from __future__ import annotations

import copy
import logging
from inspect import cleandoc
from typing import TYPE_CHECKING
from typing_extensions import override

from comfy_api.latest import ComfyExtension, io

if TYPE_CHECKING:
    from comfy.model_patcher import ModelPatcher
    from comfy.sd import CLIP, VAE
import torch

import comfy.model_management
import comfy.multigpu


class MultiGPUCFGSplitNode(io.ComfyNode):
    """
    Prepares model to have sampling accelerated via splitting work units.

    Should be placed after nodes that modify the model object itself, such as compile or attention-switch nodes.

    Other than those exceptions, this node can be placed in any order.
    """

    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="MultiGPU_WorkUnits",
            display_name="MultiGPU CFG Split",
            category="advanced/multigpu",
            description=cleandoc(cls.__doc__),
            inputs=[
                io.Model.Input("model"),
                io.Int.Input("max_gpus", default=2, min=1, step=1),
            ],
            outputs=[
                io.Model.Output(),
            ],
        )

    @classmethod
    def execute(cls, model: ModelPatcher, max_gpus: int) -> io.NodeOutput:
        model = comfy.multigpu.create_multigpu_deepclones(model, max_gpus, reuse_loaded=True)
        return io.NodeOutput(model)


def _force_fp32_cpu_compute(patcher: ModelPatcher):
    """Force fp32 inference dtype for CPU.

    PyTorch's CPU conv2d kernels fall back to software emulation for fp16/bf16
    and run ~500-600x slower than fp32, which makes a normal-sized workflow
    look frozen for hours. Routing through set_model_compute_dtype leaves the
    weights as-is and casts at use, so peak memory does not blow up."""
    dtype = patcher.model_dtype()
    if dtype in (torch.float16, torch.bfloat16):
        logging.info(f"Select Model Device: using fp32 compute dtype for CPU inference (model dtype was {dtype}).")
        patcher.set_model_compute_dtype(torch.float32)


def _remember_base_devices(patcher: ModelPatcher):
    """Stash the original load/offload device on the underlying model.

    Stored on patcher.model (which is shared with the input patcher), so
    later "default" selections can recover the loader's original routing.
    Only the first Select on a given chain writes these attrs; subsequent
    deepclones inherit them onto their freshly-loaded model below.
    """
    if not hasattr(patcher.model, "_select_base_load_device"):
        patcher.model._select_base_load_device = patcher.load_device
        patcher.model._select_base_offload_device = patcher.offload_device


def _propagate_base_devices(src_model, dst_model):
    """Carry the loader-original device attrs onto the freshly-deepcloned model."""
    if hasattr(src_model, "_select_base_load_device") and not hasattr(dst_model, "_select_base_load_device"):
        dst_model._select_base_load_device = src_model._select_base_load_device
        dst_model._select_base_offload_device = src_model._select_base_offload_device


def _retarget_patcher(patcher: ModelPatcher, target_load_device, target_offload_device):
    """Return a patcher whose actual model weights live on *target_load_device*.

    If *patcher* is already on *target_load_device* we just retarget the
    (already-cloned) patcher's metadata in place. Otherwise we call
    :meth:`ModelPatcher.deepclone_multigpu` to spawn a fresh model from
    the loader's ``cached_patcher_init`` factory -- the only safe way to
    move weights that may already be partially loaded onto another device.

    NOTE: reusing the input patcher's model when the requested device
    matches its current load_device is a deliberate fast path. Anything
    that has already mutated the original model (e.g. a prior KSampler
    invocation on the same model) will be observed here. This is by
    design and documented on the SelectXDeviceNode docstrings -- placing
    Select X Device after a node that consumes the same model is not
    recommended.
    """
    if patcher.load_device == target_load_device:
        # Fast path: weights already on the desired device, just update offload.
        patcher.offload_device = target_offload_device
        return patcher
    src_model = patcher.model
    patcher = patcher.deepclone_multigpu(new_load_device=target_load_device)
    patcher.offload_device = target_offload_device
    _propagate_base_devices(src_model, patcher.model)
    if hasattr(patcher, "register_load_device"):
        patcher.register_load_device(patcher.load_device)
    return patcher


def _apply_patcher_device(patcher: ModelPatcher, resolved, base_offload_override=None):
    """Resolve the requested device and produce a patcher routed there.

