"""
    This file is part of ComfyUI.
    Copyright (C) 2024 Comfy

    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <https://www.gnu.org/licenses/>.
"""
from __future__ import annotations

import logging
import platform
import sys
import warnings
from enum import Enum
from threading import RLock
from typing import Literal, List, Sequence, Final

import psutil
import torch
from opentelemetry.trace import get_current_span

from . import interruption
from .cli_args import args
from .cmd.main_pre import tracer
from .component_model.deprecation import _deprecate_method
from .model_management_types import ModelManageable

model_management_lock = RLock()


class VRAMState(Enum):
    DISABLED = 0  # No vram present: no need to move models to vram
    NO_VRAM = 1  # Very low vram: enable all the options to save vram
    LOW_VRAM = 2
    NORMAL_VRAM = 3
    HIGH_VRAM = 4
    SHARED = 5  # No dedicated vram: memory shared between CPU and GPU but models still need to be moved between both.


class CPUState(Enum):
    GPU = 0
    CPU = 1
    MPS = 2


# Determine VRAM State
vram_state = VRAMState.NORMAL_VRAM
set_vram_to = VRAMState.NORMAL_VRAM
cpu_state = CPUState.GPU

total_vram = 0

xpu_available = False
torch_version = ""
try:
    torch_version = torch.version.__version__
    xpu_available = (int(torch_version[0]) < 2 or (int(torch_version[0]) == 2 and int(torch_version[2]) <= 4)) and torch.xpu.is_available()
except:
    pass

lowvram_available = True
if args.deterministic:
    logging.info("Using deterministic algorithms for pytorch")
    torch.use_deterministic_algorithms(True, warn_only=True)

directml_device = None
if args.directml is not None:
    import torch_directml  # pylint: disable=import-error

    device_index = args.directml
    if device_index < 0:
        directml_device = torch_directml.device()
    else:
        directml_device = torch_directml.device(device_index)
    logging.info("Using directml with device: {}".format(torch_directml.device_name(device_index)))
    # torch_directml.disable_tiled_resources(True)
    lowvram_available = False  # TODO: need to find a way to get free memory in directml before this can be enabled by default.

try:
    import intel_extension_for_pytorch as ipex  # pylint: disable=import-error

    _ = torch.xpu.device_count()
    xpu_available = torch.xpu.is_available()
except:
    xpu_available = xpu_available or (hasattr(torch, "xpu") and torch.xpu.is_available())

try:
    if torch.backends.mps.is_available():
        cpu_state = CPUState.MPS
        import torch.mps
except:
    pass

if args.cpu:
    cpu_state = CPUState.CPU


def is_intel_xpu():
    global cpu_state
    global xpu_available
    if cpu_state == CPUState.GPU:
        if xpu_available:
            return True
    return False


def get_torch_device():
    global directml_device
    global cpu_state
    if directml_device:
        return directml_device
    if cpu_state == CPUState.MPS:
        return torch.device("mps")
    if cpu_state == CPUState.CPU:
        return torch.device("cpu")
    else:
        if is_intel_xpu():
            return torch.device("xpu", torch.xpu.current_device())
        else:
            try:
                # https://github.com/sayakpaul/diffusers-torchao/blob/bade7a6abb1cab9ef44782e6bcfab76d0237ae1f/inference/benchmark_image.py#L3
                # This setting optimizes performance on NVIDIA GPUs with Ampere architecture (e.g., A100, RTX 30 series) or newer.
                torch.set_float32_matmul_precision("high")
                return torch.device(torch.cuda.current_device())
            except:
                warnings.warn("torch.cuda.current_device() did not return a device, returning a CPU torch device")
                return torch.device("cpu")


def get_total_memory(dev=None, torch_total_too=False):
    global directml_device
    if dev is None:
        dev = get_torch_device()

    if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'):
        mem_total = psutil.virtual_memory().total
        mem_total_torch = mem_total
    else:
        if directml_device:
            mem_total = 1024 * 1024 * 1024  # TODO
            mem_total_torch = mem_total
        elif is_intel_xpu():
            mem_total = torch.xpu.get_device_properties(dev).total_memory
            mem_total_torch = mem_total
        else:
            stats = torch.cuda.memory_stats(dev)
            mem_reserved = stats['reserved_bytes.all.current']
            _, mem_total_cuda = torch.cuda.mem_get_info(dev)
            mem_total_torch = mem_reserved
            mem_total = mem_total_cuda
    if torch_total_too:
        return mem_total, mem_total_torch
    else:
        return mem_total


