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
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_clamp_min_div_linalg_vector_norm_mul_0(in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, out_ptr1, 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
    x1 = xindex // 4 % 16
    x5 = xindex % 256
    x6 = xindex // 4 % 64
    x7 = xindex
    tmp0 = tl.load(in_ptr0 + (x4 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (4 * x1 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x1 + 64 * x3), xmask, eviction_policy
        ='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x1 + 64 * x3), xmask, eviction_policy
        ='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x1 + 64 * x3), xmask, eviction_policy
        ='evict_last')
    tmp16 = tl.load(in_ptr1 + x5, xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr1 + 4 * x6, xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (1 + 4 * x6), xmask, eviction_policy='evict_last'
        )
    tmp22 = tl.load(in_ptr1 + (2 + 4 * x6), xmask, eviction_policy='evict_last'
        )
    tmp25 = tl.load(in_ptr1 + (3 + 4 * x6), xmask, eviction_policy='evict_last'
        )
    tmp32 = tl.load(in_ptr2 + (x4 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp33 = tl.load(in_ptr2 + (4 * x1 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp35 = tl.load(in_ptr2 + (1 + 4 * x1 + 64 * x3), xmask,
        eviction_policy='evict_last')
    tmp38 = tl.load(in_ptr2 + (2 + 4 * x1 + 64 * x3), xmask,
        eviction_policy='evict_last')
    tmp41 = tl.load(in_ptr2 + (3 + 4 * x1 + 64 * x3), 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
    tmp34 = tmp33 * tmp33
    tmp36 = tmp35 * tmp35
    tmp37 = tmp34 + tmp36
    tmp39 = tmp38 * tmp38
    tmp40 = tmp37 + tmp39
    tmp42 = tmp41 * tmp41
    tmp43 = tmp40 + tmp42
    tmp44 = libdevice.sqrt(tmp43)
    tmp45 = triton_helpers.maximum(tmp44, tmp13)
    tmp46 = tmp32 / tmp45
    tmp47 = tmp46 * tmp30
    tl.store(out_ptr0 + x7, tmp31, xmask)
    tl.store(out_ptr1 + x7, tmp47, xmask)


@triton.jit
def triton_per_fused_add_clamp_mse_loss_sub_sum_1(in_ptr0, in_ptr1,
    out_ptr0, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + 4 * r0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp9 = tmp7 + tmp8
    tmp11 = tmp9 + tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tmp6 - tmp13
    tmp15 = 4.0
    tmp16 = tmp14 + tmp15
    tmp17 = 0.0
    tmp18 = triton_helpers.maximum(tmp16, tmp17)
    tmp19 = 1.0
    tmp20 = triton_helpers.minimum(tmp18, tmp19)
    tmp21 = tmp20 * tmp20
    tmp22 = tl.broadcast_to(tmp21, [RBLOCK])
    tmp24 = triton_helpers.promote_to_tensor(tl.sum(tmp22, 0))
    tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp24, 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((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        buf1 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_0[grid(1024)](
            arg2_1, arg1_1, arg0_1, buf0, buf1, 1024, XBLOCK=128, num_warps
            =4, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        buf2 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused_add_clamp_mse_loss_sub_sum_1[grid(1)](buf0, buf1,
            buf2, 1, 256, num_warps=2, num_stages=1)
        del buf0
        del buf1
    return buf2,


class RankingNew(torch.nn.Module):

    def __init__(self, delta, use_cosine_similarity):
        super(RankingNew, self).__init__()
        self._cosine_similarity = torch.nn.CosineSimilarity(dim=-1)
        self.measure_similarity = self._get_similarity_function(
            use_cosine_similarity)
        self.delta = delta
        self.criterion = torch.nn.MSELoss(reduction='sum')
        if not use_cosine_similarity:
            dim = 64
            self.projector = torch.nn.Linear(dim, dim, bias=False)

    def _get_similarity_function(self, use_cosine_similarity):
        if use_cosine_similarity:
            self._cosine_similarity = torch.nn.CosineSimilarity(dim=-1)
            return self._cosine_simililarity
        else:
            return self._metrics_similarity

    def _metrics_similarity(self, x, y):
        return torch.sum(torch.square(self.projector(x) - self.projector(y)
            ), dim=1)

    def _cosine_simililarity(self, x, y):
        v = self._cosine_similarity(x.unsqueeze(1), y.unsqueeze(0))
        return v

    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: 0.030586