# 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_1/inductor_cache/f6/cf6zadwv7a5akbud7ttdbdc565ql6iiuysbozq7xwvkys7sxo7oz.py
# Topologically Sorted Source Nodes: [feat_v_1], Original ATen: [aten.linalg_vector_norm]
# Source node to ATen node mapping:
#   feat_v_1 => pow_1, sum_1
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%view, 2.0), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [-1], True), kwargs = {})
triton_per_fused_linalg_vector_norm_0 = async_compile.triton('triton_per_fused_linalg_vector_norm_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=[4, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_linalg_vector_norm_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_linalg_vector_norm_0(in_ptr0, out_ptr0, 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)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tl.store(out_ptr0 + (x0), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/o6/co65xeotc4w3dat3ahcsyaw4klhv5mxlia7b5j6qfhr2a27ig27r.py
# Topologically Sorted Source Nodes: [mul, sum_1, mul_1, sum_2, mul_2, sum_3], Original ATen: [aten.mul, aten.sum]
# Source node to ATen node mapping:
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   sum_1 => sum_3
#   sum_2 => sum_4
#   sum_3 => sum_5
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%unsqueeze, %unsqueeze_1), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [-1]), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%unsqueeze_2, %unsqueeze_3), kwargs = {})
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_1, [-1]), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%unsqueeze_4, %unsqueeze_5), kwargs = {})
#   %sum_5 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_2, [-1]), kwargs = {})
triton_per_fused_mul_sum_1 = async_compile.triton('triton_per_fused_mul_sum_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=[16, 64],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32', 8: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mul_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 3, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_mul_sum_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    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)
    r2 = rindex
    x0 = xindex % 4
    x1 = (xindex // 4)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (r2 + (64*x0)), xmask, eviction_policy='evict_last', other=0.0)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (r2 + (64*x1)), xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr2 + (r2 + (64*x0)), xmask, eviction_policy='evict_last', other=0.0)
    tmp17 = tl.load(in_ptr3 + (x0), xmask, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr2 + (r2 + (64*x1)), xmask, eviction_policy='evict_last', other=0.0)
    tmp22 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = libdevice.sqrt(tmp1)
    tmp3 = 1e-12
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = tmp0 / tmp4
    tmp8 = libdevice.sqrt(tmp7)
    tmp9 = triton_helpers.maximum(tmp8, tmp3)
    tmp10 = tmp6 / tmp9
    tmp11 = tmp5 * tmp10
    tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK])
    tmp14 = tl.where(xmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tmp18 = libdevice.sqrt(tmp17)
    tmp19 = triton_helpers.maximum(tmp18, tmp3)
    tmp20 = tmp16 / tmp19
    tmp23 = libdevice.sqrt(tmp22)
    tmp24 = triton_helpers.maximum(tmp23, tmp3)
    tmp25 = tmp21 / tmp24
    tmp26 = tmp20 * tmp25
    tmp27 = tl.broadcast_to(tmp26, [XBLOCK, RBLOCK])
    tmp29 = tl.where(xmask, tmp27, 0)
    tmp30 = tl.sum(tmp29, 1)[:, None]
    tmp31 = tmp5 * tmp25
    tmp32 = tl.broadcast_to(tmp31, [XBLOCK, RBLOCK])
    tmp34 = tl.where(xmask, tmp32, 0)
    tmp35 = tl.sum(tmp34, 1)[:, None]
    tl.store(out_ptr0 + (x3), tmp15, xmask)
    tl.store(out_ptr1 + (x3), tmp30, xmask)
    tl.store(out_ptr2 + (x3), tmp35, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/ts/ctsxkk536uyp5fec3ryq3tilzzjb36yephn2kbrcplax6x4ngjxm.py
# Topologically Sorted Source Nodes: [res, mean, res_1, mean_1, add, res_2, mean_2, mul_3, sub, add_1, loss], Original ATen: [aten.pow, aten.mean, aten.add, aten.mul, aten.sub, aten.div]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   loss => div_2
#   mean => mean
#   mean_1 => mean_1
#   mean_2 => mean_2
#   mul_3 => mul_3
#   res => pow_5
#   res_1 => pow_6
#   res_2 => pow_7
#   sub => sub
# Graph fragment:
#   %pow_5 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_3, 2), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_5,), kwargs = {})
#   %pow_6 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_4, 2), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_6,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean, %mean_1), kwargs = {})
#   %pow_7 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_5, 2), kwargs = {})
#   %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_7,), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mean_2, 2), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %mul_3), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub, 0), kwargs = {})
#   %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_1, 1), kwargs = {})
triton_per_fused_add_div_mean_mul_pow_sub_2 = async_compile.triton('triton_per_fused_add_div_mean_mul_pow_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.persistent_reduction(
    size_hints=[1, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=(4,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_mean_mul_pow_sub_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 3, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_pow_sub_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, 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)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp5 = tl.load(in_ptr1 + (r0), None)
    tmp10 = tl.load(in_ptr2 + (r0), None)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.sum(tmp2, 1)[:, None]
    tmp6 = tmp5 * tmp5
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.sum(tmp7, 1)[:, None]
    tmp11 = tmp10 * tmp10
    tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK])
    tmp14 = tl.sum(tmp12, 1)[:, None]
    tmp15 = 16.0
    tmp16 = tmp4 / tmp15
    tmp17 = tmp9 / tmp15
    tmp18 = tmp16 + tmp17
    tmp19 = tmp14 / tmp15
    tmp20 = 2.0
    tmp21 = tmp19 * tmp20
    tmp22 = tmp18 - tmp21
    tmp23 = 0.0
    tmp24 = tmp22 + tmp23
    tmp25 = 1.0
    tmp26 = tmp24 * tmp25
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp26, 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, 1), (1, 4), torch.float32)
        # Topologically Sorted Source Nodes: [feat_v_1], Original ATen: [aten.linalg_vector_norm]
        stream0 = get_raw_stream(0)
        triton_per_fused_linalg_vector_norm_0.run(arg0_1, buf0, 4, 64, grid=grid(4), stream=stream0)
        buf1 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        # Topologically Sorted Source Nodes: [feat_t_1], Original ATen: [aten.linalg_vector_norm]
        triton_per_fused_linalg_vector_norm_0.run(arg1_1, buf1, 4, 64, grid=grid(4), stream=stream0)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, sum_1, mul_1, sum_2, mul_2, sum_3], Original ATen: [aten.mul, aten.sum]
        triton_per_fused_mul_sum_1.run(arg0_1, buf0, arg1_1, buf1, buf2, buf4, buf6, 16, 64, grid=grid(16), stream=stream0)
        del arg0_1
        del arg1_1
        del buf0
        del buf1
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf8 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [res, mean, res_1, mean_1, add, res_2, mean_2, mul_3, sub, add_1, loss], Original ATen: [aten.pow, aten.mean, aten.add, aten.mul, aten.sub, aten.div]
        triton_per_fused_add_div_mean_mul_pow_sub_2.run(buf8, buf2, buf4, buf6, 1, 16, grid=grid(1), stream=stream0)
        del buf2
        del buf4
        del buf6
    return (buf8, )


