import math
import torch
import torch.nn as nn
from torch.nn import functional as F


class GraphConvLayer(nn.Module):
    """ A Graph Convolution Layer as per https://arxiv.org/pdf/1609.02907.pdf with Glorot initialisation """

    def __init__(self, in_features: 'int', out_filters: 'int',
        dropout_ratio: 'float', bias: 'bool'):
        """
        Weight matrix / filter parameters: (in_features, out_filters) or (in_features, out_features)
        """
        super(GraphConvLayer, self).__init__()
        self.in_features = in_features
        self.out_filters = out_filters
        self.weight = nn.Parameter(torch.FloatTensor(in_features, out_filters))
        self.dropout = nn.Dropout(p=dropout_ratio)
        if bias:
            self.bias = nn.Parameter(torch.FloatTensor(out_filters))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self) ->None:
        """ Reset all trainable parameters using Glorot & Bengio (2010) initialisation """
        stdv = 1.0 / math.sqrt(self.weight.size(1))
        self.weight.data.uniform_(-stdv, stdv)
        if self.bias is not None:
            self.bias.data.uniform_(-stdv, stdv)

    def forward(self, input_x: 'torch.FloatTensor', adjacency:
        'torch.sparse.FloatTensor') ->torch.FloatTensor:
        """
        input_x.shape == (num_nodes, in_features)
        support.shape == (num_nodes, out_features)
        adjacency.shape == (num_nodes, num_nodes)
        output_z.shape == (num_nodes, out_features)
        """
        input_x = self.dropout(input_x)
        support = torch.mm(input_x, self.weight)
        output_z = torch.spmm(adjacency, support)
        if self.bias is not None:
            output_z = output_z + self.bias
        return output_z


class GCN(nn.Module):

    def __init__(self, num_classes: 'int', num_input_features: 'int',
        num_hidden_features: 'int', dropout_ratio: 'float', use_bias: 'bool'):
        super(GCN, self).__init__()
        self.gc_1 = GraphConvLayer(in_features=num_input_features,
            out_filters=num_hidden_features, dropout_ratio=dropout_ratio,
            bias=use_bias)
        self.gc_2 = GraphConvLayer(in_features=num_hidden_features,
            out_filters=num_classes, dropout_ratio=dropout_ratio, bias=use_bias
            )

    def forward(self, x: 'torch.FloatTensor', adjacency:
        'torch.sparse.FloatTensor') ->torch.FloatTensor:
        z = F.relu(self.gc_1(x, adjacency))
        z = self.gc_2(z, adjacency)
        return z


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


def get_init_inputs():
    return [[], {'num_classes': 4, 'num_input_features': 4,
        'num_hidden_features': 4, 'dropout_ratio': 0.5, 'use_bias': 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 import triton_helpers
import math
import torch.nn as nn
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_add_relu_0(in_out_ptr0, in_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_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = 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, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, primals_2, out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, buf0, out=buf1)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_poi_fused_add_relu_0[grid(16)](buf2, primals_4, 16, XBLOCK=
            16, num_warps=1, num_stages=1)
        del primals_4
        buf3 = buf0
        del buf0
        extern_kernels.mm(buf2, primals_5, out=buf3)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_6, primals_3, buf3, alpha=1, beta=1,
            out=buf4)
        del buf3
        del primals_6
    return buf4, buf2, reinterpret_tensor(primals_3, (4, 4), (1, 4), 0
        ), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0
        ), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0)


class GraphConvLayer(nn.Module):
    """ A Graph Convolution Layer as per https://arxiv.org/pdf/1609.02907.pdf with Glorot initialisation """

    def __init__(self, in_features: 'int', out_filters: 'int',
        dropout_ratio: 'float', bias: 'bool'):
        """
        Weight matrix / filter parameters: (in_features, out_filters) or (in_features, out_features)
        """
        super(GraphConvLayer, self).__init__()
        self.in_features = in_features
        self.out_filters = out_filters
        self.weight = nn.Parameter(torch.FloatTensor(in_features, out_filters))
        self.dropout = nn.Dropout(p=dropout_ratio)
        if bias:
            self.bias = nn.Parameter(torch.FloatTensor(out_filters))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self) ->None:
        """ Reset all trainable parameters using Glorot & Bengio (2010) initialisation """
        stdv = 1.0 / math.sqrt(self.weight.size(1))
        self.weight.data.uniform_(-stdv, stdv)
        if self.bias is not None:
            self.bias.data.uniform_(-stdv, stdv)

    def forward(self, input_x: 'torch.FloatTensor', adjacency:
        'torch.sparse.FloatTensor') ->torch.FloatTensor:
        """
        input_x.shape == (num_nodes, in_features)
        support.shape == (num_nodes, out_features)
        adjacency.shape == (num_nodes, num_nodes)
        output_z.shape == (num_nodes, out_features)
        """
        input_x = self.dropout(input_x)
        support = torch.mm(input_x, self.weight)
        output_z = torch.spmm(adjacency, support)
        if self.bias is not None:
            output_z = output_z + self.bias
        return output_z


class GCNNew(nn.Module):

    def __init__(self, num_classes: 'int', num_input_features: 'int',
        num_hidden_features: 'int', dropout_ratio: 'float', use_bias: 'bool'):
        super(GCNNew, self).__init__()
        self.gc_1 = GraphConvLayer(in_features=num_input_features,
            out_filters=num_hidden_features, dropout_ratio=dropout_ratio,
            bias=use_bias)
        self.gc_2 = GraphConvLayer(in_features=num_hidden_features,
            out_filters=num_classes, dropout_ratio=dropout_ratio, bias=use_bias
            )

    def forward(self, input_0, input_1):
        primals_1 = self.gc_1.weight
        primals_4 = self.gc_1.bias
        primals_2 = self.gc_2.weight
        primals_6 = self.gc_2.bias
        primals_3 = input_0
        primals_5 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
