# 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/uc/cucjdjgb2gm3d2jss4tjqy4pwi4npff3w5qc6uxzydjlzmi5rc6p.py
# Topologically Sorted Source Nodes: [add_1, ll, ones_like, add_2, lgamma_1, ll_1, lgamma_2, ll_2, eta_loc, add, log_r_plus_mu, sub_2, multiply, add_3, eta_scale, sub_3, multiply_1, ll_3, ll_4, neg_ll, neg_ll_1], Original ATen: [aten.add, aten.lgamma, aten.ones_like, aten.sub, aten.log, aten.mul, aten.clamp, aten.neg, aten.mean]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_2 => add_2
#   add_3 => add_3
#   eta_loc => log
#   eta_scale => log_1
#   lgamma_1 => lgamma_1
#   lgamma_2 => lgamma_2
#   ll => lgamma
#   ll_1 => sub
#   ll_2 => sub_1
#   ll_3 => add_4
#   ll_4 => clamp_max, clamp_min
#   log_r_plus_mu => log_2
#   multiply => mul
#   multiply_1 => mul_1
#   neg_ll => neg
#   neg_ll_1 => mean
#   ones_like => full_default
#   sub_2 => sub_2
#   sub_3 => sub_3
# Graph fragment:
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_1, %arg0_1), kwargs = {})
#   %lgamma : [num_users=1] = call_function[target=torch.ops.aten.lgamma.default](args = (%add_1,), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4, 4, 4, 4], 1), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg0_1, %full_default), kwargs = {})
#   %lgamma_1 : [num_users=1] = call_function[target=torch.ops.aten.lgamma.default](args = (%add_2,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%lgamma, %lgamma_1), kwargs = {})
#   %lgamma_2 : [num_users=1] = call_function[target=torch.ops.aten.lgamma.default](args = (%getitem_1,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %lgamma_2), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%getitem,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_1, %getitem), kwargs = {})
#   %log_2 : [num_users=2] = call_function[target=torch.ops.aten.log.default](args = (%add,), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%log, %log_2), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, %sub_2), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_1, %mul), kwargs = {})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%getitem_1,), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%log_1, %log_2), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%getitem_1, %sub_3), kwargs = {})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_3, %mul_1), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add_4, -300), kwargs = {})
#   %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 300), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%clamp_max,), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%neg,), kwargs = {})
triton_per_fused_add_clamp_lgamma_log_mean_mul_neg_ones_like_sub_0 = async_compile.triton('triton_per_fused_add_clamp_lgamma_log_mean_mul_neg_ones_like_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_add_clamp_lgamma_log_mean_mul_neg_ones_like_sub_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 3, '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_add_clamp_lgamma_log_mean_mul_neg_ones_like_sub_0(in_out_ptr0, in_ptr0, in_ptr1, 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 // 64)
    r3 = rindex
    tmp0 = tl.load(in_ptr0 + (16 + r0 + (32*r2)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (r3), None)
    tmp10 = tl.load(in_ptr0 + (r0 + (32*r2)), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.lgamma(tmp2)
    tmp4 = 1.0
    tmp5 = tmp1 + tmp4
    tmp6 = libdevice.lgamma(tmp5)
    tmp7 = tmp3 - tmp6
    tmp8 = libdevice.lgamma(tmp0)
    tmp9 = tmp7 - tmp8
    tmp11 = tl_math.log(tmp10)
    tmp12 = tmp0 + tmp10
    tmp13 = tl_math.log(tmp12)
    tmp14 = tmp11 - tmp13
    tmp15 = tmp1 * tmp14
    tmp16 = tmp9 + tmp15
    tmp17 = tl_math.log(tmp0)
    tmp18 = tmp17 - tmp13
    tmp19 = tmp0 * tmp18
    tmp20 = tmp16 + tmp19
    tmp21 = -300.0
    tmp22 = triton_helpers.maximum(tmp20, tmp21)
    tmp23 = 300.0
    tmp24 = triton_helpers.minimum(tmp22, tmp23)
    tmp25 = -tmp24
    tmp26 = tl.broadcast_to(tmp25, [RBLOCK])
    tmp28 = triton_helpers.promote_to_tensor(tl.sum(tmp26, 0))
    tmp29 = 256.0
    tmp30 = tmp28 / tmp29
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp30, 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, 2, 4, 4), (32, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [add_1, ll, ones_like, add_2, lgamma_1, ll_1, lgamma_2, ll_2, eta_loc, add, log_r_plus_mu, sub_2, multiply, add_3, eta_scale, sub_3, multiply_1, ll_3, ll_4, neg_ll, neg_ll_1], Original ATen: [aten.add, aten.lgamma, aten.ones_like, aten.sub, aten.log, aten.mul, aten.clamp, aten.neg, aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_clamp_lgamma_log_mean_mul_neg_ones_like_sub_0.run(buf1, arg1_1, arg0_1, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf1, )