    For "default" we restore the loader's original load/offload pair.
    For CPU we pin both load and offload to CPU (and, on a dynamic
    patcher, downgrade to a plain ModelPatcher so the dynamic-only
    code paths are bypassed).
    For an explicit GPU we keep the loader's original offload but
    target the requested load device; if that differs from the current
    load device the patcher is deepcloned onto the new device.
    """
    _remember_base_devices(patcher)
    base_load = patcher.model._select_base_load_device
    base_offload = base_offload_override if base_offload_override is not None else patcher.model._select_base_offload_device

    if resolved is None:
        # "default" -> route back to the loader's original devices.
        return _retarget_patcher(patcher, base_load, base_offload)
    if resolved.type == "cpu":
        if patcher.is_dynamic():
            # clone(disable_dynamic=True) requires cached_patcher_init; let the
            # exception surface to the caller (Select*DeviceNode.execute), which
            # will translate it into a passthrough+log so unsupported loaders
            # don't hard-fail the workflow.
            patcher = patcher.clone(disable_dynamic=True)
        patcher.load_device = resolved
        patcher.offload_device = resolved
        return patcher
    return _retarget_patcher(patcher, resolved, base_offload)


def _prune_multigpu_collision(model: ModelPatcher, primary_device):
    """Drop any multigpu clone whose load_device matches *primary_device*.

    Without pruning, MultiGPU CFG Split would have stacked a clone on
    the same device the primary now occupies (i.e. the workflow places
    MultiGPU CFG Split before Select Model Device). Keeps the clone set
    consistent with the new primary placement.
    """
    multigpu_models = model.get_additional_models_with_key("multigpu")
    if not multigpu_models:
        return
    filtered = [m for m in multigpu_models if m.load_device != primary_device]
    if len(filtered) != len(multigpu_models):
        logging.info(f"Select Model Device: pruning MultiGPU clone on {primary_device} that now collides with the primary model.")
        model.set_additional_models("multigpu", filtered)
        if hasattr(model, "match_multigpu_clones"):
            model.match_multigpu_clones()


class SelectModelDeviceNode(io.ComfyNode):
    """
    Place the diffusion model on a specific device (default / cpu / gpu:N).

    - "default" restores the device assigned by the loader (even after a
      prior Select Model Device call).
    - "cpu" pins both the load and offload device to CPU.
    - "gpu:N" pins the load device to the Nth available GPU; the offload
      device is restored to the loader's original choice.

    When the requested device differs from the device the input model is
    already on, a fresh model is spawned via the loader's reload factory
    (cached_patcher_init) so the new patcher owns independent weights on
    the new device. Loaders that don't support multigpu (no factory) will
    cause the node to pass through unchanged with a warning.

    If the workflow already has MultiGPU CFG Split applied and the chosen
    GPU collides with one of the existing multigpu clones, that clone is
    dropped so two patchers don't end up bound to the same device.

    When the selected device does not exist on the current machine
    (e.g. a workflow built on a 2-GPU box opened on a 1-GPU box),
    the node passes the model through unchanged and logs a message
    instead of failing.

