# 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/ak/cakm5e6lwgvo4ch6lbxjmcmcar3oco47d3xyxuief4vqkwlft225.py
# Topologically Sorted Source Nodes: [sub, alpha, mul, alpha_tilde], Original ATen: [aten.rsub, aten.add, aten.mul]
# Source node to ATen node mapping:
#   alpha => add
#   alpha_tilde => add_1
#   mul => mul
#   sub => sub
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg1_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg0_1, 1.0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %add), kwargs = {})
#   %add_1 : [num_users=5] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg1_1, %mul), kwargs = {})
triton_poi_fused_add_mul_rsub_0 = async_compile.triton('triton_poi_fused_add_mul_rsub_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_rsub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_add_mul_rsub_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp3 = tl.load(in_ptr1 + (x0), xmask)
    tmp1 = 1.0
    tmp2 = tmp1 - tmp0
    tmp4 = tmp3 + tmp1
    tmp5 = tmp2 * tmp4
    tmp6 = tmp0 + tmp5
    tl.store(out_ptr0 + (x0), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/fs/cfslznycxlykpj42fqvbg2sgr2rr5ofx6xol4bhm6igaw3efhsg5.py
# Topologically Sorted Source Nodes: [strength_tilde], Original ATen: [aten.sum]
# Source node to ATen node mapping:
#   strength_tilde => sum_1
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%add_1, [-1]), kwargs = {})
triton_poi_fused_sum_1 = async_compile.triton('triton_poi_fused_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.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_sum_1(in_ptr0, 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
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tl.store(out_ptr0 + (x0), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/2i/c2izsdx2sqgkyo4pbm7trfgkofg2h276vcut2cdf4alkpa2abztc.py
# Topologically Sorted Source Nodes: [sub, alpha, mul, alpha_tilde, sum_2, lgamma, lgamma_1, sub_1, lgamma_2, sum_3, first, sub_3, sub_4, mul_1, second, loss, mean], Original ATen: [aten.rsub, aten.add, aten.mul, aten.sum, aten.lgamma, aten.sub, aten.mean]
# Source node to ATen node mapping:
#   alpha => add
#   alpha_tilde => add_1
#   first => sub_2
#   lgamma => lgamma
#   lgamma_1 => full_default
#   lgamma_2 => lgamma_2
#   loss => add_2
#   mean => mean
#   mul => mul
#   mul_1 => mul_1
#   second => sum_4
#   sub => sub
#   sub_1 => sub_1
#   sub_3 => sub_3
#   sub_4 => sub_4
#   sum_2 => sum_2
#   sum_3 => sum_3
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg1_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg0_1, 1.0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %add), kwargs = {})
#   %add_1 : [num_users=5] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg1_1, %mul), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%add_1, [-1]), kwargs = {})
#   %lgamma : [num_users=1] = call_function[target=torch.ops.aten.lgamma.default](args = (%sum_2,), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 1.7917594909667969), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%lgamma, %full_default), kwargs = {})
#   %lgamma_2 : [num_users=1] = call_function[target=torch.ops.aten.lgamma.default](args = (%add_1,), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%lgamma_2, [-1]), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_1, %sum_3), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_1, 1), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%digamma, %unsqueeze), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_3, %sub_4), kwargs = {})
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_1, [-1]), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_2, %sum_4), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%add_2,), kwargs = {})
triton_per_fused_add_lgamma_mean_mul_rsub_sub_sum_2 = async_compile.triton('triton_per_fused_add_lgamma_mean_mul_rsub_sub_sum_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, 64],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: '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': {5: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 6), equal_to_1=(5,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_lgamma_mean_mul_rsub_sub_sum_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 16, '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_lgamma_mean_mul_rsub_sub_sum_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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)
