import os
import sys
import torch
import datasets
import transformers
from typing import Any, Dict, Optional, Tuple
from transformers import HfArgumentParser, Seq2SeqTrainingArguments

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


logger = get_logger(__name__)


def get_train_args(
    args: Optional[Dict[str, Any]] = None
) -> Tuple[ModelArguments, DataArguments, Seq2SeqTrainingArguments, FinetuningArguments, GeneralArguments]:

    parser = HfArgumentParser((ModelArguments, DataArguments, Seq2SeqTrainingArguments, FinetuningArguments, GeneralArguments))

    if args is not None:
        model_args, data_args, training_args, finetuning_args, general_args = parser.parse_dict(args)
    elif len(sys.argv) == 2 and sys.argv[1].endswith(".yaml"):
        model_args, data_args, training_args, finetuning_args, general_args = parser.parse_yaml_file(os.path.abspath(sys.argv[1]))
    elif len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        model_args, data_args, training_args, finetuning_args, general_args = parser.parse_json_file(os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args, finetuning_args, general_args = parser.parse_args_into_dataclasses()

    # 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()

    assert general_args.stage == "sft" or (not training_args.predict_with_generate), \
        "`predict_with_generate` cannot be set as True at PT, RM and PPO stages."

    assert not (training_args.do_train and training_args.predict_with_generate), \
        "`predict_with_generate` cannot be set as True while training."

    assert (not training_args.do_predict) or training_args.predict_with_generate, \
        "Please enable `predict_with_generate` to save model predictions."

    assert model_args.quantization_bit is None or finetuning_args.finetuning_type == "lora", \
        "Quantization is only compatible with the LoRA method."

    if model_args.checkpoint_dir is not None:
        if finetuning_args.finetuning_type != "lora":
            assert len(model_args.checkpoint_dir) == 1, "Only LoRA tuning accepts multiple checkpoints."
        else:
            assert model_args.quantization_bit is None or len(model_args.checkpoint_dir) == 1, \
                "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):
        logger.warning("We recommend enable fp16 mixed precision training.")

    if data_args.prompt_template == "default":
        logger.warning("Please specify `prompt_template` if you are using other pre-trained models.")

    if training_args.local_rank != -1 and training_args.ddp_find_unused_parameters is None:
        logger.warning("`ddp_find_unused_parameters` needs to be set as False in DDP training.")
        training_args.ddp_find_unused_parameters = False

    training_args.optim = "adamw_torch" if training_args.optim == "adamw_hf" else training_args.optim # suppress warning

    if model_args.quantization_bit is not None:
        if training_args.fp16:
            model_args.compute_dtype = torch.float16
        elif training_args.bf16:
            model_args.compute_dtype = torch.bfloat16
        else:
            model_args.compute_dtype = torch.float32

    # Log on each process the small summary:
    logger.info(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}\n"
        + f"  distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
    )
    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, general_args


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

    parser = HfArgumentParser((ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments))

    if args is not None:
        model_args, data_args, finetuning_args, generating_args = parser.parse_dict(args)
    elif len(sys.argv) == 2 and sys.argv[1].endswith(".yaml"):
        model_args, data_args, finetuning_args, generating_args = parser.parse_yaml_file(os.path.abspath(sys.argv[1]))
    elif len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        model_args, data_args, finetuning_args, generating_args = parser.parse_json_file(os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, finetuning_args, generating_args = parser.parse_args_into_dataclasses()

    assert model_args.quantization_bit is None or finetuning_args.finetuning_type == "lora", \
        "Quantization is only compatible with the LoRA method."

    if model_args.checkpoint_dir is not None:
        if finetuning_args.finetuning_type != "lora":
            assert len(model_args.checkpoint_dir) == 1, "Only LoRA tuning accepts multiple checkpoints."
        else:
            assert model_args.quantization_bit is None or len(model_args.checkpoint_dir) == 1, \
                "Quantized model only accepts a single checkpoint."

    if data_args.prompt_template == "default":
        logger.warning("Please specify `prompt_template` if you are using other pre-trained models.")

    return model_args, data_args, finetuning_args, generating_args