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.nn.modules.loss import *
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import *
from torch.optim import *
from torch.optim.lr_scheduler import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused__unsafe_index_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 8 % 8
    x0 = xindex % 8
    x2 = xindex // 64
    x4 = xindex
    tmp0 = x1
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tmp5 = x0
    tmp6 = tmp5.to(tl.float32)
    tmp7 = tmp6 * tmp2
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.load(in_ptr0 + (tmp8 + 4 * tmp4 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + x4, tmp9, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__unsafe_index_0[grid(1024)](primals_1, buf0, 1024,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 8, 8), (256, 64, 8, 1))
        buf2 = buf1
        del buf1
        buf3 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(1024)](buf2, buf3, 
            1024, XBLOCK=128, num_warps=4, num_stages=1)
    return buf2, primals_2, buf0, buf3


class ConvRelu(nn.Module):
    """3x3 convolution followed by ReLU activation building block.
    """

    def __init__(self, num_in, num_out):
        """Creates a `ConvReLU` building block.

        Args:
          num_in: number of input feature maps
          num_out: number of output feature maps
        """
        super().__init__()
        self.block = nn.Conv2d(num_in, num_out, kernel_size=3, padding=1,
            bias=False)

    def forward(self, x):
        """
        The networks forward pass for which
            autograd synthesizes the backwards pass.

        Args:
          x: the input tensor

        Returns:
          The networks output tensor.
        """
        return F.relu(self.block(x), inplace=True)


class DecoderBlockNew(nn.Module):
    """Decoder building block upsampling resolution by a factor of two.
    """

    def __init__(self, num_in, num_out):
        """Creates a `DecoderBlock` building block.

        Args:
          num_in: number of input feature maps
          num_out: number of output feature maps
        """
        super().__init__()
        self.block = ConvRelu(num_in, num_out)

    def forward(self, input_0):
        primals_2 = self.block.block.weight
        primals_1 = input_0
        output = call([primals_1, primals_2])
        return output[0]


Measured latency_ms: 0.034568