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
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_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    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)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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 // 4) + x0), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + x0, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp15 = tl.load(in_ptr2 + (4 * x1 + (-4 + x0)), tmp12 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr3 + (-4 + x0), tmp12 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp17 = tmp15 + tmp16
    tmp18 = triton_helpers.maximum(tmp8, tmp17)
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp12, tmp18, tmp19)
    tmp21 = tl.where(tmp4, tmp11, tmp20)
    tl.store(out_ptr0 + x2, tmp21, xmask)


@triton.jit
def triton_poi_fused_relu_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    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)


@triton.jit
def triton_poi_fused_relu_threshold_backward_3(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, 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 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_4(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, 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 + x2, tmp6, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 16), (16, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 8), (8, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4, 4), (4, 1))
    assert_size_stride(primals_11, (4,), (1,))
    assert_size_stride(primals_12, (4, 4), (4, 1))
    assert_size_stride(primals_13, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 16), (16, 1), 
            0), reinterpret_tensor(primals_2, (16, 4), (1, 16), 0), out=buf0)
        del primals_2
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(64)](buf1, primals_3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (4, 4), (1, 4
            ), 0), out=buf2)
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf3)
        del primals_6
        buf4 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
        triton_poi_fused_cat_1[grid(512)](buf2, primals_5, buf3, primals_7,
            buf4, 512, XBLOCK=128, num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf4, reinterpret_tensor(primals_8, (8, 4), (1, 8
            ), 0), out=buf5)
        buf6 = buf5
        del buf5
        triton_poi_fused_relu_2[grid(256)](buf6, primals_9, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_9
        buf7 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf6, reinterpret_tensor(primals_10, (4, 4), (1, 
            4), 0), out=buf7)
        buf8 = buf7
        del buf7
        triton_poi_fused_relu_2[grid(256)](buf8, primals_11, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_11
        buf9 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_13, buf8, reinterpret_tensor(
            primals_12, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf9)
        del primals_13
        buf10 = empty_strided_cuda((64, 4), (4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_3[grid(256)](buf3,
            primals_7, buf10, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf3
        del primals_7
        buf11 = empty_strided_cuda((16, 4), (4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_4[grid(64)](buf2,
            primals_5, buf11, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf2
        del primals_5
    return (reinterpret_tensor(buf9, (16, 4, 4), (16, 4, 1), 0), primals_1,
        buf1, buf4, buf6, buf8, primals_12, primals_10, primals_8, buf10,
        buf11, primals_4)


def init_weights(layer, gain):
    for p in layer.parameters():
        if len(p.data.shape) >= 2:
            nn.init.orthogonal_(p, gain=gain)
        else:
            p.data.zero_()


def all_init_weights(m, gain=2 ** 0.5):
    init_weights(m, gain)


class CriticMlpNew(nn.Module):

    def __init__(self, obs_size, n_agent, n_action, global_encode_size,
        local_encode_size, fc1_size, fc2_size):
        super(CriticMlpNew, self).__init__()
        self.obs_size = obs_size
        self.n_agent = n_agent
        self.n_action = n_action
        self.global_encode_fc1 = nn.Linear(obs_size * n_agent,
            global_encode_size)
        self.global_encode_fc2 = nn.Linear(global_encode_size,
            global_encode_size)
        self.local_encode_fc = nn.Linear(obs_size, local_encode_size)
        self.fc1 = nn.Linear(global_encode_size + local_encode_size, fc1_size)
        self.fc2 = nn.Linear(fc1_size, fc2_size)
        self.fc3 = nn.Linear(fc2_size, n_action)
        self.apply(all_init_weights)
        init_weights(self.fc3, gain=1)

    def forward(self, input_0):
        primals_2 = self.global_encode_fc1.weight
        primals_3 = self.global_encode_fc1.bias
        primals_4 = self.global_encode_fc2.weight
        primals_5 = self.global_encode_fc2.bias
        primals_6 = self.local_encode_fc.weight
        primals_7 = self.local_encode_fc.bias
        primals_8 = self.fc1.weight
        primals_9 = self.fc1.bias
        primals_10 = self.fc2.weight
        primals_11 = self.fc2.bias
        primals_12 = self.fc3.weight
        primals_13 = self.fc3.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13])
        return output[0]


Measured latency_ms: 0.126468