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._inductor.runtime.triton_helpers import libdevice
import torch.nn as 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_red_fused__native_batch_norm_legit_leaky_relu_repeat_0(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr3, xnumel, rnumel,
    XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 256
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0 % 64, xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x0, tmp0, xmask)
    tmp3_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp3_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp3_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp1 = tl.load(in_ptr1 + (r1 + 4096 * x0), rmask & xmask,
            eviction_policy='evict_last', other=0.0)
        tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
        tmp3_mean_next, tmp3_m2_next, tmp3_weight_next = (triton_helpers.
            welford_reduce(tmp2, tmp3_mean, tmp3_m2, tmp3_weight, roffset == 0)
            )
        tmp3_mean = tl.where(rmask & xmask, tmp3_mean_next, tmp3_mean)
        tmp3_m2 = tl.where(rmask & xmask, tmp3_m2_next, tmp3_m2)
        tmp3_weight = tl.where(rmask & xmask, tmp3_weight_next, tmp3_weight)
    tmp3_tmp, tmp4_tmp, tmp5_tmp = triton_helpers.welford(tmp3_mean,
        tmp3_m2, tmp3_weight, 1)
    tmp3 = tmp3_tmp[:, None]
    tmp4 = tmp4_tmp[:, None]
    tmp5_tmp[:, None]
    tl.store(out_ptr1 + x0, tmp3, xmask)
    tmp15 = tl.load(in_ptr2 + x0 % 64, xmask, eviction_policy='evict_last')
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp6 = tl.load(in_ptr1 + (r1 + 4096 * x0), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp7 = tmp6 - tmp3
        tmp8 = 4096.0
        tmp9 = tmp4 / tmp8
        tmp10 = 1e-05
        tmp11 = tmp9 + tmp10
        tmp12 = libdevice.rsqrt(tmp11)
        tmp13 = tmp7 * tmp12
        tmp14 = tmp13 * tmp0
        tmp16 = tmp14 + tmp15
        tmp17 = 0.0
        tmp18 = tmp16 > tmp17
        tmp19 = 0.2
        tmp20 = tmp16 * tmp19
        tmp21 = tl.where(tmp18, tmp16, tmp20)
        tl.store(in_out_ptr0 + (r1 + 4096 * x0), tmp21, rmask & xmask)
    tmp22 = 4096.0
    tmp23 = tmp4 / tmp22
    tmp24 = 1e-05
    tmp25 = tmp23 + tmp24
    tmp26 = libdevice.rsqrt(tmp25)
    tl.store(out_ptr3 + x0, tmp26, xmask)


@triton.jit
def triton_red_fused__native_batch_norm_legit_add_repeat_1(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr3, out_ptr4, xnumel,
    rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 256
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0 % 64, xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x0, tmp0, xmask)
    tmp3_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp3_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp3_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp1 = tl.load(in_ptr1 + (r1 + 4096 * x0), rmask & xmask,
            eviction_policy='evict_last', other=0.0)
        tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
        tmp3_mean_next, tmp3_m2_next, tmp3_weight_next = (triton_helpers.
            welford_reduce(tmp2, tmp3_mean, tmp3_m2, tmp3_weight, roffset == 0)
            )
        tmp3_mean = tl.where(rmask & xmask, tmp3_mean_next, tmp3_mean)
        tmp3_m2 = tl.where(rmask & xmask, tmp3_m2_next, tmp3_m2)
        tmp3_weight = tl.where(rmask & xmask, tmp3_weight_next, tmp3_weight)
    tmp3_tmp, tmp4_tmp, tmp5_tmp = triton_helpers.welford(tmp3_mean,
        tmp3_m2, tmp3_weight, 1)
    tmp3 = tmp3_tmp[:, None]
    tmp4 = tmp4_tmp[:, None]
    tmp5_tmp[:, None]
    tl.store(out_ptr1 + x0, tmp3, xmask)
    x2 = xindex % 64
    tmp15 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp6 = tl.load(in_ptr1 + (r1 + 4096 * x0), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp17 = tl.load(in_ptr3 + (r1 + 4096 * x0), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp7 = tmp6 - tmp3
        tmp8 = 4096.0
        tmp9 = tmp4 / tmp8
        tmp10 = 1e-05
        tmp11 = tmp9 + tmp10
        tmp12 = libdevice.rsqrt(tmp11)
        tmp13 = tmp7 * tmp12
        tmp14 = tmp13 * tmp0
        tmp16 = tmp14 + tmp15
        tmp18 = tmp16 + tmp17
        tl.store(out_ptr3 + (r1 + 4096 * x0), tmp18, rmask & xmask)
    tmp19 = 4096.0
    tmp20 = tmp4 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr4 + x0, tmp23, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 64, 64, 64), (262144, 4096, 64, 1))
    assert_size_stride(primals_2, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_3, (64,), (1,))
    assert_size_stride(primals_4, (64,), (1,))
    assert_size_stride(primals_5, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_6, (64,), (1,))
    assert_size_stride(primals_7, (64,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf1 = empty_strided_cuda((256,), (1,), torch.float32)
        buf2 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256), torch
            .float32)
        buf6 = empty_strided_cuda((1, 256, 64, 64), (1048576, 4096, 64, 1),
            torch.float32)
        buf7 = reinterpret_tensor(buf6, (4, 64, 64, 64), (262144, 4096, 64,
            1), 0)
        del buf6
        buf5 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256), torch
            .float32)
        get_raw_stream(0)
        triton_red_fused__native_batch_norm_legit_leaky_relu_repeat_0[grid(256)
            ](buf7, primals_3, buf0, primals_4, buf1, buf2, buf5, 256, 4096,
            XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1)
        del primals_3
        del primals_4
        buf8 = extern_kernels.convolution(buf7, primals_5, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 64, 64, 64), (262144, 4096, 64, 1))
        buf9 = empty_strided_cuda((256,), (1,), torch.float32)
        buf10 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256),
            torch.float32)
        buf14 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
            torch.float32)
        buf13 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256),
            torch.float32)
        triton_red_fused__native_batch_norm_legit_add_repeat_1[grid(256)](
            primals_6, buf8, primals_7, primals_1, buf9, buf10, buf14,
            buf13, 256, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1
            )
        del primals_6
        del primals_7
    return (buf14, primals_1, primals_2, primals_5, buf0, buf1,
        reinterpret_tensor(buf5, (256,), (1,), 0), buf7, buf8, buf9,
        reinterpret_tensor(buf13, (256,), (1,), 0), reinterpret_tensor(
        buf10, (1, 256, 1, 1), (256, 1, 1, 1), 0), reinterpret_tensor(buf2,
        (1, 256, 1, 1), (256, 1, 1, 1), 0))


class _Residual_BlockNew(nn.Module):

    def __init__(self):
        super(_Residual_BlockNew, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size
            =3, stride=1, padding=1, bias=False)
        self.in1 = nn.InstanceNorm2d(64, affine=True)
        self.relu = nn.LeakyReLU(0.2, inplace=True)
        self.conv2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size
            =3, stride=1, padding=1, bias=False)
        self.in2 = nn.InstanceNorm2d(64, affine=True)

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.in1.weight
        primals_4 = self.in1.bias
        primals_5 = self.conv2.weight
        primals_6 = self.in2.weight
        primals_7 = self.in2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.131499