    NOTE: Placing Select Model Device *after* a node that has already
    consumed the same model (e.g. a KSampler that ran on this model on
    the original device) is not recommended -- any state the prior
    consumer mutated on the original model will be observed when the
    selected device matches the original (fast path). Place Select Model
    Device before any consumer of the model.
    """

    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="SelectModelDevice",
            display_name="Select Model Device",
            category="advanced/multigpu",
            description=cleandoc(cls.__doc__),
            inputs=[
                io.Model.Input("model"),
                io.Combo.Input("device", options=comfy.model_management.get_gpu_device_options()),
            ],
            outputs=[
                io.Model.Output(),
            ],
        )

    @classmethod
    def validate_inputs(cls, device="default"):
        # Allow unknown gpu:N values so portable workflows do not error
        # at validation time; runtime fallback will handle them.
        return True

    @classmethod
    def execute(cls, model: ModelPatcher, device: str = "default") -> io.NodeOutput:
        model = model.clone()
        resolved = comfy.model_management.resolve_gpu_device_option(device)
        if resolved is None and device not in (None, "default"):
            logging.info(f"Select Model Device: requested device '{device}' not available, passing through unchanged.")
            return io.NodeOutput(model)
        try:
            model = _apply_patcher_device(model, resolved)
        except RuntimeError as e:
            logging.warning(f"Select Model Device: cannot retarget model, passing through unchanged. ({e})")
            return io.NodeOutput(model)
        if resolved is not None:
            if resolved.type == "cpu":
                _force_fp32_cpu_compute(model)
            _prune_multigpu_collision(model, model.load_device)
        return io.NodeOutput(model)


class SelectCLIPDeviceNode(io.ComfyNode):
    """
    Place the CLIP text encoder on a specific device (default / cpu / gpu:N).

    - "default" restores the device assigned by the loader.
    - "cpu" pins both the load and offload device to CPU.
    - "gpu:N" pins the load device to the Nth available GPU.

    When the selected device does not exist on the current machine
    (e.g. a workflow built on a 2-GPU box opened on a 1-GPU box),
    the node passes the CLIP through unchanged and logs a message
    instead of failing.
    """

    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="SelectCLIPDevice",
            display_name="Select CLIP Device",
            category="advanced/multigpu",
            description=cleandoc(cls.__doc__),
            inputs=[
                io.Clip.Input("clip"),
                io.Combo.Input("device", options=comfy.model_management.get_gpu_device_options()),
            ],
            outputs=[
                io.Clip.Output(),
            ],
        )

    @classmethod
    def validate_inputs(cls, device="default"):
        return True

    @classmethod
    def execute(cls, clip: CLIP, device: str = "default") -> io.NodeOutput:
        clip = clip.clone()
        resolved = comfy.model_management.resolve_gpu_device_option(device)
        if resolved is None and device not in (None, "default"):
            logging.info(f"Select CLIP Device: requested device '{device}' not available, passing through unchanged.")
            return io.NodeOutput(clip)
        try:
            clip.patcher = _apply_patcher_device(clip.patcher, resolved)
        except RuntimeError as e:
            logging.warning(f"Select CLIP Device: cannot retarget CLIP, passing through unchanged. ({e})")
        return io.NodeOutput(clip)


class SelectVAEDeviceNode(io.ComfyNode):
    """
    Place the VAE on a specific device (default / gpu:N).

    - "default" restores the device assigned by the loader.
    - "gpu:N" pins the load device to the Nth available GPU; the offload
      device is set to the standard VAE offload device.

    CPU is intentionally not exposed in the UI for the VAE; if a workflow
    supplies "cpu" anyway (e.g. opened from another machine), the request
    is dropped with a log message and the VAE is passed through unchanged.

