# 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_6/inductor_cache/tc/ctchh66224xunisoqylba7qzpsl47ose6e5qu7bcgrxigfwirvf4.py
# Topologically Sorted Source Nodes: [instance_norm, output], Original ATen: [aten.repeat, aten._native_batch_norm_legit, aten.leaky_relu]
# Source node to ATen node mapping:
#   instance_norm => add, add_1, mul, mul_1, repeat, rsqrt, sub, var_mean
#   output => gt, mul_2, where
# Graph fragment:
#   %repeat : [num_users=2] = call_function[target=torch.ops.aten.repeat.default](args = (%primals_3, [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 = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view, %getitem_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %rsqrt), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %unsqueeze_1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %unsqueeze_3), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_1, 0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, 0.2), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %view_1, %mul_2), kwargs = {})
triton_red_fused__native_batch_norm_legit_leaky_relu_repeat_0 = async_compile.triton('triton_red_fused__native_batch_norm_legit_leaky_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=[256, 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: 'i32', 8: '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_leaky_relu_repeat_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, '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_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 = 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)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/st/cstrjrus2xtc6elawqi42w5evvdehzcp4d5fjmxji4eznl5szfll.py
# Topologically Sorted Source Nodes: [output_1, output_2], Original ATen: [aten.repeat, aten._native_batch_norm_legit, aten.add]
# Source node to ATen node mapping:
#   output_1 => add_2, repeat_2, rsqrt_1, var_mean_1
#   output_2 => add_4
# Graph fragment:
#   %repeat_2 : [num_users=2] = call_function[target=torch.ops.aten.repeat.default](args = (%primals_6, [4]), kwargs = {})
#   %var_mean_1 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view_5, [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_6, %primals_1), kwargs = {})
triton_red_fused__native_batch_norm_legit_add_repeat_1 = async_compile.triton('triton_red_fused__native_batch_norm_legit_add_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=[256, 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, 9), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused__native_batch_norm_legit_add_repeat_1', 'mutated_arg_names': [], '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_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 = 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)
''', 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 = 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)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        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  # reuse
        buf5 = empty_strided_cuda((1, 256, 1, 1), (256, 1, 256, 256), torch.float32)
        # Topologically Sorted Source Nodes: [instance_norm, output], Original ATen: [aten.repeat, aten._native_batch_norm_legit, aten.leaky_relu]
        stream0 = get_raw_stream(0)
        triton_red_fused__native_batch_norm_legit_leaky_relu_repeat_0.run(buf7, primals_3, buf0, primals_4, buf1, buf2, buf5, 256, 4096, grid=grid(256), stream=stream0)
        del primals_3
        del primals_4
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        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)
        # Topologically Sorted Source Nodes: [output_1, output_2], Original ATen: [aten.repeat, aten._native_batch_norm_legit, aten.add]
        triton_red_fused__native_batch_norm_legit_add_repeat_1.run(primals_6, buf8, primals_7, primals_1, buf9, buf10, buf14, buf13, 256, 4096, grid=grid(256), stream=stream0)
        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), )


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, 64, 64, 64), (262144, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
    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
from torch import add


class Residual_Block(nn.Module):

    def __init__(self):
        super(Residual_Block, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size
            =3, 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, padding=1, bias=False)
        self.in2 = nn.InstanceNorm2d(64, affine=True)

    def forward(self, x):
        identity_data = x
        output = self.relu(self.in1(self.conv1(x)))
        output = self.in2(self.conv2(output))
        output = add(output, identity_data)
        return output


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


def get_init_inputs():
    return [[], {}]

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, 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, 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]
