# AOT ID: ['0_inference']
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/bc/cbcebs6veq3jcchg2s5hkpwbnyxvsyj4d2tmg22tn336tutpb46n.py
# Topologically Sorted Source Nodes: [mean, sub, pow_1, mean_1, add, stddev, inject], Original ATen: [aten.mean, aten.sub, aten.pow, aten.add, aten.sqrt]
# Source node to ATen node mapping:
#   add => add
#   inject => mean_2
#   mean => mean
#   mean_1 => mean_1
#   pow_1 => pow_1
#   stddev => sqrt
#   sub => sub
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%arg0_1, [0], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %mean), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%pow_1, [0], True), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean_1, 1e-08), kwargs = {})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add,), kwargs = {})
#   %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%sqrt, [1], True), kwargs = {})
triton_poi_fused_add_mean_pow_sqrt_sub_0 = async_compile.triton('triton_poi_fused_add_mean_pow_sqrt_sub_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.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mean_pow_sqrt_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 16, '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_mean_pow_sqrt_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr0 + (64 + x0), xmask)
    tmp3 = tl.load(in_ptr0 + (128 + x0), xmask)
    tmp5 = tl.load(in_ptr0 + (192 + x0), xmask)
    tmp24 = tl.load(in_ptr0 + (16 + x0), xmask)
    tmp25 = tl.load(in_ptr0 + (80 + x0), xmask)
    tmp27 = tl.load(in_ptr0 + (144 + x0), xmask)
    tmp29 = tl.load(in_ptr0 + (208 + x0), xmask)
    tmp47 = tl.load(in_ptr0 + (32 + x0), xmask)
    tmp48 = tl.load(in_ptr0 + (96 + x0), xmask)
    tmp50 = tl.load(in_ptr0 + (160 + x0), xmask)
    tmp52 = tl.load(in_ptr0 + (224 + x0), xmask)
    tmp70 = tl.load(in_ptr0 + (48 + x0), xmask)
    tmp71 = tl.load(in_ptr0 + (112 + x0), xmask)
    tmp73 = tl.load(in_ptr0 + (176 + x0), xmask)
    tmp75 = tl.load(in_ptr0 + (240 + x0), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-08
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.sqrt(tmp22)
    tmp26 = tmp24 + tmp25
    tmp28 = tmp26 + tmp27
    tmp30 = tmp28 + tmp29
    tmp31 = tmp30 / tmp7
    tmp32 = tmp24 - tmp31
    tmp33 = tmp32 * tmp32
    tmp34 = tmp25 - tmp31
    tmp35 = tmp34 * tmp34
    tmp36 = tmp33 + tmp35
    tmp37 = tmp27 - tmp31
    tmp38 = tmp37 * tmp37
    tmp39 = tmp36 + tmp38
    tmp40 = tmp29 - tmp31
    tmp41 = tmp40 * tmp40
    tmp42 = tmp39 + tmp41
    tmp43 = tmp42 / tmp7
    tmp44 = tmp43 + tmp21
    tmp45 = libdevice.sqrt(tmp44)
    tmp46 = tmp23 + tmp45
    tmp49 = tmp47 + tmp48
    tmp51 = tmp49 + tmp50
    tmp53 = tmp51 + tmp52
    tmp54 = tmp53 / tmp7
    tmp55 = tmp47 - tmp54
    tmp56 = tmp55 * tmp55
    tmp57 = tmp48 - tmp54
    tmp58 = tmp57 * tmp57
    tmp59 = tmp56 + tmp58
    tmp60 = tmp50 - tmp54
    tmp61 = tmp60 * tmp60
    tmp62 = tmp59 + tmp61
    tmp63 = tmp52 - tmp54
    tmp64 = tmp63 * tmp63
    tmp65 = tmp62 + tmp64
    tmp66 = tmp65 / tmp7
    tmp67 = tmp66 + tmp21
    tmp68 = libdevice.sqrt(tmp67)
    tmp69 = tmp46 + tmp68
    tmp72 = tmp70 + tmp71
    tmp74 = tmp72 + tmp73
    tmp76 = tmp74 + tmp75
    tmp77 = tmp76 / tmp7
    tmp78 = tmp70 - tmp77
    tmp79 = tmp78 * tmp78
    tmp80 = tmp71 - tmp77
    tmp81 = tmp80 * tmp80
    tmp82 = tmp79 + tmp81
    tmp83 = tmp73 - tmp77
    tmp84 = tmp83 * tmp83
    tmp85 = tmp82 + tmp84
    tmp86 = tmp75 - tmp77
    tmp87 = tmp86 * tmp86
    tmp88 = tmp85 + tmp87
    tmp89 = tmp88 / tmp7
    tmp90 = tmp89 + tmp21
    tmp91 = libdevice.sqrt(tmp90)
    tmp92 = tmp69 + tmp91
    tmp93 = tmp92 / tmp7
    tl.store(out_ptr0 + (x0), tmp93, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/fc/cfcpnsijfdjlkqlxyjsxpa4wd2v5qtfunr2guhqosjmgxykzzohf.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%arg0_1, %expand], 1), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_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.pointwise(
    size_hints=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 320
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16) % 5
    x0 = xindex % 16
    x2 = (xindex // 80)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (16*x1) + (64*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 5, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x3), tmp10, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 1, 4, 4), (16, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mean, sub, pow_1, mean_1, add, stddev, inject], Original ATen: [aten.mean, aten.sub, aten.pow, aten.add, aten.sqrt]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_mean_pow_sqrt_sub_0.run(arg0_1, buf0, 16, grid=grid(16), stream=stream0)
        buf1 = empty_strided_cuda((4, 5, 4, 4), (80, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(arg0_1, buf0, buf1, 320, grid=grid(320), stream=stream0)
        del arg0_1
        del buf0
    return (buf1, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1])
    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.fft


class MiniBatchAverageLayer(nn.Module):
    """Minibatch stat concatenation layer. Implementation is from https://github.com/shanexn/pytorch-pggan."""

