#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. 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.
"""

import logging
import random
import sys

import datasets
import torch
import transformers
from transformers import set_seed, AutoModelForCausalLM
from trl import DataCollatorForCompletionOnlyLM

from accelerate import Accelerator
from alignment import (
    DataArguments,
    H4ArgumentParser,
    ModelArguments,
    SFTConfig,
    apply_chat_template,
    get_datasets,
    get_kbit_device_map,
    get_peft_config,
    get_quantization_config,
    get_tokenizer,
)
from trl import SFTTrainer


logger = logging.getLogger(__name__)


def main():
    parser = H4ArgumentParser((ModelArguments, DataArguments, SFTConfig))
    model_args, data_args, training_args = parser.parse()

    # Set seed for reproducibility
    set_seed(training_args.seed)

    accelerator = Accelerator()

    ###############
    # 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()

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

    ###############
    # Load datasets
    ###############
    raw_datasets = get_datasets(data_args, splits=data_args.dataset_splits)
    logger.info(
        f"Training on the following datasets and their proportions: {[split + ' : ' + str(dset.num_rows) for split, dset in raw_datasets.items()]}"
    )

    ################
    # Load tokenizer
    ################
    tokenizer = get_tokenizer(model_args, data_args)

    #####################
    # Apply chat template
    #####################
    raw_datasets = raw_datasets.map(apply_chat_template, fn_kwargs={"tokenizer": tokenizer, "task": "sft"})
    train_dataset = raw_datasets["train"]
    eval_dataset = raw_datasets["test"]

    with training_args.main_process_first(desc="Log a few random samples from the processed training set"):
        for index in random.sample(range(len(raw_datasets["train"])), 3):
            logger.info(f"Sample {index} of the processed training set:\n\n{raw_datasets['train'][index]['text']}")

    #######################
    # Load pretrained model
    #######################
    logger.info("*** Load pretrained model ***")
    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="flash_attention_2" if model_args.use_flash_attention_2 else "eager",
        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,
    )
    logger.info("*** Model loaded! ***")

    
    ########################
    # Initialize the Trainer
    ########################
    model = AutoModelForCausalLM.from_pretrained(model_args.model_name_or_path, **model_kwargs)
    # tokenizer.pad_token_id = tokenizer.eos_token_id
    # model.pad_token_id = tokenizer.eos_token_id
    if "phi-1_5" in model_args.model_name_or_path or "codes" in model_args.model_name_or_path.lower():
        tokenizer.add_tokens(['<|reserved_special_token_246|>', '<|reserved_special_token_247|>'])
        model.resize_token_embeddings(len(tokenizer))
        print('Add tokens <|reserved_special_token_246|>')
    
    if tokenizer.pad_token == tokenizer.eos_token:
        print('add Pad token')
        tokenizer.add_special_tokens({'pad_token': '[PAD]'})
        model.pad_token = tokenizer.pad_token
        model.resize_token_embeddings(len(tokenizer))

    if model_args.num_freeze_layers > 0:
        # freeze embed_tokens
        # for param in model.model.get_input_embeddings().parameters():
        #     param.requires_grad = False
        # freeze first n layers
        for layer in model.model.layers[:model_args.num_freeze_layers]:
            for param in layer.parameters():
                param.requires_grad = False
        # require grad for all other layers
        # for layer in model.model.layers[model_args.num_freeze_layers:]:
        #     for param in layer.parameters():
        #         param.requires_grad = True


    if model_args.response_template is not None:
        collator = DataCollatorForCompletionOnlyLM(
            response_template=model_args.response_template,
            tokenizer=tokenizer, mlm=False)
        packing = False
    else:
        collator = None
        packing = True

    trainer = SFTTrainer(
        # model=model_args.model_name_or_path,
        # model_init_kwargs=model_kwargs,
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        dataset_text_field="text",
        max_seq_length=training_args.max_seq_length,
        tokenizer=tokenizer,
        packing=packing,
        peft_config=get_peft_config(model_args),
        data_collator=collator,
    )

    ###############
    # Training loop
    ###############
    logger.info("*** Train ***")
    train_result = trainer.train(resume_from_checkpoint=False)
    metrics = train_result.metrics
    max_train_samples = data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
    metrics["train_samples"] = min(max_train_samples, len(train_dataset))
    trainer.log_metrics("train", metrics)
    trainer.save_metrics("train", metrics)
    trainer.save_state()

    ##########
    # Evaluate
    ##########
    if training_args.do_eval:
        logger.info("*** Evaluate ***")
        metrics = trainer.evaluate()
        max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
        metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
        trainer.log_metrics("eval", metrics)
        trainer.save_metrics("eval", metrics)

    ##################################
    # Save model and create model card
    ##################################
    logger.info("*** Save model ***")
    trainer.save_model(training_args.output_dir)
    # trainer.save_pretrained(training_args.output_dir)
    logger.info(f"Model saved to {training_args.output_dir}")

    # Save everything else on main process
    if accelerator.is_main_process:
        kwargs = {
            "finetuned_from": model_args.model_name_or_path,
            "dataset": list(data_args.dataset_mixer.keys()),
            "dataset_tags": list(data_args.dataset_mixer.keys()),
            "tags": ["alignment-handbook"],
        }
        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)

        if training_args.push_to_hub is True:
            logger.info("Pushing to hub...")
            trainer.push_to_hub()

    accelerator.wait_for_everyone()


if __name__ == "__main__":
    main()