# 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_4/inductor_cache/f6/cf6outqezdomcxvjwbjchfmsgy45k2q6wtv3oau6yri4t3ezi6d7.py
# Topologically Sorted Source Nodes: [alpha, log_1, sub, mul, loss, mean], Original ATen: [aten.add, aten.log, aten.sub, aten.mul, aten.sum, aten.mean]
# Source node to ATen node mapping:
#   alpha => add
#   log_1 => log_1
#   loss => sum_2
#   mean => mean
#   mul => mul
#   sub => sub
# Graph fragment:
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg0_1, 1.0), kwargs = {})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%unsqueeze, %log_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, %sub), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [-1]), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sum_2,), kwargs = {})
triton_per_fused_add_log_mean_mul_sub_sum_0 = async_compile.triton('triton_per_fused_add_log_mean_mul_sub_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.persistent_reduction(
    size_hints=[1, 64],
    reduction_hint=ReductionHint.DEFAULT,
    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_log_mean_mul_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 24, '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_log_mean_mul_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    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)
    r3 = rindex
    r0 = rindex % 4
    r2 = (rindex // 16)
    tmp0 = tl.load(in_ptr0 + (4*r3), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + ((16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (2 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (3 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + (4*r3), None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (1 + (4*r3)), None, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr1 + (4 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr1 + (5 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr1 + (6 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr1 + (7 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp32 = tl.load(in_ptr1 + (1 + (4*r3)), None, eviction_policy='evict_last')
    tmp38 = tl.load(in_ptr0 + (2 + (4*r3)), None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr1 + (8 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp41 = tl.load(in_ptr1 + (9 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp44 = tl.load(in_ptr1 + (10 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp47 = tl.load(in_ptr1 + (11 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp51 = tl.load(in_ptr1 + (2 + (4*r3)), None, eviction_policy='evict_last')
    tmp57 = tl.load(in_ptr0 + (3 + (4*r3)), None, eviction_policy='evict_last')
    tmp58 = tl.load(in_ptr1 + (12 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp60 = tl.load(in_ptr1 + (13 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp63 = tl.load(in_ptr1 + (14 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp66 = tl.load(in_ptr1 + (15 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp70 = tl.load(in_ptr1 + (3 + (4*r3)), None, eviction_policy='evict_last')
    tmp2 = 1.0
    tmp3 = tmp1 + tmp2
    tmp5 = tmp4 + tmp2
    tmp6 = tmp3 + tmp5
    tmp8 = tmp7 + tmp2
    tmp9 = tmp6 + tmp8
    tmp11 = tmp10 + tmp2
    tmp12 = tmp9 + tmp11
    tmp13 = tl_math.log(tmp12)
    tmp15 = tmp14 + tmp2
    tmp16 = tl_math.log(tmp15)
    tmp17 = tmp13 - tmp16
    tmp18 = tmp0 * tmp17
    tmp21 = tmp20 + tmp2
    tmp23 = tmp22 + tmp2
    tmp24 = tmp21 + tmp23
    tmp26 = tmp25 + tmp2
    tmp27 = tmp24 + tmp26
    tmp29 = tmp28 + tmp2
    tmp30 = tmp27 + tmp29
    tmp31 = tl_math.log(tmp30)
    tmp33 = tmp32 + tmp2
    tmp34 = tl_math.log(tmp33)
    tmp35 = tmp31 - tmp34
    tmp36 = tmp19 * tmp35
    tmp37 = tmp18 + tmp36
    tmp40 = tmp39 + tmp2
    tmp42 = tmp41 + tmp2
    tmp43 = tmp40 + tmp42
    tmp45 = tmp44 + tmp2
    tmp46 = tmp43 + tmp45
    tmp48 = tmp47 + tmp2
    tmp49 = tmp46 + tmp48
    tmp50 = tl_math.log(tmp49)
    tmp52 = tmp51 + tmp2
    tmp53 = tl_math.log(tmp52)
    tmp54 = tmp50 - tmp53
    tmp55 = tmp38 * tmp54
    tmp56 = tmp37 + tmp55
    tmp59 = tmp58 + tmp2
    tmp61 = tmp60 + tmp2
    tmp62 = tmp59 + tmp61
    tmp64 = tmp63 + tmp2
    tmp65 = tmp62 + tmp64
    tmp67 = tmp66 + tmp2
    tmp68 = tmp65 + tmp67
    tmp69 = tl_math.log(tmp68)
    tmp71 = tmp70 + tmp2
    tmp72 = tl_math.log(tmp71)
    tmp73 = tmp69 - tmp72
    tmp74 = tmp57 * tmp73
    tmp75 = tmp56 + tmp74
    tmp76 = tl.broadcast_to(tmp75, [XBLOCK, RBLOCK])
    tmp78 = tl.sum(tmp76, 1)[:, None]
    tmp79 = 64.0
    tmp80 = tmp78 / tmp79
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp80, 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)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [alpha, log_1, sub, mul, loss, mean], Original ATen: [aten.add, aten.log, aten.sub, aten.mul, aten.sum, aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_log_mean_mul_sub_sum_0.run(buf2, arg1_1, arg0_1, 1, 64, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf2, )


