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.utils.data
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_clone_0(in_ptr0, 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 % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, 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 % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_2(in_ptr0, 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 % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_3(in_ptr0, 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 % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_relu_threshold_backward_4(in_out_ptr0, in_out_ptr1,
    in_out_ptr2, in_out_ptr3, in_ptr0, out_ptr0, out_ptr1, out_ptr2,
    out_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_out_ptr1 + x2, xmask)
    tmp11 = tl.load(in_out_ptr2 + x2, xmask)
    tmp15 = tl.load(in_out_ptr3 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tmp8 = tmp7 + tmp1
    tmp9 = triton_helpers.maximum(tmp3, tmp8)
    tmp10 = tmp9 <= tmp5
    tmp12 = tmp11 + tmp1
    tmp13 = triton_helpers.maximum(tmp3, tmp12)
    tmp14 = tmp13 <= tmp5
    tmp16 = tmp15 + tmp1
    tmp17 = triton_helpers.maximum(tmp3, tmp16)
    tmp18 = tmp17 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)
    tl.store(in_out_ptr1 + x2, tmp9, xmask)
    tl.store(out_ptr1 + x2, tmp10, xmask)
    tl.store(in_out_ptr2 + x2, tmp13, xmask)
    tl.store(out_ptr2 + x2, tmp14, xmask)
    tl.store(in_out_ptr3 + x2, tmp17, xmask)
    tl.store(out_ptr3 + x2, tmp18, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_5(in_out_ptr0, in_out_ptr1,
    in_out_ptr2, in_out_ptr3, in_ptr0, out_ptr0, out_ptr1, out_ptr2,
    out_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_out_ptr1 + x2, xmask)
    tmp11 = tl.load(in_out_ptr2 + x2, xmask)
    tmp15 = tl.load(in_out_ptr3 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tmp8 = tmp7 + tmp1
    tmp9 = triton_helpers.maximum(tmp3, tmp8)
    tmp10 = tmp9 <= tmp5
    tmp12 = tmp11 + tmp1
    tmp13 = triton_helpers.maximum(tmp3, tmp12)
    tmp14 = tmp13 <= tmp5
    tmp16 = tmp15 + tmp1
    tmp17 = triton_helpers.maximum(tmp3, tmp16)
    tmp18 = tmp17 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)
    tl.store(in_out_ptr1 + x2, tmp9, xmask)
    tl.store(out_ptr1 + x2, tmp10, xmask)
    tl.store(in_out_ptr2 + x2, tmp13, xmask)
    tl.store(out_ptr2 + x2, tmp14, xmask)
    tl.store(in_out_ptr3 + x2, tmp17, xmask)
    tl.store(out_ptr3 + x2, tmp18, xmask)