    r0 = rindex
    r1 = rindex % 4
    r3 = (rindex // 16)
    tmp0 = tl.load(in_ptr0 + (4*r0), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (4*r0), None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr0 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr1 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr2 + (4*r0), None, eviction_policy='evict_last')
    tmp40 = tl.load(in_ptr3 + ((4*r1) + (16*r3)), None, eviction_policy='evict_last')
    tmp44 = tl.load(in_ptr2 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp45 = tl.load(in_ptr3 + (1 + (4*r1) + (16*r3)), None, eviction_policy='evict_last')
    tmp50 = tl.load(in_ptr2 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp51 = tl.load(in_ptr3 + (2 + (4*r1) + (16*r3)), None, eviction_policy='evict_last')
    tmp56 = tl.load(in_ptr2 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp57 = tl.load(in_ptr3 + (3 + (4*r1) + (16*r3)), None, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp1 - tmp0
    tmp4 = tmp3 + tmp1
    tmp5 = tmp2 * tmp4
    tmp6 = tmp0 + tmp5
    tmp8 = tmp1 - tmp7
    tmp10 = tmp9 + tmp1
    tmp11 = tmp8 * tmp10
    tmp12 = tmp7 + tmp11
    tmp13 = tmp6 + tmp12
    tmp15 = tmp1 - tmp14
    tmp17 = tmp16 + tmp1
    tmp18 = tmp15 * tmp17
    tmp19 = tmp14 + tmp18
    tmp20 = tmp13 + tmp19
    tmp22 = tmp1 - tmp21
    tmp24 = tmp23 + tmp1
    tmp25 = tmp22 * tmp24
    tmp26 = tmp21 + tmp25
    tmp27 = tmp20 + tmp26
    tmp28 = libdevice.lgamma(tmp27)
    tmp29 = 1.7917594909667969
    tmp30 = tmp28 - tmp29
    tmp31 = libdevice.lgamma(tmp6)
    tmp32 = libdevice.lgamma(tmp12)
    tmp33 = tmp31 + tmp32
    tmp34 = libdevice.lgamma(tmp19)
    tmp35 = tmp33 + tmp34
    tmp36 = libdevice.lgamma(tmp26)
    tmp37 = tmp35 + tmp36
    tmp38 = tmp6 - tmp1
    tmp41 = tmp39 - tmp40
    tmp42 = tmp38 * tmp41
    tmp43 = tmp12 - tmp1
    tmp46 = tmp44 - tmp45
    tmp47 = tmp43 * tmp46
    tmp48 = tmp42 + tmp47
    tmp49 = tmp19 - tmp1
    tmp52 = tmp50 - tmp51
    tmp53 = tmp49 * tmp52
    tmp54 = tmp48 + tmp53
    tmp55 = tmp26 - tmp1
    tmp58 = tmp56 - tmp57
    tmp59 = tmp55 * tmp58
    tmp60 = tmp54 + tmp59
    tmp61 = tmp30 - tmp37
    tmp62 = tmp61 + tmp60
    tmp63 = tl.broadcast_to(tmp62, [XBLOCK, RBLOCK])
    tmp65 = tl.sum(tmp63, 1)[:, None]
    tmp66 = 64.0
    tmp67 = tmp65 / tmp66
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp67, 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)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sub, alpha, mul, alpha_tilde], Original ATen: [aten.rsub, aten.add, aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_mul_rsub_0.run(arg1_1, arg0_1, buf2, 256, grid=grid(256), stream=stream0)
        # Topologically Sorted Source Nodes: [sub, alpha, mul, alpha_tilde, digamma], Original ATen: [aten.rsub, aten.add, aten.mul, aten.digamma]
        buf3 = torch.ops.aten.digamma.default(buf2)
        buf4 = buf3
        del buf3
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [strength_tilde], Original ATen: [aten.sum]
        triton_poi_fused_sum_1.run(buf2, buf5, 64, grid=grid(64), stream=stream0)
        del buf2
        # Topologically Sorted Source Nodes: [strength_tilde, digamma_1], Original ATen: [aten.sum, aten.digamma]
        buf6 = torch.ops.aten.digamma.default(buf5)
        del buf5
        buf7 = buf6
        del buf6
        buf9 = empty_strided_cuda((), (), torch.float32)
        buf10 = buf9; del buf9  # reuse
        # Topologically Sorted Source Nodes: [sub, alpha, mul, alpha_tilde, sum_2, lgamma, lgamma_1, sub_1, lgamma_2, sum_3, first, sub_3, sub_4, mul_1, second, loss, mean], Original ATen: [aten.rsub, aten.add, aten.mul, aten.sum, aten.lgamma, aten.sub, aten.mean]
        triton_per_fused_add_lgamma_mean_mul_rsub_sub_sum_2.run(buf10, arg1_1, arg0_1, buf4, buf7, 1, 64, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
        del buf4
        del buf7
    return (buf10, )