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)

from torch.nn import Module
import torch
import torch.utils.data
import torch.nn.functional
import torch.autograd


class MaximumLikelihoodLoss(Module):
    """
    <a id="MaximumLikelihoodLoss"></a>

    ## Type II Maximum Likelihood Loss

    The distribution $D(\\mathbf{p} ert 	extcolor{orange}{\\mathbf{lpha}})$ is a prior on the likelihood
    $Multi(\\mathbf{y} ert p)$,
     and the negative log marginal likelihood is calculated by integrating over class probabilities
     $\\mathbf{p}$.

    If target probabilities (one-hot targets) are $y_k$ for a given sample the loss is,

    egin{align}
    \\mathcal{L}(\\Theta)
    &= -\\log \\Bigg(
     \\int
      \\prod_{k=1}^K p_k^{y_k}
      rac{1}{B(	extcolor{orange}{\\mathbf{lpha}})}
      \\prod_{k=1}^K p_k^{	extcolor{orange}{lpha_k} - 1}
     d\\mathbf{p}
     \\Bigg ) \\
    &= \\sum_{k=1}^K y_k igg( \\log S - \\log 	extcolor{orange}{lpha_k} igg)
    \\end{align}
    """

    def forward(self, evidence: 'torch.Tensor', target: 'torch.Tensor'):
        """
        * `evidence` is $\\mathbf{e} \\ge 0$ with shape `[batch_size, n_classes]`
        * `target` is $\\mathbf{y}$ with shape `[batch_size, n_classes]`
        """
        alpha = evidence + 1.0
        strength = alpha.sum(dim=-1)
        loss = (target * (strength.log()[:, None] - alpha.log())).sum(dim=-1)
        return loss.mean()


