from typing import Any, Callable, Dict, List, Optional
import os
from math import prod

import torch
from torch.nn.parameter import Parameter

import vllm.envs as envs
from vllm import _custom_ops as ops
from vllm.model_executor.utils import set_weight_attrs
from vllm.distributed import get_tensor_model_parallel_world_size, get_ep_group, get_dp_group
from vllm.logger import init_logger
from vllm.config import get_current_vllm_config
from vllm.forward_context import ForwardContext, get_forward_context
from vllm.model_executor.layers.quantization import QuantizationMethods
from vllm.model_executor.layers.linear import (LinearBase,LinearMethodBase)
from vllm.model_executor.layers.quantization.base_config import (QuantizationConfig,
                                                                 QuantizeMethodBase)
from vllm.model_executor.layers.quantization.utils.w4a8_utils import w4a8_weight_repack_impl
from vllm.model_executor.layers.fused_moe import (FusedMoE, FusedMoEMethodBase,
                                                  FusedMoeWeightScaleSupported)
from vllm.model_executor.layers.fused_moe.utils import _resize_cache
from vllm.model_executor.parameter import (ChannelQuantScaleParameter,
                                           ModelWeightParameter)
from vllm.model_executor.layers.quantization.slimquant_w4a8 import SlimQuantW4A8Int8LinearMethod
from vllm.model_executor.layers.fused_moe import (
    FusedMoE, FusedMoEActivationFormat, FusedMoEConfig, FusedMoEMethodBase,
    FusedMoEPermuteExpertsUnpermute, FusedMoEPrepareAndFinalize,
    FusedMoeWeightScaleSupported)

try:
    from lmslim.layers.fused_moe.fuse_moe_w4a8_marlin import fused_experts_impl_w4a8_marlin
    from lightop import m_grouped_w4a8_gemm_nt_masked, fuse_silu_mul_quant, fuse_silu_mul_quant_ep
except Exception:
    print("INFO: Please install lmslim if you want to infer the quantitative model of moe.\n")


logger = init_logger(__name__)


class MarlinMoeWorkspace:
    """
    Singleton manager for device-specific workspace buffers used by w4a8 Marlin-MoE.
    global_reduce_buffer will take 1.5MB * cus (about 120MB for BW200) memoery in each device
    """
    _instances = {}
    def __new__(cls, device):
        if device not in cls._instances:
            instance = super().__new__(cls)
            instance._initialized = False
            cls._instances[device] = instance
        return cls._instances[device]

    def __init__(self, device):
        if self._initialized:
            return
        sms = torch.cuda.get_device_properties(device).multi_processor_count
        self.workspace = torch.zeros(
            500, dtype=torch.int, device=device, requires_grad=False
        )
        self.global_reduce_buffer = torch.zeros(
            sms * 6 * 128 * 512, dtype=torch.int, device=device, requires_grad=False
        )
        self._initialized = True

    def get_buffers(self):
        return self.workspace, self.global_reduce_buffer

def baseline_scaled_mm(a: torch.Tensor,
                      b: torch.Tensor,
                      scale_a: torch.Tensor,
                      scale_b: torch.Tensor,
                      out_dtype: torch.dtype,
                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:

    scales= scale_a* scale_b.T
    gemmout= torch.mm(
        a.to(dtype=torch.float32), b.to(dtype=torch.float32))
    output = (scales *gemmout).to(out_dtype)
    if bias is not None:
        output = output + bias
    return output.to(out_dtype)


class SlimQuantW4A8Int8MarlinConfig(QuantizationConfig):
    """Config class for W4A8 Int8 Quantization.
    - Weight: static, per-channel, symmetric
    - Activation: dynamic, per-token, symmetric
    """

    def __init__(self):
        pass

    @classmethod
    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
        return [torch.float16, torch.bfloat16]

    @classmethod
    def get_min_capability(cls) -> int:
        return 75

    @classmethod
    def get_name(self) -> str:
        return "slimquant_w4a8_marlin"

    @classmethod
    def get_config_filenames(cls) -> List[str]:
        return []

