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.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_add_linalg_vector_norm_mul_neg_reciprocal_0(in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp13 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp18 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp19 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = 4.0
    tmp3 = tmp1 * tmp2
    tmp4 = tmp0 + tmp3
    tmp5 = tmp4 * tmp4
    tmp8 = tmp7 * tmp2
    tmp9 = tmp6 + tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp5 + tmp10
    tmp14 = tmp13 * tmp2
    tmp15 = tmp12 + tmp14
    tmp16 = tmp15 * tmp15
    tmp17 = tmp11 + tmp16
    tmp20 = tmp19 * tmp2
    tmp21 = tmp18 + tmp20
    tmp22 = tmp21 * tmp21
    tmp23 = tmp17 + tmp22
    tmp24 = libdevice.sqrt(tmp23)
    tmp25 = 0.0001
    tmp26 = tmp24 + tmp25
    tmp27 = tl.full([1], 1, tl.int32)
    tmp28 = tmp27 / tmp26
    tmp29 = 3.0
    tmp30 = tmp28 * tmp29
    tmp31 = -tmp30
    tl.store(out_ptr0 + x0, tmp31, xmask)


@triton.jit
def triton_poi_fused_add_mul_neg_relu_sub_1(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    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_ptr1 + x2, xmask)
    tmp5 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr3 + x2, xmask)
    tmp2 = 4.0
    tmp3 = tmp1 * tmp2
    tmp4 = tmp0 + tmp3
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tmp9 - tmp6
    tmp11 = -tmp10
    tmp12 = tmp4 * tmp11
    tmp14 = tmp12 * tmp1
    tmp15 = tmp13 + tmp14
    tl.store(out_ptr0 + x2, tmp12, xmask)
    tl.store(out_ptr1 + x2, tmp15, xmask)


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, 1), (16, 4, 1, 64), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_linalg_vector_norm_mul_neg_reciprocal_0[grid(64)](
            arg1_1, arg0_1, buf0, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_neg_relu_sub_1[grid(256)](arg1_1, arg0_1,
            buf0, arg2_1, buf1, buf2, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        del buf0
    return buf2, buf1


class FluidGravityForceNew(nn.Module):

    def __init__(self, gravity, maxSpeed=3):
        """
        Initializes a fluid gravity model.

        Arguments:
            gravity: Gravity vector in the global frame (same as particle l) for the simulation
            maxSpeed: The maximum magnitude of the particle velocities. Higher velocities are clamped.
                      Previous work used: MAX_VEL = 0.5*0.1*NSUBSTEPS/DT
        """
        super(FluidGravityForceNew, self).__init__()
        self.gravity = gravity
        self.maxSpeed = maxSpeed
        self.relu = nn.ReLU()

    def _cap_magnitude(self, A, cap):
        d = len(A.size())
        vv = torch.norm(A, 2, d - 1, keepdim=True)
        vv = cap / (vv + 0.0001)
        vv = -(self.relu(-vv + 1.0) - 1.0)
        return A * vv

    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], output[1]


Measured latency_ms: 0.031015