# /// script
# requires-python = ">=3.10"
# dependencies = [
#     "trl>=0.12.0",
#     "peft>=0.7.0",
#     "transformers @ git+https://github.com/huggingface/transformers.git",
#     "accelerate @ git+https://github.com/huggingface/accelerate.git",
#     "bitsandbytes>=0.45.0",
#     "trackio",
#     "datasets",
# ]
# ///

"""
Agent Zero SFT: zai-org/GLM-4.7-Flash (30B MoE)
QLoRA (4-bit) on l40sx1 (48GB) with monkey-patch for CPU offload compat.
Patches both Params4bit.__new__ and quant_state.as_dict for meta tensors.
"""

import os
import torch

# === Monkey-patches for bitsandbytes + accelerate CPU offload compat ===

import bitsandbytes as bnb
from bitsandbytes import functional as bnb_func

# Patch 1: Params4bit.__new__ to accept _is_hf_initialized kwarg
_orig_params4bit_new = bnb.nn.Params4bit.__new__
def _patched_params4bit_new(cls, *args, **kwargs):
    kwargs.pop('_is_hf_initialized', None)
    return _orig_params4bit_new(cls, *args, **kwargs)
bnb.nn.Params4bit.__new__ = _patched_params4bit_new

# Patch 2: QuantState.as_dict to handle meta tensors (offset.item() fails on meta)
_orig_as_dict = bnb_func.QuantState.as_dict
def _patched_as_dict(self, packed=False):
    try:
        return _orig_as_dict(self, packed=packed)
    except RuntimeError as e:
        if "meta tensors" in str(e):
            # Return a minimal dict when on meta device
            result = {
                "quant_type": self.quant_type,
                "blocksize": self.blocksize,
            }
            if hasattr(self, 'shape'):
                result["shape"] = self.shape
            return result
        raise
bnb_func.QuantState.as_dict = _patched_as_dict

print("Patched bitsandbytes for CPU offload compat")

# === Main training script ===

import trackio
from huggingface_hub import login
login(token=os.environ["HF_TOKEN"])

from datasets import load_dataset
from peft import LoraConfig
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from trl import SFTTrainer, SFTConfig

print("Loading dataset...")
train_ds = load_dataset("wheattoast11/agent-zero-sft-v1", data_files="data/train.jsonl", split="train")
val_ds = load_dataset("wheattoast11/agent-zero-sft-v1", data_files="data/validation.jsonl", split="train")
print(f"Train: {len(train_ds)}, Val: {len(val_ds)}")

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
    llm_int8_enable_fp32_cpu_offload=True,
)

offload_dir = "/tmp/offload"
os.makedirs(offload_dir, exist_ok=True)

print("Loading model in 4-bit with CPU offload on l40sx1...")
model = AutoModelForCausalLM.from_pretrained(
    "zai-org/GLM-4.7-Flash",
    quantization_config=bnb_config,
    trust_remote_code=True,
    device_map="auto",
    max_memory={0: "44GiB", "cpu": "60GiB"},
    offload_folder=offload_dir,
    torch_dtype=torch.bfloat16,
)
tokenizer = AutoTokenizer.from_pretrained("zai-org/GLM-4.7-Flash", trust_remote_code=True)
print("Model loaded.")

if hasattr(model, 'hf_device_map'):
    devices = {}
    for v in model.hf_device_map.values():
        devices[str(v)] = devices.get(str(v), 0) + 1
    print(f"Device distribution: {devices}")

import subprocess
result = subprocess.run(['nvidia-smi'], capture_output=True, text=True)
print(result.stdout)

config = SFTConfig(
    output_dir="agent-zero-glm-4.7-v1",
    push_to_hub=True,
    hub_model_id="wheattoast11/agent-zero-glm-4.7-v1",
    hub_strategy="every_save",
    hub_private_repo=True,
    num_train_epochs=2,
    per_device_train_batch_size=1,
    gradient_accumulation_steps=16,
    learning_rate=1e-4,
    bf16=True,
    gradient_checkpointing=True,
    logging_steps=10,
    save_strategy="steps",
    save_steps=50,
    save_total_limit=2,
    eval_strategy="steps",
    eval_steps=50,
    warmup_ratio=0.1,
    lr_scheduler_type="cosine",
    report_to="trackio",
    project="agent-zero-finetune",
    run_name="glm-4.7-flash-qlora-v1",
)

peft_config = LoraConfig(
    r=16, lora_alpha=32, lora_dropout=0.05,
    bias="none", task_type="CAUSAL_LM",
    target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],
)

print("Initializing trainer...")
trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    train_dataset=train_ds,
    eval_dataset=val_ds,
    args=config,
    peft_config=peft_config,
)

print("Starting training...")
trainer.train()

print("Pushing to Hub...")
trainer.push_to_hub()
trackio.finish()
print("Done!")