# 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/ut/cut5f4hltl6d7ynm46tiuq5eqxyuetbydusf3gimebkwxzdtb2bl.py
# Topologically Sorted Source Nodes: [norm_G_s, norm_G_t, loss, add, norm_G_s_1, norm_G_t_1, loss_1, add_1, norm_G_s_2, norm_G_t_2, loss_2, add_2, norm_G_s_3, norm_G_t_3, loss_3, add_3], Original ATen: [aten.div, aten.mse_loss, aten.add]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_2 => add_2
#   add_3 => add_3
#   loss => mean, pow_5, sub
#   loss_1 => mean_1, pow_10, sub_1
#   loss_2 => mean_2, pow_15, sub_2
#   loss_3 => mean_3, pow_20, sub_3
#   norm_G_s => div
#   norm_G_s_1 => div_2
#   norm_G_s_2 => div_4
#   norm_G_s_3 => div_6
#   norm_G_t => div_1
#   norm_G_t_1 => div_3
#   norm_G_t_2 => div_5
#   norm_G_t_3 => div_7
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mm, %expand), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mm_1, %expand_1), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%div, %div_1), kwargs = {})
#   %pow_5 : [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_5,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean, 0), kwargs = {})
#   %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mm_2, %expand_2), kwargs = {})
#   %div_3 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mm_3, %expand_3), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%div_2, %div_3), kwargs = {})
#   %pow_10 : [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_10,), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %mean_1), kwargs = {})
#   %div_4 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mm_4, %expand_4), kwargs = {})
#   %div_5 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mm_5, %expand_5), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%div_4, %div_5), kwargs = {})
#   %pow_15 : [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_15,), 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, %expand_6), kwargs = {})
#   %div_7 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mm_7, %expand_7), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%div_6, %div_7), kwargs = {})
#   %pow_20 : [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_20,), 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, 16],
    reduction_hint=ReductionHint.DEFAULT,
    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': False, 'num_load': 40, '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, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r2 = rindex
    r1 = (rindex // 4)
    tmp0 = tl.load(in_ptr0 + (r2), None)
    tmp1 = tl.load(in_ptr0 + (4*r1), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + (4*r1)), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + (4*r1)), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + (4*r1)), None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (r2), None)
    tmp17 = tl.load(in_ptr1 + (4*r1), None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (1 + (4*r1)), None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr1 + (2 + (4*r1)), None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr1 + (3 + (4*r1)), None, eviction_policy='evict_last')
    tmp36 = tl.load(in_ptr2 + (r2), None)
    tmp37 = tl.load(in_ptr2 + (4*r1), None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr2 + (1 + (4*r1)), None, eviction_policy='evict_last')
    tmp42 = tl.load(in_ptr2 + (2 + (4*r1)), None, eviction_policy='evict_last')
    tmp45 = tl.load(in_ptr2 + (3 + (4*r1)), None, eviction_policy='evict_last')
    tmp51 = tl.load(in_ptr3 + (r2), None)
    tmp52 = tl.load(in_ptr3 + (4*r1), None, eviction_policy='evict_last')
