# 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_8/inductor_cache/lh/clhfjkkr7diebd2mxdfts32t7tl4bok6jmervpgm2asjty6pcsix.py
# Topologically Sorted Source Nodes: [G_1, T, mse_loss, value_1, G_4, T_1, mse_loss_1, value_2, G_7, T_2, mse_loss_2, value_3, G_10, T_3, mse_loss_3, value_4], Original ATen: [aten.div, aten.mse_loss, aten.add]
# Source node to ATen node mapping:
#   G_1 => div
#   G_10 => div_6
#   G_4 => div_2
#   G_7 => div_4
#   T => div_1
#   T_1 => div_3
#   T_2 => div_5
#   T_3 => div_7
#   mse_loss => pow_1, sub
#   mse_loss_1 => mean_1, pow_2, sub_1
#   mse_loss_2 => mean_2, pow_3, sub_2
#   mse_loss_3 => mean_3, pow_4, sub_3
#   value_1 => mean
#   value_2 => add_1
#   value_3 => add_2
#   value_4 => add_3
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mm, 256), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mm_1, 256), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%div, %div_1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_1,), kwargs = {})
#   %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mm_2, 256), kwargs = {})
#   %div_3 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mm_3, 256), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%div_2, %div_3), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_1, 2), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_2,), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean, %mean_1), kwargs = {})
#   %div_4 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mm_4, 256), kwargs = {})
#   %div_5 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mm_5, 256), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%div_4, %div_5), kwargs = {})
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_2, 2), kwargs = {})
#   %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_3,), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %mean_2), kwargs = {})
#   %div_6 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mm_6, 256), kwargs = {})
#   %div_7 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mm_7, 256), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%div_6, %div_7), kwargs = {})
#   %pow_4 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_3, 2), kwargs = {})
#   %mean_3 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_4,), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %mean_3), kwargs = {})
triton_per_fused_add_div_mse_loss_0 = async_compile.triton('triton_per_fused_add_div_mse_loss_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=[1, 256],
    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': {9: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 10), equal_to_1=(9,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_mse_loss_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 8, '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_mse_loss_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp3 = tl.load(in_ptr1 + (r0), None)
    tmp10 = tl.load(in_ptr2 + (r0), None)
    tmp12 = tl.load(in_ptr3 + (r0), None)
    tmp19 = tl.load(in_ptr4 + (r0), None)
    tmp21 = tl.load(in_ptr5 + (r0), None)
    tmp28 = tl.load(in_ptr6 + (r0), None)
    tmp30 = tl.load(in_ptr7 + (r0), None)
    tmp1 = 0.00390625
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp5 = tmp2 - tmp4
    tmp6 = tmp5 * tmp5
    tmp7 = tl.broadcast_to(tmp6, [RBLOCK])
    tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0))
    tmp11 = tmp10 * tmp1
    tmp13 = tmp12 * tmp1
    tmp14 = tmp11 - tmp13
    tmp15 = tmp14 * tmp14
    tmp16 = tl.broadcast_to(tmp15, [RBLOCK])
    tmp18 = triton_helpers.promote_to_tensor(tl.sum(tmp16, 0))
    tmp20 = tmp19 * tmp1
    tmp22 = tmp21 * tmp1
    tmp23 = tmp20 - tmp22
    tmp24 = tmp23 * tmp23
    tmp25 = tl.broadcast_to(tmp24, [RBLOCK])
    tmp27 = triton_helpers.promote_to_tensor(tl.sum(tmp25, 0))
    tmp29 = tmp28 * tmp1
    tmp31 = tmp30 * tmp1
    tmp32 = tmp29 - tmp31
    tmp33 = tmp32 * tmp32
    tmp34 = tl.broadcast_to(tmp33, [RBLOCK])
    tmp36 = triton_helpers.promote_to_tensor(tl.sum(tmp34, 0))
    tmp37 = 256.0
    tmp38 = tmp9 / tmp37
    tmp39 = tmp18 / tmp37
    tmp40 = tmp38 + tmp39
    tmp41 = tmp27 / tmp37
    tmp42 = tmp40 + tmp41
    tmp43 = tmp36 / tmp37
    tmp44 = tmp42 + tmp43
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp44, None)
''', 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, 4), (256, 64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [G], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(arg0_1, (16, 16), (16, 1), 0), reinterpret_tensor(arg0_1, (16, 16), (1, 16), 0), out=buf0)
        buf1 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [G_2], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(arg1_1, (16, 16), (16, 1), 0), reinterpret_tensor(arg1_1, (16, 16), (1, 16), 0), out=buf1)
        buf10 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [G_11], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(arg1_1, (16, 16), (16, 1), 768), reinterpret_tensor(arg1_1, (16, 16), (1, 16), 768), out=buf10)
        buf3 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [G_3], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(arg0_1, (16, 16), (16, 1), 256), reinterpret_tensor(arg0_1, (16, 16), (1, 16), 256), out=buf3)
        buf4 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [G_5], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(arg1_1, (16, 16), (16, 1), 256), reinterpret_tensor(arg1_1, (16, 16), (1, 16), 256), out=buf4)
        buf6 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [G_6], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(arg0_1, (16, 16), (16, 1), 512), reinterpret_tensor(arg0_1, (16, 16), (1, 16), 512), out=buf6)
        buf7 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [G_8], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(arg1_1, (16, 16), (16, 1), 512), reinterpret_tensor(arg1_1, (16, 16), (1, 16), 512), out=buf7)
        del arg1_1
        buf9 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [G_9], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(arg0_1, (16, 16), (16, 1), 768), reinterpret_tensor(arg0_1, (16, 16), (1, 16), 768), out=buf9)
        del arg0_1
        buf11 = empty_strided_cuda((), (), torch.float32)
        buf12 = buf11; del buf11  # reuse
        # Topologically Sorted Source Nodes: [G_1, T, mse_loss, value_1, G_4, T_1, mse_loss_1, value_2, G_7, T_2, mse_loss_2, value_3, G_10, T_3, mse_loss_3, value_4], Original ATen: [aten.div, aten.mse_loss, aten.add]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_div_mse_loss_0.run(buf12, buf0, buf1, buf3, buf4, buf6, buf7, buf9, buf10, 1, 256, grid=grid(1), stream=stream0)
        del buf0
        del buf1
        del buf10
        del buf3
        del buf4
        del buf6
        del buf7
        del buf9
    return (buf12, )


