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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch import Tensor
from torch.nn.modules.loss import _Loss
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_clamp_div_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), 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
    tl.store(out_ptr0 + x2, tmp15, xmask)


@triton.jit
def triton_per_fused_div_exp_log_neg_sum_1(in_ptr0, out_ptr0, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    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
    tmp0 = tl.load(in_ptr0 + 5 * r0, None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + 4 * r0, None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp1 = 1.4285714285714286
    tmp2 = tmp0 * tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp5 = tmp4 * tmp1
    tmp6 = tl_math.exp(tmp5)
    tmp8 = tmp7 * tmp1
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp6 + tmp9
    tmp12 = tmp11 * tmp1
    tmp13 = tl_math.exp(tmp12)
    tmp14 = tmp10 + tmp13
    tmp16 = tmp15 * tmp1
    tmp17 = tl_math.exp(tmp16)
    tmp18 = tmp14 + tmp17
    tmp19 = tmp3 / tmp18
    tmp20 = tl_math.log(tmp19)
    tmp21 = -tmp20
    tmp22 = tl.broadcast_to(tmp21, [XBLOCK, RBLOCK])
    tmp24 = tl.sum(tmp22, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp24, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clamp_div_0[grid(16)](arg0_1, buf0, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_clamp_div_0[grid(16)](arg1_1, buf1, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del arg1_1
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(buf1, (4, 4), (1, 4), 0),
            out=buf2)
        del buf0
        del buf1
        buf3 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused_div_exp_log_neg_sum_1[grid(1)](buf2, buf3, 1, 4,
            XBLOCK=1, num_warps=2, num_stages=1)
        del buf2
    return buf3,


def cos_sim_matrix(a: 'Tensor', b: 'Tensor', eps: 'float'=1e-08) ->Tensor:
    a_n, b_n = a.norm(dim=1), b.norm(dim=1)
    a_norm = a / torch.clamp(a_n.unsqueeze(1), min=eps)
    b_norm = b / torch.clamp(b_n.unsqueeze(1), min=eps)
    sim_matrix = torch.mm(a_norm, b_norm.transpose(0, 1))
    return sim_matrix


class InfoNCELossNew(_Loss):
    """Info NCE Loss. A type of contrastive loss function used for self-supervised learning.

    References:
        A. Oord, Y. Li, and O. Vinyals,
        "Representation Learning with Contrastive Predictive Coding,"
        ArXiv:1807.03748 [cs.LG], 2018. <https://arxiv.org/abs/1807.03748v2>
    """

    def __init__(self, reduction: 'str'='sum') ->None:
        """
        Args:
            reduction (str)
        """
        super().__init__(reduction=reduction)
        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: 0.015223