from typing import Dict, List
import torch
import torch.nn as nn
import math
import torch.nn.functional as F
from tabkanet.models.base_blocks import Activation, ColumnEmbedding




class BasicNetKAN(nn.Module):
    def __init__(self, 
                 output_dim: int, vocabulary: Dict[str, Dict[str, int]], num_continuous_features: int,
                 embedding_dim: int, nhead: int, num_layers: int, dim_feedforward: int, attn_dropout_rate: float,
                 mlp_hidden_dims: List[int], activation: str, ffn_dropout_rate: float):
        super(BasicNetKAN, self).__init__()
        self.kanclassifier=KAN([len(vocabulary) + num_continuous_features,(len(vocabulary) + num_continuous_features)*64,64, output_dim])
        self.norm = nn.LayerNorm([num_continuous_features])
        self.cat_count=len(vocabulary)

    def forward(self, categorical_x: torch.Tensor, continuous_x: torch.Tensor):
        continuous_x=self.norm(continuous_x)
        if self.cat_count==0:
            x = self.kanclassifier(continuous_x)
        else:
            concatenated_x = torch.cat((continuous_x, categorical_x), dim=1)
            x = self.kanclassifier(concatenated_x)
        return x
    



class KAN(torch.nn.Module):
    def __init__(
        self,
        layers_hidden,
        grid_size=5,
        spline_order=3,
        scale_noise=0.1,
        scale_base=1.0,
        scale_spline=1.0,
        base_activation=torch.nn.SiLU,
        grid_eps=0.02,
        grid_range=[-1, 1],
    ):

        super(KAN, self).__init__()
        self.grid_size = grid_size
        self.spline_order = spline_order

        self.layers = torch.nn.ModuleList()
        for in_features, out_features in zip(layers_hidden, layers_hidden[1:]):
            self.layers.append(
                KANLinear(
                    in_features,
                    out_features,
                    grid_size=grid_size,
                    spline_order=spline_order,
                    scale_noise=scale_noise,
                    scale_base=scale_base,
                    scale_spline=scale_spline,
                    base_activation=base_activation,
                    grid_eps=grid_eps,
                    grid_range=grid_range,
                )
            )

    def forward(self, x: torch.Tensor, update_grid=False): 

        for layer in self.layers:
            if update_grid:
                layer.update_grid(x)
            x = layer(x)
        return x

    def regularization_loss(self, regularize_activation=1.0, regularize_entropy=1.0):

        return sum(
            layer.regularization_loss(regularize_activation, regularize_entropy)
            for layer in self.layers
        )





class KANLinear(torch.nn.Module):
    def __init__(
        self,
        in_features,
        out_features,
        grid_size=5,  
        spline_order=3, 
        scale_noise=0.1,  
        scale_base=1.0,   
        scale_spline=1.0,    
        enable_standalone_scale_spline=True,
        base_activation=torch.nn.SiLU,  # 
        grid_eps=0.02,
        grid_range=[-1, 1],  # 
    ):
        super(KANLinear, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.grid_size = grid_size # 
        self.spline_order = spline_order

        h = (grid_range[1] - grid_range[0]) / grid_size   # 
        grid = ( # 
            (
                torch.arange(-spline_order, grid_size + spline_order + 1) * h
                + grid_range[0] 
            )
            .expand(in_features, -1)
            .contiguous()
        )
        self.register_buffer("grid", grid)  # 

        self.base_weight = torch.nn.Parameter(torch.Tensor(out_features, in_features)) # 
        self.spline_weight = torch.nn.Parameter(
            torch.Tensor(out_features, in_features, grid_size + spline_order)
        )
        if enable_standalone_scale_spline:  # 
            self.spline_scaler = torch.nn.Parameter(
                torch.Tensor(out_features, in_features)
            )

        self.scale_noise = scale_noise # 
        self.scale_base = scale_base
        self.scale_spline = scale_spline
        self.enable_standalone_scale_spline = enable_standalone_scale_spline
        self.base_activation = base_activation()
        self.grid_eps = grid_eps

        self.reset_parameters()  # 

    def reset_parameters(self):
        torch.nn.init.kaiming_uniform_(self.base_weight, a=math.sqrt(5) * self.scale_base)# 
        with torch.no_grad():
            noise = (# 
                (
                    torch.rand(self.grid_size + 1, self.in_features, self.out_features)
                    - 1 / 2
                )
                * self.scale_noise
                / self.grid_size
            )
            self.spline_weight.data.copy_( # 
                (self.scale_spline if not self.enable_standalone_scale_spline else 1.0)
                * self.curve2coeff(
                    self.grid.T[self.spline_order : -self.spline_order],
                    noise,
                )
            )
            if self.enable_standalone_scale_spline:  # 
                # torch.nn.init.constant_(self.spline_scaler, self.scale_spline)
                torch.nn.init.kaiming_uniform_(self.spline_scaler, a=math.sqrt(5) * self.scale_spline)

