import time

import torch
import torch.nn as nn
from typing import Dict, Tuple, Optional

from task import input_t, output_t


# Reference code in PyTorch
class Expert(nn.Module):
    def __init__(self, config: Dict, d_expert: Optional[int] = None):
        super().__init__()
        self.config = config
        self.act_fn = nn.SiLU()
        self.d_hidden: int = config["d_hidden"]
        self.d_expert: int = config["d_expert"] if d_expert is None else d_expert

        self.W_gate = nn.Linear(self.d_hidden, self.d_expert, bias=False)
        self.W_up = nn.Linear(self.d_hidden, self.d_expert, bias=False)
        self.W_down = nn.Linear(self.d_expert, self.d_hidden, bias=False)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        gate = self.act_fn(self.W_gate(x))
        out = self.W_down(gate * self.W_up(x))
        return out


class MoEGate(nn.Module):
    def __init__(self, config: Dict):
        super().__init__()
        self.top_k: int = config["n_experts_per_token"]
        self.num_experts: int = config["n_routed_experts"]
        self.d_hidden: int = config["d_hidden"]

        self.W_g = nn.Linear(self.d_hidden, self.num_experts, bias=False)

    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        logits = self.W_g(x)
        scores = logits.softmax(dim=-1)
        topk_scores, topk_indices = torch.topk(scores, k=self.top_k, dim=-1, sorted=False)

        return topk_indices, topk_scores


class MoE(nn.Module):
    def __init__(self, config: Dict):
        super().__init__()
        self.config = config
        self.experts = nn.ModuleList([
            Expert(config)
            for _ in range(config["n_routed_experts"])
        ])
        self.gating_network = MoEGate(config)
        shared_expert_dim = config["d_expert"] * config["n_shared_experts"]
        self.shared_expert = Expert(config=config, d_expert=shared_expert_dim)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        shared_output = self.shared_expert(x)
        expert_indices, expert_scores = self.gating_network(x)
        batch_size, seq_len, hidden_dim = x.shape
        orig_shape = x.shape
        x_flat = x.view(-1, hidden_dim)
        flat_expert_indices = expert_indices.view(-1)
        flat_expert_weights = expert_scores.view(-1, 1)
        routed_output_flat = self.moe_infer(x_flat,
                                            flat_expert_indices,
                                            flat_expert_weights)

        routed_output = routed_output_flat.view(*orig_shape)
        return routed_output + shared_output

    @torch.no_grad()
    def moe_infer(self,
                  x: torch.Tensor,
                  flat_expert_indices: torch.Tensor,
                  flat_expert_weights: torch.Tensor
                 ) -> torch.Tensor:
        expert_cache = torch.zeros_like(x)
        idxs = flat_expert_indices.argsort()
        counts = flat_expert_indices.bincount().cpu().numpy()
        tokens_per_expert = counts.cumsum()
        num_per_tok = self.config["n_experts_per_token"]
        token_idxs = idxs // num_per_tok
        for expert_id, end_idx in enumerate(tokens_per_expert):
            start_idx = 0 if expert_id == 0 else tokens_per_expert[expert_id - 1]
            if start_idx == end_idx:
                continue

            expert = self.experts[expert_id]
            exp_token_idxs = token_idxs[start_idx:end_idx]
            expert_tokens = x[exp_token_idxs]
            expert_out    = expert(expert_tokens)
            expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]])
            expert_cache.scatter_reduce_(
                0,
                exp_token_idxs.view(-1, 1).repeat(1, x.shape[-1]),
                expert_out,
                reduce='sum'
            )

        return expert_cache


def custom_kernel(data: input_t) -> output_t:
    """
    Reference implementation of DeepSeek-style Mixture of Experts using PyTorch.
    
    Args:
        data: Tuple of (input: torch.Tensor, weights: Dict[str, torch.Tensor], config: Dict)
            - input: Input tensor of shape [batch_size, seq_len, hidden_dim]
            - weights: Dictionary containing model weights
            - config: Dictionary containing model configuration parameters
            
    Returns:
        Tuple containing:
            - output: Processed tensor [batch_size, seq_len, d_model]
            - aux_data: Dictionary with auxiliary data
    """
    print(f"begin kernel {time.time()}")
    input_tensor, weights, config = data
    num_experts = config["n_routed_experts"]
    moe = MoE(config)
    print(f"begin copy {time.time()}")
    # Fill in the given weights of the model
    moe.gating_network.W_g.weight = nn.Parameter(weights['router.weight'])

    for i in range(num_experts):
        gate_proj_weight = weights[f'experts.{i}.0.weight']
        up_proj_weight = weights[f'experts.{i}.1.weight']
        down_proj_weight = weights[f'experts.{i}.2.weight']

        # Transpose weights to match expected shape for nn.Linear
        moe.experts[i].W_gate.weight = nn.Parameter(gate_proj_weight.t())
        moe.experts[i].W_up.weight = nn.Parameter(up_proj_weight.t())
        moe.experts[i].W_down.weight = nn.Parameter(down_proj_weight.t())

    moe.shared_expert.W_gate.weight = nn.Parameter(weights['shared_experts.0.weight'].t())
    moe.shared_expert.W_up.weight = nn.Parameter(weights['shared_experts.1.weight'].t())
    moe.shared_expert.W_down.weight = nn.Parameter(weights['shared_experts.2.weight'].t())

    # print(f"begin compute {time.time()}")
    output = moe(input_tensor)
    # print(f"end compute {time.time()}")

    return output

python eval.py leaderboard /tmp/tmpcsc820o3
begin kernel 1747133763.9388208
begin copy 1747133771.2300084
begin kernel 1747133780.4600997
begin copy 1747133787.7550473
begin kernel 1747133787.9601083
begin copy 1747133795.2508879
begin kernel 1747133795.4570382
begin copy 1747133802.7488463
begin kernel 1747133802.9600005
begin copy 1747133810.2482376
begin kernel 1747133818.008125
begin copy 1747133825.303804
begin kernel 1747133833.0371065
begin copy 1747133840.3288605
begin kernel 1747133848.0561411
begin copy 1747133855.3503473
begin kernel 1747133863.0724854
begin copy 1747133870.3715272
begin kernel 1747133878.1097784
begin copy 1747133885.405233
begin kernel 1747133893.1466763
begin copy 1747133900.4426577
begin kernel 1747133908.1854281
begin copy 1747133915.4845314
