import os
import sys
import torch
import datasets
import transformers
from typing import Any, Dict, Optional, Tuple
from transformers import HfArgumentParser, Seq2SeqTrainingArguments
from transformers.utils.versions import require_version
from transformers.trainer_utils import get_last_checkpoint

from llmtuner.extras.logging import get_logger
from llmtuner.hparams import (
    ModelArguments,
    DataArguments,
    FinetuningArguments,
    GeneratingArguments,
    GeneralArguments
)


logger = get_logger(__name__)


def _parse_args(parser: HfArgumentParser, args: Optional[Dict[str, Any]] = None) -> Tuple[Any]:
    if args is not None:
        return parser.parse_dict(args)
    elif len(sys.argv) == 2 and sys.argv[1].endswith(".yaml"):
        return parser.parse_yaml_file(os.path.abspath(sys.argv[1]))
    elif len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        return parser.parse_json_file(os.path.abspath(sys.argv[1]))
    else:
        return parser.parse_args_into_dataclasses()


def parse_train_args(
    args: Optional[Dict[str, Any]] = None
) -> Tuple[
    ModelArguments,
    DataArguments,
    Seq2SeqTrainingArguments,
    FinetuningArguments,
    GeneratingArguments,
    GeneralArguments
]:
    parser = HfArgumentParser((
        ModelArguments,
        DataArguments,
        Seq2SeqTrainingArguments,
        FinetuningArguments,
        GeneratingArguments,
        GeneralArguments
    ))
    return _parse_args(parser, args)


def parse_infer_args(
    args: Optional[Dict[str, Any]] = None
) -> Tuple[
    ModelArguments,
    DataArguments,
    FinetuningArguments,
    GeneratingArguments
]:
    parser = HfArgumentParser((
        ModelArguments,
        DataArguments,
        FinetuningArguments,
        GeneratingArguments
    ))
    return _parse_args(parser, args)


def get_train_args(
    args: Optional[Dict[str, Any]] = None
) -> Tuple[
    ModelArguments,
    DataArguments,
    Seq2SeqTrainingArguments,
    FinetuningArguments,
    GeneratingArguments,
    GeneralArguments
]:
    model_args, data_args, training_args, finetuning_args, generating_args, general_args = parse_train_args(args)

    # Setup logging
    if training_args.should_log:
        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
        transformers.utils.logging.set_verbosity_info()

    log_level = training_args.get_process_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()

    # Check arguments (do not check finetuning_args since it may be loaded from checkpoints)
    data_args.init_for_training()

    if general_args.stage != "pt" and data_args.template is None:
        raise ValueError("Please specify which `template` to use.")

    if general_args.stage != "sft" and training_args.predict_with_generate:
        raise ValueError("`predict_with_generate` cannot be set as True except SFT.")

    if general_args.stage == "sft" and training_args.do_predict and not training_args.predict_with_generate:
        raise ValueError("Please enable `predict_with_generate` to save model predictions.")

    if general_args.stage in ["rm", "ppo"] and finetuning_args.finetuning_type != "lora":
        raise ValueError("RM and PPO stages can only be performed with the LoRA method.")

    if general_args.stage in ["rm", "ppo"] and training_args.resume_from_checkpoint is not None:
        raise ValueError("RM and PPO stages do not support `resume_from_checkpoint`.")

    if general_args.stage in ["ppo", "dpo"] and not training_args.do_train:
        raise ValueError("PPO and DPO stages can only be performed at training.")

    if general_args.stage in ["rm", "dpo"]:
        for dataset_attr in data_args.dataset_list:
            if not dataset_attr.ranking:
                raise ValueError("Please use ranked datasets for reward modeling or DPO training.")

    if general_args.stage == "ppo" and model_args.reward_model is None:
        raise ValueError("Reward model is necessary for PPO training.")

    if general_args.stage == "ppo" and training_args.deepspeed is not None:
        raise ValueError("PPO training is incompatible with DeepSpeed, use Accelerate instead.")

    if general_args.stage == "ppo" and data_args.streaming:
        raise ValueError("Streaming mode does not suppport PPO training currently.")

    if training_args.max_steps == -1 and data_args.streaming:
        raise ValueError("Please specify `max_steps` in streaming mode.")

    if data_args.val_size > 1e-6 and data_args.val_size < 1 and data_args.streaming:
        raise ValueError("Streaming mode should have an integer val size.")

