# 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_7/inductor_cache/js/cjsiyaqsllleekt5zrbljtvzp2d6dzoqnmk5foc25chzsp7e4hce.py
# Topologically Sorted Source Nodes: [mean, std, add], Original ATen: [aten.mean, aten.std, aten.add]
# Source node to ATen node mapping:
#   add => add
#   mean => mean
#   std => sqrt, var
# Graph fragment:
#   %mean : [num_users=5] = call_function[target=torch.ops.aten.mean.dim](args = (%view, [-1]), kwargs = {})
#   %var : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%view, [-1]), kwargs = {correction: 1.0})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%var,), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%sqrt, 1e-06), kwargs = {})
triton_per_fused_add_mean_std_0 = async_compile.triton('triton_per_fused_add_mean_std_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.persistent_reduction(
    size_hints=[256, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_mean_std_0', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 4, '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_per_fused_add_mean_std_0(in_out_ptr0, in_out_ptr1, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (16*x0)), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp6 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp1 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 16.0
    tmp20 = tmp4 / tmp19
    tmp21 = 15.0
    tmp22 = tmp18 / tmp21
    tmp23 = libdevice.sqrt(tmp22)
    tmp24 = 1e-06
    tmp25 = tmp23 + tmp24
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp20, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (x0), tmp25, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/mi/cmi56mxaxnvq6dmhczru2d3fc3dtwhtru4zwufqtpzxcfcx4yyu5.py
# Topologically Sorted Source Nodes: [mean_mean, mean_std, sub_1, mul_1, add_2, correction], Original ATen: [aten.mean, aten.std, aten.sub, aten.mul, aten.add, aten.div]
# Source node to ATen node mapping:
#   add_2 => add_2
#   correction => div_1
#   mean_mean => mean_1
#   mean_std => sqrt_1, var_1
#   mul_1 => mul_1
#   sub_1 => sub_1
# Graph fragment:
#   %mean_1 : [num_users=2] = call_function[target=torch.ops.aten.mean.dim](args = (%mean, [1], True), kwargs = {})
#   %var_1 : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%mean, [1]), kwargs = {correction: 1.0, keepdim: True})
#   %sqrt_1 : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%var_1,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mean, %mean_1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_6, %sub_1), kwargs = {})
#   %add_2 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%sqrt_1, 1e-06), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_1, %add_2), kwargs = {})
triton_per_fused_add_div_mean_mul_std_sub_1 = async_compile.triton('triton_per_fused_add_div_mean_mul_std_sub_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.persistent_reduction(
    size_hints=[4, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: '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, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_mean_mul_std_sub_1', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1', 'in_out_ptr2'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 4, '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_per_fused_add_div_mean_mul_std_sub_1(in_out_ptr0, in_out_ptr1, in_out_ptr2, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (64*x0)), xmask, other=0.0)
    tmp26 = tl.load(in_out_ptr2 + (r1 + (64*x0)), xmask, other=0.0)
    tmp27 = tl.load(in_ptr1 + (r1 % 4), None, eviction_policy='evict_last')
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp6 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 64, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp1 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 64.0
    tmp20 = tmp4 / tmp19
    tmp21 = 63.0
    tmp22 = tmp18 / tmp21
    tmp23 = libdevice.sqrt(tmp22)
    tmp24 = 1e-06
    tmp25 = tmp23 + tmp24
    tmp28 = tmp26 + tmp27
    tmp29 = tmp0 - tmp20
    tmp30 = tmp28 * tmp29
    tmp31 = tmp30 / tmp25
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp20, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (x0), tmp25, xmask)
    tl.store(in_out_ptr2 + (r1 + (64*x0)), tmp31, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/uw/cuwpch3ceab56czrb3ncoa2fmpoopk4fthtuchvypheph2nga66r.py
# Topologically Sorted Source Nodes: [sub, mul, truediv, result, sub_1, mul_1, correction, add_3], Original ATen: [aten.sub, aten.mul, aten.div, aten.add]
# Source node to ATen node mapping:
#   add_3 => add_3
#   correction => div_1
#   mul => mul
#   mul_1 => mul_1
#   result => add_1
#   sub => sub
#   sub_1 => sub_1
#   truediv => div
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_1, %mean), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_3, %sub), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul, %add), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, %view_9), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mean, %mean_1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_6, %sub_1), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_1, %add_2), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %div_1), kwargs = {})
triton_poi_fused_add_div_mul_sub_2 = async_compile.triton('triton_poi_fused_add_div_mul_sub_2', '''
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.pointwise(
    size_hints=[4096], 
    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'}, '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_poi_fused_add_div_mul_sub_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, '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},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_div_mul_sub_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = (xindex // 16)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (((x4 // 16) % 64) % 4), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x4), None)
    tmp4 = tl.load(in_ptr3 + (x3), None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + (x3), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr5 + (x3), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr6 + (((x4 // 16) % 64) % 4), None, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr7 + (x3), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 - tmp4
    tmp6 = tmp2 * tmp5
    tmp8 = tmp6 / tmp7
    tmp11 = tmp9 + tmp10
    tmp12 = tmp8 + tmp11
    tmp14 = tmp12 + tmp13
    tl.store(out_ptr0 + (x4), tmp14, None)
''', 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4, ), (1, ))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 64, 1, 1), (64, 1, 256, 256), torch.float32)
        buf5 = empty_strided_cuda((4, 64, 1, 1), (64, 1, 256, 256), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 64, 1, 1), (64, 1, 1, 1), 0); del buf0  # reuse
        buf13 = reinterpret_tensor(buf5, (4, 64, 1, 1), (64, 1, 1, 1), 0); del buf5  # reuse
        # Topologically Sorted Source Nodes: [mean, std, add], Original ATen: [aten.mean, aten.std, aten.add]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_mean_std_0.run(buf1, buf13, primals_1, 256, 16, grid=grid(256), stream=stream0)
        buf11 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_4, (64, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf11)
        del primals_5
        buf2 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf8 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf3 = reinterpret_tensor(buf2, (4, 1, 1, 1), (1, 1, 1, 1), 0); del buf2  # reuse
        buf14 = reinterpret_tensor(buf8, (4, 1, 1, 1), (1, 1, 1, 1), 0); del buf8  # reuse
        buf15 = reinterpret_tensor(buf11, (4, 64, 1, 1), (64, 1, 256, 256), 0); del buf11  # reuse
        # Topologically Sorted Source Nodes: [mean_mean, mean_std, sub_1, mul_1, add_2, correction], Original ATen: [aten.mean, aten.std, aten.sub, aten.mul, aten.add, aten.div]
        triton_per_fused_add_div_mean_mul_std_sub_1.run(buf3, buf14, buf15, buf1, primals_6, 4, 64, grid=grid(4), stream=stream0)
        del primals_6
        buf10 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_4, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf10)
        del primals_2
        buf12 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_4, (64, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf12)
        del primals_7
        buf16 = empty_strided_cuda((4, 64, 4, 4), (1024, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sub, mul, truediv, result, sub_1, mul_1, correction, add_3], Original ATen: [aten.sub, aten.mul, aten.div, aten.add]
        triton_poi_fused_add_div_mul_sub_2.run(buf10, primals_3, primals_1, buf1, buf13, buf12, primals_8, buf15, buf16, 4096, grid=grid(4096), stream=stream0)
        del buf10
        del buf12
        del buf15
        del primals_3
        del primals_8
    return (buf16, primals_1, buf1, buf3, reinterpret_tensor(primals_4, (64, 4), (4, 1), 0), buf13, buf14, )