    tmp54 = tl.load(in_ptr3 + (1 + (4*r1)), None, eviction_policy='evict_last')
    tmp57 = tl.load(in_ptr3 + (2 + (4*r1)), None, eviction_policy='evict_last')
    tmp60 = tl.load(in_ptr3 + (3 + (4*r1)), None, eviction_policy='evict_last')
    tmp71 = tl.load(in_ptr4 + (r2), None)
    tmp72 = tl.load(in_ptr4 + (4*r1), None, eviction_policy='evict_last')
    tmp74 = tl.load(in_ptr4 + (1 + (4*r1)), None, eviction_policy='evict_last')
    tmp77 = tl.load(in_ptr4 + (2 + (4*r1)), None, eviction_policy='evict_last')
    tmp80 = tl.load(in_ptr4 + (3 + (4*r1)), None, eviction_policy='evict_last')
    tmp86 = tl.load(in_ptr5 + (r2), None)
    tmp87 = tl.load(in_ptr5 + (4*r1), None, eviction_policy='evict_last')
    tmp89 = tl.load(in_ptr5 + (1 + (4*r1)), None, eviction_policy='evict_last')
    tmp92 = tl.load(in_ptr5 + (2 + (4*r1)), None, eviction_policy='evict_last')
    tmp95 = tl.load(in_ptr5 + (3 + (4*r1)), None, eviction_policy='evict_last')
    tmp106 = tl.load(in_ptr6 + (r2), None)
    tmp107 = tl.load(in_ptr6 + (4*r1), None, eviction_policy='evict_last')
    tmp109 = tl.load(in_ptr6 + (1 + (4*r1)), None, eviction_policy='evict_last')
    tmp112 = tl.load(in_ptr6 + (2 + (4*r1)), None, eviction_policy='evict_last')
    tmp115 = tl.load(in_ptr6 + (3 + (4*r1)), None, eviction_policy='evict_last')
    tmp121 = tl.load(in_ptr7 + (r2), None)
    tmp122 = tl.load(in_ptr7 + (4*r1), None, eviction_policy='evict_last')
    tmp124 = tl.load(in_ptr7 + (1 + (4*r1)), None, eviction_policy='evict_last')
    tmp127 = tl.load(in_ptr7 + (2 + (4*r1)), None, eviction_policy='evict_last')
    tmp130 = tl.load(in_ptr7 + (3 + (4*r1)), None, eviction_policy='evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-12
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp0 / tmp14
    tmp18 = tmp17 * tmp17
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = libdevice.sqrt(tmp27)
    tmp29 = triton_helpers.maximum(tmp28, tmp13)
    tmp30 = tmp16 / tmp29
    tmp31 = tmp15 - tmp30
    tmp32 = tmp31 * tmp31
    tmp33 = tl.broadcast_to(tmp32, [XBLOCK, RBLOCK])
    tmp35 = tl.sum(tmp33, 1)[:, None]
    tmp38 = tmp37 * tmp37
    tmp40 = tmp39 * tmp39
    tmp41 = tmp38 + tmp40
    tmp43 = tmp42 * tmp42
    tmp44 = tmp41 + tmp43
    tmp46 = tmp45 * tmp45
    tmp47 = tmp44 + tmp46
    tmp48 = libdevice.sqrt(tmp47)
    tmp49 = triton_helpers.maximum(tmp48, tmp13)
    tmp50 = tmp36 / tmp49
    tmp53 = tmp52 * tmp52
    tmp55 = tmp54 * tmp54
    tmp56 = tmp53 + tmp55
    tmp58 = tmp57 * tmp57
    tmp59 = tmp56 + tmp58
    tmp61 = tmp60 * tmp60
    tmp62 = tmp59 + tmp61
    tmp63 = libdevice.sqrt(tmp62)
    tmp64 = triton_helpers.maximum(tmp63, tmp13)
    tmp65 = tmp51 / tmp64
    tmp66 = tmp50 - tmp65
    tmp67 = tmp66 * tmp66
    tmp68 = tl.broadcast_to(tmp67, [XBLOCK, RBLOCK])
    tmp70 = tl.sum(tmp68, 1)[:, None]
    tmp73 = tmp72 * tmp72
    tmp75 = tmp74 * tmp74
    tmp76 = tmp73 + tmp75
    tmp78 = tmp77 * tmp77
    tmp79 = tmp76 + tmp78
    tmp81 = tmp80 * tmp80
    tmp82 = tmp79 + tmp81
    tmp83 = libdevice.sqrt(tmp82)
    tmp84 = triton_helpers.maximum(tmp83, tmp13)
    tmp85 = tmp71 / tmp84
    tmp88 = tmp87 * tmp87
    tmp90 = tmp89 * tmp89
    tmp91 = tmp88 + tmp90
    tmp93 = tmp92 * tmp92
    tmp94 = tmp91 + tmp93
    tmp96 = tmp95 * tmp95
    tmp97 = tmp94 + tmp96
    tmp98 = libdevice.sqrt(tmp97)
    tmp99 = triton_helpers.maximum(tmp98, tmp13)
    tmp100 = tmp86 / tmp99
    tmp101 = tmp85 - tmp100
    tmp102 = tmp101 * tmp101
    tmp103 = tl.broadcast_to(tmp102, [XBLOCK, RBLOCK])
    tmp105 = tl.sum(tmp103, 1)[:, None]
    tmp108 = tmp107 * tmp107
