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 import nn
from torch.nn import functional as F
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_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 512
    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.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    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_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp7, 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 8), (8, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (1, 4), (4, 1))
    assert_size_stride(primals_6, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(512)](primals_1, primals_2, buf0, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 8), (8, 1), 0),
            reinterpret_tensor(primals_3, (8, 4), (1, 8), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_leaky_relu_1[grid(256)](buf1, primals_4, buf2,
            buf3, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf1
        del primals_4
        buf5 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_6, reinterpret_tensor(buf3, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_5, (4, 1), (1, 4), 0),
            alpha=1, beta=1, out=buf5)
        del primals_6
    return reinterpret_tensor(buf5, (4, 4, 4), (16, 4, 1), 0
        ), reinterpret_tensor(buf0, (64, 8), (8, 1), 0
        ), buf2, reinterpret_tensor(buf3, (64, 4), (4, 1), 0), primals_5


class MLP(nn.Module):
    """
        Multi-Layer Perceptron
        :param in_dim: int, size of input feature
        :param n_classes: int, number of output classes
        :param hidden_dim: int, size of hidden vector
        :param dropout: float, dropout rate
        :param n_layers: int, number of layers, at least 2, default = 2
        :param act: function, activation function, default = leaky_relu
    """

    def __init__(self, in_dim, n_classes, hidden_dim, dropout, n_layers=2,
        act=F.leaky_relu):
        super(MLP, self).__init__()
        self.l_in = nn.Linear(in_dim, hidden_dim)
        self.l_hs = nn.ModuleList(nn.Linear(hidden_dim, hidden_dim) for _ in
            range(n_layers - 2))
        self.l_out = nn.Linear(hidden_dim, n_classes)
        self.dropout = nn.Dropout(p=dropout)
        self.act = act
        return

    def forward(self, input):
        """
            :param input: Tensor of (batch_size, in_dim), input feature
            :returns: Tensor of (batch_size, n_classes), output class
        """
        hidden = self.act(self.l_in(self.dropout(input)))
        for l_h in self.l_hs:
            hidden = self.act(l_h(self.dropout(hidden)))
        output = self.l_out(self.dropout(hidden))
        return output


class DiscNew(nn.Module):
    """
        2-layer discriminator for MI estimator
        :param x_dim: int, size of x vector
        :param y_dim: int, size of y vector
        :param dropout: float, dropout rate
    """

    def __init__(self, x_dim, y_dim, dropout):
        super(DiscNew, self).__init__()
        self.disc = MLP(x_dim + y_dim, 1, y_dim, dropout, n_layers=2)
        return

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


Measured latency_ms: 0.034086