_MODEL,
        "--dataset",
        DATASET,
        "--output-model",
        OUTPUT_MODEL,
        "--lora-r",
        str(LORA_R),
        "--lora-alpha",
        str(LORA_ALPHA),
        "--lora-dropout",
        str(LORA_DROPOUT),
        "--learning-rate",
        str(LEARNING_RATE),
        "--batch-size",
        str(BATCH_SIZE),
        "--gradient-accumulation",
        str(GRADIENT_ACCUMULATION),
        "--max-steps",
        str(MAX_STEPS),
        "--logging-steps",
        "10",
        "--save-steps",
        "100",
        "--eval-steps",
        "100",
    ]

    print("=" * 80)
    print("Submitting Unsloth VLM Fine-tuning Job to HF Jobs")
    print("=" * 80)
    print(f"\n📦 Configuration:")
    print(f"  Base Model:    {BASE_MODEL}")
    print(f"  Dataset:       {DATASET}")
    print(f"  Output:        {OUTPUT_MODEL}")
    print(f"\n🎛️  Training Settings:")
    print(f"  Max Steps:     {MAX_STEPS:,}")
    print(f"  Batch Size:    {BATCH_SIZE}")
    print(f"  Grad Accum:    {GRADIENT_ACCUMULATION}")
    print(f"  Effective BS:  {BATCH_SIZE * GRADIENT_ACCUMULATION}")
    print(f"  Learning Rate: {LEARNING_RATE}")
    print(f"\n🔧 LoRA Settings:")
    print(f"  Rank (r):      {LORA_R}")
    print(f"  Alpha:         {LORA_ALPHA}")
    print(f"  Dropout:       {LORA_DROPOUT}")
    print(f"\n💻 Infrastructure:")
    print(f"  GPU:           {GPU_FLAVOR}")
    print(f"  Timeout:       {TIMEOUT}")
    print(f"\n🚀 Submitting job...")

    # Submit the job using run_uv_job
    job = api.run_uv_job(
        script=SCRIPT_URL,  # Can also be a local path
        script_args=script_args,
        dependencies=[],  # UV handles all dependencies from script header
        flavor=GPU_FLAVOR,
        timeout=TIMEOUT,
        env={
            "HF_HUB_ENABLE_HF_TRANSFER": "1",  # Fast downloads
        },
        secrets={
            "HF_TOKEN": HF_TOKEN,
        },
    )

    print("\n✅ Job submitted successfully!")
    print("\n📊 Job Details:")
    print(f"  Job ID:  {job.id}")
    print(f"  Status:  {job.status}")
    print(f"  URL:     https://huggingface.co/jobs/{job.id}")
    print("\n💡 Monitor your job:")
    print(f"  • Web: https://huggingface.co/jobs/{job.id}")
    print(f"  • CLI: hfjobs status {job.id}")
    print(f"  • Logs: hfjobs logs {job.id} --follow")
    print("\n🎯 Your model will be available at:")
    print(f"  https://huggingface.co/{OUTPUT_MODEL}")
    print("\n" + "=" * 80)

    return job


# =============================================================================
# MAIN
# =============================================================================


def main():
    """Main entry point."""
    job = submit_training_job()

    if job:
        # Optional: Show Python code to monitor the job
        print("\n📝 To monitor this job programmatically:")
        print("""
from huggingface_hub import HfApi

api = HfApi()
job = api.get_job("{}")
print(job.status)  # Check status
print(job.logs())  # View logs
        """.format(job.id))


if __name__ == "__main__":
    main()