import sys
import logging

import deepspeed
import datasets
from datasets import load_dataset
from peft import LoraConfig
import torch
import transformers
from trl import SFTTrainer
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments

"""
A simple example on using SFTTrainer and Accelerate to finetune Phi-3 models. For
a more advanced example, please follow HF alignment-handbook/scripts/run_sft.py.
This example has utilized DeepSpeed ZeRO3 offload to reduce the memory usage. The
script can be run on A100 or later generation GPUs. Here are some suggestions on 
futher reducing memory consumption:
    - reduce batch size
    - decrease lora dimension
    - restrict lora target modules
Please follow these steps to run the script:
1. Install dependencies: 
    conda install -c conda-forge accelerate
    pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
    pip3 install -i https://pypi.org/simple/ bitsandbytes
    pip3 install peft trl transformers datasets
    pip3 install deepspeed
2. Setup accelerate and deepspeed config based on the machine used:
    accelerate config
Here is a sample config for deepspeed zero3:
    compute_environment: LOCAL_MACHINE
    debug: false
    deepspeed_config:
      gradient_accumulation_steps: 1
      offload_optimizer_device: none
      offload_param_device: none
      zero3_init_flag: true
      zero3_save_16bit_model: true
      zero_stage: 3
    distributed_type: DEEPSPEED
    downcast_bf16: 'no'
    enable_cpu_affinity: false
    machine_rank: 0
    main_training_function: main
    mixed_precision: bf16
    num_machines: 1
    num_processes: 2
    rdzv_backend: static
    same_network: true
    tpu_env: []
    tpu_use_cluster: false
    tpu_use_sudo: false
    use_cpu: false
3. check accelerate config:
    accelerate env
4. Run the code, and make sure to use accelerate launch alongside with
    at least 2 A100 80GB GPUs:
    
    accelerate launch sample_finetune.py
"""

logger = logging.getLogger(__name__)


###################
# Hyper-parameters
###################
training_config = {
    "bf16": True,
    "do_eval": False,
    "learning_rate": 5.0e-06,
    "log_level": "info",
    "logging_steps": 20,
    "logging_strategy": "steps",
    "lr_scheduler_type": "cosine",
    "num_train_epochs": 1,
    "max_steps": -1,
    "output_dir": "./checkpoint_dir",
    "overwrite_output_dir": True,
    "per_device_eval_batch_size": 4,
    "per_device_train_batch_size": 4,
    "remove_unused_columns": True,
    "save_steps": 100,
    "save_total_limit": 1,
    "seed": 0,
    "gradient_checkpointing": True,
    "gradient_checkpointing_kwargs":{"use_reentrant": False},
    "gradient_accumulation_steps": 1,
    "warmup_ratio": 0.2,
    }

peft_config = {
    "r": 16,
    "lora_alpha": 32,
    "lora_dropout": 0.05,
    "bias": "none",
    "task_type": "CAUSAL_LM",
    "target_modules": "all-linear",
    "modules_to_save": None,
}
train_conf = TrainingArguments(**training_config)
peft_conf = LoraConfig(**peft_config)


###############
# Setup logging
###############
logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S",
    handlers=[logging.StreamHandler(sys.stdout)],
)
log_level = train_conf.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()

# Log on each process a small summary
logger.warning(
    f"Process rank: {train_conf.local_rank}, device: {train_conf.device}, n_gpu: {train_conf.n_gpu}"
    + f" distributed training: {bool(train_conf.local_rank != -1)}, 16-bits training: {train_conf.fp16}"
)
logger.info(f"Training/evaluation parameters {train_conf}")
logger.info(f"PEFT parameters {peft_conf}")


################
# Model Loading
################
checkpoint_path = "microsoft/Phi-3.5-MoE-instruct"
model_kwargs = dict(
    use_cache=False,
    trust_remote_code=True,
    attn_implementation="flash_attention_2",  # loading the model with flash-attenstion support
    torch_dtype=torch.bfloat16,
    device_map=None
)
model = AutoModelForCausalLM.from_pretrained(checkpoint_path, **model_kwargs)
tokenizer = AutoTokenizer.from_pretrained(checkpoint_path)
tokenizer.model_max_length = 2048
tokenizer.pad_token = tokenizer.unk_token  # use unk rather than eos token to prevent endless generation
tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
tokenizer.padding_side = 'right'

for m in model.modules():
    # https://github.com/microsoft/DeepSpeed/pull/4966
    if "PhiMoESparseMoeBlock" in m.__class__.__name__:
        deepspeed.utils.set_z3_leaf_modules(model, [m.__class__])
        logger.info(f"Setting zero3 leaf for model on class with name: {m.__class__.__name__}")
        break


##################
# Data Processing
##################
def apply_chat_template(
    example,
    tokenizer,
):
    messages = example["messages"]
    example["text"] = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=False)
    return example

raw_dataset = load_dataset("HuggingFaceH4/ultrachat_200k")
train_dataset = raw_dataset["train_sft"]
test_dataset = raw_dataset["test_sft"]
column_names = list(train_dataset.features)

processed_train_dataset = train_dataset.map(
    apply_chat_template,
    fn_kwargs={"tokenizer": tokenizer},
    num_proc=10,
    remove_columns=column_names,
    desc="Applying chat template to train_sft",
)

processed_test_dataset = test_dataset.map(
    apply_chat_template,
    fn_kwargs={"tokenizer": tokenizer},
    num_proc=10,
    remove_columns=column_names,
    desc="Applying chat template to test_sft",
)


###########
# Training
###########
trainer = SFTTrainer(
    model=model,
    args=train_conf,
    peft_config=peft_conf,
    train_dataset=processed_train_dataset,
    eval_dataset=processed_test_dataset,
    max_seq_length=2048,
    dataset_text_field="text",
    tokenizer=tokenizer,
    packing=True
)
train_result = trainer.train()
metrics = train_result.metrics
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()


#############
# Evaluation
#############
tokenizer.padding_side = 'left'
metrics = trainer.evaluate()
metrics["eval_samples"] = len(processed_test_dataset)
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)


# ############
# # Save model
# ############
trainer.save_model(train_conf.output_dir)