    def __init__(self, offset=1e-08):
        super().__init__()
        self.offset = offset

    def forward(self, x):
        stddev = torch.sqrt(torch.mean((x - torch.mean(x, dim=0, keepdim=
            True)) ** 2, dim=0, keepdim=True) + self.offset)
        inject_shape = list(x.size())[:]
        inject_shape[1] = 1
        inject = torch.mean(stddev, dim=1, keepdim=True)
        inject = inject.expand(inject_shape)
        return torch.cat((x, inject), dim=1)


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


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

import torch
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.fft
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_add_mean_pow_sqrt_sub_0(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + (64 + x0), xmask)
    tmp3 = tl.load(in_ptr0 + (128 + x0), xmask)
    tmp5 = tl.load(in_ptr0 + (192 + x0), xmask)
    tmp24 = tl.load(in_ptr0 + (16 + x0), xmask)
    tmp25 = tl.load(in_ptr0 + (80 + x0), xmask)
    tmp27 = tl.load(in_ptr0 + (144 + x0), xmask)
    tmp29 = tl.load(in_ptr0 + (208 + x0), xmask)
    tmp47 = tl.load(in_ptr0 + (32 + x0), xmask)
    tmp48 = tl.load(in_ptr0 + (96 + x0), xmask)
    tmp50 = tl.load(in_ptr0 + (160 + x0), xmask)
    tmp52 = tl.load(in_ptr0 + (224 + x0), xmask)
    tmp70 = tl.load(in_ptr0 + (48 + x0), xmask)
    tmp71 = tl.load(in_ptr0 + (112 + x0), xmask)
    tmp73 = tl.load(in_ptr0 + (176 + x0), xmask)
    tmp75 = tl.load(in_ptr0 + (240 + x0), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-08
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.sqrt(tmp22)
    tmp26 = tmp24 + tmp25
    tmp28 = tmp26 + tmp27
    tmp30 = tmp28 + tmp29
    tmp31 = tmp30 / tmp7
    tmp32 = tmp24 - tmp31
    tmp33 = tmp32 * tmp32
    tmp34 = tmp25 - tmp31
    tmp35 = tmp34 * tmp34
    tmp36 = tmp33 + tmp35
    tmp37 = tmp27 - tmp31
    tmp38 = tmp37 * tmp37
    tmp39 = tmp36 + tmp38
    tmp40 = tmp29 - tmp31
    tmp41 = tmp40 * tmp40
    tmp42 = tmp39 + tmp41
    tmp43 = tmp42 / tmp7
    tmp44 = tmp43 + tmp21
    tmp45 = libdevice.sqrt(tmp44)
    tmp46 = tmp23 + tmp45
    tmp49 = tmp47 + tmp48
    tmp51 = tmp49 + tmp50
    tmp53 = tmp51 + tmp52
    tmp54 = tmp53 / tmp7
    tmp55 = tmp47 - tmp54
    tmp56 = tmp55 * tmp55
    tmp57 = tmp48 - tmp54
    tmp58 = tmp57 * tmp57
    tmp59 = tmp56 + tmp58
    tmp60 = tmp50 - tmp54
    tmp61 = tmp60 * tmp60
    tmp62 = tmp59 + tmp61
    tmp63 = tmp52 - tmp54
    tmp64 = tmp63 * tmp63
    tmp65 = tmp62 + tmp64
    tmp66 = tmp65 / tmp7
    tmp67 = tmp66 + tmp21
    tmp68 = libdevice.sqrt(tmp67)
    tmp69 = tmp46 + tmp68
    tmp72 = tmp70 + tmp71
    tmp74 = tmp72 + tmp73
    tmp76 = tmp74 + tmp75
    tmp77 = tmp76 / tmp7
    tmp78 = tmp70 - tmp77
    tmp79 = tmp78 * tmp78
    tmp80 = tmp71 - tmp77
    tmp81 = tmp80 * tmp80
    tmp82 = tmp79 + tmp81
    tmp83 = tmp73 - tmp77
    tmp84 = tmp83 * tmp83
    tmp85 = tmp82 + tmp84
    tmp86 = tmp75 - tmp77
    tmp87 = tmp86 * tmp86
    tmp88 = tmp85 + tmp87
    tmp89 = tmp88 / tmp7
    tmp90 = tmp89 + tmp21
    tmp91 = libdevice.sqrt(tmp90)
    tmp92 = tmp69 + tmp91
    tmp93 = tmp92 / tmp7
    tl.store(out_ptr0 + x0, tmp93, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 320
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 5
    x0 = xindex % 16
    x2 = xindex // 80
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 5, tl.int64)
    tmp9 = tl.load(in_ptr1 + x0, tmp6 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x3, tmp10, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 1, 4, 4), (16, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mean_pow_sqrt_sub_0[grid(16)](arg0_1, buf0, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 5, 4, 4), (80, 16, 4, 1), torch.float32)
        triton_poi_fused_cat_1[grid(320)](arg0_1, buf0, buf1, 320, XBLOCK=
            128, num_warps=4, num_stages=1)
        del arg0_1
        del buf0
    return buf1,


class MiniBatchAverageLayerNew(nn.Module):
    """Minibatch stat concatenation layer. Implementation is from https://github.com/shanexn/pytorch-pggan."""

    def __init__(self, offset=1e-08):
        super().__init__()
        self.offset = offset

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]
