import math
import torch
from types import MethodType
from typing import TYPE_CHECKING, Optional, Tuple

from transformers import (
    AutoConfig,
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    PretrainedConfig,
    PreTrainedModel,
    PreTrainedTokenizerBase
)
from transformers.utils.versions import require_version
from trl import AutoModelForCausalLMWithValueHead

try:
    from transformers.integrations import is_deepspeed_zero3_enabled
except ImportError: # https://github.com/huggingface/transformers/releases/tag/v4.33.1
    from transformers.deepspeed import is_deepspeed_zero3_enabled

from llmtuner.extras.logging import get_logger
from llmtuner.extras.misc import count_parameters, get_current_device, infer_optim_dtype, try_download_model_from_ms
from llmtuner.extras.packages import is_flash_attn2_available
from llmtuner.hparams import FinetuningArguments
from llmtuner.model.adapter import init_adapter
from llmtuner.model.utils import load_valuehead_params, prepare_model_for_training, resize_embedding_layer

if TYPE_CHECKING:
    from transformers import PreTrainedTokenizer
    from llmtuner.hparams import ModelArguments


logger = get_logger(__name__)


require_version("transformers>=4.36.0", "To fix: pip install transformers>=4.36.0")
require_version("datasets>=2.14.3", "To fix: pip install datasets>=2.14.3")
require_version("accelerate>=0.21.0", "To fix: pip install accelerate>=0.21.0")
require_version("peft>=0.7.1", "To fix: pip install peft>=0.7.1")
require_version("trl>=0.7.4", "To fix: pip install trl>=0.7.4")


def load_model_and_tokenizer(
    model_args: "ModelArguments",
    finetuning_args: "FinetuningArguments",
    is_trainable: Optional[bool] = False,
    add_valuehead: Optional[bool] = False
) -> Tuple[PreTrainedModel, "PreTrainedTokenizer"]:
    r"""
    Loads pretrained model and tokenizer.

    Support both training and inference.
    """

    try_download_model_from_ms(model_args)

    config_kwargs = {
        "trust_remote_code": True,
        "cache_dir": model_args.cache_dir,
        "revision": model_args.model_revision,
        "token": model_args.hf_hub_token
    }

    tokenizer = AutoTokenizer.from_pretrained(
        model_args.model_name_or_path,
        use_fast=model_args.use_fast_tokenizer,
        split_special_tokens=model_args.split_special_tokens,
        padding_side="right", # training with left-padded tensors in fp16 precision may cause overflow
        **config_kwargs
    )

    if finetuning_args.finetuning_type != "lora" and model_args.checkpoint_dir is not None:
        logger.info("Use `model_name_or_path` to specify the model trained with full/freeze method.")
        model_to_load = model_args.checkpoint_dir[0]
    else:
        model_to_load = model_args.model_name_or_path

    config = AutoConfig.from_pretrained(model_to_load, **config_kwargs)

    # Fix tokenizer (for ChatGLM2 and ChatGLM3)
    if getattr(config, "model_type", None) == "chatglm":
        tokenizer._pad = MethodType(PreTrainedTokenizerBase._pad, tokenizer)

    # Set model dtype
    if model_args.compute_dtype is None: # priority: bf16 > fp16 > fp32
        model_args.compute_dtype = infer_optim_dtype(model_dtype=getattr(config, "torch_dtype", None))
    setattr(config, "torch_dtype", model_args.compute_dtype)

    # Fix config (for Qwen)
    if getattr(config, "model_type", None) == "qwen":
        for dtype_name, dtype in [("fp16", torch.float16), ("bf16", torch.bfloat16), ("fp32", torch.float32)]:
            setattr(config, dtype_name, getattr(config, "torch_dtype", None) == dtype)

    # Set RoPE scaling
    if model_args.rope_scaling is not None:
        if not hasattr(config, "rope_scaling"):
            logger.warning("Current model does not support RoPE scaling.")
        else:
            if is_trainable:
                if model_args.rope_scaling == "dynamic":
                    logger.warning(
                        "Dynamic NTK may not work well with fine-tuning. "
                        "See: https://github.com/huggingface/transformers/pull/24653"
                    )

                current_max_length = getattr(config, "max_position_embeddings", None)
                if current_max_length and model_args.model_max_length > current_max_length:
                    scaling_factor = float(math.ceil(model_args.model_max_length / current_max_length))
                else:
                    logger.warning("Input length is smaller than max length. Consider increase input length.")
                    scaling_factor = 1.0
            else:
                scaling_factor = 2.0

            setattr(config, "rope_scaling", {"type": model_args.rope_scaling, "factor": scaling_factor})
            logger.info("Using {} scaling strategy and setting scaling factor to {}".format(
                model_args.rope_scaling, scaling_factor
            ))

    # Set FlashAttention-2
    if model_args.flash_attn:
        if not is_flash_attn2_available():
            logger.warning("FlashAttention-2 is not installed.")
        elif getattr(config, "model_type", None) == "qwen":
            logger.info("Current model automatically enables FlashAttention if installed.")
        else:
            setattr(config, "attn_implementation", "flash_attention_2")
            logger.info("Using FlashAttention-2 for faster training and inference.")