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 KLDivergenceLoss(Module):
    """
    <a id="KLDivergenceLoss"></a>

    ## KL Divergence Regularization Loss

    This tries to shrink the total evidence to zero if the sample cannot be correctly classified.

    First we calculate $	ilde{lpha}_k = y_k + (1 - y_k) 	extcolor{orange}{lpha_k}$ the
    Dirichlet parameters after remove the correct evidence.

    egin{align}
    &KL \\Big[ D(\\mathbf{p} ert \\mathbf{	ilde{lpha}}) \\Big \\Vert
    D(\\mathbf{p} ert <1, \\dots, 1>\\Big] \\
    &= \\log \\Bigg( rac{\\Gamma \\Big( \\sum_{k=1}^K 	ilde{lpha}_k \\Big)}
    {\\Gamma(K) \\prod_{k=1}^K \\Gamma(	ilde{lpha}_k)} \\Bigg)
    + \\sum_{k=1}^K (	ilde{lpha}_k - 1)
    \\Big[ \\psi(	ilde{lpha}_k) - \\psi(	ilde{S}) \\Big]
    \\end{align}

    where $\\Gamma(\\cdot)$ is the gamma function,
    $\\psi(\\cdot)$ is the $digamma$ function and
    $	ilde{S} = \\sum_{k=1}^K 	ilde{lpha}_k$
    """