# we're required to call get_device_name early on to initialize the methods get_total_memory will call
if torch.cuda.is_available() and hasattr(torch.version, "hip") and torch.version.hip is not None:
    logging.info(f"Detected HIP device: {torch.cuda.get_device_name(torch.cuda.current_device())}")
total_vram = get_total_memory(get_torch_device()) / (1024 * 1024)
total_ram = psutil.virtual_memory().total / (1024 * 1024)
logging.debug("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram))

try:
    logging.debug("pytorch version: {}".format(torch.version.__version__))
except:
    pass


class _ComfyOutOfMemoryException(RuntimeError):
    pass


try:
    OOM_EXCEPTION = torch.cuda.OutOfMemoryError
except:
    OOM_EXCEPTION = _ComfyOutOfMemoryException

XFORMERS_VERSION = ""
XFORMERS_ENABLED_VAE = True
if args.disable_xformers:
    XFORMERS_IS_AVAILABLE = False
else:
    try:
        import xformers  # pylint: disable=import-error
        import xformers.ops  # pylint: disable=import-error

        XFORMERS_IS_AVAILABLE = True
        try:
            XFORMERS_IS_AVAILABLE = xformers._has_cpp_library
        except:
            pass
        try:
            XFORMERS_VERSION = xformers.version.__version__
            logging.debug("xformers version: {}".format(XFORMERS_VERSION))
            if XFORMERS_VERSION.startswith("0.0.18"):
                logging.warning("\nWARNING: This version of xformers has a major bug where you will get black images when generating high resolution images.")
                logging.warning("Please downgrade or upgrade xformers to a different version.\n")
                XFORMERS_ENABLED_VAE = False
        except:
            pass
    except:
        XFORMERS_IS_AVAILABLE = False


def is_nvidia():
    global cpu_state
    if cpu_state == CPUState.GPU:
        if torch.version.cuda:
            return True
    return False


def is_amd():
    global cpu_state
    if cpu_state == CPUState.GPU:
        if torch.version.hip:
            return True
    return False


ENABLE_PYTORCH_ATTENTION = False
if args.use_pytorch_cross_attention:
    ENABLE_PYTORCH_ATTENTION = True
    XFORMERS_IS_AVAILABLE = False

VAE_DTYPES = [torch.float32]

try:
    if is_nvidia() or is_amd():
        if int(torch_version[0]) >= 2:
            if ENABLE_PYTORCH_ATTENTION == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
                ENABLE_PYTORCH_ATTENTION = True
            if torch.cuda.is_bf16_supported() and torch.cuda.get_device_properties(torch.cuda.current_device()).major >= 8:
                VAE_DTYPES = [torch.bfloat16] + VAE_DTYPES
    if is_intel_xpu():
        if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
            ENABLE_PYTORCH_ATTENTION = True
except:
    pass

if is_intel_xpu():
    VAE_DTYPES = [torch.bfloat16] + VAE_DTYPES

if args.cpu_vae:
    VAE_DTYPES = [torch.float32]

if ENABLE_PYTORCH_ATTENTION:
    torch.backends.cuda.enable_math_sdp(True)
    torch.backends.cuda.enable_flash_sdp(True)
    torch.backends.cuda.enable_mem_efficient_sdp(True)

if args.lowvram:
    set_vram_to = VRAMState.LOW_VRAM
    lowvram_available = True
elif args.novram:
    set_vram_to = VRAMState.NO_VRAM
elif args.highvram or args.gpu_only:
    vram_state = VRAMState.HIGH_VRAM

FORCE_FP32 = False
FORCE_FP16 = False
FORCE_BF16 = False
if args.force_fp32:
    logging.info("Forcing FP32, if this improves things please report it.")
    FORCE_FP32 = True

if args.force_fp16 or cpu_state == CPUState.MPS:
    logging.info("Forcing FP16.")
    FORCE_FP16 = True

if args.force_bf16:
    logging.info("Force BF16")
    FORCE_BF16 = True

if lowvram_available:
    if set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM):
        vram_state = set_vram_to

if cpu_state != CPUState.GPU:
    vram_state = VRAMState.DISABLED

if cpu_state == CPUState.MPS:
    vram_state = VRAMState.SHARED

logging.debug(f"Set vram state to: {vram_state.name}")

DISABLE_SMART_MEMORY = args.disable_smart_memory

if DISABLE_SMART_MEMORY:
    logging.debug("Disabling smart memory management")


def get_torch_device_name(device):
    if hasattr(device, 'type'):
        if device.type == "cuda":
            try:
                allocator_backend = torch.cuda.get_allocator_backend()
            except:
                allocator_backend = ""
            return "{} {} : {}".format(device, torch.cuda.get_device_name(device), allocator_backend)
        else:
            return "{}".format(device.type)
    elif is_intel_xpu():
        return "{} {}".format(device, torch.xpu.get_device_name(device))
    else:
        return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device))


try:
    logging.debug("Device: {}".format(get_torch_device_name(get_torch_device())))
except:
    logging.warning("Could not pick default device.")

