# 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/7o/c7ozsbxou6iod6kiylclvdodovsjopbnnhezxcz5j5d76a7te7ls.py
# Topologically Sorted Source Nodes: [theta, mul, abs_1, neg, exp, add, log, clamp, add_1, mul_1, mul_2, sub, metric_loss], Original ATen: [aten.div, aten.mul, aten.abs, aten.neg, aten.exp, aten.add, aten.log, aten.clamp, aten.sub, aten.mean]
# Source node to ATen node mapping:
#   abs_1 => abs_1
#   add => add
#   add_1 => add_1
#   clamp => clamp_min
#   exp => exp
#   log => log
#   metric_loss => mean
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   neg => neg
#   sub => sub
#   theta => div
# Graph fragment:
#   %div : [num_users=3] = call_function[target=torch.ops.aten.div.Tensor](args = (%mm, 2), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, 4), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%mul,), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%abs_1,), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%exp, 1), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add,), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%div, 0), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%log, %clamp_min), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, 4), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %div), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_1, %mul_2), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sub,), kwargs = {})
triton_per_fused_abs_add_clamp_div_exp_log_mean_mul_neg_sub_0 = async_compile.triton('triton_per_fused_abs_add_clamp_div_exp_log_mean_mul_neg_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.persistent_reduction(
    size_hints=[1, 256],
    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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_abs_add_clamp_div_exp_log_mean_mul_neg_sub_0', 'mutated_arg_names': [], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 1, '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_abs_add_clamp_div_exp_log_mean_mul_neg_sub_0(in_ptr0, in_ptr1, out_ptr0, 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 % 16
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + (r2), None)
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 4.0
    tmp4 = tmp2 * tmp3
    tmp5 = tl_math.abs(tmp4)
    tmp6 = -tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp8 = 1.0
    tmp9 = tmp7 + tmp8
    tmp10 = tl_math.log(tmp9)
    tmp11 = 0.0
    tmp12 = triton_helpers.maximum(tmp2, tmp11)
    tmp13 = tmp10 + tmp12
    tmp15 = tmp14 * tmp3
    tmp16 = tmp15 * tmp2
    tmp17 = tmp13 - tmp16
    tmp18 = tl.broadcast_to(tmp17, [RBLOCK])
    tmp20 = triton_helpers.promote_to_tensor(tl.sum(tmp18, 0))
    tl.store(out_ptr0 + (tl.full([1], 0, tl.int32)), tmp20, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/yy/cyyibubdlrcesxl24phdsiosxhj2rbadfbfuiqnc7u7m3hnyu6rn.py
# Topologically Sorted Source Nodes: [theta, mul, abs_1, neg, exp, add, log, clamp, add_1, mul_1, mul_2, sub, metric_loss, abs_2, sub_1, cosh, log_1, quantization_loss, mul_3, loss], Original ATen: [aten.div, aten.mul, aten.abs, aten.neg, aten.exp, aten.add, aten.log, aten.clamp, aten.sub, aten.mean, aten.cosh]
# Source node to ATen node mapping:
#   abs_1 => abs_1
#   abs_2 => abs_2
#   add => add
#   add_1 => add_1
#   clamp => clamp_min
#   cosh => cosh
#   exp => exp
#   log => log
#   log_1 => log_1
#   loss => add_2
#   metric_loss => mean
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   neg => neg
#   quantization_loss => mean_1
#   sub => sub
#   sub_1 => sub_1
#   theta => div
# Graph fragment:
#   %div : [num_users=3] = call_function[target=torch.ops.aten.div.Tensor](args = (%mm, 2), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, 4), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%mul,), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%abs_1,), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%exp, 1), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add,), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%div, 0), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%log, %clamp_min), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, 4), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %div), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_1, %mul_2), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sub,), kwargs = {})
#   %abs_2 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%arg0_1,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%abs_2, 1), kwargs = {})
#   %cosh : [num_users=1] = call_function[target=torch.ops.aten.cosh.default](args = (%sub_1,), kwargs = {})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%cosh,), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%log_1,), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mean_1, 4), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean, %mul_3), kwargs = {})
triton_per_fused_abs_add_clamp_cosh_div_exp_log_mean_mul_neg_sub_1 = async_compile.triton('triton_per_fused_abs_add_clamp_cosh_div_exp_log_mean_mul_neg_sub_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=[1, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_abs_add_clamp_cosh_div_exp_log_mean_mul_neg_sub_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 1, '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_abs_add_clamp_cosh_div_exp_log_mean_mul_neg_sub_1(in_out_ptr0, in_ptr0, 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)
    tmp9 = tl.load(in_out_ptr0 + (0))
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK, 1])
    tmp1 = tl_math.abs(tmp0)
    tmp2 = 1.0
    tmp3 = tmp1 - tmp2
    tmp4 = libdevice.cosh(tmp3)
    tmp5 = tl_math.log(tmp4)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.sum(tmp6, 1)[:, None]
    tmp11 = 256.0
    tmp12 = tmp10 / tmp11
    tmp13 = 16.0
    tmp14 = tmp8 / tmp13
    tmp15 = 4.0
    tmp16 = tmp14 * tmp15
    tmp17 = tmp12 + tmp16
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp17, 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, 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: [matmul], Original ATen: [aten.mm]
        extern_kernels.mm(arg0_1, reinterpret_tensor(arg0_1, (4, 4), (1, 4), 0), out=buf0)
        buf1 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [theta, mul, abs_1, neg, exp, add, log, clamp, add_1, mul_1, mul_2, sub, metric_loss], Original ATen: [aten.div, aten.mul, aten.abs, aten.neg, aten.exp, aten.add, aten.log, aten.clamp, aten.sub, aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused_abs_add_clamp_div_exp_log_mean_mul_neg_sub_0.run(buf0, arg1_1, buf1, 1, 256, grid=grid(1), stream=stream0)
        del arg1_1
        del buf0
        buf3 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [theta, mul, abs_1, neg, exp, add, log, clamp, add_1, mul_1, mul_2, sub, metric_loss, abs_2, sub_1, cosh, log_1, quantization_loss, mul_3, loss], Original ATen: [aten.div, aten.mul, aten.abs, aten.neg, aten.exp, aten.add, aten.log, aten.clamp, aten.sub, aten.mean, aten.cosh]
        triton_per_fused_abs_add_clamp_cosh_div_exp_log_mean_mul_neg_sub_1.run(buf3, arg0_1, 1, 16, grid=grid(1), stream=stream0)
        del arg0_1
    return (buf3, )


