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_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 12800
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 200
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 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(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 6400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x0 = xindex % 100
    x2 = xindex % 1600
    x3 = xindex // 1600
    tmp0 = tl.load(in_out_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr0 + 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(in_out_ptr0 + x4, tmp4, xmask)
    tl.store(out_ptr0 + (x2 + 1664 * x3), tmp6, 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, (200, 4), (4, 1))
    assert_size_stride(primals_2, (200,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (100, 200), (200, 1))
    assert_size_stride(primals_5, (100,), (1,))
    assert_size_stride(primals_6, (1, 100), (100, 1))
    assert_size_stride(primals_7, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 200), (200, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 200), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 200), (3200, 800, 200, 1), 0)
        del buf0
        buf7 = empty_strided_cuda((4, 4, 4, 200), (3200, 800, 200, 1),
            torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(12800)](buf1,
            primals_2, buf7, 12800, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 100), (100, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 200), (200, 1), 0),
            reinterpret_tensor(primals_4, (200, 100), (1, 200), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 100), (1600, 400, 100, 1), 0)
        del buf2
        buf6 = empty_strided_cuda((4, 4, 4, 100), (1664, 400, 100, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(6400)](buf3,
            primals_5, buf6, 6400, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf5 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 100),
            (100, 1), 0), reinterpret_tensor(primals_6, (100, 1), (1, 100),
            0), alpha=1, beta=1, out=buf5)
        del primals_7
    return reinterpret_tensor(buf5, (4, 4, 4, 1), (16, 4, 1, 1), 0
        ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (64, 200), (200, 1), 0
        ), reinterpret_tensor(buf3, (64, 100), (100, 1), 0
        ), primals_6, buf6, primals_4, buf7


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


class VNetNew(nn.Module):

    def __init__(self, observation_space, h1=200, h2=100):
        super(VNetNew, self).__init__()
        self.fc1 = nn.Linear(observation_space.shape[0], h1)
        self.fc2 = nn.Linear(h1, h2)
        self.output_layer = nn.Linear(h2, 1)
        self.apply(weight_init)

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


Measured latency_ms: 0.096153