current_loaded_models: Final[List["LoadedModel"]] = []


def module_size(module):
    module_mem = 0
    sd = module.state_dict()
    for k in sd:
        t = sd[k]
        module_mem += t.nelement() * t.element_size()
    return module_mem


class LoadedModel:
    def __init__(self, model: ModelManageable):
        self.model = model
        self.device = model.load_device
        self.weights_loaded = False
        self.real_model = None
        self.currently_used = True

    def model_memory(self):
        return self.model.model_size()

    def model_offloaded_memory(self):
        return self.model.model_size() - self.model.loaded_size()

    def model_memory_required(self, device):
        if device == self.model.current_loaded_device():
            return self.model_offloaded_memory()
        else:
            return self.model_memory()

    def model_load(self, lowvram_model_memory=0, force_patch_weights=False):
        patch_model_to = self.device

        self.model.model_patches_to(self.device)
        self.model.model_patches_to(self.model.model_dtype())

        load_weights = not self.weights_loaded

        if self.model.loaded_size() > 0:
            use_more_vram = lowvram_model_memory
            if use_more_vram == 0:
                use_more_vram = 1e32
            self.model_use_more_vram(use_more_vram)
        else:
            try:
                self.real_model = self.model.patch_model(device_to=patch_model_to, lowvram_model_memory=lowvram_model_memory, load_weights=load_weights, force_patch_weights=force_patch_weights)
            except Exception as e:
                self.model.unpatch_model(self.model.offload_device)
                self.model_unload()
                raise e

        if is_intel_xpu() and not args.disable_ipex_optimize and 'ipex' in globals() and self.real_model is not None:
            with torch.no_grad():
                self.real_model = ipex.optimize(self.real_model.eval(), inplace=True, graph_mode=True, concat_linear=True)

        self.weights_loaded = True
        return self.real_model

    def should_reload_model(self, force_patch_weights=False):
        if force_patch_weights and self.model.lowvram_patch_counter() > 0:
            return True
        return False

    def model_unload(self, memory_to_free=None, unpatch_weights=True):
        if memory_to_free is not None:
            if memory_to_free < self.model.loaded_size():
                freed = self.model.partially_unload(self.model.offload_device, memory_to_free)
                if freed >= memory_to_free:
                    return False
        self.model.unpatch_model(self.model.offload_device, unpatch_weights=unpatch_weights)
        self.model.model_patches_to(self.model.offload_device)
        self.weights_loaded = self.weights_loaded and not unpatch_weights
        self.real_model = None
        return True

    def model_use_more_vram(self, extra_memory):
        return self.model.partially_load(self.device, extra_memory)

    def __eq__(self, other):
        return self.model is other.model

    def __str__(self):
        if self.model is not None:
            return f"<LoadedModel {str(self.model)}>"
        else:
            return f"<LoadedModel>"


def use_more_memory(extra_memory, loaded_models, device):
    for m in loaded_models:
        if m.device == device:
            extra_memory -= m.model_use_more_vram(extra_memory)
            if extra_memory <= 0:
                break


def offloaded_memory(loaded_models, device):
    offloaded_mem = 0
    for m in loaded_models:
        if m.device == device:
            offloaded_mem += m.model_offloaded_memory()
    return offloaded_mem


WINDOWS = any(platform.win32_ver())

EXTRA_RESERVED_VRAM = 400 * 1024 * 1024
if WINDOWS:
    EXTRA_RESERVED_VRAM = 600 * 1024 * 1024  # Windows is higher because of the shared vram issue

if args.reserve_vram is not None:
    EXTRA_RESERVED_VRAM = args.reserve_vram * 1024 * 1024 * 1024
    logging.debug("Reserving {}MB vram for other applications.".format(EXTRA_RESERVED_VRAM / (1024 * 1024)))


def extra_reserved_memory():
    return EXTRA_RESERVED_VRAM


def minimum_inference_memory():
    return (1024 * 1024 * 1024) * 0.8 + extra_reserved_memory()


def unload_model_clones(model, unload_weights_only=True, force_unload=True) -> bool | Literal[None]:
    with model_management_lock:
        return _unload_model_clones(model, unload_weights_only, force_unload)


def _unload_model_clones(model, unload_weights_only=True, force_unload=True) -> bool | Literal[None]:
    to_unload = []
    for i in range(len(current_loaded_models)):
        if model.is_clone(current_loaded_models[i].model):
            to_unload = [i] + to_unload

    if len(to_unload) == 0:
        return True

    same_weights = 0
    for i in to_unload:
        if model.clone_has_same_weights(current_loaded_models[i].model):
            same_weights += 1

