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import os
import sys
import math
import torch
import numpy as np
from datasets import load_dataset
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, DataCollatorForLanguageModeling, TrainingArguments
from trl import SFTConfig, SFTTrainer

"""# **Initial Configs**"""

# -----------------------
# Safety checks
# -----------------------
if not torch.cuda.is_available():
    print("ERROR: CUDA not available. This script requires a GPU.")
    sys.exit(1)

device = "cuda"
print("CUDA device:", torch.cuda.get_device_name(0))
print(f"Total GPU memory (GB): {torch.cuda.get_device_properties(0).total_memory / 1024**3:.2f}")

# -----------------------
# Config
# -----------------------
MODEL_NAME = "microsoft/Phi-3.5-mini-instruct"
DATASET_NAME = "dassarthak18/FStarDataset-V2-Conversation"
OUTPUT_DIR = "./phi3.5-mini-lora"
MAX_SEQ_LENGTH = 2048             # safe starting point
PER_DEVICE_BATCH_SIZE = 4         # per GPU
GRAD_ACCUM_STEPS = 4              # effective batch size = 16
NUM_EPOCHS = 6
LEARNING_RATE = 2e-4

# LoRA params
LORA_R = 32
LORA_ALPHA = 64
LORA_DROPOUT = 0.05

# PEFT target modules for Phi-3.5
TARGET_MODULES = [
    "q_proj", "k_proj", "v_proj", "o_proj",
    "gate_proj", "up_proj", "down_proj"
]

# ---------- Robust FlashAttention detection & fallback ----------
def detect_flash_attn():
    try:
        # try the compiled extension import which fails on GLIBC mismatch
        import flash_attn_2_cuda  # noqa: F401
        print("flash-attn compiled extension import: OK")
        return "flash_attention_2"
    except Exception as e:
        print("flash-attn import failed (will fallback). Reason:", repr(e))
        return "eager"

attn_impl = detect_flash_attn()
print("Using attn_implementation =", attn_impl)
# ----------------------------------------------------------------

# -----------------------
# Tokenizer
# -----------------------
print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

"""# **Load Model and Dataset**"""
"""
# -----------------------
# Load model in 4-bit (bitsandbytes)
# -----------------------
print("Loading model in 4-bit (QLoRA) — this uses bitsandbytes.")
# Important: load_in_4bit requires bitsandbytes installed and a compatible transformers version.
# bnb_4bit_compute_dtype uses fp16 (bf16 not supported)
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=torch.float16,
)
"""
print("Loading model...")
model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    device_map="auto",
    trust_remote_code=True,
    dtype=torch.float16,
    attn_implementation=attn_impl,
    #quantization_config=bnb_config,
)

# Required tweaks for long training
model.config.use_cache = False
#model.gradient_checkpointing_enable()

"""
# Prepare model for k-bit training (adjusts layer norms, enables gradients for some params)
print("Preparing model for k-bit (4-bit) training...")
model = prepare_model_for_kbit_training(model)
"""

# -----------------------
# Apply LoRA (PEFT) on top of 4-bit model
# -----------------------
print("Applying LoRA adapters (PEFT)...")
lora_config = LoraConfig(
    r=LORA_R,
    lora_alpha=LORA_ALPHA,
    lora_dropout=LORA_DROPOUT,
    bias="none",
    target_modules=TARGET_MODULES,
    task_type="CAUSAL_LM"
)

model = get_peft_model(model, lora_config)

# Ensure LoRA params are trainable
for n, p in model.named_parameters():
    # By design, most parameters remain frozen; LoRA adapters and some qkv adapters are trainable
    if "lora" in n or "adapter" in n:
        p.requires_grad = True

# Print summary of trainable params
print("\nTrainable parameters (should be LoRA params, small fraction):")
model.print_trainable_parameters()

# -----------------------
# Load and preprocess conversation dataset
# -----------------------
print(f"Loading dataset: {DATASET_NAME} ...")
raw_ds = load_dataset(DATASET_NAME)
print("Available splits:", raw_ds.keys())

def conversation_to_text(example):
    conv = example.get("messages")
    parts = []
    for turn in conv:
        role = str(turn.get("role", "")).strip().lower()
        content = str(turn.get("content", "")).strip()
        if not content:
            continue
        if role == "user":
            parts.append(f"<user>\n{content}\n</user>")
        elif role == "assistant":
            parts.append(f"<assistant>\n{content}\n</assistant>")
        else:
            parts.append(content)
    text = "\n".join(parts).strip()
    return {"text": text}

print("Converting conversations -> single text field ...")
processed = raw_ds.map(conversation_to_text, remove_columns=raw_ds["train"].column_names, num_proc=32)

