import torch, copy
from models.utils import init_weights_on_device


def cast_to(weight, dtype, device):
    r = torch.empty_like(weight, dtype=dtype, device=device)
    r.copy_(weight)
    return r


class AutoTorchModule(torch.nn.Module):
    def __init__(self):
        super().__init__()

    def check_free_vram(self):
        gpu_mem_state = torch.cuda.mem_get_info(self.computation_device)
        used_memory = (gpu_mem_state[1] - gpu_mem_state[0]) / (1024**3)
        return used_memory < self.vram_limit

    def offload(self):
        if self.state != 0:
            self.to(dtype=self.offload_dtype, device=self.offload_device)
            self.state = 0

    def onload(self):
        if self.state != 1:
            self.to(dtype=self.onload_dtype, device=self.onload_device)
            self.state = 1

    def keep(self):
        if self.state != 2:
            self.to(dtype=self.computation_dtype, device=self.computation_device)
            self.state = 2


class AutoWrappedModule(AutoTorchModule):
    def __init__(
        self,
        module: torch.nn.Module,
        offload_dtype,
        offload_device,
        onload_dtype,
        onload_device,
        computation_dtype,
        computation_device,
        vram_limit,
        **kwargs,
    ):
        super().__init__()
        self.module = module.to(dtype=offload_dtype, device=offload_device)
        self.offload_dtype = offload_dtype
        self.offload_device = offload_device
        self.onload_dtype = onload_dtype
        self.onload_device = onload_device
        self.computation_dtype = computation_dtype
        self.computation_device = computation_device
        self.vram_limit = vram_limit
        self.state = 0

    def forward(self, *args, **kwargs):
        if self.state == 2:
            module = self.module
        else:
            if (
                self.onload_dtype == self.computation_dtype
                and self.onload_device == self.computation_device
            ):
                module = self.module
            elif self.vram_limit is not None and self.check_free_vram():
                self.keep()
                module = self.module
            else:
                module = copy.deepcopy(self.module).to(
                    dtype=self.computation_dtype, device=self.computation_device
                )
        return module(*args, **kwargs)


class WanAutoCastLayerNorm(torch.nn.LayerNorm, AutoTorchModule):
    def __init__(
        self,
        module: torch.nn.LayerNorm,
        offload_dtype,
        offload_device,
        onload_dtype,
        onload_device,
        computation_dtype,
        computation_device,
        vram_limit,
        **kwargs,
    ):
        with init_weights_on_device(device=torch.device("meta")):
            super().__init__(
                module.normalized_shape,
                eps=module.eps,
                elementwise_affine=module.elementwise_affine,
                bias=module.bias is not None,
                dtype=offload_dtype,
                device=offload_device,
            )
        self.weight = module.weight
        self.bias = module.bias
        self.offload_dtype = offload_dtype
        self.offload_device = offload_device
        self.onload_dtype = onload_dtype
        self.onload_device = onload_device
        self.computation_dtype = computation_dtype
        self.computation_device = computation_device
        self.vram_limit = vram_limit
        self.state = 0

    def forward(self, x, *args, **kwargs):
        if self.state == 2:
            weight, bias = self.weight, self.bias
        else:
            if (
                self.onload_dtype == self.computation_dtype
                and self.onload_device == self.computation_device
            ):
                weight, bias = self.weight, self.bias
            elif self.vram_limit is not None and self.check_free_vram():
                self.keep()
                weight, bias = self.weight, self.bias
            else:
                weight = (
                    None
                    if self.weight is None
                    else cast_to(
                        self.weight, self.computation_dtype, self.computation_device
                    )
                )
                bias = (
                    None
                    if self.bias is None
                    else cast_to(
                        self.bias, self.computation_dtype, self.computation_device
                    )
                )
        with torch.amp.autocast(device_type=x.device.type):
            x = torch.nn.functional.layer_norm(
                x.float(), self.normalized_shape, weight, bias, self.eps
            ).type_as(x)
        return x


