import gc
import torch
from typing import TYPE_CHECKING, Tuple
from transformers import InfNanRemoveLogitsProcessor, LogitsProcessorList

try:
    from transformers.utils import (
        is_torch_bf16_cpu_available,
        is_torch_bf16_gpu_available,
        is_torch_cuda_available,
        is_torch_npu_available
    )
    _is_fp16_available = is_torch_npu_available() or is_torch_cuda_available()
    _is_bf16_available = is_torch_bf16_gpu_available() or is_torch_bf16_cpu_available
except ImportError:
    _is_fp16_available = torch.cuda.is_available()
    _is_bf16_available = torch.cuda.is_bf16_supported()

if TYPE_CHECKING:
    from transformers.modeling_utils import PreTrainedModel


class AverageMeter:
    r"""
    Computes and stores the average and current value.
    """
    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count


def count_parameters(model: torch.nn.Module) -> Tuple[int, int]:
    r"""
    Returns the number of trainable parameters and number of all parameters in the model.
    """
    trainable_params, all_param = 0, 0
    for param in model.parameters():
        num_params = param.numel()
        # if using DS Zero 3 and the weights are initialized empty
        if num_params == 0 and hasattr(param, "ds_numel"):
            num_params = param.ds_numel

        # Due to the design of 4bit linear layers from bitsandbytes, multiply the number of parameters by 2
        if param.__class__.__name__ == "Params4bit":
            num_params = num_params * 2

        all_param += num_params
        if param.requires_grad:
            trainable_params += num_params

    return trainable_params, all_param


def infer_optim_dtype(model_dtype: torch.dtype) -> torch.dtype:
    r"""
    Infers the optimal dtype according to the model_dtype and device compatibility.
    """
    if _is_bf16_available and model_dtype == torch.bfloat16:
        return torch.bfloat16
    elif _is_fp16_available:
        return torch.float16
    else:
        return torch.float32


def get_logits_processor() -> LogitsProcessorList:
    r"""
    Gets logits processor that removes NaN and Inf logits.
    """
    logits_processor = LogitsProcessorList()
    logits_processor.append(InfNanRemoveLogitsProcessor())
    return logits_processor


def torch_gc() -> None:
    r"""
    Collects GPU memory.
    """
    gc.collect()
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
        torch.cuda.ipc_collect()


def dispatch_model(model: "PreTrainedModel") -> "PreTrainedModel":
    r"""
    Dispatches a pre-trained model to GPUs with balanced memory.
    Borrowed from: https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/modeling_utils.py#L2803
    """
    if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False): # do nothing
        return model

    if torch.cuda.device_count() > 1:
        from accelerate import dispatch_model
        from accelerate.utils import infer_auto_device_map, get_balanced_memory

        if model._no_split_modules is None:
            raise ValueError("The model class needs to implement the `_no_split_modules` attribute.")

        kwargs = {"dtype": model.dtype, "no_split_module_classes": model._no_split_modules}
        max_memory = get_balanced_memory(model, **kwargs)
        # Make sure tied weights are tied before creating the device map.
        model.tie_weights()
        device_map = infer_auto_device_map(model, max_memory=max_memory, **kwargs)
        return dispatch_model(model, device_map)
    else:
        return model.cuda()