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
from torch.nn.init import kaiming_uniform_
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_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 32000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x0 = xindex % 500
    x2 = xindex // 2000
    x3 = xindex % 2000
    tmp0 = tl.load(in_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x3 + 2016 * x2), tmp4, xmask)
    tl.store(out_ptr1 + (x3 + 2048 * x2), tmp6, xmask)


@triton.jit
def triton_poi_fused_relu_view_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 32000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 500
    x1 = xindex // 500
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 500 * (x1 % 4) + 2016 * (x1 // 4)), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (500, 8), (8, 1))
    assert_size_stride(primals_4, (500,), (1,))
    assert_size_stride(primals_5, (500, 500), (500, 1))
    assert_size_stride(primals_6, (500,), (1,))
    assert_size_stride(primals_7, (4, 500), (500, 1))
    assert_size_stride(primals_8, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(512)](primals_1, primals_2, buf0, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((64, 500), (500, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 8), (8, 1), 0),
            reinterpret_tensor(primals_3, (8, 500), (1, 8), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4, 4, 500), (8064, 2016, 500, 1),
            torch.float32)
        buf9 = empty_strided_cuda((4, 4, 4, 500), (8192, 2048, 500, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(32000)](buf1,
            primals_4, buf2, buf9, 32000, XBLOCK=256, num_warps=4, num_stages=1
            )
        del primals_4
        buf3 = buf1
        del buf1
        triton_poi_fused_relu_view_2[grid(32000)](buf2, buf3, 32000, XBLOCK
            =256, num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((64, 500), (500, 1), torch.float32)
        extern_kernels.mm(buf3, reinterpret_tensor(primals_5, (500, 500), (
            1, 500), 0), out=buf4)
        buf5 = buf2
        del buf2
        buf8 = empty_strided_cuda((4, 4, 4, 500), (8192, 2048, 500, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(32000)](buf4,
            primals_6, buf5, buf8, 32000, XBLOCK=256, num_warps=4, num_stages=1
            )
        del primals_6
        buf6 = buf4
        del buf4
        triton_poi_fused_relu_view_2[grid(32000)](buf5, buf6, 32000, XBLOCK
            =256, num_warps=4, num_stages=1)
        del buf5
        buf7 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_8, buf6, reinterpret_tensor(primals_7,
            (500, 4), (1, 500), 0), alpha=1, beta=1, out=buf7)
        del primals_8
    return reinterpret_tensor(buf7, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(buf0, (64, 8), (8, 1), 0
        ), buf3, buf6, primals_7, buf8, primals_5, buf9


def weight_init(m):
    if m.__class__.__name__ == 'Linear':
        m.weight.data.copy_(kaiming_uniform_(m.weight.data))
        m.bias.data.fill_(0)


class ModelNetNew(nn.Module):

    def __init__(self, observation_space, action_space, h1=500, h2=500):
        super(ModelNetNew, self).__init__()
        self.fc1 = nn.Linear(observation_space.shape[0] + action_space.
            shape[0], h1)
        self.fc2 = nn.Linear(h1, h2)
        self.output_layer = nn.Linear(h2, observation_space.shape[0])
        self.fc1.apply(weight_init)
        self.fc2.apply(weight_init)
        self.output_layer.apply(weight_init)

    def forward(self, input_0, input_1):
        primals_3 = self.fc1.weight
        primals_4 = self.fc1.bias
        primals_5 = self.fc2.weight
        primals_6 = self.fc2.bias
        primals_7 = self.output_layer.weight
        primals_8 = self.output_layer.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0]


Measured latency_ms: 0.098504