    if same_weights == len(to_unload):
        unload_weight = False
    else:
        unload_weight = True

    if not force_unload:
        if unload_weights_only and unload_weight == False:
            return None
    else:
        unload_weight = True

    for i in to_unload:
        logging.debug("unload clone {} {}".format(i, unload_weight))
        current_loaded_models.pop(i).model_unload(unpatch_weights=unload_weight)

    return unload_weight


@tracer.start_as_current_span("Free Memory")
def free_memory(memory_required, device, keep_loaded=[]) -> List[LoadedModel]:
    span = get_current_span()
    span.set_attribute("memory_required", memory_required)
    with model_management_lock:
        unloaded_models = _free_memory(memory_required, device, keep_loaded)
        span.set_attribute("unloaded_models", list(map(str, unloaded_models)))
        return unloaded_models


def _free_memory(memory_required, device, keep_loaded=[]) -> List[LoadedModel]:
    unloaded_model = []
    can_unload = []
    unloaded_models = []

    for i in range(len(current_loaded_models) - 1, -1, -1):
        shift_model = current_loaded_models[i]
        if shift_model.device == device:
            if shift_model not in keep_loaded:
                can_unload.append((-shift_model.model_offloaded_memory(), sys.getrefcount(shift_model.model), shift_model.model_memory(), i))
                shift_model.currently_used = False

    for x in sorted(can_unload):
        i = x[-1]
        memory_to_free = None
        if not DISABLE_SMART_MEMORY:
            free_mem = get_free_memory(device)
            if free_mem > memory_required:
                break
            memory_to_free = memory_required - free_mem
        logging.debug(f"Unloading {current_loaded_models[i].model.model.__class__.__name__}")
        if current_loaded_models[i].model_unload(memory_to_free):
            unloaded_model.append(i)

    for i in sorted(unloaded_model, reverse=True):
        unloaded_models.append(current_loaded_models.pop(i))

    if len(unloaded_model) > 0:
        soft_empty_cache()
    else:
        if vram_state != VRAMState.HIGH_VRAM:
            mem_free_total, mem_free_torch = get_free_memory(device, torch_free_too=True)
            if mem_free_torch > mem_free_total * 0.25:
                soft_empty_cache()
    return unloaded_models


@tracer.start_as_current_span("Load Models GPU")
def load_models_gpu(models: Sequence[ModelManageable], memory_required: int = 0, force_patch_weights=False, minimum_memory_required=None, force_full_load=False) -> None:
    span = get_current_span()
    if memory_required != 0:
        span.set_attribute("memory_required", memory_required)
    with model_management_lock:
        _load_models_gpu(models, memory_required, force_patch_weights, minimum_memory_required, force_full_load)
        to_load = list(map(str, models))
        span.set_attribute("models", to_load)
        logging.info(f"Loaded {to_load}")


def _load_models_gpu(models: Sequence[ModelManageable], memory_required: int = 0, force_patch_weights=False, minimum_memory_required=None, force_full_load=False) -> None:
    global vram_state

    inference_memory = minimum_inference_memory()
    extra_mem = max(inference_memory, memory_required + extra_reserved_memory())
    if minimum_memory_required is None:
        minimum_memory_required = extra_mem
    else:
        minimum_memory_required = max(inference_memory, minimum_memory_required + extra_reserved_memory())

    models = set(models)

    models_to_load = []
    models_already_loaded = []
    models_freed = []
    for x in models:
        loaded_model = LoadedModel(x)
        loaded = None

        try:
            loaded_model_index = current_loaded_models.index(loaded_model)
        except:
            loaded_model_index = None

        if loaded_model_index is not None:
            loaded = current_loaded_models[loaded_model_index]
            if loaded.should_reload_model(force_patch_weights=force_patch_weights):  # TODO: cleanup this model reload logic
                current_loaded_models.pop(loaded_model_index).model_unload(unpatch_weights=True)
                loaded = None
            else:
                loaded.currently_used = True
                models_already_loaded.append(loaded)

        if loaded is None:
            models_to_load.append(loaded_model)

    if len(models_to_load) == 0:
        devs = set(map(lambda a: a.device, models_already_loaded))
        for d in devs:
            if d != torch.device("cpu"):
                free_memory(extra_mem + offloaded_memory(models_already_loaded, d), d, models_already_loaded)
                free_mem = get_free_memory(d)
                if free_mem < minimum_memory_required:
                    models_to_load = free_memory(minimum_memory_required, d)
                    models_freed += models_to_load
                else:
                    use_more_memory(free_mem - minimum_memory_required, models_already_loaded, d)
        if len(models_to_load) == 0:
            return

    total_memory_required = {}
    for loaded_model in models_to_load:
        unload_model_clones(loaded_model.model, unload_weights_only=True, force_unload=False)  # unload clones where the weights are different
        total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device)

