# 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_6/inductor_cache/lg/clg3djsr2hxnxqdfy6k2rwkpomfuwg2lerr2kevbwhb4ehsko2tm.py
# Topologically Sorted Source Nodes: [mean, var, feat_var, sqrt], Original ATen: [aten.mean, aten.var, aten.add, aten.sqrt]
# Source node to ATen node mapping:
#   feat_var => add
#   mean => mean
#   sqrt => sqrt
#   var => var
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%view_2, [2]), kwargs = {})
#   %var : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%view, [2]), kwargs = {correction: 1})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%var, 1e-05), kwargs = {})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add,), kwargs = {})
triton_per_fused_add_mean_sqrt_var_0 = async_compile.triton('triton_per_fused_add_mean_sqrt_var_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=[16, 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_sqrt_var_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_sqrt_var_0(in_out_ptr0, in_out_ptr1, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    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 = 1e-05
    tmp24 = tmp22 + tmp23
    tmp25 = libdevice.sqrt(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_6/inductor_cache/d6/cd6wxiyfzhko7dbkh2mls5x54sd3vao4qdzazftcyd6o4z66tdvd.py
# Topologically Sorted Source Nodes: [iCov], Original ATen: [aten.div]
# Source node to ATen node mapping:
#   iCov => div
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%bmm, 64), kwargs = {})
triton_poi_fused_div_1 = async_compile.triton('triton_poi_fused_div_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, '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_div_1(in_out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = 0.015625
    tmp2 = tmp0 * tmp1
    tl.store(in_out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf1 = buf0; del buf0  # reuse
        buf5 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [mean, var, feat_var, sqrt], Original ATen: [aten.mean, aten.var, aten.add, aten.sqrt]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_mean_sqrt_var_0.run(buf1, buf5, arg0_1, 16, 16, grid=grid(16), stream=stream0)
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [G], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(arg0_1, (4, 4, 16), (64, 16, 1), 0), reinterpret_tensor(arg0_1, (4, 16, 4), (64, 1, 16), 0), out=buf6)
        del arg0_1
        buf7 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [iCov], Original ATen: [aten.div]
        triton_poi_fused_div_1.run(buf7, 64, grid=grid(64), stream=stream0)
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf11 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf9 = buf8; del buf8  # reuse
        buf13 = buf11; del buf11  # reuse
        # Topologically Sorted Source Nodes: [mean_1, var_1, feat_var_1, sqrt_1], Original ATen: [aten.mean, aten.var, aten.add, aten.sqrt]
        triton_per_fused_add_mean_sqrt_var_0.run(buf9, buf13, arg1_1, 16, 16, grid=grid(16), stream=stream0)
        buf14 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [G_1], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(arg1_1, (4, 4, 16), (64, 16, 1), 0), reinterpret_tensor(arg1_1, (4, 16, 4), (64, 1, 16), 0), out=buf14)
        del arg1_1
        buf15 = buf14; del buf14  # reuse
        # Topologically Sorted Source Nodes: [tCov], Original ATen: [aten.div]
        triton_poi_fused_div_1.run(buf15, 64, grid=grid(64), stream=stream0)
    return (reinterpret_tensor(buf1, (4, 4, 1, 1), (4, 1, 1, 1), 0), reinterpret_tensor(buf5, (4, 4, 1, 1), (4, 1, 1, 1), 0), buf7, reinterpret_tensor(buf9, (4, 4, 1, 1), (4, 1, 1, 1), 0), reinterpret_tensor(buf13, (4, 4, 1, 1), (4, 1, 1, 1), 0), buf15, )


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)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_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


def calc_mean_std(feat, eps=1e-05):
    size = feat.size()
    assert len(size) == 4
    N, C = size[:2]
    feat_var = feat.view(N, C, -1).var(dim=2) + eps
    feat_std = feat_var.sqrt().view(N, C, 1, 1)
    feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1)
    return feat_mean, feat_std


class GramMatrix(nn.Module):

    def forward(self, input):
        b, c, h, w = input.size()
        f = input.view(b, c, h * w)
        G = torch.bmm(f, f.transpose(1, 2))
        return G.div_(c * h * w)


class styleLoss_v2(nn.Module):

    def forward(self, input, target):
        _ib, _ic, _ih, _iw = input.size()
        mean_x, var_x = calc_mean_std(input)
        iCov = GramMatrix()(input)
        mean_y, var_y = calc_mean_std(target)
        tCov = GramMatrix()(target)
        loss = nn.MSELoss(size_average=True)(mean_x, mean_y) + nn.MSELoss(
            size_average=True)(var_x, var_y) + nn.MSELoss(size_average=True)(
            iCov, tCov)
        return loss


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


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.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_per_fused_add_mean_sqrt_var_0(in_out_ptr0, in_out_ptr1, in_ptr0,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 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, :]
    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 = 1e-05
    tmp24 = tmp22 + tmp23
    tmp25 = libdevice.sqrt(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_poi_fused_div_1(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = 0.015625
    tmp2 = tmp0 * tmp1
    tl.store(in_out_ptr0 + x0, tmp2, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf1 = buf0
        del buf0
        buf5 = buf3
        del buf3
        get_raw_stream(0)
        triton_per_fused_add_mean_sqrt_var_0[grid(16)](buf1, buf5, arg0_1, 
            16, 16, XBLOCK=8, num_warps=2, num_stages=1)
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(arg0_1, (4, 4, 16), (64, 16, 
            1), 0), reinterpret_tensor(arg0_1, (4, 16, 4), (64, 1, 16), 0),
            out=buf6)
        del arg0_1
        buf7 = buf6
        del buf6
        triton_poi_fused_div_1[grid(64)](buf7, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf11 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf9 = buf8
        del buf8
        buf13 = buf11
        del buf11
        triton_per_fused_add_mean_sqrt_var_0[grid(16)](buf9, buf13, arg1_1,
            16, 16, XBLOCK=8, num_warps=2, num_stages=1)
        buf14 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(arg1_1, (4, 4, 16), (64, 16, 
            1), 0), reinterpret_tensor(arg1_1, (4, 16, 4), (64, 1, 16), 0),
            out=buf14)
        del arg1_1
        buf15 = buf14
        del buf14
        triton_poi_fused_div_1[grid(64)](buf15, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
    return reinterpret_tensor(buf1, (4, 4, 1, 1), (4, 1, 1, 1), 0
        ), reinterpret_tensor(buf5, (4, 4, 1, 1), (4, 1, 1, 1), 0
        ), buf7, reinterpret_tensor(buf9, (4, 4, 1, 1), (4, 1, 1, 1), 0
        ), reinterpret_tensor(buf13, (4, 4, 1, 1), (4, 1, 1, 1), 0), buf15


def calc_mean_std(feat, eps=1e-05):
    size = feat.size()
    assert len(size) == 4
    N, C = size[:2]
    feat_var = feat.view(N, C, -1).var(dim=2) + eps
    feat_std = feat_var.sqrt().view(N, C, 1, 1)
    feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1)
    return feat_mean, feat_std


class GramMatrix(nn.Module):

    def forward(self, input):
        b, c, h, w = input.size()
        f = input.view(b, c, h * w)
        G = torch.bmm(f, f.transpose(1, 2))
        return G.div_(c * h * w)


class styleLoss_v2New(nn.Module):

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]