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, 2, 4, 4), (32, 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


class LossLoglikelihoodNb(torch.nn.Module):

    def __init__(self, average=True):
        super(LossLoglikelihoodNb, self).__init__()
        self.average = average

    def forward(self, preds, target):
        """Implements the negative log likelihood loss as VAE reconstruction loss"""
        x = target
        loc, scale = torch.chunk(preds, chunks=2, dim=1)
        eta_loc = torch.log(loc)
        eta_scale = torch.log(scale)
        log_r_plus_mu = torch.log(scale + loc)
        ll = torch.lgamma(scale + x)
        ll = ll - torch.lgamma(x + torch.ones_like(x))
        ll = ll - torch.lgamma(scale)
        ll = ll + torch.multiply(x, eta_loc - log_r_plus_mu) + torch.multiply(
            scale, eta_scale - log_r_plus_mu)
        ll = torch.clamp(ll, min=-300, max=300)
        neg_ll = -ll
        if self.average:
            neg_ll = torch.mean(neg_ll)
        else:
            neg_ll = neg_ll.sum(dim=1).sum(dim=1)
        return neg_ll


def get_inputs():
    return [torch.rand([4, 2, 4, 4]), torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {}]

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, math as tl_math
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_add_clamp_lgamma_log_mean_mul_neg_ones_like_sub_0(
    in_out_ptr0, in_ptr0, in_ptr1, 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 // 64
    r3 = rindex
    tmp0 = tl.load(in_ptr0 + (16 + r0 + 32 * r2), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + r3, None)
    tmp10 = tl.load(in_ptr0 + (r0 + 32 * r2), None, eviction_policy=
        'evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.lgamma(tmp2)
    tmp4 = 1.0
    tmp5 = tmp1 + tmp4
    tmp6 = libdevice.lgamma(tmp5)
    tmp7 = tmp3 - tmp6
    tmp8 = libdevice.lgamma(tmp0)
    tmp9 = tmp7 - tmp8
    tmp11 = tl_math.log(tmp10)
    tmp12 = tmp0 + tmp10
    tmp13 = tl_math.log(tmp12)
    tmp14 = tmp11 - tmp13
    tmp15 = tmp1 * tmp14
    tmp16 = tmp9 + tmp15
    tmp17 = tl_math.log(tmp0)
    tmp18 = tmp17 - tmp13
    tmp19 = tmp0 * tmp18
    tmp20 = tmp16 + tmp19
    tmp21 = -300.0
    tmp22 = triton_helpers.maximum(tmp20, tmp21)
    tmp23 = 300.0
    tmp24 = triton_helpers.minimum(tmp22, tmp23)
    tmp25 = -tmp24
    tmp26 = tl.broadcast_to(tmp25, [RBLOCK])
    tmp28 = triton_helpers.promote_to_tensor(tl.sum(tmp26, 0))
    tmp29 = 256.0
    tmp30 = tmp28 / tmp29
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp30, 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, 2, 4, 4), (32, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_add_clamp_lgamma_log_mean_mul_neg_ones_like_sub_0[grid
            (1)](buf1, arg1_1, arg0_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf1,


class LossLoglikelihoodNbNew(torch.nn.Module):

    def __init__(self, average=True):
        super(LossLoglikelihoodNbNew, self).__init__()
        self.average = average

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