    @classmethod
    def from_config(cls, config: Dict[str, Any]) -> "SlimQuantW4A8Int8MarlinConfig":
        return cls()
    @classmethod
    def override_quantization_method(
            cls, hf_quant_cfg, user_quant) -> Optional[QuantizationMethods]:
        if hf_quant_cfg.get("quant_method") == "slimquant_w4a8" \
                and user_quant in ("slimquant_w4a8_marlin", "slimquant_marlin"):
            return cls.get_name()
        return None
    def get_quant_method(
        self,
        layer: torch.nn.Module,
        prefix: str,
    ) -> Optional["QuantizeMethodBase"]:

        if isinstance(layer, LinearBase):
            return SlimQuantW4A8Int8LinearMethod(self)
        elif isinstance(layer, FusedMoE):
            return SlimQuantW4A8Int8MarlinMoEMethod(self)
        return None

    def get_scaled_act_names(self) -> List[str]:
        return []


class SlimQuantW4A8Int8MarlinMoEMethod:
    """MoE method for W4A8INT8 Marlin.
    Supports loading INT8 checkpoints with static weight scale and
    dynamic/static activation scale.
    Args:
        quant_config: The quantization config.
    """

    def __new__(cls, *args, **kwargs):

        if not hasattr(cls, "_initialized"):
            original_init = cls.__init__
            new_cls = type(
                cls.__name__,
                (FusedMoEMethodBase,),
                {
                    "__init__": original_init,
                    **{k: v for k, v in cls.__dict__.items() if k != "__dict__"},
                },
            )
            obj = super(new_cls, new_cls).__new__(new_cls)
            obj.__init__(*args, **kwargs)
            return obj
        return super().__new__(cls)

    def __init__(self, quant_config):
        self.quant_config = quant_config
        self.fused_experts = self.w4a8_fused_moe_marlin_forward
        vllm_config = get_current_vllm_config()
        parallel_config = vllm_config.parallel_config
        dp_size = get_dp_group().world_size
        self.use_deepep = dp_size > 1 and parallel_config.enable_expert_parallel and \
            (envs.VLLM_ALL2ALL_BACKEND == "deepep_high_throughput" or \
             envs.VLLM_ALL2ALL_BACKEND == "deepep_low_latency")
        
        self.ep_size = get_ep_group().world_size

        if self.use_deepep:
            all2all_manager = get_ep_group().device_communicator.all2all_manager
            assert all2all_manager is not None
            self.num_dispatchers = all2all_manager.world_size


    def create_weights(
        self,
        layer: torch.nn.Module,
        num_experts: int,
        hidden_size: int,
        intermediate_size: int,
        params_dtype: torch.dtype,
        **extra_weight_attrs,
    ):
        tp_size = get_tensor_model_parallel_world_size()

        if self.use_deepep:
            self.N = 2 * intermediate_size
            self.K = hidden_size

        # WEIGHTS
        w13_weight = torch.nn.Parameter(
            torch.empty(
                num_experts, 2 * intermediate_size, hidden_size//2, dtype=torch.int8
            ),
            requires_grad=False,
        )
        layer.register_parameter("w13_weight", w13_weight)
        set_weight_attrs(w13_weight, extra_weight_attrs)

        w2_weight = torch.nn.Parameter(
            torch.empty(num_experts, hidden_size, intermediate_size//2, dtype=torch.int8),
            requires_grad=False,
        )
        layer.register_parameter("w2_weight", w2_weight)
        set_weight_attrs(w2_weight, extra_weight_attrs)

        w13_weight_scale = torch.nn.Parameter(
            torch.ones(num_experts, 2 * intermediate_size, 1, dtype=torch.float32),
            requires_grad=False,
        )
        w2_weight_scale = torch.nn.Parameter(
            torch.ones(num_experts, hidden_size, 1, dtype=torch.float32),
            requires_grad=False,
        )
        layer.register_parameter("w13_weight_scale", w13_weight_scale)
        layer.register_parameter("w2_weight_scale", w2_weight_scale)

        extra_weight_attrs.update(
            {"quant_method": FusedMoeWeightScaleSupported.CHANNEL.value}
        )

        set_weight_attrs(w13_weight_scale, extra_weight_attrs)
        set_weight_attrs(w2_weight_scale, extra_weight_attrs)

        w13_input_scale = None
        layer.register_parameter("w13_input_scale", w13_input_scale)

        w2_input_scale = None
        layer.register_parameter("w2_input_scale", w2_input_scale)

