from __future__ import annotations

from abc import ABC, abstractmethod
from contextlib import ExitStack, contextmanager
from functools import partial
from typing import TYPE_CHECKING, Callable, Dict

import torch
from diffusers import UNet2DConditionModel

from invokeai.backend.util.devices import TorchDevice

if TYPE_CHECKING:
    from invokeai.backend.stable_diffusion.denoise_context import DenoiseContext
    from invokeai.backend.stable_diffusion.extensions import ExtensionBase


class ExtModifiersApi(ABC):
    @abstractmethod
    def pre_denoise_loop(self, ctx: DenoiseContext):
        pass

    @abstractmethod
    def post_denoise_loop(self, ctx: DenoiseContext):
        pass

    @abstractmethod
    def pre_step(self, ctx: DenoiseContext):
        pass

    @abstractmethod
    def post_step(self, ctx: DenoiseContext):
        pass

    @abstractmethod
    def modify_noise_prediction(self, ctx: DenoiseContext):
        pass

    @abstractmethod
    def pre_unet_forward(self, ctx: DenoiseContext):
        pass

    @abstractmethod
    def pre_unet_load(self, ctx: DenoiseContext, ext_manager: ExtensionsManager):
        pass


class ExtOverridesApi(ABC):
    @abstractmethod
    def step(self, orig_func: Callable, ctx: DenoiseContext, ext_manager: ExtensionsManager):
        pass

    @abstractmethod
    def combine_noise(self, orig_func: Callable, ctx: DenoiseContext):
        pass


class ProxyCallsClass:
    def __init__(self, handler):
        self._handler = handler

    def __getattr__(self, item):
        return partial(self._handler, item)


class ModifierInjectionPoint:
    def __init__(self):
        self.first = []
        self.any = []
        self.last = []

    def add(self, func: Callable, order: str):
        if order == "first":
            self.first.append(func)
        elif order == "last":
            self.last.append(func)
        else:  # elif order == "any":
            self.any.append(func)

    def __call__(self, *args, **kwargs):
        for func in self.first:
            func(*args, **kwargs)
        for func in self.any:
            func(*args, **kwargs)
        for func in reversed(self.last):
            func(*args, **kwargs)


class ExtensionsManager:
    def __init__(self):
        self.extensions = []

        self._overrides = {}
        self._modifiers = {}

        self.modifiers: ExtModifiersApi = ProxyCallsClass(self.call_modifier)
        self.overrides: ExtOverridesApi = ProxyCallsClass(self.call_override)

    def add_extension(self, ext: ExtensionBase):
        self.extensions.append(ext)
        ordered_extensions = sorted(self.extensions, reverse=True, key=lambda ext: ext.priority)

        self._overrides.clear()
        self._modifiers.clear()

        for ext in ordered_extensions:
            for inj_info in ext.injections:
                if inj_info.type == "modifier":
                    if inj_info.name not in self._modifiers:
                        self._modifiers[inj_info.name] = ModifierInjectionPoint()
                    self._modifiers[inj_info.name].add(inj_info.function, inj_info.order)

                else:
                    if inj_info.name in self._overrides:
                        raise Exception(f"Already overloaded - {inj_info.name}")
                    self._overrides[inj_info.name] = inj_info.function

    def call_modifier(self, name: str, *args, **kwargs):
        if name in self._modifiers:
            self._modifiers[name](*args, **kwargs)

    def call_override(self, name: str, orig_func: Callable, *args, **kwargs):
        if name in self._overrides:
            return self._overrides[name](orig_func, *args, **kwargs)
        else:
            return orig_func(*args, **kwargs)

    # TODO: is there any need in such high abstarction
    # @contextmanager
    # def patch_extensions(self):
    #    exit_stack = ExitStack()
    #    try:
    #        for ext in self.extensions:
    #            exit_stack.enter_context(ext.patch_extension(self))
    #
    #        yield None
    #
    #    finally:
    #        exit_stack.close()

    @contextmanager
    def patch_attention_processor(self, unet: UNet2DConditionModel, attn_processor_cls: object):
        unet_orig_processors = unet.attn_processors
        exit_stack = ExitStack()
        try:
            # just to be sure that attentions have not same processor instance
            attn_procs = {}
            for name in unet.attn_processors.keys():
                attn_procs[name] = attn_processor_cls()
            unet.set_attn_processor(attn_procs)

            for ext in self.extensions:
                exit_stack.enter_context(ext.patch_attention_processor(attn_processor_cls))

            yield None

        finally:
            unet.set_attn_processor(unet_orig_processors)
            exit_stack.close()

    @contextmanager
    def patch_unet(self, state_dict: Dict[str, torch.Tensor], unet: UNet2DConditionModel):
        exit_stack = ExitStack()
        try:
            changed_keys = set()
            changed_unknown_keys = {}

            ordered_extensions = sorted(self.extensions, reverse=True, key=lambda ext: ext.priority)
            for ext in ordered_extensions:
                patch_result = exit_stack.enter_context(ext.patch_unet(state_dict, unet))
                if patch_result is None:
                    continue
                new_keys, new_unk_keys = patch_result
                changed_keys.update(new_keys)
                # skip already seen keys, as new weight might be changed
                for k, v in new_unk_keys.items():
                    if k in changed_unknown_keys:
                        continue
                    changed_unknown_keys[k] = v

            yield None

        finally:
            exit_stack.close()
            assert hasattr(unet, "get_submodule")  # mypy not picking up fact that torch.nn.Module has get_submodule()
            with torch.no_grad():
                for module_key in changed_keys:
                    weight = state_dict[module_key]
                    unet.get_submodule(module_key).weight.copy_(
                        weight, non_blocking=TorchDevice.get_non_blocking(weight.device)
                    )
                for module_key, weight in changed_unknown_keys.items():
                    unet.get_submodule(module_key).weight.copy_(
                        weight, non_blocking=TorchDevice.get_non_blocking(weight.device)
                    )