# 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/4m/c4mbktqccghizbaoiq4sp5jwan6vfxof7affhyhr32khwihn5hy7.py
# Topologically Sorted Source Nodes: [alpha, p], Original ATen: [aten.add, aten.div]
# Source node to ATen node mapping:
#   alpha => add
#   p => div
# Graph fragment:
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg0_1, 1.0), kwargs = {})
#   %div : [num_users=3] = call_function[target=torch.ops.aten.div.Tensor](args = (%add, %unsqueeze), kwargs = {})
triton_poi_fused_add_div_0 = async_compile.triton('triton_poi_fused_add_div_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: '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_add_div_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_add_div_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp3 = tl.load(in_ptr0 + ((4*x0) + (64*x2)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + (4*x0) + (64*x2)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + (4*x0) + (64*x2)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + (4*x0) + (64*x2)), xmask, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 + tmp1
    tmp4 = tmp3 + tmp1
    tmp6 = tmp5 + tmp1
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 + tmp1
    tmp10 = tmp7 + tmp9
    tmp12 = tmp11 + tmp1
    tmp13 = tmp10 + tmp12
    tmp14 = tmp2 / tmp13
    tl.store(out_ptr0 + (x3), tmp14, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/kb/ckb3ux77dgnn2btgpdwtpvd62ighfwj3upxaxlwlv65qub5yeef2.py
# Topologically Sorted Source Nodes: [sub, err, sub_1, mul, add_1, var, add_2, loss, mean], Original ATen: [aten.sub, aten.pow, aten.rsub, aten.mul, aten.add, aten.div, aten.sum, aten.mean]
# Source node to ATen node mapping:
#   add_1 => add_1
#   add_2 => add_2
#   err => pow_1
#   loss => sum_2
#   mean => mean
#   mul => mul
#   sub => sub
#   sub_1 => sub_1
#   var => div_1
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %div), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %div), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, %sub_1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%unsqueeze_1, 1), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul, %add_1), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%pow_1, %div_1), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%add_2, [-1]), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sum_2,), kwargs = {})
triton_per_fused_add_div_mean_mul_pow_rsub_sub_sum_1 = async_compile.triton('triton_per_fused_add_div_mean_mul_pow_rsub_sub_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.persistent_reduction(
    size_hints=[1, 64],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: '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': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=(4,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_mean_mul_pow_rsub_sub_sum_1', '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_div_mean_mul_pow_rsub_sub_sum_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, 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 + (4*r3), None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + ((16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + (1 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr2 + (2 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr2 + (3 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr0 + (1 + (4*r3)), None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr1 + (1 + (4*r3)), None, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr2 + (4 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr2 + (5 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp32 = tl.load(in_ptr2 + (6 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr2 + (7 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp42 = tl.load(in_ptr0 + (2 + (4*r3)), None, eviction_policy='evict_last')
    tmp43 = tl.load(in_ptr1 + (2 + (4*r3)), None, eviction_policy='evict_last')
    tmp48 = tl.load(in_ptr2 + (8 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp50 = tl.load(in_ptr2 + (9 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp53 = tl.load(in_ptr2 + (10 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp56 = tl.load(in_ptr2 + (11 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp63 = tl.load(in_ptr0 + (3 + (4*r3)), None, eviction_policy='evict_last')
    tmp64 = tl.load(in_ptr1 + (3 + (4*r3)), None, eviction_policy='evict_last')
    tmp69 = tl.load(in_ptr2 + (12 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp71 = tl.load(in_ptr2 + (13 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp74 = tl.load(in_ptr2 + (14 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp77 = tl.load(in_ptr2 + (15 + (16*r0) + (64*r2)), None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = 1.0
    tmp5 = tmp4 - tmp1
    tmp6 = tmp1 * tmp5
    tmp8 = tmp7 + tmp4
    tmp10 = tmp9 + tmp4
    tmp11 = tmp8 + tmp10
    tmp13 = tmp12 + tmp4
    tmp14 = tmp11 + tmp13
    tmp16 = tmp15 + tmp4
    tmp17 = tmp14 + tmp16
    tmp18 = tmp17 + tmp4
    tmp19 = tmp6 / tmp18
    tmp20 = tmp3 + tmp19
    tmp23 = tmp21 - tmp22
    tmp24 = tmp23 * tmp23
    tmp25 = tmp4 - tmp22
    tmp26 = tmp22 * tmp25
    tmp28 = tmp27 + tmp4
    tmp30 = tmp29 + tmp4
    tmp31 = tmp28 + tmp30
    tmp33 = tmp32 + tmp4
    tmp34 = tmp31 + tmp33
    tmp36 = tmp35 + tmp4
    tmp37 = tmp34 + tmp36
    tmp38 = tmp37 + tmp4
    tmp39 = tmp26 / tmp38
    tmp40 = tmp24 + tmp39
    tmp41 = tmp20 + tmp40
    tmp44 = tmp42 - tmp43
    tmp45 = tmp44 * tmp44
    tmp46 = tmp4 - tmp43
    tmp47 = tmp43 * tmp46
    tmp49 = tmp48 + tmp4
    tmp51 = tmp50 + tmp4
    tmp52 = tmp49 + tmp51
    tmp54 = tmp53 + tmp4
    tmp55 = tmp52 + tmp54
    tmp57 = tmp56 + tmp4
    tmp58 = tmp55 + tmp57
    tmp59 = tmp58 + tmp4
    tmp60 = tmp47 / tmp59
    tmp61 = tmp45 + tmp60
    tmp62 = tmp41 + tmp61
    tmp65 = tmp63 - tmp64
    tmp66 = tmp65 * tmp65
    tmp67 = tmp4 - tmp64
    tmp68 = tmp64 * tmp67
    tmp70 = tmp69 + tmp4
    tmp72 = tmp71 + tmp4
    tmp73 = tmp70 + tmp72
    tmp75 = tmp74 + tmp4
    tmp76 = tmp73 + tmp75
    tmp78 = tmp77 + tmp4
    tmp79 = tmp76 + tmp78
    tmp80 = tmp79 + tmp4
    tmp81 = tmp68 / tmp80
    tmp82 = tmp66 + tmp81
    tmp83 = tmp62 + tmp82
    tmp84 = tl.broadcast_to(tmp83, [XBLOCK, RBLOCK])
    tmp86 = tl.sum(tmp84, 1)[:, None]
    tmp87 = 64.0
    tmp88 = tmp86 / tmp87
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp88, 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)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [alpha, p], Original ATen: [aten.add, aten.div]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_div_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0)
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [sub, err, sub_1, mul, add_1, var, add_2, loss, mean], Original ATen: [aten.sub, aten.pow, aten.rsub, aten.mul, aten.add, aten.div, aten.sum, aten.mean]
        triton_per_fused_add_div_mean_mul_pow_rsub_sub_sum_1.run(buf3, arg1_1, buf0, arg0_1, 1, 64, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
        del buf0
    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, 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 SquaredErrorBayesRisk(Module):
    """
    <a id="SquaredErrorBayesRisk"></a>

