import torch
from torch._inductor.select_algorithm import extern_kernels
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.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_mul_pow_sub_sum_0(in_ptr0, in_ptr1, in_ptr2, 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 % 16
    x2 = xindex // 64
    x1 = xindex // 16 % 4
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + (x0 + 64 * x1), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr1 + (16 + x0 + 64 * x1), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp10 = tl.load(in_ptr1 + (32 + x0 + 64 * x1), xmask, eviction_policy=
        'evict_last')
    tmp14 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp15 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask, eviction_policy=
        'evict_last')
    tmp19 = tl.load(in_ptr2 + x3, xmask)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp6 = tmp4 - tmp5
    tmp7 = tmp6 * tmp6
    tmp8 = tmp3 + tmp7
    tmp11 = tmp9 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 - tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tmp13 + tmp17
    tmp20 = tmp18 * tmp19
    tl.store(out_ptr0 + x3, tmp20, xmask)


@triton.jit
def triton_per_fused_add_mean_mul_pow_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)
    r0 = rindex % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp1 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp4 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp5 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp9 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp10 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp14 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp15 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp22 = tl.load(in_ptr2 + (r0 + 64 * r1), None)
    tmp23 = tl.load(in_ptr2 + (16 + r0 + 64 * r1), None)
    tmp25 = tl.load(in_ptr2 + (32 + r0 + 64 * r1), None)
    tmp27 = tl.load(in_ptr2 + (48 + r0 + 64 * r1), None)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp6 = tmp4 - tmp5
    tmp7 = tmp6 * tmp6
    tmp8 = tmp3 + tmp7
    tmp11 = tmp9 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 - tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = tl.broadcast_to(tmp18, [XBLOCK, RBLOCK])
    tmp21 = tl.sum(tmp19, 1)[:, None]
    tmp24 = tmp22 + tmp23
    tmp26 = tmp24 + tmp25
    tmp28 = tmp26 + tmp27
    tmp29 = tl.broadcast_to(tmp28, [XBLOCK, RBLOCK])
    tmp31 = tl.sum(tmp29, 1)[:, None]
    tmp32 = 64.0
    tmp33 = tmp21 / tmp32
    tmp34 = 0.5
    tmp35 = tmp33 * tmp34
    tmp36 = tmp31 / tmp32
    tmp37 = 4.0
    tmp38 = tmp36 * tmp37
    tmp39 = tmp35 + tmp38
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp39, None)


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, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(arg2_1, (16, 4, 4), (16, 4, 1
            ), 0), reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1), 0),
            out=buf0)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_pow_sub_sum_0[grid(256)](arg0_1, arg1_1,
            arg2_1, buf2, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del arg1_1
        del arg2_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf1
        del buf1
        triton_per_fused_add_mean_mul_pow_sub_sum_1[grid(1)](buf4, arg0_1,
            buf0, buf2, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del buf0
        del buf2
    return buf4,


class LocalDictionaryLossNew(torch.nn.Module):

    def __init__(self, penalty):
        super(LocalDictionaryLossNew, self).__init__()
        self.penalty = penalty

    def forward_detailed(self, A, y, x):
        weight = (y.unsqueeze(1) - A.unsqueeze(0)).pow(2).sum(dim=2)
        a = 0.5 * (y - x @ A).pow(2).sum(dim=1).mean()
        b = (weight * x).sum(dim=1).mean()
        return a, b, a + b * self.penalty

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


Measured latency_ms: 0.037384