    for loaded_model in models_already_loaded:
        total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device)

    for loaded_model in models_to_load:
        weights_unloaded = unload_model_clones(loaded_model.model, unload_weights_only=False, force_unload=False)  # unload the rest of the clones where the weights can stay loaded
        if weights_unloaded is not None:
            loaded_model.weights_loaded = not weights_unloaded

    for device in total_memory_required:
        if device != torch.device("cpu"):
            models_freed += free_memory(total_memory_required[device] * 1.1 + extra_mem, device, models_already_loaded)

    for loaded_model in models_to_load:
        model = loaded_model.model
        torch_dev = model.load_device
        if is_device_cpu(torch_dev):
            vram_set_state = VRAMState.DISABLED
        else:
            vram_set_state = vram_state
        lowvram_model_memory = 0
        if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM) and not force_full_load:
            model_size = loaded_model.model_memory_required(torch_dev)
            current_free_mem = get_free_memory(torch_dev)
            lowvram_model_memory = max(64 * (1024 * 1024), (current_free_mem - minimum_memory_required), min(current_free_mem * 0.4, current_free_mem - minimum_inference_memory()))
            if model_size <= lowvram_model_memory:  # only switch to lowvram if really necessary
                lowvram_model_memory = 0

        if vram_set_state == VRAMState.NO_VRAM:
            lowvram_model_memory = 64 * 1024 * 1024

        cur_loaded_model = loaded_model.model_load(lowvram_model_memory, force_patch_weights=force_patch_weights)
        current_loaded_models.insert(0, loaded_model)

    devs = set(map(lambda a: a.device, models_already_loaded))
    for d in devs:
        if d != torch.device("cpu"):
            free_mem = get_free_memory(d)
            if free_mem > minimum_memory_required:
                use_more_memory(free_mem - minimum_memory_required, models_already_loaded, d)

    span = get_current_span()
    span.set_attribute("models_to_load", list(map(str, models_to_load)))
    span.set_attribute("models_freed", list(map(str, models_freed)))


@_deprecate_method(message="Use load_models_gpu instead", version="0.0.2")
def load_model_gpu(model):
    return load_models_gpu([model])


def loaded_models(only_currently_used=False):
    with model_management_lock:
        output = []
        for m in current_loaded_models:
            if only_currently_used:
                if not m.currently_used:
                    continue

            output.append(m.model)
        return output


def cleanup_models(keep_clone_weights_loaded=False):
    with model_management_lock:
        to_delete = []
        for i in range(len(current_loaded_models)):
            # TODO: very fragile function needs improvement
            num_refs = sys.getrefcount(current_loaded_models[i].model)
            if num_refs <= 2:
                if not keep_clone_weights_loaded:
                    to_delete = [i] + to_delete
                # TODO: find a less fragile way to do this.
                elif sys.getrefcount(current_loaded_models[i].real_model) <= 3:  # references from .real_model + the .model
                    to_delete = [i] + to_delete

        for i in to_delete:
            x = current_loaded_models.pop(i)
            x.model_unload()
            del x


def dtype_size(dtype):
    dtype_size = 4
    if dtype == torch.float16 or dtype == torch.bfloat16:
        dtype_size = 2
    elif dtype == torch.float32:
        dtype_size = 4
    else:
        try:
            dtype_size = dtype.itemsize
        except:  # Old pytorch doesn't have .itemsize
            pass
    return dtype_size


def unet_offload_device():
    if vram_state == VRAMState.HIGH_VRAM:
        return get_torch_device()
    else:
        return torch.device("cpu")


def unet_initial_load_device(parameters, dtype):
    torch_dev = get_torch_device()
    if vram_state == VRAMState.HIGH_VRAM:
        return torch_dev

    cpu_dev = torch.device("cpu")
    if DISABLE_SMART_MEMORY:
        return cpu_dev

    model_size = dtype_size(dtype) * parameters

    mem_dev = get_free_memory(torch_dev)
    mem_cpu = get_free_memory(cpu_dev)
    if mem_dev > mem_cpu and model_size < mem_dev:
        return torch_dev
    else:
        return cpu_dev


def maximum_vram_for_weights(device=None) -> int:
    return get_total_memory(device) * 0.88 - minimum_inference_memory()


def unet_dtype(device=None, model_params=0, supported_dtypes=(torch.float16, torch.bfloat16, torch.float32)):
    if model_params < 0:
        model_params = 1000000000000000000000
    if args.bf16_unet:
        return torch.bfloat16
    if args.fp16_unet:
        return torch.float16
    if args.fp8_e4m3fn_unet:
        return torch.float8_e4m3fn
    if args.fp8_e5m2_unet:
        return torch.float8_e5m2

