# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/2o/c2o5xjfj64zz7nwpyoieudcndnaiobc6b7jtmt4fzc5odoye7s7g.py
# Topologically Sorted Source Nodes: [conv2d, instance_norm, out], Original ATen: [aten.convolution, aten.repeat, aten._native_batch_norm_legit, aten.relu]
# Source node to ATen node mapping:
#   conv2d => convolution
#   instance_norm => add, repeat, rsqrt, var_mean
#   out => relu
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %repeat : [num_users=2] = call_function[target=torch.ops.aten.repeat.default](args = (%primals_4, [4]), kwargs = {})
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
triton_red_fused__native_batch_norm_legit_convolution_relu_repeat_0 = async_compile.triton('triton_red_fused__native_batch_norm_legit_convolution_relu_repeat_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.reduction(
    size_hints=[16, 4096],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: 'i32', 9: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused__native_batch_norm_legit_convolution_relu_repeat_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 2, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_red_fused__native_batch_norm_legit_convolution_relu_repeat_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr3, out_ptr4, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 3844
    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 % 4), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x0), tmp0, xmask)
    x1 = xindex % 4
    tmp2 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp5_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp5_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp5_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r3 = rindex
        tmp1 = tl.load(in_out_ptr0 + (r3 + (3844*x0)), rmask & xmask, eviction_policy='evict_last', other=0.0)
        tmp3 = tmp1 + tmp2
        tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
        tmp5_mean_next, tmp5_m2_next, tmp5_weight_next = triton_helpers.welford_reduce(
            tmp4, tmp5_mean, tmp5_m2, tmp5_weight, roffset == 0
        )
        tmp5_mean = tl.where(rmask & xmask, tmp5_mean_next, tmp5_mean)
        tmp5_m2 = tl.where(rmask & xmask, tmp5_m2_next, tmp5_m2)
        tmp5_weight = tl.where(rmask & xmask, tmp5_weight_next, tmp5_weight)
        tl.store(in_out_ptr0 + (r3 + (3844*x0)), tmp3, rmask & xmask)
    tmp5_tmp, tmp6_tmp, tmp7_tmp = triton_helpers.welford(
        tmp5_mean, tmp5_m2, tmp5_weight, 1
    )
    tmp5 = tmp5_tmp[:, None]
    tmp6 = tmp6_tmp[:, None]
    tmp7 = tmp7_tmp[:, None]
    tl.store(out_ptr1 + (x0), tmp5, xmask)
    tmp17 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r3 = rindex
        tmp8 = tl.load(in_out_ptr0 + (r3 + (3844*x0)), rmask & xmask, eviction_policy='evict_first', other=0.0)
        tmp9 = tmp8 - tmp5
        tmp10 = 3844.0
        tmp11 = tmp6 / tmp10
        tmp12 = 1e-05
        tmp13 = tmp11 + tmp12
        tmp14 = libdevice.rsqrt(tmp13)
        tmp15 = tmp9 * tmp14
        tmp16 = tmp15 * tmp0
        tmp18 = tmp16 + tmp17
        tmp19 = tl.full([1, 1], 0, tl.int32)
        tmp20 = triton_helpers.maximum(tmp19, tmp18)
        tl.store(out_ptr3 + (r3 + (3844*x0)), tmp20, rmask & xmask)
    tmp21 = 3844.0
    tmp22 = tmp6 / tmp21
    tmp23 = 1e-05
    tmp24 = tmp22 + tmp23
    tmp25 = libdevice.rsqrt(tmp24)
    tl.store(out_ptr4 + (x0), tmp25, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/hj/chjzqltjsxd7fqbzbunofdlo6342adq3i2nw3viyvqbpxrkzc3jx.py
# Topologically Sorted Source Nodes: [conv2d_1, out_1, add], Original ATen: [aten.convolution, aten.repeat, aten._native_batch_norm_legit, aten.add]
# Source node to ATen node mapping:
#   add => add_4
#   conv2d_1 => convolution_1
#   out_1 => add_2, repeat_2, rsqrt_1, var_mean_1
# Graph fragment:
#   %convolution_1 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_6, %primals_7, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %repeat_2 : [num_users=2] = call_function[target=torch.ops.aten.repeat.default](args = (%primals_8, [4]), kwargs = {})
#   %var_mean_1 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view_2, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_2, 1e-05), kwargs = {})
#   %rsqrt_1 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_2,), kwargs = {})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_3, %slice_4), kwargs = {})
triton_red_fused__native_batch_norm_legit_add_convolution_repeat_1 = async_compile.triton('triton_red_fused__native_batch_norm_legit_add_convolution_repeat_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.reduction(