    ## Bayes Risk with Squared Error Loss

    Here the cost function is squared error,
    $$\\sum_{k=1}^K (y_k - p_k)^2 = \\Vert \\mathbf{y} - \\mathbf{p} \\Vert_2^2$$

    We integrate this cost over all $\\mathbf{p}$

    egin{align}
    \\mathcal{L}(\\Theta)
    &= -\\log \\Bigg(
     \\int
      \\Big[ \\sum_{k=1}^K (y_k - p_k)^2 \\Big]
      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 \\mathbb{E} \\Big[ y_k^2 -2 y_k p_k + p_k^2 \\Big] \\
    &= \\sum_{k=1}^K \\Big( y_k^2 -2 y_k \\mathbb{E}[p_k] + \\mathbb{E}[p_k^2] \\Big)
    \\end{align}

    Where $$\\mathbb{E}[p_k] = \\hat{p}_k = rac{	extcolor{orange}{lpha_k}}{S}$$
    is the expected probability when sampled from the Dirichlet distribution
    and $$\\mathbb{E}[p_k^2] = \\mathbb{E}[p_k]^2 + 	ext{Var}(p_k)$$
     where
    $$	ext{Var}(p_k) = rac{	extcolor{orange}{lpha_k}(S - 	extcolor{orange}{lpha_k})}{S^2 (S + 1)}
    = rac{\\hat{p}_k(1 - \\hat{p}_k)}{S + 1}$$
     is the variance.