    When the selected device does not exist on the current machine
    (e.g. a workflow built on a 2-GPU box opened on a 1-GPU box),
    the node passes the VAE through unchanged and logs a message
    instead of failing.
    """

    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="SelectVAEDevice",
            display_name="Select VAE Device",
            category="advanced/multigpu",
            description=cleandoc(cls.__doc__),
            inputs=[
                io.Vae.Input("vae"),
                io.Combo.Input("device", options=comfy.model_management.get_gpu_device_options_no_cpu()),
            ],
            outputs=[
                io.Vae.Output(),
            ],
        )

    @classmethod
    def validate_inputs(cls, device="default"):
        return True

    @classmethod
    def execute(cls, vae: VAE, device: str = "default") -> io.NodeOutput:
        # VAE has no .clone(); shallow-copy the wrapper and clone the patcher
        # so we can retarget load/offload device without affecting the input VAE.
        vae = copy.copy(vae)
        vae.patcher = vae.patcher.clone()
        resolved = comfy.model_management.resolve_gpu_device_option(device)
        if resolved is None and device not in (None, "default"):
            logging.info(f"Select VAE Device: requested device '{device}' not available, passing through unchanged.")
            return io.NodeOutput(vae)
        if resolved is not None and resolved.type == "cpu":
            logging.info("Select VAE Device: CPU is not a supported choice, passing through unchanged.")
            return io.NodeOutput(vae)
        if not hasattr(vae, "_select_base_device"):
            vae._select_base_device = vae.device
        try:
            vae.patcher = _apply_patcher_device(
                vae.patcher, resolved,
                base_offload_override=comfy.model_management.vae_offload_device(),
            )
        except RuntimeError as e:
            logging.warning(f"Select VAE Device: cannot retarget VAE, passing through unchanged. ({e})")
            return io.NodeOutput(vae)
        # Keep VAE wrapper in sync with whatever model the patcher now owns;
        # deepclone_multigpu may have produced a fresh first_stage_model.
        vae.first_stage_model = vae.patcher.model
        vae.device = vae._select_base_device if resolved is None else resolved
        return io.NodeOutput(vae)


class MultiGPUOptionsNode(io.ComfyNode):
    """
    Select the relative speed of GPUs in the special case they have significantly different performance from one another.

    NOTE (not registered yet, see MultiGPUExtension.get_node_list below):
    The output GPUOptionsGroup is plumbed through create_multigpu_deepclones() and stored on
    model.model_options['multigpu_options'] via GPUOptionsGroup.register(), but the cond
    scheduler in comfy/samplers.py (calc_cond_batch_outer_multigpu) does NOT yet consult
    relative_speed when distributing conds across devices; it uses a uniform conds_per_device
    round-robin via next_available_device(). Before re-enabling this node, wire its
    relative_speed into the scheduler (e.g. via comfy.multigpu.load_balance_devices(),
    which already implements the proportional split) so the input actually affects work
    distribution.
    """

    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="MultiGPU_Options",
            display_name="MultiGPU Options",
            category="advanced/multigpu",
            description=cleandoc(cls.__doc__),
            inputs=[
                io.Int.Input("device_index", default=0, min=0, max=64),
                io.Float.Input("relative_speed", default=1.0, min=0.0, step=0.01),
                io.Custom("GPU_OPTIONS").Input("gpu_options", optional=True),
            ],
            outputs=[
                io.Custom("GPU_OPTIONS").Output(),
            ],
        )

    @classmethod
    def execute(cls, device_index: int, relative_speed: float, gpu_options: comfy.multigpu.GPUOptionsGroup = None) -> io.NodeOutput:
        if not gpu_options:
            gpu_options = comfy.multigpu.GPUOptionsGroup()
        else:
            gpu_options = gpu_options.clone()

        opt = comfy.multigpu.GPUOptions(device_index=device_index, relative_speed=relative_speed)
        gpu_options.add(opt)

        return io.NodeOutput(gpu_options)


class MultiGPUExtension(ComfyExtension):
    @override
    async def get_node_list(self) -> list[type[io.ComfyNode]]:
        return [
            MultiGPUCFGSplitNode,
            SelectModelDeviceNode,
            SelectCLIPDeviceNode,
            SelectVAEDeviceNode,
            # MultiGPUOptionsNode,
        ]


async def comfy_entrypoint() -> MultiGPUExtension:
    return MultiGPUExtension()