    size_hints=[16, 4096],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '*fp32', 9: 'i32', 10: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused__native_batch_norm_legit_add_convolution_repeat_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 2, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_red_fused__native_batch_norm_legit_add_convolution_repeat_1(in_out_ptr0, 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 = 16
    rnumel = 3600
    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 % 4), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x0), tmp0, xmask)
    x1 = xindex % 4
    tmp2 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp5_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp5_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp5_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r3 = rindex
        tmp1 = tl.load(in_out_ptr0 + (r3 + (3600*x0)), rmask & xmask, eviction_policy='evict_last', other=0.0)
        tmp3 = tmp1 + tmp2
        tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
        tmp5_mean_next, tmp5_m2_next, tmp5_weight_next = triton_helpers.welford_reduce(
            tmp4, tmp5_mean, tmp5_m2, tmp5_weight, roffset == 0
        )
        tmp5_mean = tl.where(rmask & xmask, tmp5_mean_next, tmp5_mean)
        tmp5_m2 = tl.where(rmask & xmask, tmp5_m2_next, tmp5_m2)
        tmp5_weight = tl.where(rmask & xmask, tmp5_weight_next, tmp5_weight)
        tl.store(in_out_ptr0 + (r3 + (3600*x0)), tmp3, rmask & xmask)
    tmp5_tmp, tmp6_tmp, tmp7_tmp = triton_helpers.welford(
        tmp5_mean, tmp5_m2, tmp5_weight, 1
    )
    tmp5 = tmp5_tmp[:, None]
    tmp6 = tmp6_tmp[:, None]
    tmp7 = tmp7_tmp[:, None]
    tl.store(out_ptr1 + (x0), tmp5, xmask)
    tmp17 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r3 = rindex
        r4 = rindex % 60
        r5 = (rindex // 60)
        tmp8 = tl.load(in_out_ptr0 + (r3 + (3600*x0)), rmask & xmask, eviction_policy='evict_first', other=0.0)
        tmp19 = tl.load(in_ptr3 + (130 + r4 + (64*r5) + (4096*x0)), rmask & xmask, eviction_policy='evict_first', other=0.0)
        tmp9 = tmp8 - tmp5
        tmp10 = 3600.0
        tmp11 = tmp6 / tmp10
        tmp12 = 1e-05
        tmp13 = tmp11 + tmp12
        tmp14 = libdevice.rsqrt(tmp13)
        tmp15 = tmp9 * tmp14
        tmp16 = tmp15 * tmp0
        tmp18 = tmp16 + tmp17
        tmp20 = tmp18 + tmp19
        tl.store(out_ptr3 + (r3 + (3600*x0)), tmp20, rmask & xmask)
    tmp21 = 3600.0
    tmp22 = tmp6 / tmp21
    tmp23 = 1e-05
    tmp24 = tmp22 + tmp23
    tmp25 = libdevice.rsqrt(tmp24)
    tl.store(out_ptr4 + (x0), tmp25, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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, (4, 4, 64, 64), (16384, 4096, 64, 1))
    assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, ), (1, ))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, ), (1, ))
    assert_size_stride(primals_9, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 62, 62), (15376, 3844, 62, 1))
        buf2 = empty_strided_cuda((16, ), (1, ), torch.float32)
        buf1 = buf0; del buf0  # reuse
        buf3 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        buf7 = empty_strided_cuda((4, 4, 62, 62), (15376, 3844, 62, 1), torch.float32)
        buf6 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d, instance_norm, out], Original ATen: [aten.convolution, aten.repeat, aten._native_batch_norm_legit, aten.relu]
        stream0 = get_raw_stream(0)
        triton_red_fused__native_batch_norm_legit_convolution_relu_repeat_0.run(buf1, primals_4, primals_3, primals_5, buf2, buf3, buf7, buf6, 16, 3844, grid=grid(16), stream=stream0)
        del primals_3
        del primals_4
        del primals_5
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf7, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 4, 60, 60), (14400, 3600, 60, 1))
        buf10 = empty_strided_cuda((16, ), (1, ), torch.float32)
        buf9 = buf8; del buf8  # reuse
        buf11 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        buf15 = empty_strided_cuda((4, 4, 60, 60), (14400, 3600, 60, 1), torch.float32)
        buf14 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_1, out_1, add], Original ATen: [aten.convolution, aten.repeat, aten._native_batch_norm_legit, aten.add]
        triton_red_fused__native_batch_norm_legit_add_convolution_repeat_1.run(buf9, primals_8, primals_7, primals_9, primals_1, buf10, buf11, buf15, buf14, 16, 3600, grid=grid(16), stream=stream0)
        del primals_7
        del primals_8
        del primals_9
    return (buf15, primals_1, primals_2, primals_6, buf1, buf2, reinterpret_tensor(buf6, (16, ), (1, ), 0), buf7, buf9, buf10, reinterpret_tensor(buf14, (16, ), (1, ), 0), reinterpret_tensor(buf11, (1, 16, 1, 1), (16, 1, 1, 1), 0), reinterpret_tensor(buf3, (1, 16, 1, 1), (16, 1, 1, 1), 0), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 64, 64), (16384, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import torch.nn as nn


