import torch
import torch.nn as nn
from torch.nn import init


class ChebConv(nn.Module):
    """
    The ChebNet convolution operation.

    :param in_c: int, number of input channels.
    :param out_c: int, number of output channels.
    :param K: int, the order of Chebyshev Polynomial.
    """

    def __init__(self, in_c, out_c, K, bias=True, normalize=True):
        super(ChebConv, self).__init__()
        self.normalize = normalize
        self.weight = nn.Parameter(torch.Tensor(K + 1, 1, in_c, out_c))
        init.xavier_normal_(self.weight)
        if bias:
            self.bias = nn.Parameter(torch.Tensor(1, 1, out_c))
            init.zeros_(self.bias)
        else:
            self.register_parameter('bias', None)
        self.K = K + 1

    def forward(self, inputs, graph):
        """
        :param inputs: the input data, [B, N, C]
        :param graph: the graph structure, [N, N]
        :return: convolution result, [B, N, D]
        """
        L = ChebConv.get_laplacian(graph, self.normalize)
        mul_L = self.cheb_polynomial(L).unsqueeze(1)
        result = torch.matmul(mul_L, inputs)
        result = torch.matmul(result, self.weight)
        result = torch.sum(result, dim=0) + self.bias
        return result

    def cheb_polynomial(self, laplacian):
        """
        Compute the Chebyshev Polynomial, according to the graph laplacian.

        :param laplacian: the graph laplacian, [N, N].
        :return: the multi order Chebyshev laplacian, [K, N, N].
        """
        N = laplacian.size(0)
        multi_order_laplacian = torch.zeros([self.K, N, N], device=
            laplacian.device, dtype=torch.float)
        multi_order_laplacian[0] = torch.eye(N, device=laplacian.device,
            dtype=torch.float)
        if self.K == 1:
            return multi_order_laplacian
        else:
            multi_order_laplacian[1] = laplacian
            if self.K == 2:
                return multi_order_laplacian
            else:
                for k in range(2, self.K):
                    multi_order_laplacian[k] = 2 * torch.mm(laplacian,
                        multi_order_laplacian[k - 1]) - multi_order_laplacian[
                        k - 2]
        return multi_order_laplacian

    @staticmethod
    def get_laplacian(graph, normalize):
        """
        return the laplacian of the graph.

        :param graph: the graph structure without self loop, [N, N].
        :param normalize: whether to used the normalized laplacian.
        :return: graph laplacian.
        """
        if normalize:
            D = torch.diag(torch.sum(graph, dim=-1) ** (-1 / 2))
            L = torch.eye(graph.size(0), device=graph.device, dtype=graph.dtype
                ) - torch.mm(torch.mm(D, graph), D)
        else:
            D = torch.diag(torch.sum(graph, dim=-1))
            L = D - graph
        return L


def get_inputs():
    return [torch.rand([4, 4]), torch.rand([4, 4])]


def get_init_inputs():
    return [[], {'in_c': 4, 'out_c': 4, 'K': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from torch.nn import init
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_diag_embed_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp0 = x0
    tmp1 = x1
    tmp2 = tmp0 == tmp1
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp9 = tmp7 + tmp8
    tmp10 = -0.5
    tmp11 = libdevice.pow(tmp9, tmp10)
    tmp12 = 0.0
    tmp13 = tl.where(tmp2, tmp11, tmp12)
    tl.store(out_ptr0 + x2, tmp13, xmask)


@triton.jit
def triton_poi_fused_diag_embed_eye_sub_1(in_out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp6 = tl.load(in_out_ptr0 + x2, xmask)
    tmp0 = x1
    tmp1 = x0
    tmp2 = tmp0 == tmp1
    tmp3 = 1.0
    tmp4 = 0.0
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp7 = tmp5 - tmp6
    tl.store(in_out_ptr0 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused_diag_embed_eye_zeros_2(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 16
    x3 = xindex % 16
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x4 = xindex
    tmp3 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp0 = x2
    tmp1 = tl.full([1], 1, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = tmp0 == tmp4
    tmp6 = x1
    tmp7 = x0
    tmp8 = tmp6 == tmp7
    tmp9 = 1.0
    tmp10 = 0.0
    tmp11 = tl.where(tmp8, tmp9, tmp10)
    tmp12 = tl.where(tmp5, tmp11, tmp10)
    tmp13 = tl.where(tmp2, tmp3, tmp12)
    tl.store(out_ptr0 + x4, tmp13, xmask)