    tmp110 = tmp109 * tmp109
    tmp111 = tmp108 + tmp110
    tmp113 = tmp112 * tmp112
    tmp114 = tmp111 + tmp113
    tmp116 = tmp115 * tmp115
    tmp117 = tmp114 + tmp116
    tmp118 = libdevice.sqrt(tmp117)
    tmp119 = triton_helpers.maximum(tmp118, tmp13)
    tmp120 = tmp106 / tmp119
    tmp123 = tmp122 * tmp122
    tmp125 = tmp124 * tmp124
    tmp126 = tmp123 + tmp125
    tmp128 = tmp127 * tmp127
    tmp129 = tmp126 + tmp128
    tmp131 = tmp130 * tmp130
    tmp132 = tmp129 + tmp131
    tmp133 = libdevice.sqrt(tmp132)
    tmp134 = triton_helpers.maximum(tmp133, tmp13)
    tmp135 = tmp121 / tmp134
    tmp136 = tmp120 - tmp135
    tmp137 = tmp136 * tmp136
    tmp138 = tl.broadcast_to(tmp137, [XBLOCK, RBLOCK])
    tmp140 = tl.sum(tmp138, 1)[:, None]
    tmp141 = 16.0
    tmp142 = tmp35 / tmp141
    tmp143 = 0.0
    tmp144 = tmp142 + tmp143
    tmp145 = tmp70 / tmp141
    tmp146 = tmp144 + tmp145
    tmp147 = tmp105 / tmp141
    tmp148 = tmp146 + tmp147
    tmp149 = tmp140 / tmp141
    tmp150 = tmp148 + tmp149
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp150, 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), (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)
        # Topologically Sorted Source Nodes: [G_s], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(arg0_1, (4, 16), (16, 1), 0), reinterpret_tensor(arg0_1, (16, 4), (1, 16), 0), out=buf0)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [G_t], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(arg1_1, (4, 16), (16, 1), 0), reinterpret_tensor(arg1_1, (16, 4), (1, 16), 0), out=buf1)
        buf12 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [G_s_3], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(arg0_1, (4, 16), (16, 1), 192), reinterpret_tensor(arg0_1, (16, 4), (1, 16), 192), out=buf12)
        buf13 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [G_t_3], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(arg1_1, (4, 16), (16, 1), 192), reinterpret_tensor(arg1_1, (16, 4), (1, 16), 192), out=buf13)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [G_s_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(arg0_1, (4, 16), (16, 1), 64), reinterpret_tensor(arg0_1, (16, 4), (1, 16), 64), out=buf4)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [G_t_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(arg1_1, (4, 16), (16, 1), 64), reinterpret_tensor(arg1_1, (16, 4), (1, 16), 64), out=buf5)
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [G_s_2], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(arg0_1, (4, 16), (16, 1), 128), reinterpret_tensor(arg0_1, (16, 4), (1, 16), 128), out=buf8)
        del arg0_1
        buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [G_t_2], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(arg1_1, (4, 16), (16, 1), 128), reinterpret_tensor(arg1_1, (16, 4), (1, 16), 128), out=buf9)
        del arg1_1
        buf11 = empty_strided_cuda((), (), torch.float32)
        buf16 = buf11; del buf11  # reuse
        # Topologically Sorted Source Nodes: [norm_G_s, norm_G_t, loss, add, norm_G_s_1, norm_G_t_1, loss_1, add_1, norm_G_s_2, norm_G_t_2, loss_2, add_2, norm_G_s_3, norm_G_t_3, loss_3, add_3], Original ATen: [aten.div, aten.mse_loss, aten.add]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_div_mse_loss_0.run(buf16, buf0, buf1, buf4, buf5, buf8, buf9, buf12, buf13, 1, 16, grid=grid(1), stream=stream0)
        del buf0
        del buf1
        del buf12
        del buf13
        del buf4
        del buf5
        del buf8
        del buf9
    return (buf16, )


