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booxel-cpu
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import sys
import contextlib
from functools import lru_cache

import torch
#from modules import errors

if sys.platform == "darwin":
    from modules import mac_specific


def has_mps() -> bool:
    if sys.platform != "darwin":
        return False
    else:
        return mac_specific.has_mps


def get_cuda_device_string():
    return "cuda"


def get_optimal_device_name():
    if torch.cuda.is_available():
        return get_cuda_device_string()

    if has_mps():
        return "mps"

    return "cpu"


def get_optimal_device():
    return torch.device(get_optimal_device_name())


def get_device_for(task):
    return get_optimal_device()


def torch_gc():

    if torch.cuda.is_available():
        with torch.cuda.device(get_cuda_device_string()):
            torch.cuda.empty_cache()
            torch.cuda.ipc_collect()

    if has_mps():
        mac_specific.torch_mps_gc()


def enable_tf32():
    if torch.cuda.is_available():

        # 启用基准选项似乎能让一系列显卡在无法使用 fp16 时使用 fp16
        # see https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/4407
        if any(torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())):
            torch.backends.cudnn.benchmark = True

        torch.backends.cuda.matmul.allow_tf32 = True
        torch.backends.cudnn.allow_tf32 = True


enable_tf32()
#errors.run(enable_tf32, "Enabling TF32")

cpu = torch.device("cpu")
device = device_interrogate = device_gfpgan = device_esrgan = device_codeformer = torch.device("cuda")
dtype = torch.float16
dtype_vae = torch.float16
dtype_unet = torch.float16
unet_needs_upcast = False


def cond_cast_unet(input):
    return input.to(dtype_unet) if unet_needs_upcast else input


def cond_cast_float(input):
    return input.float() if unet_needs_upcast else input


def randn(seed, shape):
    torch.manual_seed(seed)
    return torch.randn(shape, device=device)


def randn_without_seed(shape):
    return torch.randn(shape, device=device)


def autocast(disable=False):
    if disable:
        return contextlib.nullcontext()

    return torch.autocast("cuda")


def without_autocast(disable=False):
    return torch.autocast("cuda", enabled=False) if torch.is_autocast_enabled() and not disable else contextlib.nullcontext()


class NansException(Exception):
    pass


def test_for_nans(x, where):
    if not torch.all(torch.isnan(x)).item():
        return

    if where == "unet":
        message = "在 Unet 中生成了一个包含所有 NaNs 的张量。"

    elif where == "vae":
        message = "在 VAE 中生成了一个包含所有 NaN 的张量。"

    else:
        message = "产生了一个包含所有 NaN 的张量。"

    message += " 使用 --disable-nan-check 命令行参数禁用此检查。"

    raise NansException(message)


@lru_cache
def first_time_calculation():
    """

    只要用 pytorch 层进行任何计算，第一次计算就会分配约 700MB 内存，耗时约 2.7 秒，至少在 NVIDIA 上是这样。

    """

    x = torch.zeros((1, 1)).to(device, dtype)
    linear = torch.nn.Linear(1, 1).to(device, dtype)
    linear(x)

    x = torch.zeros((1, 1, 3, 3)).to(device, dtype)
    conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype)
    conv2d(x)