import torch
import torch.nn.functional as func


class CriticNetwork(torch.nn.Module):

    def __init__(self, s_space, a_space):
        super(CriticNetwork, self).__init__()
        self.s_dense = torch.nn.Linear(s_space, 50)
        self.a_dense = torch.nn.Linear(a_space, 50)
        self.q_dense = torch.nn.Linear(50, 1)

    def forward(self, s, a):
        phi_s = self.s_dense(s)
        phi_a = self.a_dense(a)
        pre_q = func.relu(phi_s + phi_a)
        q_value = self.q_dense(pre_q)
        return q_value


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'s_space': 4, 'a_space': 4}]

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
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_add_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 3200
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 50
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = tl.full([1], 0, tl.int32)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp9 = 0.0
    tmp10 = tmp8 <= tmp9
    tl.store(in_out_ptr0 + x2, tmp8, xmask)
    tl.store(out_ptr0 + x2, tmp10, 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, (50, 4), (4, 1))
    assert_size_stride(primals_2, (50,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (50, 4), (4, 1))
    assert_size_stride(primals_5, (50,), (1,))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_7, (1, 50), (50, 1))
    assert_size_stride(primals_8, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 50), (50, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 50), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((64, 50), (50, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_6, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 50), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = reinterpret_tensor(buf0, (4, 4, 4, 50), (800, 200, 50, 1), 0)
        del buf0
        buf5 = empty_strided_cuda((4, 4, 4, 50), (800, 200, 50, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_add_relu_threshold_backward_0[grid(3200)](buf2,
            primals_2, buf1, primals_5, buf5, 3200, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf1
        del primals_2
        del primals_5
        buf4 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_8, reinterpret_tensor(buf2, (64, 50),
            (50, 1), 0), reinterpret_tensor(primals_7, (50, 1), (1, 50), 0),
            alpha=1, beta=1, out=buf4)
        del primals_8
    return reinterpret_tensor(buf4, (4, 4, 4, 1), (16, 4, 1, 1), 0
        ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(primals_6, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf2, (64, 50), (50, 1), 0), primals_7, buf5


class CriticNetworkNew(torch.nn.Module):

    def __init__(self, s_space, a_space):
        super(CriticNetworkNew, self).__init__()
        self.s_dense = torch.nn.Linear(s_space, 50)
        self.a_dense = torch.nn.Linear(a_space, 50)
        self.q_dense = torch.nn.Linear(50, 1)

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