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
import torch.nn.functional as F


class CMMD(nn.Module):

    def __init__(self, num_pos):
        super(CMMD, self).__init__()
        self.num_pos = num_pos

    def forward(self, feat_v, feat_t):
        feat_v = feat_v.view(feat_v.size(0), -1)
        feat_v = F.normalize(feat_v, dim=-1)
        feat_v_s = torch.split(feat_v, self.num_pos)
        feat_t = feat_t.view(feat_t.size(0), -1)
        feat_t = F.normalize(feat_t, dim=-1)
        feat_t_s = torch.split(feat_t, self.num_pos)
        losses = [self.mmd_loss(f_v, f_t) for f_v, f_t in zip(feat_v_s,
            feat_t_s)]
        loss = sum(losses) / len(losses)
        return loss

    def mmd_loss(self, f_v, f_t):
        return self.poly_kernel(f_v, f_v).mean() + self.poly_kernel(f_t, f_t
            ).mean() - 2 * self.poly_kernel(f_v, f_t).mean()

    def poly_kernel(self, a, b):
        a = a.unsqueeze(0)
        b = b.unsqueeze(1)
        res = (a * b).sum(-1).pow(2)
        return res


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


def get_init_inputs():
    return [[], {'num_pos': 4}]

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 import triton_helpers
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


@triton.jit
def triton_per_fused_linalg_vector_norm_0(in_ptr0, out_ptr0, 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)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp5, xmask)