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.functional as F
import torch.nn as nn


class InstanceSimilarity(nn.Module):
    """
    Instance Similarity based loss
    """

    def __init__(self, mse=True):
        super(InstanceSimilarity, self).__init__()
        self.mse = mse

    def _loss(self, fm_s, fm_t):
        fm_s = fm_s.view(fm_s.size(0), -1)
        G_s = torch.mm(fm_s, fm_s.t())
        norm_G_s = F.normalize(G_s, p=2, dim=1)
        fm_t = fm_t.view(fm_t.size(0), -1)
        G_t = torch.mm(fm_t, fm_t.t())
        norm_G_t = F.normalize(G_t, p=2, dim=1)
        loss = F.mse_loss(norm_G_s, norm_G_t) if self.mse else F.l1_loss(
            norm_G_s, norm_G_t)
        return loss

    def forward(self, g_s, g_t):
        return sum(self._loss(f_s, f_t) for f_s, f_t in zip(g_s, g_t))


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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn.functional as F
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):
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r2 = rindex
    r1 = rindex // 4
    tmp0 = tl.load(in_ptr0 + r2, None)
    tmp1 = tl.load(in_ptr0 + 4 * r1, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * r1), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * r1), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * r1), None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + r2, None)
    tmp17 = tl.load(in_ptr1 + 4 * r1, None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (1 + 4 * r1), None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr1 + (2 + 4 * r1), None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr1 + (3 + 4 * r1), None, eviction_policy='evict_last')
    tmp36 = tl.load(in_ptr2 + r2, None)
    tmp37 = tl.load(in_ptr2 + 4 * r1, None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr2 + (1 + 4 * r1), None, eviction_policy='evict_last')
    tmp42 = tl.load(in_ptr2 + (2 + 4 * r1), None, eviction_policy='evict_last')
    tmp45 = tl.load(in_ptr2 + (3 + 4 * r1), None, eviction_policy='evict_last')
    tmp51 = tl.load(in_ptr3 + r2, None)
    tmp52 = tl.load(in_ptr3 + 4 * r1, None, eviction_policy='evict_last')
    tmp54 = tl.load(in_ptr3 + (1 + 4 * r1), None, eviction_policy='evict_last')
    tmp57 = tl.load(in_ptr3 + (2 + 4 * r1), None, eviction_policy='evict_last')
    tmp60 = tl.load(in_ptr3 + (3 + 4 * r1), None, eviction_policy='evict_last')
    tmp71 = tl.load(in_ptr4 + r2, None)
    tmp72 = tl.load(in_ptr4 + 4 * r1, None, eviction_policy='evict_last')
    tmp74 = tl.load(in_ptr4 + (1 + 4 * r1), None, eviction_policy='evict_last')
    tmp77 = tl.load(in_ptr4 + (2 + 4 * r1), None, eviction_policy='evict_last')
    tmp80 = tl.load(in_ptr4 + (3 + 4 * r1), None, eviction_policy='evict_last')
    tmp86 = tl.load(in_ptr5 + r2, None)
    tmp87 = tl.load(in_ptr5 + 4 * r1, None, eviction_policy='evict_last')
    tmp89 = tl.load(in_ptr5 + (1 + 4 * r1), None, eviction_policy='evict_last')
    tmp92 = tl.load(in_ptr5 + (2 + 4 * r1), None, eviction_policy='evict_last')
    tmp95 = tl.load(in_ptr5 + (3 + 4 * r1), None, eviction_policy='evict_last')
    tmp106 = tl.load(in_ptr6 + r2, None)
    tmp107 = tl.load(in_ptr6 + 4 * r1, None, eviction_policy='evict_last')
    tmp109 = tl.load(in_ptr6 + (1 + 4 * r1), None, eviction_policy='evict_last'
        )
    tmp112 = tl.load(in_ptr6 + (2 + 4 * r1), None, eviction_policy='evict_last'
        )
    tmp115 = tl.load(in_ptr6 + (3 + 4 * r1), None, eviction_policy='evict_last'
