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._inductor.runtime.triton_helpers import math as tl_math
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_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    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_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, 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 % 64
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (64 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (128 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (192 + x0), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, 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 % 64
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (64 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (128 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (192 + x0), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, 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, (200, 200), (200, 1))
    assert_size_stride(primals_5, (200,), (1,))
    assert_size_stride(primals_6, (4, 200), (200, 1))
    assert_size_stride(primals_7, (4,), (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 = empty_strided_cuda((4, 4, 4, 200), (3200, 800, 200, 1),
            torch.bool)
        buf2 = empty_strided_cuda((4, 4, 4, 200), (3200, 800, 200, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_leaky_relu_0[grid(12800)](buf0, primals_2, buf1,
            buf2, 12800, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
        buf3 = buf0
        del buf0
        extern_kernels.mm(reinterpret_tensor(buf2, (64, 200), (200, 1), 0),
            reinterpret_tensor(primals_4, (200, 200), (1, 200), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4, 200), (3200, 800, 200, 1),
            torch.bool)
        buf5 = empty_strided_cuda((4, 4, 4, 200), (3200, 800, 200, 1),
            torch.float32)
        triton_poi_fused_leaky_relu_0[grid(12800)](buf3, primals_5, buf4,
            buf5, 12800, XBLOCK=256, num_warps=4, num_stages=1)
        del buf3
        del primals_5
        buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf5, (64, 200),
            (200, 1), 0), reinterpret_tensor(primals_6, (200, 4), (1, 200),
            0), alpha=1, beta=1, out=buf6)
        del primals_7
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(256)](buf6, buf7, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf8 = reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf6
        triton_poi_fused__softmax_2[grid(256)](buf7, buf8, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf7
    return buf8, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf1, reinterpret_tensor(buf2, (64, 200), (200, 1), 0
        ), buf4, reinterpret_tensor(buf5, (64, 200), (200, 1), 0
        ), buf8, primals_6, primals_4


class DiscretePolicyFunctionNew(nn.Module):
    """fully connected 200x200 hidden layers"""

    def __init__(self, state_dim, action_dim):
        super(DiscretePolicyFunctionNew, self).__init__()
        self.fc1 = nn.Linear(state_dim, 200)
        self.fc2 = nn.Linear(200, 200)
        self.out = nn.Linear(200, action_dim)

    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.out.weight
        primals_7 = self.out.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: 0.072676