@triton.jit
def triton_poi_fused_mul_sub_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16
    x0 = xindex % 16
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + x2, xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 2, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = 2.0
    tmp5 = tmp3 * tmp4
    tmp7 = tmp5 - tmp6
    tmp9 = tl.where(tmp2, tmp7, tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_mul_sub_4(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16
    x0 = xindex % 16
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (16 + x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + x2, xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 3, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = 2.0
    tmp5 = tmp3 * tmp4
    tmp7 = tmp5 - tmp6
    tmp9 = tl.where(tmp2, tmp7, tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_mul_sub_5(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16
    x0 = xindex % 16
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (32 + x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + x2, xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 4, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = 2.0
    tmp5 = tmp3 * tmp4
    tmp7 = tmp5 - tmp6
    tmp9 = tl.where(tmp2, tmp7, tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_add_sum_6(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x2), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x2), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x2), xmask)
    tmp7 = tl.load(in_ptr0 + (64 + x2), xmask)
    tmp9 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp8 = tmp6 + tmp7
    tmp10 = tmp8 + tmp9
    tl.store(out_ptr0 + x2, tmp10, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (5, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_4, (1, 1, 4), (4, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_diag_embed_0[grid(16)](primals_1, buf0, 16, XBLOCK
            =16, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf0, primals_1, out=buf1)
        del primals_1
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf1, buf0, out=buf2)
        del buf0
        buf3 = buf2
        del buf2
        triton_poi_fused_diag_embed_eye_sub_1[grid(16)](buf3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((5, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_diag_embed_eye_zeros_2[grid(80)](buf3, buf4, 80,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf5 = buf1
        del buf1
        extern_kernels.mm(buf3, reinterpret_tensor(buf4, (4, 4), (4, 1), 16
            ), out=buf5)
        buf6 = empty_strided_cuda((5, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_mul_sub_3[grid(80)](buf5, buf4, buf6, 80, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf7 = buf5
        del buf5
        extern_kernels.mm(buf3, reinterpret_tensor(buf6, (4, 4), (4, 1), 32
            ), out=buf7)
        buf8 = buf4
        del buf4
        triton_poi_fused_mul_sub_4[grid(80)](buf7, buf6, buf8, 80, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf9 = buf7
        del buf7
        extern_kernels.mm(buf3, reinterpret_tensor(buf8, (4, 4), (4, 1), 48
            ), out=buf9)
        del buf3
        buf10 = buf6
        del buf6
        triton_poi_fused_mul_sub_5[grid(80)](buf9, buf8, buf10, 80, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf11 = reinterpret_tensor(buf8, (20, 4), (4, 1), 0)
        del buf8
        extern_kernels.mm(reinterpret_tensor(buf10, (20, 4), (4, 1), 0),
            primals_2, out=buf11)
        del primals_2
        buf12 = buf10
        del buf10
        extern_kernels.bmm(reinterpret_tensor(buf11, (5, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(primals_3, (5, 4, 4), (16, 4, 1), 0),
            out=buf12)
        del primals_3
        buf13 = reinterpret_tensor(buf9, (1, 4, 4), (16, 4, 1), 0)
        del buf9
        triton_poi_fused_add_sum_6[grid(16)](buf12, primals_4, buf13, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del buf12
        del primals_4
    return buf13, reinterpret_tensor(buf11, (5, 4, 4), (16, 1, 4), 0)


class ChebConvNew(nn.Module):
    """
    The ChebNet convolution operation.

    :param in_c: int, number of input channels.
    :param out_c: int, number of output channels.
    :param K: int, the order of Chebyshev Polynomial.
    """

    def __init__(self, in_c, out_c, K, bias=True, normalize=True):
        super(ChebConvNew, self).__init__()
        self.normalize = normalize
        self.weight = nn.Parameter(torch.Tensor(K + 1, 1, in_c, out_c))
        init.xavier_normal_(self.weight)
        if bias:
            self.bias = nn.Parameter(torch.Tensor(1, 1, out_c))
            init.zeros_(self.bias)
        else:
            self.register_parameter('bias', None)
        self.K = K + 1

    def cheb_polynomial(self, laplacian):
        """
        Compute the Chebyshev Polynomial, according to the graph laplacian.

        :param laplacian: the graph laplacian, [N, N].
        :return: the multi order Chebyshev laplacian, [K, N, N].
        """
        N = laplacian.size(0)
        multi_order_laplacian = torch.zeros([self.K, N, N], device=
            laplacian.device, dtype=torch.float)
        multi_order_laplacian[0] = torch.eye(N, device=laplacian.device,
            dtype=torch.float)
        if self.K == 1:
            return multi_order_laplacian
        else:
            multi_order_laplacian[1] = laplacian
            if self.K == 2:
                return multi_order_laplacian
            else:
                for k in range(2, self.K):
                    multi_order_laplacian[k] = 2 * torch.mm(laplacian,
                        multi_order_laplacian[k - 1]) - multi_order_laplacian[
                        k - 2]
        return multi_order_laplacian

    @staticmethod
    def get_laplacian(graph, normalize):
        """
        return the laplacian of the graph.

        :param graph: the graph structure without self loop, [N, N].
        :param normalize: whether to used the normalized laplacian.
        :return: graph laplacian.
        """
        if normalize:
            D = torch.diag(torch.sum(graph, dim=-1) ** (-1 / 2))
            L = torch.eye(graph.size(0), device=graph.device, dtype=graph.dtype
                ) - torch.mm(torch.mm(D, graph), D)
        else:
            D = torch.diag(torch.sum(graph, dim=-1))
            L = D - graph
        return L

    def forward(self, input_0, input_1):
        primals_3 = self.weight
        primals_4 = self.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