@triton.jit
def triton_poi_fused_stack_6(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 16
    x0 = xindex % 4
    x2 = xindex // 64
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + 4 * (-4 + x1) + 16 * x2), tmp9 & xmask,
        other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 12, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr2 + (x0 + 4 * (-8 + x1) + 16 * x2), tmp14 & xmask,
        other=0.0)
    tmp16 = tmp0 >= tmp12
    tl.full([1], 16, tl.int64)
    tmp19 = tl.load(in_ptr3 + (x0 + 4 * (-12 + x1) + 16 * x2), tmp16 &
        xmask, other=0.0)
    tmp20 = tl.where(tmp14, tmp15, tmp19)
    tmp21 = tl.where(tmp9, tmp10, tmp20)
    tmp22 = tl.where(tmp4, tmp5, tmp21)
    tl.store(out_ptr0 + x3, tmp22, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (32, 4), (4, 1))
    assert_size_stride(primals_3, (32,), (1,))
    assert_size_stride(primals_4, (64, 32), (32, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (4, 64), (64, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(64)](primals_1, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 32), (1, 4), 0), out=buf1)
        buf12 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clone_1[grid(64)](primals_1, buf12, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf13 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf12, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 32), (1, 4), 0), out=buf13)
        buf18 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clone_2[grid(64)](primals_1, buf18, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf19 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf18, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 32), (1, 4), 0), out=buf19)
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clone_3[grid(64)](primals_1, buf6, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_1
        buf7 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf6, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 32), (1, 4), 0), out=buf7)
        del primals_2
        buf2 = reinterpret_tensor(buf1, (4, 4, 32), (128, 32, 1), 0)
        del buf1
        buf32 = empty_strided_cuda((4, 4, 32), (128, 32, 1), torch.bool)
        buf8 = reinterpret_tensor(buf7, (4, 4, 32), (128, 32, 1), 0)
        del buf7
        buf30 = empty_strided_cuda((4, 4, 32), (128, 32, 1), torch.bool)
        buf14 = reinterpret_tensor(buf13, (4, 4, 32), (128, 32, 1), 0)
        del buf13
        buf28 = empty_strided_cuda((4, 4, 32), (128, 32, 1), torch.bool)
        buf20 = reinterpret_tensor(buf19, (4, 4, 32), (128, 32, 1), 0)
        del buf19
        buf26 = empty_strided_cuda((4, 4, 32), (128, 32, 1), torch.bool)
        triton_poi_fused_add_relu_threshold_backward_4[grid(512)](buf2,
            buf8, buf14, buf20, primals_3, buf32, buf30, buf28, buf26, 512,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf3 = empty_strided_cuda((16, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 32), (32, 1), 0),
            reinterpret_tensor(primals_4, (32, 64), (1, 32), 0), out=buf3)
        buf15 = empty_strided_cuda((16, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf14, (16, 32), (32, 1), 0),
            reinterpret_tensor(primals_4, (32, 64), (1, 32), 0), out=buf15)
        buf21 = empty_strided_cuda((16, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf20, (16, 32), (32, 1), 0),
            reinterpret_tensor(primals_4, (32, 64), (1, 32), 0), out=buf21)
        buf9 = empty_strided_cuda((16, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf8, (16, 32), (32, 1), 0),
            reinterpret_tensor(primals_4, (32, 64), (1, 32), 0), out=buf9)
        buf4 = reinterpret_tensor(buf3, (4, 4, 64), (256, 64, 1), 0)
        del buf3
        buf31 = empty_strided_cuda((4, 4, 64), (256, 64, 1), torch.bool)
        buf10 = reinterpret_tensor(buf9, (4, 4, 64), (256, 64, 1), 0)
        del buf9
        buf29 = empty_strided_cuda((4, 4, 64), (256, 64, 1), torch.bool)
        buf16 = reinterpret_tensor(buf15, (4, 4, 64), (256, 64, 1), 0)
        del buf15
        buf27 = empty_strided_cuda((4, 4, 64), (256, 64, 1), torch.bool)
        buf22 = reinterpret_tensor(buf21, (4, 4, 64), (256, 64, 1), 0)
        del buf21
        buf25 = empty_strided_cuda((4, 4, 64), (256, 64, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_5[grid(1024)](buf4, buf10,
            buf16, buf22, primals_5, buf31, buf29, buf27, buf25, 1024,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf5 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf4, (16, 64),
            (64, 1), 0), reinterpret_tensor(primals_6, (64, 4), (1, 64), 0),
            alpha=1, beta=1, out=buf5)
        buf11 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf10, (16, 64),
            (64, 1), 0), reinterpret_tensor(primals_6, (64, 4), (1, 64), 0),
            alpha=1, beta=1, out=buf11)
        buf17 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf16, (16, 64),
            (64, 1), 0), reinterpret_tensor(primals_6, (64, 4), (1, 64), 0),
            alpha=1, beta=1, out=buf17)
        buf23 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf22, (16, 64),
            (64, 1), 0), reinterpret_tensor(primals_6, (64, 4), (1, 64), 0),
            alpha=1, beta=1, out=buf23)
        del primals_7
        buf24 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        triton_poi_fused_stack_6[grid(256)](buf5, buf11, buf17, buf23,
            buf24, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf11
        del buf17
        del buf23
        del buf5
    return (reinterpret_tensor(buf24, (4, 4, 4, 4), (64, 16, 4, 1), 0),
        reinterpret_tensor(buf0, (16, 4), (4, 1), 0), reinterpret_tensor(
        buf2, (16, 32), (32, 1), 0), reinterpret_tensor(buf4, (16, 64), (64,
        1), 0), reinterpret_tensor(buf6, (16, 4), (4, 1), 0),
        reinterpret_tensor(buf8, (16, 32), (32, 1), 0), reinterpret_tensor(
        buf10, (16, 64), (64, 1), 0), reinterpret_tensor(buf12, (16, 4), (4,
        1), 0), reinterpret_tensor(buf14, (16, 32), (32, 1), 0),
        reinterpret_tensor(buf16, (16, 64), (64, 1), 0), reinterpret_tensor
        (buf18, (16, 4), (4, 1), 0), reinterpret_tensor(buf20, (16, 32), (
        32, 1), 0), reinterpret_tensor(buf22, (16, 64), (64, 1), 0),
        primals_6, buf25, primals_4, buf26, buf27, buf28, buf29, buf30,
        buf31, buf32)


class EncoderNetNew(nn.Module):

    def __init__(self, pedestrian_num, input_size, hidden_size):
        super(EncoderNetNew, self).__init__()
        self.pedestrian_num = pedestrian_num
        self.input_size = input_size
        self.hidden_size = hidden_size
        hidden1_size = 32
        hidden2_size = 64
        self.fc1 = torch.nn.Linear(input_size, hidden1_size)
        self.fc2 = torch.nn.Linear(hidden1_size, hidden2_size)
        self.fc3 = torch.nn.Linear(hidden2_size, hidden_size)

    def forward(self, input_0):
        primals_2 = self.fc1.weight
        primals_3 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_6 = self.fc3.weight
        primals_7 = self.fc3.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.032103