    # Set shift short attention (S^2-Attn)
    if is_trainable and model_args.shift_attn:
        logger.warning("Shift short attention is temporarily invalid due to breaking changes.")
        # if getattr(config, "model_type", None) == "llama":
        #     setattr(config, "group_size_ratio", 0.25)
        #     logger.info("Using shift short attention with group_size_ratio=1/4.")
        # else:
        #     logger.warning("Current model does not support shift short attention.")

    # Quantization configurations (using gptq or awq)
    if getattr(config, "quantization_config", None):
        if model_args.quantization_bit is not None: # remove bnb quantization
            model_args.quantization_bit = None
        config_kwargs["device_map"] = {"": get_current_device()}
        quantization_config = getattr(config, "quantization_config", None)
        logger.info("Loading {}-bit pre-quantized model.".format(quantization_config.get("bits", -1)))

    # Quantization configurations (using bitsandbytes)
    if model_args.quantization_bit is not None:
        if is_deepspeed_zero3_enabled():
            raise ValueError("DeepSpeed ZeRO-3 is incompatible with quantization.")

        if model_args.quantization_bit == 8:
            require_version("bitsandbytes>=0.37.0", "To fix: pip install bitsandbytes>=0.37.0")
            config_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True)

        if model_args.quantization_bit == 4:
            require_version("bitsandbytes>=0.39.0", "To fix: pip install bitsandbytes>=0.39.0")
            config_kwargs["quantization_config"] = BitsAndBytesConfig(
                load_in_4bit=True,
                bnb_4bit_compute_dtype=model_args.compute_dtype,
                bnb_4bit_use_double_quant=model_args.double_quantization,
                bnb_4bit_quant_type=model_args.quantization_type
            )

        config_kwargs["device_map"] = {"": get_current_device()}
        logger.info("Quantizing model to {} bit.".format(model_args.quantization_bit))

    # Load pre-trained models (without valuehead)
    model = AutoModelForCausalLM.from_pretrained(
        model_to_load,
        config=config,
        torch_dtype=model_args.compute_dtype,
        low_cpu_mem_usage=(not is_deepspeed_zero3_enabled()),
        **config_kwargs
    )

    # Resize token embeddings
    resize_embedding_layer(model, tokenizer)

    # Disable custom generate method (for Qwen and Baichuan2)
    if isinstance(model, PreTrainedModel) and "GenerationMixin" not in str(model.generate.__func__):
        model.generate = MethodType(PreTrainedModel.generate, model)

    # Fix LM head (for ChatGLM2 and ChatGLM3)
    if getattr(config, "model_type", None) == "chatglm":
        setattr(model, "lm_head", model.transformer.output_layer)
        setattr(model, "_keys_to_ignore_on_save", ["lm_head.weight"])

    # Register auto class to save the custom code files
    if isinstance(config, PretrainedConfig) and "AutoConfig" in getattr(config, "auto_map", {}):
        config.__class__.register_for_auto_class()
    if isinstance(model, PreTrainedModel) and "AutoModelForCausalLM" in getattr(config, "auto_map", {}):
        model.__class__.register_for_auto_class()
    if isinstance(tokenizer, PreTrainedTokenizerBase) and "AutoTokenizer" in tokenizer.init_kwargs.get("auto_map", {}):
        tokenizer.__class__.register_for_auto_class()

    # Initialize adapters
    model = prepare_model_for_training(model=model, finetuning_args=finetuning_args) if is_trainable else model
    model = init_adapter(model, model_args, finetuning_args, is_trainable)

    # Prepare model with valuehead for RLHF
    if add_valuehead:
        model: "AutoModelForCausalLMWithValueHead" = AutoModelForCausalLMWithValueHead.from_pretrained(model)
        ignore_modules = [name for name, _ in model.named_parameters() if "pretrained_model" in name]
        setattr(model, "_keys_to_ignore_on_save", ignore_modules)
        setattr(model, "tie_weights", MethodType(lambda _: None, model)) # use empty method
        vhead_path = (
            model_args.checkpoint_dir[-1] if model_args.checkpoint_dir is not None else model_args.model_name_or_path
        )
        vhead_params = load_valuehead_params(vhead_path, model_args)
        if vhead_params is not None:
            model.load_state_dict(vhead_params, strict=False)
            logger.info("Loaded valuehead from checkpoint: {}".format(vhead_path))

    # Prepare model for inference
    if not is_trainable:
        model.requires_grad_(False) # fix all model params
        model = model.to(model_args.compute_dtype) if not getattr(model, "quantization_method", None) else model
        model.eval()
    else:
        model.train()

    trainable_params, all_param = count_parameters(model)
    logger.info("trainable params: {:d} || all params: {:d} || trainable%: {:.4f}".format(
        trainable_params, all_param, 100 * trainable_params / all_param
    ))

    if not is_trainable:
        logger.info("This IS expected that the trainable params is 0 if you are using model for inference only.")

    return model, tokenizer