    def b_splines(self, x: torch.Tensor):

        assert x.dim() == 2 and x.size(1) == self.in_features

        grid: torch.Tensor = ( # 形状为 (in_features, grid_size + 2 * spline_order + 1)
            self.grid
        )  # (in_features, grid_size + 2 * spline_order + 1)
        x = x.unsqueeze(-1)
        bases = ((x >= grid[:, :-1]) & (x < grid[:, 1:])).to(x.dtype)
        for k in range(1, self.spline_order + 1):
            bases = (
                (x - grid[:, : -(k + 1)])
                / (grid[:, k:-1] - grid[:, : -(k + 1)])
                * bases[:, :, :-1]
            ) + (
                (grid[:, k + 1 :] - x)
                / (grid[:, k + 1 :] - grid[:, 1:(-k)])
                * bases[:, :, 1:]
            )

        assert bases.size() == (
            x.size(0),
            self.in_features,
            self.grid_size + self.spline_order,
        )
        return bases.contiguous()

    def curve2coeff(self, x: torch.Tensor, y: torch.Tensor):

        assert x.dim() == 2 and x.size(1) == self.in_features
        assert y.size() == (x.size(0), self.in_features, self.out_features)
        # 
        A = self.b_splines(x).transpose(
            0, 1 #  (in_features, batch_size, grid_size + spline_order)
        )  # (in_features, batch_size, grid_size + spline_order)
        B = y.transpose(0, 1)  # (in_features, batch_size, out_features)  (in_features, batch_size, out_features)
        solution = torch.linalg.lstsq(   
            A, B
        ).solution  # (in_features, grid_size + spline_order, out_features)   (in_features, grid_size + spline_order, out_features)
        result = solution.permute( 
            2, 0, 1
        )  # (out_features, in_features, grid_size + spline_order)

        assert result.size() == (
            self.out_features,
            self.in_features,
            self.grid_size + self.spline_order,
        )
        return result.contiguous()

    @property
    def scaled_spline_weight(self):

        return self.spline_weight * (
            self.spline_scaler.unsqueeze(-1)
            if self.enable_standalone_scale_spline
            else 1.0
        )

    def forward(self, x: torch.Tensor): 

        assert x.dim() == 2 and x.size(1) == self.in_features

        base_output = F.linear(self.base_activation(x), self.base_weight) 
        spline_output = F.linear( 
            self.b_splines(x).view(x.size(0), -1),
            self.scaled_spline_weight.view(self.out_features, -1),
        )
        return base_output + spline_output  

    @torch.no_grad()
    def update_grid(self, x: torch.Tensor, margin=0.01): 
        assert x.dim() == 2 and x.size(1) == self.in_features
        batch = x.size(0)

        splines = self.b_splines(x)  # (batch, in, coeff)  
        splines = splines.permute(1, 0, 2)  # (in, batch, coeff)  
        orig_coeff = self.scaled_spline_weight  # (out, in, coeff)
        orig_coeff = orig_coeff.permute(1, 2, 0)  # (in, coeff, out)  
        unreduced_spline_output = torch.bmm(splines, orig_coeff)  # (in, batch, out)
        unreduced_spline_output = unreduced_spline_output.permute(
            1, 0, 2
        )  # (batch, in, out)

        # sort each channel individually to collect data distribution
        x_sorted = torch.sort(x, dim=0)[0] 
        grid_adaptive = x_sorted[
            torch.linspace(
                0, batch - 1, self.grid_size + 1, dtype=torch.int64, device=x.device
            )
        ]

        uniform_step = (x_sorted[-1] - x_sorted[0] + 2 * margin) / self.grid_size
        grid_uniform = (
            torch.arange(
                self.grid_size + 1, dtype=torch.float32, device=x.device
            ).unsqueeze(1)
            * uniform_step
            + x_sorted[0]
            - margin
        )

        grid = self.grid_eps * grid_uniform + (1 - self.grid_eps) * grid_adaptive
        grid = torch.cat(
            [
                grid[:1]
                - uniform_step
                * torch.arange(self.spline_order, 0, -1, device=x.device).unsqueeze(1),
                grid,
                grid[-1:]
                + uniform_step
                * torch.arange(1, self.spline_order + 1, device=x.device).unsqueeze(1),
            ],
            dim=0,
        )

        self.grid.copy_(grid.T)   
        self.spline_weight.data.copy_(self.curve2coeff(x, unreduced_spline_output))

    def regularization_loss(self, regularize_activation=1.0, regularize_entropy=1.0):

        l1_fake = self.spline_weight.abs().mean(-1)
        regularization_loss_activation = l1_fake.sum()
        p = l1_fake / regularization_loss_activation
        regularization_loss_entropy = -torch.sum(p * p.log())
        return (
            regularize_activation * regularization_loss_activation
            + regularize_entropy * regularization_loss_entropy
        )

