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
from torch import nn
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 = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 32
    x1 = xindex // 32
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 16, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (16 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 32, tl.int64)
    tmp9 = tl.load(in_ptr1 + (16 * x1 + (-16 + 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_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 128
    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')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, 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), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (32, 32), (32, 1))
    assert_size_stride(primals_4, (32,), (1,))
    assert_size_stride(primals_5, (32, 32), (32, 1))
    assert_size_stride(primals_6, (32,), (1,))
    assert_size_stride(primals_7, (1, 32), (32, 1))
    assert_size_stride(primals_8, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(128)](primals_1, primals_2, buf0, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(primals_3, (32, 32), (1,
            32), 0), out=buf1)
        del primals_3
        buf2 = buf1
        del buf1
        triton_poi_fused_relu_1[grid(128)](buf2, primals_4, 128, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(primals_5, (32, 32), (1,
            32), 0), out=buf3)
        buf4 = buf3
        del buf3
        triton_poi_fused_relu_1[grid(128)](buf4, primals_6, 128, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_6
        buf6 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_8, buf4, reinterpret_tensor(primals_7,
            (32, 1), (1, 32), 0), alpha=1, beta=1, out=buf6)
        del primals_8
    return buf6, buf0, buf2, buf4, primals_7, primals_5


class MADDPGCritic3New(nn.Module):
    """
	Critic which takes observation-action pairs of all agents and returns one q value for all
	"""

    def __init__(self, n_agents: 'int', act_dim: 'int', obs_dim: 'int',
        history: 'int'=0, hidden_dim: 'int'=32):
        super(MADDPGCritic3New, self).__init__()
        in_features = n_agents * ((history + 1) * obs_dim + act_dim)
        self.linear1 = nn.Linear(in_features=in_features, out_features=
            hidden_dim)
        self.linear2 = nn.Linear(in_features=hidden_dim, out_features=
            hidden_dim)
        self.linear3 = nn.Linear(in_features=hidden_dim, out_features=1)
        self.activation = nn.ReLU()

    def forward(self, input_0, input_1):
        primals_3 = self.linear1.weight
        primals_4 = self.linear1.bias
        primals_5 = self.linear2.weight
        primals_6 = self.linear2.bias
        primals_7 = self.linear3.weight
        primals_8 = self.linear3.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: 0.063014