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 torch.nn as nn
import torch.utils.data
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_relu_0(in_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
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(in_out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.full([1], 0, tl.int32)
    tmp11 = triton_helpers.maximum(tmp10, tmp9)
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + x2, tmp14, xmask)


@triton.jit
def triton_poi_fused_relu_2(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)


@triton.jit
def triton_poi_fused_mean_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + (4 + x0), xmask)
    tmp3 = tl.load(in_ptr0 + (8 + x0), xmask)
    tmp5 = tl.load(in_ptr0 + (12 + x0), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tl.store(out_ptr0 + x0, tmp8, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_4(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
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = 0.0
    tmp4 = tmp2 <= tmp3
    tl.store(out_ptr0 + x0, tmp4, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10) = 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, 4), (4, 1))
    assert_size_stride(primals_5, (4, 8), (8, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 4), (4, 1))
    assert_size_stride(primals_10, (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_2, primals_1, out=buf0)
        del primals_1
        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_relu_0[grid(16)](buf2, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        buf3 = buf0
        del buf0
        extern_kernels.mm(buf2, primals_4, out=buf3)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, buf3, out=buf4)
        buf5 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        triton_poi_fused_cat_1[grid(32)](primals_2, buf4, buf5, 32, XBLOCK=
            32, num_warps=1, num_stages=1)
        buf6 = buf3
        del buf3
        extern_kernels.mm(buf5, reinterpret_tensor(primals_5, (8, 4), (1, 8
            ), 0), out=buf6)
        buf7 = buf6
        del buf6
        triton_poi_fused_relu_2[grid(16)](buf7, primals_6, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_6
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf7, reinterpret_tensor(primals_7, (4, 4), (1, 4
            ), 0), out=buf8)
        buf9 = buf8
        del buf8
        triton_poi_fused_relu_2[grid(16)](buf9, primals_8, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_8
        buf10 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_10, buf9, reinterpret_tensor(primals_9,
            (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf10)
        del primals_10
        buf11 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_mean_3[grid(4)](buf10, buf11, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del buf10
        buf12 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_4[grid(16)](buf4, buf12, 
            16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf4
    return (buf11, buf2, buf5, buf7, buf9, primals_9, primals_7, primals_5,
        buf12, reinterpret_tensor(primals_3, (4, 4), (1, 4), 0),
        reinterpret_tensor(primals_4, (4, 4), (1, 4), 0),
        reinterpret_tensor(primals_2, (4, 4), (1, 4), 0))


class Readout(nn.Module):
    """
    This module learns a single graph level representation for a molecule given GraphSAGE generated embeddings
    """

    def __init__(self, attr_dim, embedding_dim, hidden_dim, output_dim,
        num_cats):
        super(Readout, self).__init__()
        self.attr_dim = attr_dim
        self.hidden_dim = hidden_dim
        self.output_dim = output_dim
        self.num_cats = num_cats
        self.layer1 = nn.Linear(attr_dim + embedding_dim, hidden_dim)
        self.layer2 = nn.Linear(hidden_dim, output_dim)
        self.output = nn.Linear(output_dim, num_cats)
        self.act = nn.ReLU()

    def forward(self, node_features, node_embeddings):
        combined_rep = torch.cat((node_features, node_embeddings), dim=1)
        hidden_rep = self.act(self.layer1(combined_rep))
        graph_rep = self.act(self.layer2(hidden_rep))
        logits = torch.mean(self.output(graph_rep), dim=0)
        return logits


class GCN(nn.Module):
    """
    Graph Convolutional Network based on https://arxiv.org/abs/1609.02907

    """

    def __init__(self, feat_dim, hidden_dim1, hidden_dim2, dropout,
        is_sparse=False):
        """Dense version of GAT."""
        super(GCN, self).__init__()
        self.W1 = nn.Parameter(torch.FloatTensor(feat_dim, hidden_dim1))
        self.W2 = nn.Parameter(torch.FloatTensor(hidden_dim1, hidden_dim2))
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout(p=dropout)
        nn.init.xavier_uniform_(self.W1.data)
        nn.init.xavier_uniform_(self.W2.data)
        self.is_sparse = is_sparse

    def forward(self, x, adj):
        support = torch.mm(x, self.W1)
        embeddings = torch.sparse.mm(adj, support
            ) if self.is_sparse else torch.mm(adj, support)
        embeddings = self.relu(embeddings)
        embeddings = self.dropout(embeddings)
        support = torch.mm(embeddings, self.W2)
        embeddings = torch.sparse.mm(adj, support
            ) if self.is_sparse else torch.mm(adj, support)
        embeddings = self.relu(embeddings)
        return embeddings


class GCNClassificationNew(nn.Module):
    """
    Network that consolidates GCN + Readout into a single nn.Module
    """

    def __init__(self, feat_dim, hidden_dim, node_embedding_dim, dropout,
        readout_hidden_dim, graph_embedding_dim, num_categories, sparse_adj
        =False):
        super(GCNClassificationNew, self).__init__()
        self.gcn = GCN(feat_dim, hidden_dim, node_embedding_dim, dropout,
            is_sparse=sparse_adj)
        self.readout = Readout(feat_dim, node_embedding_dim,
            readout_hidden_dim, graph_embedding_dim, num_categories)

    def forward(self, input_0, input_1):
        primals_1 = self.gcn.W1
        primals_2 = self.gcn.W2
        primals_5 = self.readout.layer1.weight
        primals_6 = self.readout.layer1.bias
        primals_3 = self.readout.layer2.weight
        primals_8 = self.readout.layer2.bias
        primals_4 = self.readout.output.weight
        primals_10 = self.readout.output.bias
        primals_7 = input_0
        primals_9 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9, primals_10])
        return output[0]


Measured latency_ms: 0.049359