    if training_args.do_train and training_args.predict_with_generate:
        raise ValueError("`predict_with_generate` cannot be set as True while training.")

    if training_args.do_train and finetuning_args.finetuning_type == "lora" and finetuning_args.lora_target is None:
        raise ValueError("Please specify `lora_target` in LoRA training.")

    if model_args.quantization_bit is not None and finetuning_args.finetuning_type != "lora":
        raise ValueError("Quantization is only compatible with the LoRA method.")

    if model_args.checkpoint_dir is not None:
        if finetuning_args.finetuning_type != "lora":
            if len(model_args.checkpoint_dir) != 1:
                raise ValueError("Only LoRA tuning accepts multiple checkpoints.")
        elif model_args.quantization_bit is not None and len(model_args.checkpoint_dir) != 1:
                raise ValueError("Quantized model only accepts a single checkpoint.")

    if model_args.quantization_bit is not None and (not training_args.do_train):
        logger.warning("Evaluating model in 4/8-bit mode may cause lower scores.")

    if training_args.do_train and (not training_args.fp16) and (not training_args.bf16):
        logger.warning("We recommend enable mixed precision training.")

    # postprocess data_args
    if data_args.max_samples is not None and data_args.streaming:
        logger.warning("`max_samples` is incompatible with `streaming`. Disabling max_samples.")
        data_args.max_samples = None

    # postprocess training_args
    if (
        training_args.local_rank != -1
        and training_args.ddp_find_unused_parameters is None
        and finetuning_args.finetuning_type == "lora"
    ):
        logger.warning("`ddp_find_unused_parameters` needs to be set as False for LoRA in DDP training.")
        training_args_dict = training_args.to_dict()
        training_args_dict.update(dict(ddp_find_unused_parameters=False))
        training_args = Seq2SeqTrainingArguments(**training_args_dict)

    if (
        training_args.resume_from_checkpoint is None
        and training_args.do_train
        and os.path.isdir(training_args.output_dir)
        and not training_args.overwrite_output_dir
    ):
        # require_version("transformers>=4.31.0", "Resuming training requires transformers>=4.31.0.")
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
            raise ValueError("Output directory already exists and is not empty. Use `overwrite_output_dir`.")

        if last_checkpoint is not None:
            training_args_dict = training_args.to_dict()
            training_args_dict.update(dict(resume_from_checkpoint=last_checkpoint))
            training_args = Seq2SeqTrainingArguments(**training_args_dict)
            logger.info(
                "Resuming from checkpoint. Change `output_dir` or use `overwrite_output_dir` to avoid."
            )

    # postprocess model_args
    if training_args.bf16:
        if not torch.cuda.is_bf16_supported():
            raise ValueError("Current device does not support bf16 training.")
        model_args.compute_dtype = torch.bfloat16
    else:
        model_args.compute_dtype = torch.float16

    model_args.model_max_length = data_args.max_source_length + data_args.max_target_length

    # Log on each process the small summary:
    logger.info("Process rank: {}, device: {}, n_gpu: {}\n  distributed training: {}, compute dtype: {}".format(
        training_args.local_rank, training_args.device, training_args.n_gpu,
        bool(training_args.local_rank != -1), str(model_args.compute_dtype)
    ))
    logger.info(f"Training/evaluation parameters {training_args}")

    # Set seed before initializing model.
    transformers.set_seed(training_args.seed)

    return model_args, data_args, training_args, finetuning_args, generating_args, general_args


def get_infer_args(
    args: Optional[Dict[str, Any]] = None
) -> Tuple[
    ModelArguments,
    DataArguments,
    FinetuningArguments,
    GeneratingArguments
]:
    model_args, data_args, finetuning_args, generating_args = parse_infer_args(args)

    if data_args.template is None:
        raise ValueError("Please specify which `template` to use.")

    if model_args.quantization_bit is not None and finetuning_args.finetuning_type != "lora":
        raise ValueError("Quantization is only compatible with the LoRA method.")

    if model_args.checkpoint_dir is not None:
        if finetuning_args.finetuning_type != "lora":
            if len(model_args.checkpoint_dir) != 1:
                raise ValueError("Only LoRA tuning accepts multiple checkpoints.")
        elif model_args.quantization_bit is not None and len(model_args.checkpoint_dir) != 1:
                raise ValueError("Quantized model only accepts a single checkpoint.")

    return model_args, data_args, finetuning_args, generating_args