class ResidualBlock(nn.Module):
    """Redisual network block for style transfer."""

    def __init__(self, nchannels):
        """Create a block of a residual network."""
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(nchannels, nchannels, kernel_size=3)
        self.conv2 = nn.Conv2d(nchannels, nchannels, kernel_size=3)
        self.norm_conv1 = nn.InstanceNorm2d(nchannels, affine=True)
        self.norm_conv2 = nn.InstanceNorm2d(nchannels, affine=True)
        self.nonlinearity = nn.ReLU()

    def forward(self, x):
        """Forward the input through the block."""
        residual = x[:, :, 2:-2, 2:-2]
        out = self.nonlinearity(self.norm_conv1(self.conv1(x)))
        out = self.norm_conv2(self.conv2(out))
        return out + residual


def get_inputs():
    return [torch.rand([4, 4, 64, 64])]


def get_init_inputs():
    return [[], {'nchannels': 4}]

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_convolution_relu_repeat_0(
    in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr3,
    out_ptr4, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 16
    rnumel = 3844
    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 % 4, xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x0, tmp0, xmask)
    x1 = xindex % 4
    tmp2 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp5_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp5_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp5_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r3 = rindex
        tmp1 = tl.load(in_out_ptr0 + (r3 + 3844 * x0), rmask & xmask,
            eviction_policy='evict_last', other=0.0)
        tmp3 = tmp1 + tmp2
        tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
        tmp5_mean_next, tmp5_m2_next, tmp5_weight_next = (triton_helpers.
            welford_reduce(tmp4, tmp5_mean, tmp5_m2, tmp5_weight, roffset == 0)
            )
        tmp5_mean = tl.where(rmask & xmask, tmp5_mean_next, tmp5_mean)
        tmp5_m2 = tl.where(rmask & xmask, tmp5_m2_next, tmp5_m2)
        tmp5_weight = tl.where(rmask & xmask, tmp5_weight_next, tmp5_weight)
        tl.store(in_out_ptr0 + (r3 + 3844 * x0), tmp3, rmask & xmask)
    tmp5_tmp, tmp6_tmp, tmp7_tmp = triton_helpers.welford(tmp5_mean,
        tmp5_m2, tmp5_weight, 1)
    tmp5 = tmp5_tmp[:, None]
    tmp6 = tmp6_tmp[:, None]
    tmp7_tmp[:, None]
    tl.store(out_ptr1 + x0, tmp5, xmask)
    tmp17 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r3 = rindex
        tmp8 = tl.load(in_out_ptr0 + (r3 + 3844 * x0), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp9 = tmp8 - tmp5
        tmp10 = 3844.0
        tmp11 = tmp6 / tmp10
        tmp12 = 1e-05
        tmp13 = tmp11 + tmp12
        tmp14 = libdevice.rsqrt(tmp13)
        tmp15 = tmp9 * tmp14
        tmp16 = tmp15 * tmp0
        tmp18 = tmp16 + tmp17
        tmp19 = tl.full([1, 1], 0, tl.int32)
        tmp20 = triton_helpers.maximum(tmp19, tmp18)
        tl.store(out_ptr3 + (r3 + 3844 * x0), tmp20, rmask & xmask)
    tmp21 = 3844.0
    tmp22 = tmp6 / tmp21
    tmp23 = 1e-05
    tmp24 = tmp22 + tmp23
    tmp25 = libdevice.rsqrt(tmp24)
    tl.store(out_ptr4 + x0, tmp25, xmask)