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        layer.w13_weight_scale = Parameter(
            layer.w13_weight_scale.data, requires_grad=False
        )
        layer.w2_weight_scale = Parameter(
            layer.w2_weight_scale.data, requires_grad=False
        )

        layer.w13_weight = Parameter(w4a8_weight_repack_impl(layer.w13_weight), requires_grad=False)
        layer.w2_weight = Parameter(w4a8_weight_repack_impl(layer.w2_weight), requires_grad=False)

    
    def w4a8_fused_moe_marlin_forward(self,
        x: torch.Tensor,
        w1: torch.Tensor,
        w2: torch.Tensor,
        topk_ids: torch.Tensor,
        topk_weights: torch.Tensor,
        global_num_experts: int = -1,
        expert_map: Optional[torch.Tensor] = None,
        apply_router_weight_on_input: bool = False,
        activation: str = "silu",
        w1_scale: Optional[torch.Tensor] = None,
        w2_scale: Optional[torch.Tensor] = None,
        a1_scale: Optional[torch.Tensor] = None,
        a2_scale: Optional[torch.Tensor] = None,
        expert_num_tokens: Optional[torch.Tensor] = None,
        use_nn_moe: Optional[bool] = False,
        routed_scaling_factor: Optional[float] = None,
        shared_output: Optional[torch.Tensor] = None,
        q_x: Optional[torch.Tensor] = None,
        **_  ):
            workspace, global_reduce_buffer = MarlinMoeWorkspace(x.device).get_buffers()
            return fused_experts_impl_w4a8_marlin(
                x if q_x is None else q_x,
                w1,
                w2,
                topk_ids=topk_ids,
                topk_weights=topk_weights,
                workspace=workspace,
                global_reduce_buffer=global_reduce_buffer,
                inplace=True,
                use_int4_w4a8=True,
                per_channel_quant=True,
                activation=activation,
                expert_map=expert_map,
                apply_router_weight_on_input=apply_router_weight_on_input,
                global_num_experts=global_num_experts,
                w1_scale=w1_scale,
                w2_scale=w2_scale,
                a1_scale=a1_scale,
                a2_scale=a2_scale,
                use_nn_moe=use_nn_moe,
                shared_output=shared_output,
                routed_scaling_factor=routed_scaling_factor,
            )
    

    def groupgemm_workspace_shapes(self,
                                   a: torch.Tensor,
                                   aq: torch.Tensor,
                                   M: int,
                                   N: int,
                                   K: int,
                                   topk: int,
                                   global_num_experts: int,
                                   local_num_experts: int,):
        assert a.dim() == 2
        # FIXME (varun): We should be able to dispatch only from the leader
        # DP ranks in the case of TP > 1. At the moment, all the Ranks
        # end up sending their tokens. This needs to be fixed.
        num_dispatchers = self.num_dispatchers
        num_experts = local_num_experts
        max_num_tokens = a.size(
            0) if self.max_num_tokens_per_rank is None else self.max_num_tokens_per_rank
        workspace13 = (num_experts, max_num_tokens * num_dispatchers,
                       max(K, N))
        workspace2 = (num_experts, max_num_tokens * num_dispatchers, (N // 2))
        output = (num_experts, max_num_tokens * num_dispatchers, K)
        return (workspace13, workspace2, output, a.dtype)


    def w4a8_groupgemm_marlin_forward(self,
        x: torch.Tensor,
        w1: torch.Tensor,
        w2: torch.Tensor,
        topk_ids: torch.Tensor,
        topk_weights: torch.Tensor,
        global_num_experts: int = -1,
        expert_map: Optional[torch.Tensor] = None,
        apply_router_weight_on_input: bool = False,
        activation: str = "silu",
        w1_scale: Optional[torch.Tensor] = None,
        w2_scale: Optional[torch.Tensor] = None,
        a1_scale: Optional[torch.Tensor] = None,
        a2_scale: Optional[torch.Tensor] = None,
        expert_num_tokens: Optional[torch.Tensor] = None,
        use_nn_moe: Optional[bool] = False,
        routed_scaling_factor: Optional[float] = None,
        shared_output: Optional[torch.Tensor] = None,
        q_x: Optional[torch.Tensor] = None,
        **_  ):
            
            import vllm.model_executor.layers.fused_moe.modular_kernel as mk
            local_num_experts = w1.size(0)

            E, max_num_tokens, _, _, top_k = mk._moe_problem_size(
                q_x, w1, w2, topk_ids)
            
            N, K = self.N, self.K
            (workspace13_shape, workspace2_shape, fused_out_shape,
                 workspace_dtype) = self.groupgemm_workspace_shapes(
                     x, q_x, max_num_tokens, N, K, top_k, global_num_experts,
                     local_num_experts)
            workspace13 = torch.empty(prod(workspace13_shape),
                                      device=x.device,
                                      dtype=workspace_dtype)
            workspace1 = _resize_cache(workspace13, (E, max_num_tokens, N))
            fused_out = _resize_cache(workspace13, fused_out_shape)