class AutoWrappedLinear(torch.nn.Linear, AutoTorchModule):
    def __init__(
        self,
        module: torch.nn.Linear,
        offload_dtype,
        offload_device,
        onload_dtype,
        onload_device,
        computation_dtype,
        computation_device,
        vram_limit,
        name="",
        **kwargs,
    ):
        with init_weights_on_device(device=torch.device("meta")):
            super().__init__(
                in_features=module.in_features,
                out_features=module.out_features,
                bias=module.bias is not None,
                dtype=offload_dtype,
                device=offload_device,
            )
        self.weight = module.weight
        self.bias = module.bias
        self.offload_dtype = offload_dtype
        self.offload_device = offload_device
        self.onload_dtype = onload_dtype
        self.onload_device = onload_device
        self.computation_dtype = computation_dtype
        self.computation_device = computation_device
        self.vram_limit = vram_limit
        self.state = 0
        self.name = name
        self.lora_A_weights = []
        self.lora_B_weights = []
        self.lora_merger = None

    def forward(self, x, *args, **kwargs):
        if self.state == 2:
            weight, bias = self.weight, self.bias
        else:
            if (
                self.onload_dtype == self.computation_dtype
                and self.onload_device == self.computation_device
            ):
                weight, bias = self.weight, self.bias
            elif self.vram_limit is not None and self.check_free_vram():
                self.keep()
                weight, bias = self.weight, self.bias
            else:
                weight = cast_to(
                    self.weight, self.computation_dtype, self.computation_device
                )
                bias = (
                    None
                    if self.bias is None
                    else cast_to(
                        self.bias, self.computation_dtype, self.computation_device
                    )
                )
        out = torch.nn.functional.linear(x, weight, bias)

        if len(self.lora_A_weights) == 0:
            # No LoRA
            return out
        elif self.lora_merger is None:
            # Native LoRA inference
            for lora_A, lora_B in zip(self.lora_A_weights, self.lora_B_weights):
                out = out + x @ lora_A.T @ lora_B.T
        else:
            # LoRA fusion
            lora_output = []
            for lora_A, lora_B in zip(self.lora_A_weights, self.lora_B_weights):
                lora_output.append(x @ lora_A.T @ lora_B.T)
            lora_output = torch.stack(lora_output)
            out = self.lora_merger(out, lora_output)
        return out


def enable_vram_management_recursively(
    model: torch.nn.Module,
    module_map: dict,
    module_config: dict,
    max_num_param=None,
    overflow_module_config: dict = None,
    total_num_param=0,
    vram_limit=None,
    name_prefix="",
):
    for name, module in model.named_children():
        layer_name = name if name_prefix == "" else name_prefix + "." + name
        for source_module, target_module in module_map.items():
            if isinstance(module, source_module):
                num_param = sum(p.numel() for p in module.parameters())
                if (
                    max_num_param is not None
                    and total_num_param + num_param > max_num_param
                ):
                    module_config_ = overflow_module_config
                else:
                    module_config_ = module_config
                module_ = target_module(
                    module, **module_config_, vram_limit=vram_limit, name=layer_name
                )
                setattr(model, name, module_)
                total_num_param += num_param
                break
        else:
            total_num_param = enable_vram_management_recursively(
                module,
                module_map,
                module_config,
                max_num_param,
                overflow_module_config,
                total_num_param,
                vram_limit=vram_limit,
                name_prefix=layer_name,
            )
    return total_num_param


def enable_vram_management(
    model: torch.nn.Module,
    module_map: dict,
    module_config: dict,
    max_num_param=None,
    overflow_module_config: dict = None,
    vram_limit=None,
):
    enable_vram_management_recursively(
        model,
        module_map,
        module_config,
        max_num_param,
        overflow_module_config,
        total_num_param=0,
        vram_limit=vram_limit,
    )
    model.vram_management_enabled = True