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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
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_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)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-12
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp0 / tmp14
    tl.store(out_ptr0 + x3, tmp15, xmask)


@triton.jit
def triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_1(in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex // 256
    x4 = xindex % 64
    x0 = xindex % 16
    x6 = xindex % 256
    x2 = xindex // 64 % 4
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + (x4 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (32 + x0 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr0 + (48 + x0 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp16 = tl.load(in_ptr0 + x6, xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp19 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp22 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp25 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-08
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp0 / tmp14
    tmp18 = tmp17 * tmp17
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = libdevice.sqrt(tmp27)
    tmp29 = triton_helpers.maximum(tmp28, tmp13)
    tmp30 = tmp16 / tmp29
    tmp31 = tmp15 * tmp30
    tl.store(out_ptr0 + x7, tmp31, xmask)


@triton.jit
def triton_per_fused_div_exp_log_mean_mul_neg_sum_2(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)
    r0 = rindex % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r0 + 256 * r1), None)
    tmp1 = tl.load(in_ptr0 + (16 + r0 + 256 * r1), None)
    tmp3 = tl.load(in_ptr0 + (32 + r0 + 256 * r1), None)
    tmp5 = tl.load(in_ptr0 + (48 + r0 + 256 * r1), None)
    tmp10 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp12 = tl.load(in_ptr0 + (64 + r0 + 256 * r1), None)
    tmp13 = tl.load(in_ptr0 + (80 + r0 + 256 * r1), None)
    tmp15 = tl.load(in_ptr0 + (96 + r0 + 256 * r1), None)
    tmp17 = tl.load(in_ptr0 + (112 + r0 + 256 * r1), None)
    tmp21 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp24 = tl.load(in_ptr0 + (128 + r0 + 256 * r1), None)
    tmp25 = tl.load(in_ptr0 + (144 + r0 + 256 * r1), None)
    tmp27 = tl.load(in_ptr0 + (160 + r0 + 256 * r1), None)
    tmp29 = tl.load(in_ptr0 + (176 + r0 + 256 * r1), None)
    tmp33 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp36 = tl.load(in_ptr0 + (192 + r0 + 256 * r1), None)
    tmp37 = tl.load(in_ptr0 + (208 + r0 + 256 * r1), None)
    tmp39 = tl.load(in_ptr0 + (224 + r0 + 256 * r1), None)
    tmp41 = tl.load(in_ptr0 + (240 + r0 + 256 * r1), None)
    tmp45 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 2.0
    tmp8 = tmp6 * tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp11 = tmp9 * tmp10
    tmp14 = tmp12 + tmp13
    tmp16 = tmp14 + tmp15
    tmp18 = tmp16 + tmp17
    tmp19 = tmp18 * tmp7
    tmp20 = tl_math.exp(tmp19)
    tmp22 = tmp20 * tmp21
    tmp23 = tmp11 + tmp22
    tmp26 = tmp24 + tmp25
    tmp28 = tmp26 + tmp27
    tmp30 = tmp28 + tmp29
    tmp31 = tmp30 * tmp7
    tmp32 = tl_math.exp(tmp31)
    tmp34 = tmp32 * tmp33
    tmp35 = tmp23 + tmp34
    tmp38 = tmp36 + tmp37
    tmp40 = tmp38 + tmp39
    tmp42 = tmp40 + tmp41
    tmp43 = tmp42 * tmp7
    tmp44 = tl_math.exp(tmp43)
    tmp46 = tmp44 * tmp45
    tmp47 = tmp35 + tmp46
    tmp48 = tmp9 + tmp20
    tmp49 = tmp48 + tmp32
    tmp50 = tmp49 + tmp44
    tmp51 = tmp47 / tmp50
    tmp52 = tl_math.log(tmp51)
    tmp53 = -tmp52
    tmp54 = tl.broadcast_to(tmp53, [XBLOCK, RBLOCK])
    tmp56 = tl.sum(tmp54, 1)[:, None]
    tmp57 = 64.0
    tmp58 = tmp56 / tmp57
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp58, 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_div_0[grid(256)](arg0_1, buf0, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_1[grid(1024)](
            buf0, buf1, 1024, XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf4
        del buf4
        triton_per_fused_div_exp_log_mean_mul_neg_sum_2[grid(1)](buf5, buf1,
            arg1_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg1_1
        del buf1
    return buf5,


class NTXentNew(nn.Module):

    def __init__(self, metric: 'str'='CosineSimilarity', temperature:
        'float'=0.5, reduction: 'str'='mean'):
        super().__init__()
        if metric not in ['CosineSimilarity']:
            raise ValueError('Undefined metric!')
        if reduction not in ['AvgNonInf', 'mean', 'sum', 'none']:
            raise ValueError('Undefined reduction!')
        self.metric = metric
        self.t = temperature
        self.reduction = reduction

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


Measured latency_ms: 0.039606