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, 4, 4), (1024, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = 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])
    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
import torch.jit
import torch.nn


class AdaptiveInstanceNorm(nn.Module):

    def __init__(self, in_size, ada_size):
        super(AdaptiveInstanceNorm, self).__init__()
        self.scale = nn.Linear(ada_size, in_size)
        self.bias = nn.Linear(ada_size, in_size)

    def forward(self, inputs, style):
        in_view = inputs.view(inputs.size(0), inputs.size(1), 1, 1, -1)
        mean = in_view.mean(dim=-1)
        std = in_view.std(dim=-1)
        scale = self.scale(style).view(style.size(0), -1, 1, 1)
        bias = self.bias(style).view(style.size(0), -1, 1, 1)
        return scale * (inputs - mean) / (std + 1e-06) + bias


class AdaptiveInstanceNormPP(AdaptiveInstanceNorm):

    def __init__(self, in_size, ada_size):
        super(AdaptiveInstanceNormPP, self).__init__(in_size, ada_size)
        self.mean_scale = nn.Linear(ada_size, in_size)

    def forward(self, inputs, style):
        in_view = inputs.view(inputs.size(0), inputs.size(1), 1, 1, -1)
        mean = in_view.mean(dim=-1)
        mean_mean = mean.mean(dim=1, keepdim=True)
        std = in_view.std(dim=-1)
        mean_std = mean.std(dim=1, keepdim=True)
        scale = self.scale(style).view(style.size(0), -1, 1, 1)
        mean_scale = self.mean_scale(style).view(style.size(0), -1, 1, 1)
        bias = self.bias(style).view(style.size(0), -1, 1, 1)
        result = scale * (inputs - mean) / (std + 1e-06) + bias
        correction = mean_scale * (mean - mean_mean) / (mean_std + 1e-06)
        return result + correction


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


def get_init_inputs():
    return [[], {'in_size': 4, 'ada_size': 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.triton_helpers import libdevice
import torch.nn as nn
import torch.jit
import torch.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_per_fused_add_mean_std_0(in_out_ptr0, in_out_ptr1, in_ptr0,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp6 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp1 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 16.0
    tmp20 = tmp4 / tmp19
    tmp21 = 15.0
    tmp22 = tmp18 / tmp21
    tmp23 = libdevice.sqrt(tmp22)
    tmp24 = 1e-06
    tmp25 = tmp23 + tmp24
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp20, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x0, tmp25, xmask)


