# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
Supervised fine-tuning script for decoder language models.

Usage:

# One 1 node of 8 x H100s
accelerate launch --config_file=recipes/accelerate_configs/zero3.yaml src/open_r1/sft.py \
    --model_name_or_path Qwen/Qwen2.5-1.5B-Instruct \
    --dataset_name HuggingFaceH4/Bespoke-Stratos-17k \
    --learning_rate 2.0e-5 \
    --num_train_epochs 1 \
    --packing \
    --max_seq_length 4096 \
    --per_device_train_batch_size 2 \
    --gradient_accumulation_steps 8 \
    --gradient_checkpointing \
    --bf16 \
    --logging_steps 5 \
    --eval_strategy steps \
    --eval_steps 100 \
    --output_dir data/Qwen2.5-1.5B-Open-R1-Distill
"""

import logging
import os
import sys

import datasets
import torch
import transformers
from datasets import load_dataset
from transformers import set_seed
from transformers.trainer_utils import get_last_checkpoint

from configs import SFTConfig
from utils import get_tokenizer
from utils.callbacks import get_callbacks
from utils.wandb_logging import init_wandb_training
from trl import (
    ModelConfig,
    ScriptArguments,
    SFTTrainer,
    TrlParser,
    get_kbit_device_map,
    get_peft_config,
    get_quantization_config,
)


logger = logging.getLogger(__name__)


def main(script_args, training_args, model_args):
    # Set seed for reproducibility
    set_seed(training_args.seed)

    ###############
    # 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 = training_args.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()

    logger.info(f"Model parameters {model_args}")
    logger.info(f"Script parameters {script_args}")
    logger.info(f"Training parameters {training_args}")

    # Check for last checkpoint
    last_checkpoint = None
    if os.path.isdir(training_args.output_dir):
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
    if last_checkpoint is not None and training_args.resume_from_checkpoint is None:
        logger.info(f"Checkpoint detected, resuming training at {last_checkpoint=}.")

    if "wandb" in training_args.report_to:
        init_wandb_training(training_args)

    ################
    # Load datasets
    ################
    def make_math(example):
        '''
        answer = example["messages"][1]['content'].split('</think>\n\n')[-1]
        #text = "User: {}\nPlease reason step by step, and put your final answer within \\boxed{{}}.\n\nAssistant: {}".format(example["problem"], answer)
        text = "Question: {}\n\nAnswer: Let's think step by step. {}".format(example["problem"], answer)
        '''
        #text = "User: {}\nPlease reason step by step, and put your final answer within \\boxed{{}}.\n\nAssistant: {}".format(example["problem"], example["messages"][1]['content'])
        text = "Question: {}\n\nAnswer: Let's think step by step. {}".format(example["problem"], example["messages"][1]['content'])
        return {"text": text+"\n\n"}

    def make_lamini(example):
        text = "Question: {}\n\nAnswer: {}".format(example["instruction"], example["response"])
        return {"text": text+"\n\n"}

    def make_generate(example):
        text = "User: {}\nPlease reason step by step, and put your final answer within \\boxed{{}}.\n\nAssistant: {}".format(example["question"], example["response"])
        return {"text": text+"\n\n"}

    if 'jsonl' in script_args.dataset_name:
        dataset = load_dataset('json',data_files=script_args.dataset_name)
        
    else:
        dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)
    
    if 'jsonl' in script_args.dataset_name.lower():
        dataset = dataset.map(make_generate, num_proc=64)
    elif 'r1' in script_args.dataset_name.lower():
        dataset = dataset.map(make_math, remove_columns="messages", num_proc=64)
    elif 'lamini' in script_args.dataset_name.lower():
        dataset = dataset.map(make_lamini, num_proc=64)
    else:
        exit()
    
    
    
    ################
    # Load tokenizer
    ################
    tokenizer = get_tokenizer(model_args, training_args)
    tokenizer.pad_token = tokenizer.eos_token

    ###################
    # Model init kwargs
    ###################
    logger.info("*** Initializing model kwargs ***")
    torch_dtype = (
        model_args.torch_dtype if model_args.torch_dtype in ["auto", None] else getattr(torch, model_args.torch_dtype)
    )
    quantization_config = get_quantization_config(model_args)
    model_kwargs = dict(
        revision=model_args.model_revision,
        trust_remote_code=model_args.trust_remote_code,
        attn_implementation=model_args.attn_implementation,
        torch_dtype=torch_dtype,
        use_cache=False if training_args.gradient_checkpointing else True,
        device_map=get_kbit_device_map() if quantization_config is not None else None,
        quantization_config=quantization_config,
    )
    training_args.model_init_kwargs = model_kwargs
    
    ############################
    # Initialize the SFT Trainer
    ############################
    trainer = SFTTrainer(
        model=model_args.model_name_or_path,
        args=training_args,
        train_dataset=dataset[script_args.dataset_train_split].select(range(10000)),
        eval_dataset=dataset[script_args.dataset_test_split] if training_args.eval_strategy != "no" else None,
        processing_class=tokenizer,
        peft_config=get_peft_config(model_args),
        callbacks=get_callbacks(training_args, model_args),
    )

    ###############
    # Training loop
    ###############
    logger.info("*** Train ***")
    checkpoint = None
    if training_args.resume_from_checkpoint is not None:
        checkpoint = training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
        checkpoint = last_checkpoint
    train_result = trainer.train(resume_from_checkpoint=checkpoint)
    metrics = train_result.metrics
    metrics["train_samples"] = len(dataset[script_args.dataset_train_split])
    trainer.log_metrics("train", metrics)
    trainer.save_metrics("train", metrics)
    trainer.save_state()

    ##################################
    # Save model and create model card
    ##################################
    logger.info("*** Save model ***")
    if model_args.model_name_or_path.endswith('.bin'):
        model.half()
        torch.save({
            'model': trainer.model, 
            'tokenizer': trainer.tokenizer,
        }, training_args.output_dir)
    else:
        trainer.save_model(training_args.output_dir)
        logger.info(f"Model saved to {training_args.output_dir}")

    # Save everything else on main process
    kwargs = {
        "dataset_name": script_args.dataset_name,
        "tags": ["open-r1"],
    }
    if trainer.accelerator.is_main_process:
        trainer.create_model_card(**kwargs)
        # Restore k,v cache for fast inference
        trainer.model.config.use_cache = True
        trainer.model.config.save_pretrained(training_args.output_dir)

    ##########
    # Evaluate
    ##########
    if training_args.do_eval:
        logger.info("*** Evaluate ***")
        metrics = trainer.evaluate()
        metrics["eval_samples"] = len(dataset[script_args.dataset_test_split])
        trainer.log_metrics("eval", metrics)
        trainer.save_metrics("eval", metrics)

    


if __name__ == "__main__":
    parser = TrlParser((ScriptArguments, SFTConfig, ModelConfig))
    script_args, training_args, model_args = parser.parse_args_and_config()
    main(script_args, training_args, model_args)
