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.cuda
import torch.backends.cudnn
import torch.backends.mkl
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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 256
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 64 * x2 + 262144 * y1), tmp0, ymask)


@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 64 * x2 + 576 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, None)


@triton.jit
def triton_poi_fused_convolution_relu_3(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, 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, None)


@triton.jit
def triton_poi_fused_add_convolution_relu_4(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    xnumel = 256
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4096
    y1 = yindex // 4096
    tmp0 = tl.load(in_ptr0 + (x2 + 256 * y3), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x2 + 256 * y3), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = tl.full([1, 1], 0, tl.int32)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tl.store(out_ptr0 + (y0 + 4096 * x2 + 1048576 * y1), tmp6, xmask)


@triton.jit
def triton_poi_fused_threshold_backward_5(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = yindex // 256
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), None, eviction_policy=
        'evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 <= tmp1
    tl.store(out_ptr0 + (y0 + 256 * x2 + 1048576 * y1), tmp2, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (256, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_2, (256,), (1,))
    assert_size_stride(primals_3, (4, 64, 64, 64), (262144, 4096, 64, 1))
    assert_size_stride(primals_4, (64, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_7, (64,), (1,))
    assert_size_stride(primals_8, (256, 64, 1, 1), (64, 1, 1, 1))
    assert_size_stride(primals_9, (256,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 64, 64, 64), (262144, 1, 4096, 64),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(256, 4096)](primals_3, buf0, 256, 4096,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_3
        buf1 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch.
            float32)
        triton_poi_fused_1[grid(4096, 9)](primals_6, buf1, 4096, 9, XBLOCK=
            16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_6
        buf2 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 256, 64, 64), (1048576, 1, 16384, 256))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_2[grid(4194304)](buf3, primals_2, 
            4194304, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_2
        buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 64, 64, 64), (262144, 1, 4096, 64))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_relu_3[grid(1048576)](buf5, primals_5,
            1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_5
        buf6 = extern_kernels.convolution(buf5, buf1, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 64, 64, 64), (262144, 1, 4096, 64))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_relu_3[grid(1048576)](buf7, primals_7,
            1048576, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_7
        buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 256, 64, 64), (1048576, 1, 16384, 256))
        buf9 = empty_strided_cuda((4, 256, 64, 64), (1048576, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_add_convolution_relu_4[grid(16384, 256)](buf8,
            primals_9, buf3, buf9, 16384, 256, XBLOCK=32, YBLOCK=32,
            num_warps=4, num_stages=1)
        del buf8
        del primals_9
        buf10 = empty_strided_cuda((4, 256, 64, 64), (1048576, 1, 16384, 
            256), torch.bool)
        triton_poi_fused_threshold_backward_5[grid(1024, 4096)](buf9, buf10,
            1024, 4096, XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
    return (buf9, primals_1, buf0, primals_4, buf1, primals_8, buf3, buf5,
        buf7, buf10)


class Bottleneck_v2New(nn.Module):

    def __init__(self):
        super(Bottleneck_v2New, self).__init__()
        self.conv = nn.Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1),
            bias=True)
        self.conv1 = nn.Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1),
            bias=True)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1),
            padding=(1, 1), bias=True)
        self.conv3 = nn.Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1),
            bias=True)

    def forward(self, input_0):
        primals_1 = self.conv.weight
        primals_2 = self.conv.bias
        primals_4 = self.conv1.weight
        primals_5 = self.conv1.bias
        primals_6 = self.conv2.weight
        primals_7 = self.conv2.bias
        primals_8 = self.conv3.weight
        primals_9 = self.conv3.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]


Measured latency_ms: 0.01946