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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride


@triton.jit
def triton_poi_fused_relu_0(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(in_out_ptr0 + x0, tmp2, None)


@triton.jit
def triton_poi_fused_relu_1(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(in_out_ptr0 + x0, tmp2, None)


@triton.jit
def triton_poi_fused_relu_2(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(in_out_ptr0 + x0, tmp2, None)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (256, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_2, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_3, (32, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_4, (8, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_5, (2, 8, 3, 3), (72, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 256, 64, 64), (1048576, 4096, 64, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(4194304)](buf1, 4194304, XBLOCK=1024,
            num_warps=4, num_stages=1)
        buf2 = extern_kernels.convolution(buf1, primals_3, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_relu_1[grid(524288)](buf3, 524288, XBLOCK=1024,
            num_warps=4, num_stages=1)
        buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 8, 64, 64), (32768, 4096, 64, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_relu_2[grid(131072)](buf5, 131072, XBLOCK=512,
            num_warps=8, num_stages=1)
        buf6 = extern_kernels.convolution(buf5, primals_5, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 2, 64, 64), (8192, 4096, 64, 1))
    return (buf6, primals_1, primals_2, primals_3, primals_4, primals_5,
        buf1, buf3, buf5)


class ResidualNetworkSegmentNew(nn.Module):
    """Modified ResidualNetworkSegment model class"""

    def __init__(self, block, num_blocks, width, depth):
        super(ResidualNetworkSegmentNew, self).__init__()
        assert (depth - 4
            ) % 4 == 0, 'Depth not compatible with recurrent architectue.'
        self.in_planes = int(width * 64)
        self.conv1 = nn.Conv2d(3, int(width * 64), kernel_size=3, stride=1,
            padding=1, bias=False)
        layers = []
        for j in range((depth - 4) // 4):
            for i in range(len(num_blocks)):
                layers.append(self._make_layer(block, int(width * 64),
                    num_blocks[i], stride=1))
        self.recur_block = nn.Sequential(*layers)
        self.conv2 = nn.Conv2d(int(width * 64), 32, kernel_size=3, stride=1,
            padding=1, bias=False)
        self.conv3 = nn.Conv2d(32, 8, kernel_size=3, stride=1, padding=1,
            bias=False)
        self.conv4 = nn.Conv2d(8, 2, kernel_size=3, stride=1, padding=1,
            bias=False)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for strd in strides:
            layers.append(block(self.in_planes, planes, strd))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_3 = self.conv2.weight
        primals_4 = self.conv3.weight
        primals_5 = self.conv4.weight
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.225851