    fp8_dtype = None
    try:
        for dtype in [torch.float8_e4m3fn, torch.float8_e5m2]:
            if dtype in supported_dtypes:
                fp8_dtype = dtype
                break
    except:
        pass

    if fp8_dtype is not None:
        free_model_memory = maximum_vram_for_weights(device)
        if model_params * 2 > free_model_memory:
            return fp8_dtype

    for dt in supported_dtypes:
        if dt == torch.float16 and should_use_fp16(device=device, model_params=model_params):
            if torch.float16 in supported_dtypes:
                return torch.float16
        if dt == torch.bfloat16 and should_use_bf16(device, model_params=model_params):
            if torch.bfloat16 in supported_dtypes:
                return torch.bfloat16

    for dt in supported_dtypes:
        if dt == torch.float16 and should_use_fp16(device=device, model_params=model_params, manual_cast=True):
            if torch.float16 in supported_dtypes:
                return torch.float16
        if dt == torch.bfloat16 and should_use_bf16(device, model_params=model_params, manual_cast=True):
            if torch.bfloat16 in supported_dtypes:
                return torch.bfloat16

    return torch.float32


# None means no manual cast
def unet_manual_cast(weight_dtype, inference_device, supported_dtypes=(torch.float16, torch.bfloat16, torch.float32)):
    if weight_dtype == torch.float32:
        return None

    fp16_supported = should_use_fp16(inference_device, prioritize_performance=False)
    if fp16_supported and weight_dtype == torch.float16:
        return None

    bf16_supported = should_use_bf16(inference_device)
    if bf16_supported and weight_dtype == torch.bfloat16:
        return None

    fp16_supported = should_use_fp16(inference_device, prioritize_performance=True)
    for dt in supported_dtypes:
        if dt == torch.float16 and fp16_supported:
            return torch.float16
        if dt == torch.bfloat16 and bf16_supported:
            return torch.bfloat16

    return torch.float32


def text_encoder_offload_device():
    if args.gpu_only:
        return get_torch_device()
    else:
        return torch.device("cpu")


def text_encoder_device():
    if args.gpu_only:
        return get_torch_device()
    elif vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.NORMAL_VRAM:
        if should_use_fp16(prioritize_performance=False):
            return get_torch_device()
        else:
            return torch.device("cpu")
    else:
        return torch.device("cpu")


def text_encoder_initial_device(load_device, offload_device, model_size=0):
    if load_device == offload_device or model_size <= 1024 * 1024 * 1024:
        return offload_device

    if is_device_mps(load_device):
        return offload_device

    mem_l = get_free_memory(load_device)
    mem_o = get_free_memory(offload_device)
    if mem_l > (mem_o * 0.5) and model_size * 1.2 < mem_l:
        return load_device
    else:
        return offload_device


def text_encoder_dtype(device=None):
    if args.fp8_e4m3fn_text_enc:
        return torch.float8_e4m3fn
    elif args.fp8_e5m2_text_enc:
        return torch.float8_e5m2
    elif args.fp16_text_enc:
        return torch.float16
    elif args.fp32_text_enc:
        return torch.float32

    if is_device_cpu(device):
        return torch.float16

    return torch.float16


def intermediate_device():
    if args.gpu_only:
        return get_torch_device()
    else:
        return torch.device("cpu")


def vae_device():
    if args.cpu_vae:
        return torch.device("cpu")
    return get_torch_device()


def vae_offload_device():
    if args.gpu_only:
        return get_torch_device()
    else:
        return torch.device("cpu")


def vae_dtype(device=None, allowed_dtypes=[]):
    global VAE_DTYPES
    if args.fp16_vae:
        return torch.float16
    elif args.bf16_vae:
        return torch.bfloat16
    elif args.fp32_vae:
        return torch.float32

    for d in allowed_dtypes:
        if d == torch.float16 and should_use_fp16(device, prioritize_performance=False):
            return d
        if d in VAE_DTYPES:
            return d

    return VAE_DTYPES[0]


def get_autocast_device(dev):
    if hasattr(dev, 'type'):
        return dev.type
    return "cuda"


def supports_dtype(device, dtype):  # TODO
    if dtype == torch.float32:
        return True
    if is_device_cpu(device):
        return False
    if dtype == torch.float16:
        return True
    if dtype == torch.bfloat16:
        return True
    return False


def supports_cast(device, dtype):  # TODO
    if dtype == torch.float32:
        return True
    if dtype == torch.float16:
        return True
    if directml_device:  # TODO: test this
        return False
    if dtype == torch.bfloat16:
        return True
    if is_device_mps(device):
        return False
    if dtype == torch.float8_e4m3fn:
        return True
    if dtype == torch.float8_e5m2:
        return True
    return False