# Filter empty examples
processed = processed.filter(lambda e: e["text"].strip() != "")

train_ds = processed["train"]
eval_ds = processed["validation"]
test_ds = processed["test"]

print(f"Train examples: {len(train_ds)}")
print(f"Validation examples: {len(eval_ds)}")
print(f"Test examples: {len(test_ds)}")

'''
MAX_EVAL = 1000
if len(eval_ds) > MAX_EVAL:
    eval_ds = eval_ds.select(range(MAX_EVAL))
    print(f"Validation truncated to {MAX_EVAL} for memory.")
if len(test_ds) > MAX_EVAL:
    test_ds = test_ds.select(range(MAX_EVAL))
    print(f"Test truncated to {MAX_EVAL} for memory.")
'''

# -----------------------
# Tokenization (truncation to MAX_SEQ_LENGTH)
# -----------------------
def tokenize_fn(batch):
    texts = batch["text"]
    # ensure strings
    if isinstance(texts, list):
        texts = [str(t) for t in texts]
    else:
        texts = [str(texts)]
    return tokenizer(
        texts,
        truncation=True,
        max_length=MAX_SEQ_LENGTH,
        padding=False,  # dynamic padding handled by collator
    )

print("Tokenizing datasets...")
tokenized_train = train_ds.map(tokenize_fn, batched=True, remove_columns=train_ds.column_names, num_proc=32)
tokenized_eval = eval_ds.map(tokenize_fn, batched=True, remove_columns=eval_ds.column_names, num_proc=32)
tokenized_test = test_ds.map(tokenize_fn, batched=True, remove_columns=test_ds.column_names, num_proc=32)

print(f"Tokenized train size: {len(tokenized_train)}")
print(f"Tokenized eval size: {len(tokenized_eval)}")
print(f"Tokenized test size: {len(tokenized_test)}")

# -----------------------
# Data collator
# -----------------------
data_collator = DataCollatorForLanguageModeling(
    tokenizer=tokenizer,
    mlm=False,
    pad_to_multiple_of=8
)

"""# **Training**"""

# -----------------------
# Training args (single GPU optimized)
# -----------------------
training_args = SFTConfig(
    output_dir=OUTPUT_DIR,
    max_length=MAX_SEQ_LENGTH,
    num_train_epochs=NUM_EPOCHS,
    per_device_train_batch_size=PER_DEVICE_BATCH_SIZE,
    per_device_eval_batch_size=PER_DEVICE_BATCH_SIZE,
    gradient_accumulation_steps=GRAD_ACCUM_STEPS,
    learning_rate=LEARNING_RATE,
    lr_scheduler_type="cosine",
    warmup_ratio=0.05,
    logging_steps=25,
    save_steps=250,
    eval_steps=250,
    save_total_limit=3,
    eval_strategy="steps",
    load_best_model_at_end=True,
    metric_for_best_model="eval_loss",
    greater_is_better=False,
    fp16=True,
    bf16=False,
    packing=False,
    dataloader_num_workers=2,
    dataloader_pin_memory=True,
    report_to="tensorboard",
    #gradient_checkpointing=True,
    seed=42,
)

# -----------------------
# SFTTrainer setup
# -----------------------
trainer = SFTTrainer(
    model=model,
    processing_class=tokenizer,
    args=training_args,
    train_dataset=tokenized_train,
    eval_dataset=tokenized_eval,
    data_collator=data_collator,
)

# -----------------------
# Train
# -----------------------
print("\n" + "="*40)
print("Starting training (FP16 LoRA, no quantization)...")
print("="*40 + "\n")

trainer.train()

"""# **Evaluation**"""

# -----------------------
# Evaluate on test split (final)
# -----------------------
print("\nEvaluating on test split...")
test_metrics = trainer.evaluate(eval_dataset=tokenized_test)
test_loss = test_metrics.get("eval_loss", None)

if test_loss is not None:
    try:
        test_ppl = float(np.exp(test_loss))
    except OverflowError:
        test_ppl = float("inf")
    print(f"\nTest Loss: {test_loss:.4f}")
    print(f"Test Perplexity: {test_ppl:.2f}")
else:
    print("Test eval_loss not present in metrics:", test_metrics)

# -----------------------
# Save adapters and tokenizer
# -----------------------
print("\nSaving LoRA adapters and tokenizer...")
os.makedirs(OUTPUT_DIR, exist_ok=True)
# Save only PEFT adapters (keeps model quantized + small)
model.save_pretrained(OUTPUT_DIR)
tokenizer.save_pretrained(OUTPUT_DIR)

print("\nDone. Model (quantized + LoRA adapters) saved to:", OUTPUT_DIR)
print("You can load the adapter with from_pretrained and the same bnb/4bit settings.")