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
import torch.nn as nn
import torch.nn.functional as func
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_relu_threshold_backward_0(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = 0.0
    tmp4 = tmp2 <= tmp3
    tl.store(out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr1 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused_add_clamp_ge_le_logical_and_neg_relu_rsub_1(in_ptr0,
    in_ptr1, 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
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp2 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp1 = -tmp0
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = tmp1 + tmp4
    tmp6 = 0.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = 1.0
    tmp9 = triton_helpers.minimum(tmp7, tmp8)
    tmp10 = tmp8 - tmp9
    tmp11 = tmp5 >= tmp6
    tmp12 = tmp5 <= tmp8
    tmp13 = tmp11 & tmp12
    tl.store(out_ptr0 + x2, tmp10, xmask)
    tl.store(out_ptr1 + x2, tmp13, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_2(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = 0.0
    tmp4 = tmp2 <= tmp3
    tl.store(out_ptr0 + x0, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (1, 4), (4, 1))
    assert_size_stride(primals_3, (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), torch.float32)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(16)](primals_1,
            buf0, buf5, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(buf0, (4, 4), (1, 4), 0), out=buf1)
        del buf0
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_add_clamp_ge_le_logical_and_neg_relu_rsub_1[grid(256)
            ](buf1, primals_2, buf2, buf3, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf1
        buf4 = empty_strided_cuda((1, 4), (4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_2[grid(4)](primals_2, buf4,
            4, XBLOCK=4, num_warps=1, num_stages=1)
        del primals_2
    return buf2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf3, buf4, buf5


class predicates1New(nn.Module):

    def __init__(self, num_predicates, body_len):
        super().__init__()
        self.weights = nn.Parameter(torch.zeros(body_len, num_predicates).
            uniform_(0.0, 0.1))
        self.beta = nn.Parameter(torch.ones(1, body_len))

    def get_params(self):
        weights = func.relu(self.weights)
        beta = func.relu(self.beta)
        return beta, weights

    def forward(self, input_0):
        primals_1 = self.weights
        primals_2 = self.beta
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.034716