    This gives,

    egin{align}
    \\mathcal{L}(\\Theta)
    &= \\sum_{k=1}^K \\Big( y_k^2 -2 y_k \\mathbb{E}[p_k] + \\mathbb{E}[p_k^2] \\Big) \\
    &= \\sum_{k=1}^K \\Big( y_k^2 -2 y_k \\mathbb{E}[p_k] +  \\mathbb{E}[p_k]^2 + 	ext{Var}(p_k) \\Big) \\
    &= \\sum_{k=1}^K \\Big( ig( y_k -\\mathbb{E}[p_k] ig)^2 + 	ext{Var}(p_k) \\Big) \\
    &= \\sum_{k=1}^K \\Big( ( y_k -\\hat{p}_k)^2 + rac{\\hat{p}_k(1 - \\hat{p}_k)}{S + 1} \\Big)
    \\end{align}

    This first part of the equation $ig(y_k -\\mathbb{E}[p_k]ig)^2$ is the error term and
    the second part is the variance.
    """

    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)
        p = alpha / strength[:, None]
        err = (target - p) ** 2
        var = p * (1 - p) / (strength[:, None] + 1)
        loss = (err + var).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.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_div_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp3 = tl.load(in_ptr0 + (4 * x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + 4 * x0 + 64 * x2), xmask, eviction_policy
        ='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + 4 * x0 + 64 * x2), xmask, eviction_policy
        ='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x0 + 64 * x2), xmask,
        eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 + tmp1
    tmp4 = tmp3 + tmp1
    tmp6 = tmp5 + tmp1
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 + tmp1
    tmp10 = tmp7 + tmp9
    tmp12 = tmp11 + tmp1
    tmp13 = tmp10 + tmp12
    tmp14 = tmp2 / tmp13
    tl.store(out_ptr0 + x3, tmp14, xmask)


@triton.jit
def triton_per_fused_add_div_mean_mul_pow_rsub_sub_sum_1(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, 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 + 4 * r3, None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + (16 * r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr2 + (1 + 16 * r0 + 64 * r2), None, eviction_policy
        ='evict_last')
    tmp12 = tl.load(in_ptr2 + (2 + 16 * r0 + 64 * r2), None,
        eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr2 + (3 + 16 * r0 + 64 * r2), None,
        eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr0 + (1 + 4 * r3), None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr1 + (1 + 4 * r3), None, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr2 + (4 + 16 * r0 + 64 * r2), None,
        eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr2 + (5 + 16 * r0 + 64 * r2), None,
        eviction_policy='evict_last')
    tmp32 = tl.load(in_ptr2 + (6 + 16 * r0 + 64 * r2), None,
        eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr2 + (7 + 16 * r0 + 64 * r2), None,
        eviction_policy='evict_last')
    tmp42 = tl.load(in_ptr0 + (2 + 4 * r3), None, eviction_policy='evict_last')
    tmp43 = tl.load(in_ptr1 + (2 + 4 * r3), None, eviction_policy='evict_last')
    tmp48 = tl.load(in_ptr2 + (8 + 16 * r0 + 64 * r2), None,
        eviction_policy='evict_last')
    tmp50 = tl.load(in_ptr2 + (9 + 16 * r0 + 64 * r2), None,
        eviction_policy='evict_last')
    tmp53 = tl.load(in_ptr2 + (10 + 16 * r0 + 64 * r2), None,
        eviction_policy='evict_last')
    tmp56 = tl.load(in_ptr2 + (11 + 16 * r0 + 64 * r2), None,
        eviction_policy='evict_last')
    tmp63 = tl.load(in_ptr0 + (3 + 4 * r3), None, eviction_policy='evict_last')
    tmp64 = tl.load(in_ptr1 + (3 + 4 * r3), None, eviction_policy='evict_last')
    tmp69 = tl.load(in_ptr2 + (12 + 16 * r0 + 64 * r2), None,
        eviction_policy='evict_last')
    tmp71 = tl.load(in_ptr2 + (13 + 16 * r0 + 64 * r2), None,
        eviction_policy='evict_last')
    tmp74 = tl.load(in_ptr2 + (14 + 16 * r0 + 64 * r2), None,
        eviction_policy='evict_last')
    tmp77 = tl.load(in_ptr2 + (15 + 16 * r0 + 64 * r2), None,
        eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = 1.0
    tmp5 = tmp4 - tmp1
    tmp6 = tmp1 * tmp5
    tmp8 = tmp7 + tmp4
    tmp10 = tmp9 + tmp4
    tmp11 = tmp8 + tmp10
    tmp13 = tmp12 + tmp4
    tmp14 = tmp11 + tmp13
    tmp16 = tmp15 + tmp4
    tmp17 = tmp14 + tmp16
    tmp18 = tmp17 + tmp4
    tmp19 = tmp6 / tmp18
    tmp20 = tmp3 + tmp19
    tmp23 = tmp21 - tmp22
    tmp24 = tmp23 * tmp23
    tmp25 = tmp4 - tmp22
    tmp26 = tmp22 * tmp25
    tmp28 = tmp27 + tmp4
    tmp30 = tmp29 + tmp4
    tmp31 = tmp28 + tmp30
    tmp33 = tmp32 + tmp4
    tmp34 = tmp31 + tmp33
    tmp36 = tmp35 + tmp4
    tmp37 = tmp34 + tmp36
    tmp38 = tmp37 + tmp4
    tmp39 = tmp26 / tmp38
    tmp40 = tmp24 + tmp39
    tmp41 = tmp20 + tmp40
    tmp44 = tmp42 - tmp43
    tmp45 = tmp44 * tmp44
    tmp46 = tmp4 - tmp43
    tmp47 = tmp43 * tmp46
    tmp49 = tmp48 + tmp4
    tmp51 = tmp50 + tmp4
    tmp52 = tmp49 + tmp51
    tmp54 = tmp53 + tmp4
    tmp55 = tmp52 + tmp54
    tmp57 = tmp56 + tmp4
    tmp58 = tmp55 + tmp57
    tmp59 = tmp58 + tmp4
    tmp60 = tmp47 / tmp59
    tmp61 = tmp45 + tmp60
    tmp62 = tmp41 + tmp61
    tmp65 = tmp63 - tmp64
    tmp66 = tmp65 * tmp65
    tmp67 = tmp4 - tmp64
    tmp68 = tmp64 * tmp67
    tmp70 = tmp69 + tmp4
    tmp72 = tmp71 + tmp4
    tmp73 = tmp70 + tmp72
    tmp75 = tmp74 + tmp4
    tmp76 = tmp73 + tmp75
    tmp78 = tmp77 + tmp4
    tmp79 = tmp76 + tmp78
    tmp80 = tmp79 + tmp4
    tmp81 = tmp68 / tmp80
    tmp82 = tmp66 + tmp81
    tmp83 = tmp62 + tmp82
    tmp84 = tl.broadcast_to(tmp83, [XBLOCK, RBLOCK])
    tmp86 = tl.sum(tmp84, 1)[:, None]
    tmp87 = 64.0
    tmp88 = tmp86 / tmp87
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp88, 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)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_div_0[grid(256)](arg0_1, buf0, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2
        del buf2
        triton_per_fused_add_div_mean_mul_pow_rsub_sub_sum_1[grid(1)](buf3,
            arg1_1, buf0, arg0_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        del buf0
    return buf3,