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, 4), (256, 64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4, 4), (256, 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
import torch.nn.functional as F


def gram_matrix(input):
    a, b, c, d = input.size()
    features = input.view(a * b, c * d)
    G = torch.mm(features, features.t())
    return G / (a * b * c * d)


class Gram_StyleLoss(nn.Module):

    def __init__(self):
        super(Gram_StyleLoss, self).__init__()

    def forward(self, input, target):
        value = torch.tensor(0.0).type_as(input[0])
        for in_m, in_n in zip(input, target):
            G = gram_matrix(in_m)
            T = gram_matrix(in_n)
            value += F.mse_loss(G, T)
        return value


def get_inputs():
    return [torch.rand([4, 4, 4, 4, 4]), torch.rand([4, 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 import triton_helpers
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_div_mse_loss_0(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp3 = tl.load(in_ptr1 + r0, None)
    tmp10 = tl.load(in_ptr2 + r0, None)
    tmp12 = tl.load(in_ptr3 + r0, None)
    tmp19 = tl.load(in_ptr4 + r0, None)
    tmp21 = tl.load(in_ptr5 + r0, None)
    tmp28 = tl.load(in_ptr6 + r0, None)
    tmp30 = tl.load(in_ptr7 + r0, None)
    tmp1 = 0.00390625
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp5 = tmp2 - tmp4
    tmp6 = tmp5 * tmp5
    tmp7 = tl.broadcast_to(tmp6, [RBLOCK])
    tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0))
    tmp11 = tmp10 * tmp1
    tmp13 = tmp12 * tmp1
    tmp14 = tmp11 - tmp13
    tmp15 = tmp14 * tmp14
    tmp16 = tl.broadcast_to(tmp15, [RBLOCK])
    tmp18 = triton_helpers.promote_to_tensor(tl.sum(tmp16, 0))
    tmp20 = tmp19 * tmp1
    tmp22 = tmp21 * tmp1
    tmp23 = tmp20 - tmp22
    tmp24 = tmp23 * tmp23
    tmp25 = tl.broadcast_to(tmp24, [RBLOCK])
    tmp27 = triton_helpers.promote_to_tensor(tl.sum(tmp25, 0))
    tmp29 = tmp28 * tmp1
    tmp31 = tmp30 * tmp1
    tmp32 = tmp29 - tmp31
    tmp33 = tmp32 * tmp32
    tmp34 = tl.broadcast_to(tmp33, [RBLOCK])
    tmp36 = triton_helpers.promote_to_tensor(tl.sum(tmp34, 0))
    tmp37 = 256.0
    tmp38 = tmp9 / tmp37
    tmp39 = tmp18 / tmp37
    tmp40 = tmp38 + tmp39
    tmp41 = tmp27 / tmp37
    tmp42 = tmp40 + tmp41
    tmp43 = tmp36 / tmp37
    tmp44 = tmp42 + tmp43
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp44, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(arg0_1, (16, 16), (16, 1), 0),
            reinterpret_tensor(arg0_1, (16, 16), (1, 16), 0), out=buf0)
        buf1 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(arg1_1, (16, 16), (16, 1), 0),
            reinterpret_tensor(arg1_1, (16, 16), (1, 16), 0), out=buf1)
        buf10 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(arg1_1, (16, 16), (16, 1), 768
            ), reinterpret_tensor(arg1_1, (16, 16), (1, 16), 768), out=buf10)
        buf3 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(arg0_1, (16, 16), (16, 1), 256
            ), reinterpret_tensor(arg0_1, (16, 16), (1, 16), 256), out=buf3)
        buf4 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(arg1_1, (16, 16), (16, 1), 256
            ), reinterpret_tensor(arg1_1, (16, 16), (1, 16), 256), out=buf4)
        buf6 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(arg0_1, (16, 16), (16, 1), 512
            ), reinterpret_tensor(arg0_1, (16, 16), (1, 16), 512), out=buf6)
        buf7 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(arg1_1, (16, 16), (16, 1), 512
            ), reinterpret_tensor(arg1_1, (16, 16), (1, 16), 512), out=buf7)
        del arg1_1
        buf9 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(arg0_1, (16, 16), (16, 1), 768
            ), reinterpret_tensor(arg0_1, (16, 16), (1, 16), 768), out=buf9)
        del arg0_1
        buf11 = empty_strided_cuda((), (), torch.float32)
        buf12 = buf11
        del buf11
        get_raw_stream(0)
        triton_per_fused_add_div_mse_loss_0[grid(1)](buf12, buf0, buf1,
            buf3, buf4, buf6, buf7, buf9, buf10, 1, 256, num_warps=2,
            num_stages=1)
        del buf0
        del buf1
        del buf10
        del buf3
        del buf4
        del buf6
        del buf7
        del buf9
    return buf12,


def gram_matrix(input):
    a, b, c, d = input.size()
    features = input.view(a * b, c * d)
    G = torch.mm(features, features.t())
    return G / (a * b * c * d)


class Gram_StyleLossNew(nn.Module):

    def __init__(self):
        super(Gram_StyleLossNew, self).__init__()

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