            # (from deepgemm docs) : A value hint (which is a value on CPU)
            # for the M expectation of each batch, correctly setting this value
            # may lead to better performance.
            #expected_m = max_num_tokens
            ori_bs = x.shape[0]
            expected_m = ori_bs * self.ep_size

            m_grouped_w4a8_gemm_nt_masked((q_x, a1_scale), 
                                  (w1, w1_scale),
                                    workspace1,
                                    expert_num_tokens, 
                                    expected_m,
                                    )

            assert expert_num_tokens is not None
            a2q, a2q_scale = fuse_silu_mul_quant_ep(workspace1, expert_num_tokens)
            m_grouped_w4a8_gemm_nt_masked((a2q, a2q_scale),
                                          (w2, w2_scale),
                                          fused_out,
                                          expert_num_tokens,
                                          expected_m)

            return fused_out

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        router_logits: torch.Tensor,
        top_k: int,
        renormalize: bool,
        use_grouped_topk: bool = False,
        topk_group: Optional[int] = None,
        num_expert_group: Optional[int] = None,
        global_num_experts: int = -1,
        expert_map: Optional[torch.Tensor] = None,
        custom_routing_function: Optional[Callable] = None,
        scoring_func: str = "softmax",
        e_score_correction_bias: Optional[torch.Tensor] = None,
        apply_router_weight_on_input: bool = False,
        activation: str = "silu",
        enable_eplb: bool = False,
        use_nn_moe: Optional[bool] = False,
        routed_scaling_factor: Optional[float] = None,
        use_fused_gate: Optional[bool] = False,
        shared_output: Optional[torch.Tensor] = None,
        **_  
    ) -> torch.Tensor:
        # if enable_eplb:
        #     raise NotImplementedError(
        #         "EPLB not supported for `SlimQuantW4A8Int8MarlinMoEMethod` yet.")
        # Expert selection
        topk_weights, topk_ids = FusedMoE.select_experts(
            hidden_states=x,
            router_logits=router_logits,
            use_grouped_topk=use_grouped_topk,
            top_k=top_k,
            renormalize=renormalize,
            topk_group=topk_group,
            num_expert_group=num_expert_group,
            custom_routing_function=custom_routing_function,
            scoring_func=scoring_func,
            e_score_correction_bias=e_score_correction_bias,
            indices_type=torch.int64 if self.use_deepep else None,
            routed_scaling_factor=routed_scaling_factor,
            use_fused_gate=use_fused_gate
        )
        return self.fused_experts(
            x,
            layer.w13_weight,
            layer.w2_weight,
            topk_weights=topk_weights,
            topk_ids=topk_ids,
            activation=activation,
            global_num_experts=global_num_experts,
            expert_map=expert_map,
            w1_scale=(layer.w13_weight_scale),
            w2_scale=(layer.w2_weight_scale),
            a1_scale=layer.w13_input_scale,
            a2_scale=layer.w2_input_scale,
            apply_router_weight_on_input=apply_router_weight_on_input,
            use_nn_moe=use_nn_moe,
            shared_output=shared_output,
            routed_scaling_factor=routed_scaling_factor,
        )
    
    def select_gemm_impl(
        self,
        prepare_finalize: FusedMoEPrepareAndFinalize,
        moe: FusedMoEConfig,
    ) -> FusedMoEPermuteExpertsUnpermute:
        from vllm.model_executor.layers.fused_moe import (
            TritonOrGroupGemmExperts)

        if (prepare_finalize.activation_format ==
                FusedMoEActivationFormat.BatchedExperts):
            max_num_tokens_per_rank = (
                prepare_finalize.max_num_tokens_per_rank())
            self.max_num_tokens_per_rank = max_num_tokens_per_rank
            assert max_num_tokens_per_rank is not None
            logger.info(
                "TritonOrGroupGemmExperts(%s): "
                "max_tokens_per_rank=%s, block_size=%s, per_act_token=%s",
                self.__class__.__name__, max_num_tokens_per_rank,
                None, True)
            return TritonOrGroupGemmExperts(
                use_int4_w4a8=True,
                per_act_token_quant=True,
                fused_experts=self.w4a8_groupgemm_marlin_forward
            )
        else:
            logger.info(
                "TritonOrGroupGemmExperts(%s): block_size=%s, per_act_token=%s",
                self.__class__.__name__, None,
                False)
            
            return TritonOrGroupGemmExperts(
                # use_int4_w4a8=True,
                # per_act_token_quant=True,
                fused_experts=self.w4a8_fused_moe_marlin_forward
            )