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, 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


class DNHloss(nn.Module):
    """DNH loss function

    Arguments:
        nn {[type]} -- [description]
    """

    def __init__(self, lamda):
        """Initializer class

        Arguments:
            lamda {[type]} -- [description]
        """
        super(DNHloss, self).__init__()
        self.lamda = lamda

    def forward(self, H, S):
        """Forward H and S

        Arguments:
            H {[type]} -- [description]
            S {[type]} -- [description]

        Returns:
            [type] -- [description]
        """
        theta = H @ H.t() / 2
        metric_loss = (torch.log(1 + torch.exp(-(self.lamda * theta).abs())
            ) + theta.clamp(min=0) - self.lamda * S * theta).mean()
        quantization_loss = self.logcosh(H.abs() - 1).mean()
        loss = metric_loss + self.lamda * quantization_loss
        return loss

    def logcosh(self, x):
        """log cos

        Arguments:
            x {[type]} -- [description]

        Returns:
            [type] -- [description]
        """
        return torch.log(torch.cosh(x))


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


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

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, math as tl_math
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_abs_add_clamp_div_exp_log_mean_mul_neg_sub_0(in_ptr0,
    in_ptr1, out_ptr0, 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 % 16
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + r2, None)
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 4.0
    tmp4 = tmp2 * tmp3
    tmp5 = tl_math.abs(tmp4)
    tmp6 = -tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp8 = 1.0
    tmp9 = tmp7 + tmp8
    tmp10 = tl_math.log(tmp9)
    tmp11 = 0.0
    tmp12 = triton_helpers.maximum(tmp2, tmp11)
    tmp13 = tmp10 + tmp12
    tmp15 = tmp14 * tmp3
    tmp16 = tmp15 * tmp2
    tmp17 = tmp13 - tmp16
    tmp18 = tl.broadcast_to(tmp17, [RBLOCK])
    tmp20 = triton_helpers.promote_to_tensor(tl.sum(tmp18, 0))
    tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp20, None)


@triton.jit
def triton_per_fused_abs_add_clamp_cosh_div_exp_log_mean_mul_neg_sub_1(
    in_out_ptr0, in_ptr0, 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)
    tmp9 = tl.load(in_out_ptr0 + 0)
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK, 1])
    tmp1 = tl_math.abs(tmp0)
    tmp2 = 1.0
    tmp3 = tmp1 - tmp2
    tmp4 = libdevice.cosh(tmp3)
    tmp5 = tl_math.log(tmp4)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.sum(tmp6, 1)[:, None]
    tmp11 = 256.0
    tmp12 = tmp10 / tmp11
    tmp13 = 16.0
    tmp14 = tmp8 / tmp13
    tmp15 = 4.0
    tmp16 = tmp14 * tmp15
    tmp17 = tmp12 + tmp16
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp17, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (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(arg0_1, reinterpret_tensor(arg0_1, (4, 4), (1, 4),
            0), out=buf0)
        buf1 = empty_strided_cuda((), (), torch.float32)
        get_raw_stream(0)
        triton_per_fused_abs_add_clamp_div_exp_log_mean_mul_neg_sub_0[grid(1)](
            buf0, arg1_1, buf1, 1, 256, num_warps=2, num_stages=1)
        del arg1_1
        del buf0
        buf3 = buf1
        del buf1
        triton_per_fused_abs_add_clamp_cosh_div_exp_log_mean_mul_neg_sub_1[grid
            (1)](buf3, arg0_1, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
    return buf3,


class DNHlossNew(nn.Module):
    """DNH loss function

    Arguments:
        nn {[type]} -- [description]
    """

    def __init__(self, lamda):
        """Initializer class

        Arguments:
            lamda {[type]} -- [description]
        """
        super(DNHlossNew, self).__init__()
        self.lamda = lamda

    def logcosh(self, x):
        """log cos

        Arguments:
            x {[type]} -- [description]

        Returns:
            [type] -- [description]
        """
        return torch.log(torch.cosh(x))

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