def pick_weight_dtype(dtype, fallback_dtype, device=None):
    if dtype is None:
        dtype = fallback_dtype
    elif dtype_size(dtype) > dtype_size(fallback_dtype):
        dtype = fallback_dtype

    if not supports_cast(device, dtype):
        dtype = fallback_dtype

    return dtype


def device_supports_non_blocking(device):
    if is_device_mps(device):
        return False  # pytorch bug? mps doesn't support non blocking
    if is_intel_xpu():
        return False
    if args.deterministic:  # TODO: figure out why deterministic breaks non blocking from gpu to cpu (previews)
        return False
    if directml_device:
        return False
    return True


def device_should_use_non_blocking(device):
    if not device_supports_non_blocking(device):
        return False
    return False
    # return True #TODO: figure out why this causes memory issues on Nvidia and possibly others


def force_channels_last():
    if args.force_channels_last:
        return True

    # TODO
    return False


def cast_to_device(tensor, device, dtype, copy=False):
    with model_management_lock:
        device_supports_cast = False
        if tensor.dtype == torch.float32 or tensor.dtype == torch.float16:
            device_supports_cast = True
        elif tensor.dtype == torch.bfloat16:
            if hasattr(device, 'type') and device.type.startswith("cuda"):
                device_supports_cast = True
            elif is_intel_xpu():
                device_supports_cast = True

        non_blocking = device_should_use_non_blocking(device)

        if device_supports_cast:
            if copy:
                if tensor.device == device:
                    return tensor.to(dtype, copy=copy, non_blocking=non_blocking)
                return tensor.to(device, copy=copy, non_blocking=non_blocking).to(dtype, non_blocking=non_blocking)
            else:
                return tensor.to(device, non_blocking=non_blocking).to(dtype, non_blocking=non_blocking)
        else:
            return tensor.to(device, dtype, copy=copy, non_blocking=non_blocking)

FLASH_ATTENTION_ENABLED = False
if not args.disable_flash_attn:
    try:
        import flash_attn
        FLASH_ATTENTION_ENABLED = True
    except ImportError:
        pass

SAGE_ATTENTION_ENABLED = False
if not args.disable_sage_attention:
    try:
        import sageattention
        SAGE_ATTENTION_ENABLED = True
    except ImportError:
        pass


def xformers_enabled():
    global directml_device
    global cpu_state
    if cpu_state != CPUState.GPU:
        return False
    if is_intel_xpu():
        return False
    if directml_device:
        return False
    return XFORMERS_IS_AVAILABLE

def flash_attn_enabled():
    global directml_device
    global cpu_state
    if cpu_state != CPUState.GPU:
        return False
    if is_intel_xpu():
        return False
    if directml_device:
        return False
    return FLASH_ATTENTION_ENABLED

def sage_attention_enabled():
    global directml_device
    global cpu_state
    if cpu_state != CPUState.GPU:
        return False
    if is_intel_xpu():
        return False
    if directml_device:
        return False
    if xformers_enabled():
        return False
    return SAGE_ATTENTION_ENABLED


def xformers_enabled_vae():
    enabled = xformers_enabled()
    if not enabled:
        return False

    return XFORMERS_ENABLED_VAE


def pytorch_attention_enabled():
    global ENABLE_PYTORCH_ATTENTION
    return ENABLE_PYTORCH_ATTENTION


def pytorch_attention_flash_attention():
    global ENABLE_PYTORCH_ATTENTION
    if ENABLE_PYTORCH_ATTENTION:
        # TODO: more reliable way of checking for flash attention?
        if is_nvidia():  # pytorch flash attention only works on Nvidia
            return True
        if is_intel_xpu():
            return True
    return False


def force_upcast_attention_dtype():
    upcast = args.force_upcast_attention
    try:
        macos_version = tuple(int(n) for n in platform.mac_ver()[0].split("."))
        if (14, 5) <= macos_version < (14, 7):  # black image bug on recent versions of MacOS
            upcast = True
    except:
        pass
    if upcast:
        return torch.float32
    else:
        return None


def get_free_memory(dev=None, torch_free_too=False):
    global directml_device
    if dev is None:
        dev = get_torch_device()

    if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'):
        mem_free_total = psutil.virtual_memory().available
        mem_free_torch = mem_free_total
    else:
        if directml_device:
            mem_free_total = 1024 * 1024 * 1024  # TODO
            mem_free_torch = mem_free_total
        elif is_intel_xpu():
            mem_free_total = torch.xpu.get_device_properties(dev).total_memory
            mem_free_torch = mem_free_total
        else:
            stats = torch.cuda.memory_stats(dev)
            mem_active = stats['active_bytes.all.current']
            mem_reserved = stats['reserved_bytes.all.current']
            mem_free_cuda, _ = torch.cuda.mem_get_info(dev)
            mem_free_torch = mem_reserved - mem_active
            mem_free_total = mem_free_cuda + mem_free_torch