def get_inputs():
    return [torch.rand([4, 4, 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.triton_helpers import math as tl_math
from torch.nn import Module
import torch.utils.data
import torch.nn.functional
import torch.autograd
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_log_mean_mul_sub_sum_0(in_out_ptr0, in_ptr0,
    in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    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)
    r3 = rindex
    r0 = rindex % 4
    r2 = rindex // 16
    tmp0 = tl.load(in_ptr0 + 4 * r3, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (16 * r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 16 * r0 + 64 * r2), None, eviction_policy
        ='evict_last')
    tmp7 = tl.load(in_ptr1 + (2 + 16 * r0 + 64 * r2), None, eviction_policy
        ='evict_last')
    tmp10 = tl.load(in_ptr1 + (3 + 16 * r0 + 64 * r2), None,
        eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + 4 * r3, None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (1 + 4 * r3), None, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr1 + (4 + 16 * r0 + 64 * r2), None,
        eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr1 + (5 + 16 * r0 + 64 * r2), None,
        eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr1 + (6 + 16 * r0 + 64 * r2), None,
        eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr1 + (7 + 16 * r0 + 64 * r2), None,
        eviction_policy='evict_last')
    tmp32 = tl.load(in_ptr1 + (1 + 4 * r3), None, eviction_policy='evict_last')
    tmp38 = tl.load(in_ptr0 + (2 + 4 * r3), None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr1 + (8 + 16 * r0 + 64 * r2), None,
        eviction_policy='evict_last')
    tmp41 = tl.load(in_ptr1 + (9 + 16 * r0 + 64 * r2), None,
        eviction_policy='evict_last')
    tmp44 = tl.load(in_ptr1 + (10 + 16 * r0 + 64 * r2), None,
        eviction_policy='evict_last')
    tmp47 = tl.load(in_ptr1 + (11 + 16 * r0 + 64 * r2), None,
        eviction_policy='evict_last')
    tmp51 = tl.load(in_ptr1 + (2 + 4 * r3), None, eviction_policy='evict_last')
    tmp57 = tl.load(in_ptr0 + (3 + 4 * r3), None, eviction_policy='evict_last')
    tmp58 = tl.load(in_ptr1 + (12 + 16 * r0 + 64 * r2), None,
        eviction_policy='evict_last')
    tmp60 = tl.load(in_ptr1 + (13 + 16 * r0 + 64 * r2), None,
        eviction_policy='evict_last')
    tmp63 = tl.load(in_ptr1 + (14 + 16 * r0 + 64 * r2), None,
        eviction_policy='evict_last')
    tmp66 = tl.load(in_ptr1 + (15 + 16 * r0 + 64 * r2), None,
        eviction_policy='evict_last')
    tmp70 = tl.load(in_ptr1 + (3 + 4 * r3), None, eviction_policy='evict_last')
    tmp2 = 1.0
    tmp3 = tmp1 + tmp2
    tmp5 = tmp4 + tmp2
    tmp6 = tmp3 + tmp5
    tmp8 = tmp7 + tmp2
    tmp9 = tmp6 + tmp8
    tmp11 = tmp10 + tmp2
    tmp12 = tmp9 + tmp11
    tmp13 = tl_math.log(tmp12)
    tmp15 = tmp14 + tmp2
    tmp16 = tl_math.log(tmp15)
    tmp17 = tmp13 - tmp16
    tmp18 = tmp0 * tmp17
    tmp21 = tmp20 + tmp2
    tmp23 = tmp22 + tmp2
    tmp24 = tmp21 + tmp23
    tmp26 = tmp25 + tmp2
    tmp27 = tmp24 + tmp26
    tmp29 = tmp28 + tmp2
    tmp30 = tmp27 + tmp29
    tmp31 = tl_math.log(tmp30)
    tmp33 = tmp32 + tmp2
    tmp34 = tl_math.log(tmp33)
    tmp35 = tmp31 - tmp34
    tmp36 = tmp19 * tmp35
    tmp37 = tmp18 + tmp36
    tmp40 = tmp39 + tmp2
    tmp42 = tmp41 + tmp2
    tmp43 = tmp40 + tmp42
    tmp45 = tmp44 + tmp2
    tmp46 = tmp43 + tmp45
    tmp48 = tmp47 + tmp2
    tmp49 = tmp46 + tmp48
    tmp50 = tl_math.log(tmp49)
    tmp52 = tmp51 + tmp2
    tmp53 = tl_math.log(tmp52)
    tmp54 = tmp50 - tmp53
    tmp55 = tmp38 * tmp54
    tmp56 = tmp37 + tmp55
    tmp59 = tmp58 + tmp2
    tmp61 = tmp60 + tmp2
    tmp62 = tmp59 + tmp61
    tmp64 = tmp63 + tmp2
    tmp65 = tmp62 + tmp64
    tmp67 = tmp66 + tmp2
    tmp68 = tmp65 + tmp67
    tmp69 = tl_math.log(tmp68)
    tmp71 = tmp70 + tmp2
    tmp72 = tl_math.log(tmp71)
    tmp73 = tmp69 - tmp72
    tmp74 = tmp57 * tmp73
    tmp75 = tmp56 + tmp74
    tmp76 = tl.broadcast_to(tmp75, [XBLOCK, RBLOCK])
    tmp78 = tl.sum(tmp76, 1)[:, None]
    tmp79 = 64.0
    tmp80 = tmp78 / tmp79
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp80, 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)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_per_fused_add_log_mean_mul_sub_sum_0[grid(1)](buf2, arg1_1,
            arg0_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf2,


class MaximumLikelihoodLossNew(Module):
    """
    <a id="MaximumLikelihoodLoss"></a>

    ## Type II Maximum Likelihood Loss

    The distribution $D(\\mathbf{p} ert 	extcolor{orange}{\\mathbf{lpha}})$ is a prior on the likelihood
    $Multi(\\mathbf{y} ert p)$,
     and the negative log marginal likelihood is calculated by integrating over class probabilities
     $\\mathbf{p}$.

    If target probabilities (one-hot targets) are $y_k$ for a given sample the loss is,

    egin{align}
    \\mathcal{L}(\\Theta)
    &= -\\log \\Bigg(
     \\int
      \\prod_{k=1}^K p_k^{y_k}
      rac{1}{B(	extcolor{orange}{\\mathbf{lpha}})}
      \\prod_{k=1}^K p_k^{	extcolor{orange}{lpha_k} - 1}
     d\\mathbf{p}
     \\Bigg ) \\
    &= \\sum_{k=1}^K y_k igg( \\log S - \\log 	extcolor{orange}{lpha_k} igg)
    \\end{align}
    """

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