        )
    tmp121 = tl.load(in_ptr7 + r2, None)
    tmp122 = tl.load(in_ptr7 + 4 * r1, None, eviction_policy='evict_last')
    tmp124 = tl.load(in_ptr7 + (1 + 4 * r1), None, eviction_policy='evict_last'
        )
    tmp127 = tl.load(in_ptr7 + (2 + 4 * r1), None, eviction_policy='evict_last'
        )
    tmp130 = tl.load(in_ptr7 + (3 + 4 * r1), None, eviction_policy='evict_last'
        )
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-12
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp0 / tmp14
    tmp18 = tmp17 * tmp17
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = libdevice.sqrt(tmp27)
    tmp29 = triton_helpers.maximum(tmp28, tmp13)
    tmp30 = tmp16 / tmp29
    tmp31 = tmp15 - tmp30
    tmp32 = tmp31 * tmp31
    tmp33 = tl.broadcast_to(tmp32, [XBLOCK, RBLOCK])
    tmp35 = tl.sum(tmp33, 1)[:, None]
    tmp38 = tmp37 * tmp37
    tmp40 = tmp39 * tmp39
    tmp41 = tmp38 + tmp40
    tmp43 = tmp42 * tmp42
    tmp44 = tmp41 + tmp43
    tmp46 = tmp45 * tmp45
    tmp47 = tmp44 + tmp46
    tmp48 = libdevice.sqrt(tmp47)
    tmp49 = triton_helpers.maximum(tmp48, tmp13)
    tmp50 = tmp36 / tmp49
    tmp53 = tmp52 * tmp52
    tmp55 = tmp54 * tmp54
    tmp56 = tmp53 + tmp55
    tmp58 = tmp57 * tmp57
    tmp59 = tmp56 + tmp58
    tmp61 = tmp60 * tmp60
    tmp62 = tmp59 + tmp61
    tmp63 = libdevice.sqrt(tmp62)
    tmp64 = triton_helpers.maximum(tmp63, tmp13)
    tmp65 = tmp51 / tmp64
    tmp66 = tmp50 - tmp65
    tmp67 = tmp66 * tmp66
    tmp68 = tl.broadcast_to(tmp67, [XBLOCK, RBLOCK])
    tmp70 = tl.sum(tmp68, 1)[:, None]
    tmp73 = tmp72 * tmp72
    tmp75 = tmp74 * tmp74
    tmp76 = tmp73 + tmp75
    tmp78 = tmp77 * tmp77
    tmp79 = tmp76 + tmp78
    tmp81 = tmp80 * tmp80
    tmp82 = tmp79 + tmp81
    tmp83 = libdevice.sqrt(tmp82)
    tmp84 = triton_helpers.maximum(tmp83, tmp13)
    tmp85 = tmp71 / tmp84
    tmp88 = tmp87 * tmp87
    tmp90 = tmp89 * tmp89
    tmp91 = tmp88 + tmp90
    tmp93 = tmp92 * tmp92
    tmp94 = tmp91 + tmp93
    tmp96 = tmp95 * tmp95
    tmp97 = tmp94 + tmp96
    tmp98 = libdevice.sqrt(tmp97)
    tmp99 = triton_helpers.maximum(tmp98, tmp13)
    tmp100 = tmp86 / tmp99
    tmp101 = tmp85 - tmp100
    tmp102 = tmp101 * tmp101
    tmp103 = tl.broadcast_to(tmp102, [XBLOCK, RBLOCK])
    tmp105 = tl.sum(tmp103, 1)[:, None]
    tmp108 = tmp107 * tmp107
    tmp110 = tmp109 * tmp109
    tmp111 = tmp108 + tmp110
    tmp113 = tmp112 * tmp112
    tmp114 = tmp111 + tmp113
    tmp116 = tmp115 * tmp115
    tmp117 = tmp114 + tmp116
    tmp118 = libdevice.sqrt(tmp117)
    tmp119 = triton_helpers.maximum(tmp118, tmp13)
    tmp120 = tmp106 / tmp119
    tmp123 = tmp122 * tmp122
    tmp125 = tmp124 * tmp124
    tmp126 = tmp123 + tmp125
    tmp128 = tmp127 * tmp127
    tmp129 = tmp126 + tmp128
    tmp131 = tmp130 * tmp130
    tmp132 = tmp129 + tmp131
    tmp133 = libdevice.sqrt(tmp132)
    tmp134 = triton_helpers.maximum(tmp133, tmp13)
    tmp135 = tmp121 / tmp134
    tmp136 = tmp120 - tmp135
    tmp137 = tmp136 * tmp136
    tmp138 = tl.broadcast_to(tmp137, [XBLOCK, RBLOCK])
    tmp140 = tl.sum(tmp138, 1)[:, None]
    tmp141 = 16.0
    tmp142 = tmp35 / tmp141
    tmp143 = 0.0
    tmp144 = tmp142 + tmp143
    tmp145 = tmp70 / tmp141
    tmp146 = tmp144 + tmp145
    tmp147 = tmp105 / tmp141
    tmp148 = tmp146 + tmp147
    tmp149 = tmp140 / tmp141
    tmp150 = tmp148 + tmp149
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp150, None)


