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
import torch.cuda
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_per_fused_add_mean_norm_sub_0(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_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
    tmp0 = tl.load(in_ptr0 + 4 * r0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr2 + 4 * r0, None, eviction_policy='evict_last')
    tmp32 = tl.load(in_ptr2 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp37 = tl.load(in_ptr2 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp42 = tl.load(in_ptr2 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = 1e-06
    tmp4 = tmp2 + tmp3
    tmp5 = tmp4 * tmp4
    tmp8 = tmp6 - tmp7
    tmp9 = tmp8 + tmp3
    tmp10 = tmp9 * tmp9
    tmp11 = tmp5 + tmp10
    tmp14 = tmp12 - tmp13
    tmp15 = tmp14 + tmp3
    tmp16 = tmp15 * tmp15
    tmp17 = tmp11 + tmp16
    tmp20 = tmp18 - tmp19
    tmp21 = tmp20 + tmp3
    tmp22 = tmp21 * tmp21
    tmp23 = tmp17 + tmp22
    tmp24 = libdevice.sqrt(tmp23)
    tmp25 = tl.broadcast_to(tmp24, [XBLOCK, RBLOCK])
    tmp27 = tl.sum(tmp25, 1)[:, None]
    tmp29 = tmp0 - tmp28
    tmp30 = tmp29 + tmp3
    tmp31 = tmp30 * tmp30
    tmp33 = tmp6 - tmp32
    tmp34 = tmp33 + tmp3
    tmp35 = tmp34 * tmp34
    tmp36 = tmp31 + tmp35
    tmp38 = tmp12 - tmp37
    tmp39 = tmp38 + tmp3
    tmp40 = tmp39 * tmp39
    tmp41 = tmp36 + tmp40
    tmp43 = tmp18 - tmp42
    tmp44 = tmp43 + tmp3
    tmp45 = tmp44 * tmp44
    tmp46 = tmp41 + tmp45
    tmp47 = libdevice.sqrt(tmp46)
    tmp48 = tl.broadcast_to(tmp47, [XBLOCK, RBLOCK])
    tmp50 = tl.sum(tmp48, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp27, None)
    tl.store(out_ptr1 + tl.full([XBLOCK, 1], 0, tl.int32), tmp50, None)


@triton.jit
def triton_per_fused_add_clamp_mean_mul_norm_pow_rsub_sub_1(in_out_ptr0,
    in_ptr0, in_ptr1, 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 + r0, None)
    tmp3 = tl.load(in_out_ptr0 + 0)
    tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp9 = tl.load(in_ptr1 + 0)
    tmp10 = tl.broadcast_to(tmp9, [RBLOCK])
    tmp1 = 1.0
    tmp2 = tmp1 - tmp0
    tmp5 = 64.0
    tmp6 = tmp4 / tmp5
    tmp7 = 0.2
    tmp8 = tmp6 + tmp7
    tmp11 = tmp10 / tmp5
    tmp12 = tmp8 - tmp11
    tmp13 = 0.0
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp14 * tmp14
    tmp16 = tmp2 * tmp15
    tmp17 = tmp11 + tmp7
    tmp18 = tmp17 - tmp6
    tmp19 = triton_helpers.maximum(tmp18, tmp13)
    tmp20 = tmp19 * tmp19
    tmp21 = tmp0 * tmp20
    tmp22 = tmp16 + tmp21
    tmp23 = tl.broadcast_to(tmp22, [RBLOCK])
    tmp25 = triton_helpers.promote_to_tensor(tl.sum(tmp23, 0))
    tmp26 = 256.0
    tmp27 = tmp25 / tmp26
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp27, None)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_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))
    assert_size_stride(arg3_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf1 = empty_strided_cuda((), (), torch.float32)
        get_raw_stream(0)
        triton_per_fused_add_mean_norm_sub_0[grid(1)](arg1_1, arg2_1,
            arg0_1, buf0, buf1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        buf2 = buf0
        del buf0
        buf3 = buf2
        del buf2
        triton_per_fused_add_clamp_mean_mul_norm_pow_rsub_sub_1[grid(1)](buf3,
            arg3_1, buf1, 1, 256, num_warps=2, num_stages=1)
        del arg3_1
        del buf1
    return buf3,


class ContrastiveLossNew(torch.nn.Module):
    """
    Triplet loss function based on Contrastive loss function.
    Based on: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
    """

    def __init__(self, margin=0.2):
        super(ContrastiveLossNew, self).__init__()
        self.margin = margin

    def forward(self, input_0, input_1, input_2, input_3):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        arg3_1 = input_3
        output = call([arg0_1, arg1_1, arg2_1, arg3_1])
        return output[0]


Measured latency_ms: 0.029589