@triton.jit
def triton_per_fused_mul_sum_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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)
    r2 = rindex
    x0 = xindex % 4
    x1 = xindex // 4
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (r2 + 64 * x0), xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (r2 + 64 * x1), xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp7 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr2 + (r2 + 64 * x0), xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp17 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr2 + (r2 + 64 * x1), xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp22 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp2 = libdevice.sqrt(tmp1)
    tmp3 = 1e-12
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = tmp0 / tmp4
    tmp8 = libdevice.sqrt(tmp7)
    tmp9 = triton_helpers.maximum(tmp8, tmp3)
    tmp10 = tmp6 / tmp9
    tmp11 = tmp5 * tmp10
    tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK])
    tmp14 = tl.where(xmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tmp18 = libdevice.sqrt(tmp17)
    tmp19 = triton_helpers.maximum(tmp18, tmp3)
    tmp20 = tmp16 / tmp19
    tmp23 = libdevice.sqrt(tmp22)
    tmp24 = triton_helpers.maximum(tmp23, tmp3)
    tmp25 = tmp21 / tmp24
    tmp26 = tmp20 * tmp25
    tmp27 = tl.broadcast_to(tmp26, [XBLOCK, RBLOCK])
    tmp29 = tl.where(xmask, tmp27, 0)
    tmp30 = tl.sum(tmp29, 1)[:, None]
    tmp31 = tmp5 * tmp25
    tmp32 = tl.broadcast_to(tmp31, [XBLOCK, RBLOCK])
    tmp34 = tl.where(xmask, tmp32, 0)
    tmp35 = tl.sum(tmp34, 1)[:, None]
    tl.store(out_ptr0 + x3, tmp15, xmask)
    tl.store(out_ptr1 + x3, tmp30, xmask)
    tl.store(out_ptr2 + x3, tmp35, xmask)


@triton.jit
def triton_per_fused_add_div_mean_mul_pow_sub_2(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, 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)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp5 = tl.load(in_ptr1 + r0, None)
    tmp10 = tl.load(in_ptr2 + r0, None)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.sum(tmp2, 1)[:, None]
    tmp6 = tmp5 * tmp5
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.sum(tmp7, 1)[:, None]
    tmp11 = tmp10 * tmp10
    tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK])
    tmp14 = tl.sum(tmp12, 1)[:, None]
    tmp15 = 16.0
    tmp16 = tmp4 / tmp15
    tmp17 = tmp9 / tmp15
    tmp18 = tmp16 + tmp17
    tmp19 = tmp14 / tmp15
    tmp20 = 2.0
    tmp21 = tmp19 * tmp20
    tmp22 = tmp18 - tmp21
    tmp23 = 0.0
    tmp24 = tmp22 + tmp23
    tmp25 = 1.0
    tmp26 = tmp24 * tmp25
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp26, 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, 1), (1, 4), torch.float32)
        get_raw_stream(0)
        triton_per_fused_linalg_vector_norm_0[grid(4)](arg0_1, buf0, 4, 64,
            XBLOCK=1, num_warps=2, num_stages=1)
        buf1 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        triton_per_fused_linalg_vector_norm_0[grid(4)](arg1_1, buf1, 4, 64,
            XBLOCK=1, num_warps=2, num_stages=1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_per_fused_mul_sum_1[grid(16)](arg0_1, buf0, arg1_1, buf1,
            buf2, buf4, buf6, 16, 64, XBLOCK=8, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
        del buf0
        del buf1
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf8 = buf3
        del buf3
        triton_per_fused_add_div_mean_mul_pow_sub_2[grid(1)](buf8, buf2,
            buf4, buf6, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del buf2
        del buf4
        del buf6
    return buf8,


class CMMDNew(nn.Module):

    def __init__(self, num_pos):
        super(CMMDNew, self).__init__()
        self.num_pos = num_pos

    def mmd_loss(self, f_v, f_t):
        return self.poly_kernel(f_v, f_v).mean() + self.poly_kernel(f_t, f_t
            ).mean() - 2 * self.poly_kernel(f_v, f_t).mean()

    def poly_kernel(self, a, b):
        a = a.unsqueeze(0)
        b = b.unsqueeze(1)
        res = (a * b).sum(-1).pow(2)
        return res

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