    if torch_free_too:
        return (mem_free_total, mem_free_torch)
    else:
        return mem_free_total


def cpu_mode():
    global cpu_state
    return cpu_state == CPUState.CPU


def mps_mode():
    global cpu_state
    return cpu_state == CPUState.MPS


def is_device_type(device, type):
    if hasattr(device, 'type'):
        if (device.type == type):
            return True
    return False


def is_device_cpu(device):
    return is_device_type(device, 'cpu')


def is_device_mps(device):
    return is_device_type(device, 'mps')


def is_device_cuda(device):
    return is_device_type(device, 'cuda')


def should_use_fp16(device=None, model_params=0, prioritize_performance=True, manual_cast=False):
    global directml_device

    if device is not None:
        if is_device_cpu(device):
            return False

    if FORCE_FP16:
        return True

    if device is not None:
        if is_device_mps(device):
            return True

    if FORCE_FP32:
        return False

    if directml_device:
        return False

    if mps_mode():
        return True

    if cpu_mode():
        return False

    if is_intel_xpu():
        return True

    if is_amd():
        return True
    try:
        props = torch.cuda.get_device_properties(device)
        if props.major >= 8:
            return True

        if props.major < 6:
            return False
    except AssertionError:
        logging.warning("Torch was not compiled with cuda support")
        return False

    # FP16 is confirmed working on a 1080 (GP104) and on latest pytorch actually seems faster than fp32
    nvidia_10_series = ["1080", "1070", "titan x", "p3000", "p3200", "p4000", "p4200", "p5000", "p5200", "p6000", "1060", "1050", "p40", "p100", "p6", "p4"]
    for x in nvidia_10_series:
        if x in props.name.lower():
            if WINDOWS or manual_cast:
                return True
            else:
                return False  # weird linux behavior where fp32 is faster

    if manual_cast:
        free_model_memory = maximum_vram_for_weights(device)
        if (not prioritize_performance) or model_params * 4 > free_model_memory:
            return True

    if props.major < 7:
        return False

    # FP16 is just broken on these cards
    nvidia_16_series = ["1660", "1650", "1630", "T500", "T550", "T600", "MX550", "MX450", "CMP 30HX", "T2000", "T1000", "T1200"]
    for x in nvidia_16_series:
        if x in props.name:
            return False

    return True


def should_use_bf16(device=None, model_params=0, prioritize_performance=True, manual_cast=False):
    if FORCE_BF16:
        return True

    if device is not None:
        if is_device_cpu(device):  # TODO ? bf16 works on CPU but is extremely slow
            return False

    if device is not None:
        if is_device_mps(device):
            return True

    if FORCE_FP32:
        return False

    if directml_device:
        return False

    if mps_mode():
        return True

    if cpu_mode():
        return False

    if is_intel_xpu():
        return True

    if device is None:
        device = torch.device("cuda")

    try:
        props = torch.cuda.get_device_properties(device)
        if props.major >= 8:
            return True
    except AssertionError:
        logging.warning("Torch was not compiled with CUDA support")
        return False

    bf16_works = torch.cuda.is_bf16_supported()

    if bf16_works or manual_cast:
        free_model_memory = maximum_vram_for_weights(device)
        if (not prioritize_performance) or model_params * 4 > free_model_memory:
            return True

    return False


def supports_fp8_compute(device=None):
    props = torch.cuda.get_device_properties(device)
    if props.major >= 9:
        return True
    if props.major < 8:
        return False
    if props.minor < 9:
        return False
    return True


def soft_empty_cache(force=False):
    with model_management_lock:
        global cpu_state
        if cpu_state == CPUState.MPS:
            torch.mps.empty_cache()
        elif is_intel_xpu():
            torch.xpu.empty_cache()  # pylint: disable=no-member
        elif torch.cuda.is_available():
            if force or is_nvidia():  # This seems to make things worse on ROCm so I only do it for cuda
                torch.cuda.empty_cache()
                torch.cuda.ipc_collect()


def unload_all_models():
    with model_management_lock:
        free_memory(1e30, get_torch_device())


def resolve_lowvram_weight(weight, model, key):  # TODO: remove
    warnings.warn("The comfy.model_management.resolve_lowvram_weight function will be removed soon, please stop using it.", category=DeprecationWarning)
    return weight


def interrupt_current_processing(value=True):
    interruption.interrupt_current_processing(value)


def processing_interrupted():
    interruption.processing_interrupted()


def throw_exception_if_processing_interrupted():
    interruption.throw_exception_if_processing_interrupted()