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)
        extern_kernels.mm(reinterpret_tensor(arg0_1, (4, 16), (16, 1), 0),
            reinterpret_tensor(arg0_1, (16, 4), (1, 16), 0), out=buf0)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(arg1_1, (4, 16), (16, 1), 0),
            reinterpret_tensor(arg1_1, (16, 4), (1, 16), 0), out=buf1)
        buf12 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(arg0_1, (4, 16), (16, 1), 192),
            reinterpret_tensor(arg0_1, (16, 4), (1, 16), 192), out=buf12)
        buf13 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(arg1_1, (4, 16), (16, 1), 192),
            reinterpret_tensor(arg1_1, (16, 4), (1, 16), 192), out=buf13)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(arg0_1, (4, 16), (16, 1), 64),
            reinterpret_tensor(arg0_1, (16, 4), (1, 16), 64), out=buf4)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(arg1_1, (4, 16), (16, 1), 64),
            reinterpret_tensor(arg1_1, (16, 4), (1, 16), 64), out=buf5)
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(arg0_1, (4, 16), (16, 1), 128),
            reinterpret_tensor(arg0_1, (16, 4), (1, 16), 128), out=buf8)
        del arg0_1
        buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(arg1_1, (4, 16), (16, 1), 128),
            reinterpret_tensor(arg1_1, (16, 4), (1, 16), 128), out=buf9)
        del arg1_1
        buf11 = empty_strided_cuda((), (), torch.float32)
        buf16 = buf11
        del buf11
        get_raw_stream(0)
        triton_per_fused_add_div_mse_loss_0[grid(1)](buf16, buf0, buf1,
            buf4, buf5, buf8, buf9, buf12, buf13, 1, 16, XBLOCK=1,
            num_warps=2, num_stages=1)
        del buf0
        del buf1
        del buf12
        del buf13
        del buf4
        del buf5
        del buf8
        del buf9
    return buf16,


class InstanceSimilarityNew(nn.Module):
    """
    Instance Similarity based loss
    """

    def __init__(self, mse=True):
        super(InstanceSimilarityNew, self).__init__()
        self.mse = mse

    def _loss(self, fm_s, fm_t):
        fm_s = fm_s.view(fm_s.size(0), -1)
        G_s = torch.mm(fm_s, fm_s.t())
        norm_G_s = F.normalize(G_s, p=2, dim=1)
        fm_t = fm_t.view(fm_t.size(0), -1)
        G_t = torch.mm(fm_t, fm_t.t())
        norm_G_t = F.normalize(G_t, p=2, dim=1)
        loss = F.mse_loss(norm_G_s, norm_G_t) if self.mse else F.l1_loss(
            norm_G_s, norm_G_t)
        return loss

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