@triton.jit
def triton_per_fused_add_div_mean_mul_std_sub_1(in_out_ptr0, in_out_ptr1,
    in_out_ptr2, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp26 = tl.load(in_out_ptr2 + (r1 + 64 * x0), xmask, other=0.0)
    tmp27 = tl.load(in_ptr1 + r1 % 4, None, eviction_policy='evict_last')
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp6 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 64, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp1 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 64.0
    tmp20 = tmp4 / tmp19
    tmp21 = 63.0
    tmp22 = tmp18 / tmp21
    tmp23 = libdevice.sqrt(tmp22)
    tmp24 = 1e-06
    tmp25 = tmp23 + tmp24
    tmp28 = tmp26 + tmp27
    tmp29 = tmp0 - tmp20
    tmp30 = tmp28 * tmp29
    tmp31 = tmp30 / tmp25
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp20, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x0, tmp25, xmask)
    tl.store(in_out_ptr2 + (r1 + 64 * x0), tmp31, xmask)


@triton.jit
def triton_poi_fused_add_div_mul_sub_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex // 16
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x3, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x4 // 16 % 64 % 4, None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr2 + x4, None)
    tmp4 = tl.load(in_ptr3 + x3, None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x3, None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr5 + x3, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr6 + x4 // 16 % 64 % 4, None, eviction_policy=
        'evict_last')
    tmp13 = tl.load(in_ptr7 + x3, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 - tmp4
    tmp6 = tmp2 * tmp5
    tmp8 = tmp6 / tmp7
    tmp11 = tmp9 + tmp10
    tmp12 = tmp8 + tmp11
    tmp14 = tmp12 + tmp13
    tl.store(out_ptr0 + x4, tmp14, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8) = args
    args.clear()
    assert_size_stride(primals_1, (4, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 64, 1, 1), (64, 1, 256, 256), torch.
            float32)
        buf5 = empty_strided_cuda((4, 64, 1, 1), (64, 1, 256, 256), torch.
            float32)
        buf1 = reinterpret_tensor(buf0, (4, 64, 1, 1), (64, 1, 1, 1), 0)
        del buf0
        buf13 = reinterpret_tensor(buf5, (4, 64, 1, 1), (64, 1, 1, 1), 0)
        del buf5
        get_raw_stream(0)
        triton_per_fused_add_mean_std_0[grid(256)](buf1, buf13, primals_1, 
            256, 16, XBLOCK=8, num_warps=2, num_stages=1)
        buf11 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_4, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf11)
        del primals_5
        buf2 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf8 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf3 = reinterpret_tensor(buf2, (4, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf2
        buf14 = reinterpret_tensor(buf8, (4, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf8
        buf15 = reinterpret_tensor(buf11, (4, 64, 1, 1), (64, 1, 256, 256), 0)
        del buf11
        triton_per_fused_add_div_mean_mul_std_sub_1[grid(4)](buf3, buf14,
            buf15, buf1, primals_6, 4, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del primals_6
        buf10 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_4, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf10)
        del primals_2
        buf12 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_4, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf12)
        del primals_7
        buf16 = empty_strided_cuda((4, 64, 4, 4), (1024, 16, 4, 1), torch.
            float32)
        triton_poi_fused_add_div_mul_sub_2[grid(4096)](buf10, primals_3,
            primals_1, buf1, buf13, buf12, primals_8, buf15, buf16, 4096,
            XBLOCK=128, num_warps=4, num_stages=1)
        del buf10
        del buf12
        del buf15
        del primals_3
        del primals_8
    return buf16, primals_1, buf1, buf3, reinterpret_tensor(primals_4, (64,
        4), (4, 1), 0), buf13, buf14


class AdaptiveInstanceNorm(nn.Module):

    def __init__(self, in_size, ada_size):
        super(AdaptiveInstanceNorm, self).__init__()
        self.scale = nn.Linear(ada_size, in_size)
        self.bias = nn.Linear(ada_size, in_size)

    def forward(self, inputs, style):
        in_view = inputs.view(inputs.size(0), inputs.size(1), 1, 1, -1)
        mean = in_view.mean(dim=-1)
        std = in_view.std(dim=-1)
        scale = self.scale(style).view(style.size(0), -1, 1, 1)
        bias = self.bias(style).view(style.size(0), -1, 1, 1)
        return scale * (inputs - mean) / (std + 1e-06) + bias


class AdaptiveInstanceNormPPNew(AdaptiveInstanceNorm):

    def __init__(self, in_size, ada_size):
        super(AdaptiveInstanceNormPPNew, self).__init__(in_size, ada_size)
        self.mean_scale = nn.Linear(ada_size, in_size)

    def forward(self, input_0, input_1):
        primals_2 = self.scale.weight
        primals_3 = self.scale.bias
        primals_5 = self.bias.weight
        primals_6 = self.bias.bias
        primals_7 = self.mean_scale.weight
        primals_8 = self.mean_scale.bias
        primals_1 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0]
