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
from torch import 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_relu_threshold_backward_0(in_out_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    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)
    tmp3 = 0.0
    tmp4 = tmp2 <= tmp3
    tl.store(in_out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr0 + x0, tmp4, 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), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0)
        del buf0
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(64)](buf1, buf3, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_4, reinterpret_tensor(buf1, (16, 4), (
            4, 1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf2)
        del primals_4
    return reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0
        ), reinterpret_tensor(primals_2, (16, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (16, 4), (4, 1), 0), primals_3, buf3


class NPBlockRelu2d(nn.Module):
    """Block for Neural Processes."""

    def __init__(self, in_channels, out_channels, dropout=0, batchnorm=
        False, bias=False):
        super().__init__()
        self.linear = nn.Linear(in_channels, out_channels, bias=bias)
        self.act = nn.ReLU()
        self.dropout = nn.Dropout2d(dropout)
        self.norm = nn.BatchNorm2d(out_channels) if batchnorm else False

    def forward(self, x):
        x = self.act(self.linear(x))
        x = x.permute(0, 2, 1)[:, :, :, None]
        if self.norm:
            x = self.norm(x)
        x = self.dropout(x)
        return x[:, :, :, 0].permute(0, 2, 1)


class BatchMLPNew(nn.Module):
    """Apply MLP to the final axis of a 3D tensor (reusing already defined MLPs).
    Args:
        input: input tensor of shape [B,n,d_in].
        output_sizes: An iterable containing the output sizes of the MLP as defined
            in `basic.Linear`.
    Returns:
        tensor of shape [B,n,d_out] where d_out=output_size
    """

    def __init__(self, input_size, output_size, num_layers=2, dropout=0,
        batchnorm=False):
        super().__init__()
        self.input_size = input_size
        self.output_size = output_size
        self.num_layers = num_layers
        self.initial = NPBlockRelu2d(input_size, output_size, dropout=
            dropout, batchnorm=batchnorm)
        self.encoder = nn.Sequential(*[NPBlockRelu2d(output_size,
            output_size, dropout=dropout, batchnorm=batchnorm) for _ in
            range(num_layers - 2)])
        self.final = nn.Linear(output_size, output_size)

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


Measured latency_ms: 0.032229