@triton.jit
def triton_red_fused__native_batch_norm_legit_add_convolution_repeat_1(
    in_out_ptr0, 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 = 16
    rnumel = 3600
    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 % 4, xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x0, tmp0, xmask)
    x1 = xindex % 4
    tmp2 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp5_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp5_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp5_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r3 = rindex
        tmp1 = tl.load(in_out_ptr0 + (r3 + 3600 * x0), rmask & xmask,
            eviction_policy='evict_last', other=0.0)
        tmp3 = tmp1 + tmp2
        tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
        tmp5_mean_next, tmp5_m2_next, tmp5_weight_next = (triton_helpers.
            welford_reduce(tmp4, tmp5_mean, tmp5_m2, tmp5_weight, roffset == 0)
            )
        tmp5_mean = tl.where(rmask & xmask, tmp5_mean_next, tmp5_mean)
        tmp5_m2 = tl.where(rmask & xmask, tmp5_m2_next, tmp5_m2)
        tmp5_weight = tl.where(rmask & xmask, tmp5_weight_next, tmp5_weight)
        tl.store(in_out_ptr0 + (r3 + 3600 * x0), tmp3, rmask & xmask)
    tmp5_tmp, tmp6_tmp, tmp7_tmp = triton_helpers.welford(tmp5_mean,
        tmp5_m2, tmp5_weight, 1)
    tmp5 = tmp5_tmp[:, None]
    tmp6 = tmp6_tmp[:, None]
    tmp7_tmp[:, None]
    tl.store(out_ptr1 + x0, tmp5, xmask)
    tmp17 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r3 = rindex
        r4 = rindex % 60
        r5 = rindex // 60
        tmp8 = tl.load(in_out_ptr0 + (r3 + 3600 * x0), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp19 = tl.load(in_ptr3 + (130 + r4 + 64 * r5 + 4096 * x0), rmask &
            xmask, eviction_policy='evict_first', other=0.0)
        tmp9 = tmp8 - tmp5
        tmp10 = 3600.0
        tmp11 = tmp6 / tmp10
        tmp12 = 1e-05
        tmp13 = tmp11 + tmp12
        tmp14 = libdevice.rsqrt(tmp13)
        tmp15 = tmp9 * tmp14
        tmp16 = tmp15 * tmp0
        tmp18 = tmp16 + tmp17
        tmp20 = tmp18 + tmp19
        tl.store(out_ptr3 + (r3 + 3600 * x0), tmp20, rmask & xmask)
    tmp21 = 3600.0
    tmp22 = tmp6 / tmp21
    tmp23 = 1e-05
    tmp24 = tmp22 + tmp23
    tmp25 = libdevice.rsqrt(tmp24)
    tl.store(out_ptr4 + x0, tmp25, xmask)


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, (4, 4, 64, 64), (16384, 4096, 64, 1))
    assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 62, 62), (15376, 3844, 62, 1))
        buf2 = empty_strided_cuda((16,), (1,), torch.float32)
        buf1 = buf0
        del buf0
        buf3 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        buf7 = empty_strided_cuda((4, 4, 62, 62), (15376, 3844, 62, 1),
            torch.float32)
        buf6 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        get_raw_stream(0)
        triton_red_fused__native_batch_norm_legit_convolution_relu_repeat_0[
            grid(16)](buf1, primals_4, primals_3, primals_5, buf2, buf3,
            buf7, buf6, 16, 3844, XBLOCK=1, RBLOCK=2048, num_warps=16,
            num_stages=1)
        del primals_3
        del primals_4
        del primals_5
        buf8 = extern_kernels.convolution(buf7, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 4, 60, 60), (14400, 3600, 60, 1))
        buf10 = empty_strided_cuda((16,), (1,), torch.float32)
        buf9 = buf8
        del buf8
        buf11 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.
            float32)
        buf15 = empty_strided_cuda((4, 4, 60, 60), (14400, 3600, 60, 1),
            torch.float32)
        buf14 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.
            float32)
        triton_red_fused__native_batch_norm_legit_add_convolution_repeat_1[grid
            (16)](buf9, primals_8, primals_7, primals_9, primals_1, buf10,
            buf11, buf15, buf14, 16, 3600, XBLOCK=1, RBLOCK=2048, num_warps
            =16, num_stages=1)
        del primals_7
        del primals_8
        del primals_9
    return (buf15, primals_1, primals_2, primals_6, buf1, buf2,
        reinterpret_tensor(buf6, (16,), (1,), 0), buf7, buf9, buf10,
        reinterpret_tensor(buf14, (16,), (1,), 0), reinterpret_tensor(buf11,
        (1, 16, 1, 1), (16, 1, 1, 1), 0), reinterpret_tensor(buf3, (1, 16, 
        1, 1), (16, 1, 1, 1), 0))


class ResidualBlockNew(nn.Module):
    """Redisual network block for style transfer."""

    def __init__(self, nchannels):
        """Create a block of a residual network."""
        super(ResidualBlockNew, self).__init__()
        self.conv1 = nn.Conv2d(nchannels, nchannels, kernel_size=3)
        self.conv2 = nn.Conv2d(nchannels, nchannels, kernel_size=3)
        self.norm_conv1 = nn.InstanceNorm2d(nchannels, affine=True)
        self.norm_conv2 = nn.InstanceNorm2d(nchannels, affine=True)
        self.nonlinearity = nn.ReLU()

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.conv1.bias
        primals_6 = self.conv2.weight
        primals_4 = self.conv2.bias
        primals_5 = self.norm_conv1.weight
        primals_7 = self.norm_conv1.bias
        primals_8 = self.norm_conv2.weight
        primals_9 = self.norm_conv2.bias
        primals_1 = 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]