    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
        n_classes = evidence.shape[-1]
        alpha_tilde = target + (1 - target) * alpha
        strength_tilde = alpha_tilde.sum(dim=-1)
        first = torch.lgamma(alpha_tilde.sum(dim=-1)) - torch.lgamma(
            alpha_tilde.new_tensor(float(n_classes))) - torch.lgamma(
            alpha_tilde).sum(dim=-1)
        second = ((alpha_tilde - 1) * (torch.digamma(alpha_tilde) - torch.
            digamma(strength_tilde)[:, None])).sum(dim=-1)
        loss = first + second
        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 libdevice
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_poi_fused_add_mul_rsub_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp3 = tl.load(in_ptr1 + x0, xmask)
    tmp1 = 1.0
    tmp2 = tmp1 - tmp0
    tmp4 = tmp3 + tmp1
    tmp5 = tmp2 * tmp4
    tmp6 = tmp0 + tmp5
    tl.store(out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_sum_1(in_ptr0, 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
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tl.store(out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_per_fused_add_lgamma_mean_mul_rsub_sub_sum_2(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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)
    r0 = rindex
    r1 = rindex % 4
    r3 = rindex // 16
    tmp0 = tl.load(in_ptr0 + 4 * r0, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr2 + 4 * r0, None, eviction_policy='evict_last')
    tmp40 = tl.load(in_ptr3 + (4 * r1 + 16 * r3), None, eviction_policy=
        'evict_last')
    tmp44 = tl.load(in_ptr2 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp45 = tl.load(in_ptr3 + (1 + 4 * r1 + 16 * r3), None, eviction_policy
        ='evict_last')
    tmp50 = tl.load(in_ptr2 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp51 = tl.load(in_ptr3 + (2 + 4 * r1 + 16 * r3), None, eviction_policy
        ='evict_last')
    tmp56 = tl.load(in_ptr2 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp57 = tl.load(in_ptr3 + (3 + 4 * r1 + 16 * r3), None, eviction_policy
        ='evict_last')
    tmp1 = 1.0
    tmp2 = tmp1 - tmp0
    tmp4 = tmp3 + tmp1
    tmp5 = tmp2 * tmp4
    tmp6 = tmp0 + tmp5
    tmp8 = tmp1 - tmp7
    tmp10 = tmp9 + tmp1
    tmp11 = tmp8 * tmp10
    tmp12 = tmp7 + tmp11
    tmp13 = tmp6 + tmp12
    tmp15 = tmp1 - tmp14
    tmp17 = tmp16 + tmp1
    tmp18 = tmp15 * tmp17
    tmp19 = tmp14 + tmp18
    tmp20 = tmp13 + tmp19
    tmp22 = tmp1 - tmp21
    tmp24 = tmp23 + tmp1
    tmp25 = tmp22 * tmp24
    tmp26 = tmp21 + tmp25
    tmp27 = tmp20 + tmp26
    tmp28 = libdevice.lgamma(tmp27)
    tmp29 = 1.7917594909667969
    tmp30 = tmp28 - tmp29
    tmp31 = libdevice.lgamma(tmp6)
    tmp32 = libdevice.lgamma(tmp12)
    tmp33 = tmp31 + tmp32
    tmp34 = libdevice.lgamma(tmp19)
    tmp35 = tmp33 + tmp34
    tmp36 = libdevice.lgamma(tmp26)
    tmp37 = tmp35 + tmp36
    tmp38 = tmp6 - tmp1
    tmp41 = tmp39 - tmp40
    tmp42 = tmp38 * tmp41
    tmp43 = tmp12 - tmp1
    tmp46 = tmp44 - tmp45
    tmp47 = tmp43 * tmp46
    tmp48 = tmp42 + tmp47
    tmp49 = tmp19 - tmp1
    tmp52 = tmp50 - tmp51
    tmp53 = tmp49 * tmp52
    tmp54 = tmp48 + tmp53
    tmp55 = tmp26 - tmp1
    tmp58 = tmp56 - tmp57
    tmp59 = tmp55 * tmp58
    tmp60 = tmp54 + tmp59
    tmp61 = tmp30 - tmp37
    tmp62 = tmp61 + tmp60
    tmp63 = tl.broadcast_to(tmp62, [XBLOCK, RBLOCK])
    tmp65 = tl.sum(tmp63, 1)[:, None]
    tmp66 = 64.0
    tmp67 = tmp65 / tmp66
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp67, 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)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_rsub_0[grid(256)](arg1_1, arg0_1, buf2, 
            256, XBLOCK=128, num_warps=4, num_stages=1)
        buf3 = torch.ops.aten.digamma.default(buf2)
        buf4 = buf3
        del buf3
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_sum_1[grid(64)](buf2, buf5, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf2
        buf6 = torch.ops.aten.digamma.default(buf5)
        del buf5
        buf7 = buf6
        del buf6
        buf9 = empty_strided_cuda((), (), torch.float32)
        buf10 = buf9
        del buf9
        triton_per_fused_add_lgamma_mean_mul_rsub_sub_sum_2[grid(1)](buf10,
            arg1_1, arg0_1, buf4, buf7, 1, 64, XBLOCK=1, num_warps=2,
            num_stages=1)
        del arg0_1
        del arg1_1
        del buf4
        del buf7
    return buf10,


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

    ## KL Divergence Regularization Loss

    This tries to shrink the total evidence to zero if the sample cannot be correctly classified.

    First we calculate $	ilde{lpha}_k = y_k + (1 - y_k) 	extcolor{orange}{lpha_k}$ the
    Dirichlet parameters after remove the correct evidence.

    egin{align}
    &KL \\Big[ D(\\mathbf{p} ert \\mathbf{	ilde{lpha}}) \\Big \\Vert
    D(\\mathbf{p} ert <1, \\dots, 1>\\Big] \\
    &= \\log \\Bigg( rac{\\Gamma \\Big( \\sum_{k=1}^K 	ilde{lpha}_k \\Big)}
    {\\Gamma(K) \\prod_{k=1}^K \\Gamma(	ilde{lpha}_k)} \\Bigg)
    + \\sum_{k=1}^K (	ilde{lpha}_k - 1)
    \\Big[ \\psi(	ilde{lpha}_k) - \\psi(	ilde{S}) \\Big]
    \\end{align}

    where $\\Gamma(\\cdot)$ is the gamma function,
    $\\psi(\\cdot)$ is the $digamma$ function and
    $	ilde{S} = \\sum_{k=1}^K 	ilde{lpha}_k$
    """

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