import os
import torch
import inspect
from datasets import load_from_disk
from transformers import (
    AutoTokenizer,
    AutoModelForSequenceClassification,
    TrainingArguments,
    Trainer
)
from peft import LoraConfig, get_peft_model, TaskType
from sklearn.metrics import accuracy_score, f1_score

# ✅ Hugging Face Token
hf_token = "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"

# ✅ 检查 TrainingArguments 来源
from transformers import TrainingArguments
print("🧠 当前 TrainingArguments 来源:", inspect.getfile(TrainingArguments))

# ✅ 模型与 LoRA 配置
base_model = "Qwen/Qwen2-0.5B-Instruct"
output_dir = "./qwen_lora_checkpoint"

lora_config = LoraConfig(
    r=8,
    lora_alpha=16,
    lora_dropout=0.05,
    bias="none",
    task_type=TaskType.SEQ_CLS,
    target_modules=["q_proj", "v_proj"]
)

# ✅ 加载 tokenizer，并设置 pad_token
tokenizer = AutoTokenizer.from_pretrained(
    base_model,
    token=hf_token,
    trust_remote_code=True
)
tokenizer.pad_token = tokenizer.eos_token
pad_token_id = tokenizer.pad_token_id

# ✅ 加载模型（只加载一次），并设置 pad_token_id
base = AutoModelForSequenceClassification.from_pretrained(
    base_model,
    token=hf_token,
    trust_remote_code=True,
    num_labels=2
)
base.config.pad_token_id = pad_token_id

# ✅ 应用 LoRA
model = get_peft_model(base, lora_config)

# ✅ 加载数据
dataset = load_from_disk("./qwen_classification_dataset")

def preprocess(example):
    return tokenizer(example["text"], truncation=True, padding="max_length", max_length=512)

tokenized_dataset = dataset.map(preprocess, batched=True)
tokenized_dataset = tokenized_dataset.rename_column("label", "labels")
tokenized_dataset.set_format(type="torch", columns=["input_ids", "attention_mask", "labels"])

# ✅ 训练参数（自动使用 GPU / fp16）
training_args = TrainingArguments(
    output_dir=output_dir,
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    learning_rate=2e-5,
    num_train_epochs=3,
    evaluation_strategy="epoch",
    save_strategy="epoch",
    logging_dir=f"{output_dir}/logs",
    save_total_limit=2,
    load_best_model_at_end=True,
    metric_for_best_model="accuracy",
    remove_unused_columns=False,
    report_to="none",
    fp16=torch.cuda.is_available(),  # 自动开启 fp16
    gradient_accumulation_steps=2,
    dataloader_pin_memory=True,
)

# ✅ 评估函数
def compute_metrics(eval_pred):
    logits, labels = eval_pred
    preds = torch.argmax(torch.tensor(logits), dim=1)
    acc = accuracy_score(labels, preds)
    f1 = f1_score(labels, preds)
    return {"accuracy": acc, "f1": f1}

# ✅ 构建 Trainer
trainer = Trainer(
    model=model,
    tokenizer=tokenizer,
    args=training_args,
    train_dataset=tokenized_dataset["train"],
    eval_dataset=tokenized_dataset["validation"],
    compute_metrics=compute_metrics,
)

# ✅ 开始训练
trainer.train()

# ✅ 保存模型和 tokenizer
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)

print(f"✅ 微调完成，模型保存在 {output_dir}")
