# 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_6/inductor_cache/xp/cxp3n4erp2bfm2zjjzavoa542wl2zmildh36kbphrkxvtlr74pw5.py
# Topologically Sorted Source Nodes: [log_P_2, sum_1, sum_2, sum_log_P, neg], Original ATen: [aten.mul, aten.sum, aten.div, aten.neg]
# Source node to ATen node mapping:
#   log_P_2 => mul
#   neg => neg
#   sum_1 => sum_1
#   sum_2 => sum_2
#   sum_log_P => div
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_2, %arg0_1), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [1]), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%arg0_1, [1]), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, %sum_2), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%div,), kwargs = {})
triton_poi_fused_div_mul_neg_sum_0 = async_compile.triton('triton_poi_fused_div_mul_neg_sum_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.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '*i64', 2: '*fp32', 3: '*fp32', 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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_mul_neg_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, '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},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_div_mul_neg_sum_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + (x0 + (64*x1)), xmask)
    tmp10 = tl.load(in_ptr2 + (16 + x0 + (64*x1)), xmask)
    tmp13 = tl.load(in_ptr2 + (32 + x0 + (64*x1)), xmask)
    tmp16 = tl.load(in_ptr2 + (48 + x0 + (64*x1)), xmask)
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert(((0 <= tmp4) & (tmp4 < 4)) | ~(xmask), "index out of bounds: 0 <= tmp4 < 4")
    tmp6 = tl.load(in_ptr1 + (tmp4 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp7 = tmp6.to(tl.float32)
    tmp9 = tmp7 * tmp8
    tmp11 = tmp7 * tmp10
    tmp12 = tmp9 + tmp11
    tmp14 = tmp7 * tmp13
    tmp15 = tmp12 + tmp14
    tmp17 = tmp7 * tmp16
    tmp18 = tmp15 + tmp17
    tmp19 = tmp8 + tmp10
    tmp20 = tmp19 + tmp13
    tmp21 = tmp20 + tmp16
    tmp22 = tmp18 / tmp21
    tmp23 = -tmp22
    tl.store(out_ptr0 + (x2), tmp23, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_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), (16, 4, 1))
    assert_size_stride(arg2_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [log_P_2, sum_1, sum_2, sum_log_P, neg], Original ATen: [aten.mul, aten.sum, aten.div, aten.neg]
        stream0 = get_raw_stream(0)
        triton_poi_fused_div_mul_neg_sum_0.run(arg2_1, arg1_1, arg0_1, buf0, 64, grid=grid(64), stream=stream0)
        del arg0_1
        del arg1_1
        del arg2_1
    return (buf0, )


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), (16, 4, 1), device='cuda:0', dtype=torch.int64)
    arg2_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.int64)
    fn = lambda: call([arg0_1, arg1_1, arg2_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.multiprocessing
import torch.utils.data


class StandardNLL(torch.nn.modules.loss._Loss):
    """
    Shape:
        log_prob:   batch x time x class
        y_true:     batch x time
        mask:       batch x time
        output:     batch
    """

    def forward(self, log_prob, y_true, mask):
        mask = mask.float()
        log_P = torch.gather(log_prob.view(-1, log_prob.size(2)), 1, y_true
            .contiguous().view(-1, 1))
        log_P = log_P.view(y_true.size(0), y_true.size(1))
        log_P = log_P * mask
        sum_log_P = torch.sum(log_P, dim=1) / torch.sum(mask, dim=1)
        return -sum_log_P


def get_inputs():
    return [torch.ones([4, 4, 4], dtype=torch.int64), torch.ones([4, 4],
        dtype=torch.int64), 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
import torch.multiprocessing
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_div_mul_neg_sum_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + (x0 + 64 * x1), xmask)
    tmp10 = tl.load(in_ptr2 + (16 + x0 + 64 * x1), xmask)
    tmp13 = tl.load(in_ptr2 + (32 + x0 + 64 * x1), xmask)
    tmp16 = tl.load(in_ptr2 + (48 + x0 + 64 * x1), xmask)
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp4 < 4')
    tmp6 = tl.load(in_ptr1 + (tmp4 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp7 = tmp6.to(tl.float32)
    tmp9 = tmp7 * tmp8
    tmp11 = tmp7 * tmp10
    tmp12 = tmp9 + tmp11
    tmp14 = tmp7 * tmp13
    tmp15 = tmp12 + tmp14
    tmp17 = tmp7 * tmp16
    tmp18 = tmp15 + tmp17
    tmp19 = tmp8 + tmp10
    tmp20 = tmp19 + tmp13
    tmp21 = tmp20 + tmp16
    tmp22 = tmp18 / tmp21
    tmp23 = -tmp22
    tl.store(out_ptr0 + x2, tmp23, xmask)


def call(args):
    arg0_1, arg1_1, arg2_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), (16, 4, 1))
    assert_size_stride(arg2_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_div_mul_neg_sum_0[grid(64)](arg2_1, arg1_1, arg0_1,
            buf0, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
    return buf0,


class StandardNLLNew(torch.nn.modules.loss._Loss):
    """
    Shape:
        log_prob:   batch x time x class
        y_true:     batch x time
        mask:       batch x time
        output:     batch
    """

    def forward(self, input_0, input_1, input_2):
        arg1_1 = input_0
        arg2_1 = input_1
        arg0_1 = input_2
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]