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

    ## Bayes Risk with Squared Error Loss

    Here the cost function is squared error,
    $$\\sum_{k=1}^K (y_k - p_k)^2 = \\Vert \\mathbf{y} - \\mathbf{p} \\Vert_2^2$$

    We integrate this cost over all $\\mathbf{p}$

    egin{align}
    \\mathcal{L}(\\Theta)
    &= -\\log \\Bigg(
     \\int
      \\Big[ \\sum_{k=1}^K (y_k - p_k)^2 \\Big]
      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 \\mathbb{E} \\Big[ y_k^2 -2 y_k p_k + p_k^2 \\Big] \\
    &= \\sum_{k=1}^K \\Big( y_k^2 -2 y_k \\mathbb{E}[p_k] + \\mathbb{E}[p_k^2] \\Big)
    \\end{align}

    Where $$\\mathbb{E}[p_k] = \\hat{p}_k = rac{	extcolor{orange}{lpha_k}}{S}$$
    is the expected probability when sampled from the Dirichlet distribution
    and $$\\mathbb{E}[p_k^2] = \\mathbb{E}[p_k]^2 + 	ext{Var}(p_k)$$
     where
    $$	ext{Var}(p_k) = rac{	extcolor{orange}{lpha_k}(S - 	extcolor{orange}{lpha_k})}{S^2 (S + 1)}
    = rac{\\hat{p}_k(1 - \\hat{p}_k)}{S + 1}$$
     is the variance.

    This gives,

    egin{align}
    \\mathcal{L}(\\Theta)
    &= \\sum_{k=1}^K \\Big( y_k^2 -2 y_k \\mathbb{E}[p_k] + \\mathbb{E}[p_k^2] \\Big) \\
    &= \\sum_{k=1}^K \\Big( y_k^2 -2 y_k \\mathbb{E}[p_k] +  \\mathbb{E}[p_k]^2 + 	ext{Var}(p_k) \\Big) \\
    &= \\sum_{k=1}^K \\Big( ig( y_k -\\mathbb{E}[p_k] ig)^2 + 	ext{Var}(p_k) \\Big) \\
    &= \\sum_{k=1}^K \\Big( ( y_k -\\hat{p}_k)^2 + rac{\\hat{p}_k(1 - \\hat{p}_k)}{S + 1} \\Big)
    \\end{align}

    This first part of the equation $ig(y_k -\\mathbb{E}[p_k]ig)^2$ is the error term and
    the second part is the variance.
    """

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