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 libdevice, 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_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    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 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, xmask)


@triton.jit
def triton_poi_fused_arange_1(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = x0
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused__softmax_index_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp4 < 4')
    tmp6 = tl.load(in_ptr1 + (4 * tmp4 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp7 = tl.load(in_ptr1 + (1 + 4 * tmp4 + 16 * x0), xmask,
        eviction_policy='evict_last')
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = tl.load(in_ptr1 + (2 + 4 * tmp4 + 16 * x0), xmask,
        eviction_policy='evict_last')
    tmp10 = triton_helpers.maximum(tmp8, tmp9)
    tmp11 = tl.load(in_ptr1 + (3 + 4 * tmp4 + 16 * x0), xmask,
        eviction_policy='evict_last')
    tmp12 = triton_helpers.maximum(tmp10, tmp11)
    tmp13 = tmp6 - tmp12
    tmp14 = tl_math.exp(tmp13)
    tmp15 = tmp7 - tmp12
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp14 + tmp16
    tmp18 = tmp9 - tmp12
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp17 + tmp19
    tmp21 = tmp11 - tmp12
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp20 + tmp22
    tl.store(out_ptr0 + x0, tmp12, xmask)
    tl.store(out_ptr1 + x0, tmp23, xmask)


@triton.jit
def triton_poi_fused__softmax_index_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp4 < 4')
    tmp6 = tl.load(in_ptr1 + (x0 + 4 * tmp4 + 16 * x1), xmask)
    tmp8 = tmp6 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp11 = tmp9 / tmp10
    tl.store(out_ptr0 + x2, tmp11, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (16, 4), (4, 1))
    assert_size_stride(primals_5, (16,), (1,))
    assert_size_stride(primals_6, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 4),
            (1, 4), 0), out=buf0)
        del primals_1
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_tanh_0[grid(16)](buf1, primals_2, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf1, reinterpret_tensor(primals_4,
            (4, 16), (1, 4), 0), alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4,), (1,), torch.int64)
        triton_poi_fused_arange_1[grid(4)](buf3, 4, XBLOCK=4, num_warps=1,
            num_stages=1)
        buf4 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf5 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        triton_poi_fused__softmax_index_2[grid(4)](primals_6, buf2, buf4,
            buf5, 4, XBLOCK=4, num_warps=1, num_stages=1)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__softmax_index_3[grid(16)](primals_6, buf2, buf4,
            buf5, buf6, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf2
        del buf4
        del buf5
    return buf6, primals_3, primals_6, buf1, buf3, buf6, primals_4


class DIAYNActionModelNew(nn.Module):
    """The model that computes one score per action"""

    def __init__(self, n_observations, n_actions, n_hidden, n_policies):
        super().__init__()
        self.linear = nn.Linear(n_observations, n_hidden)
        self.linear2 = nn.Linear(n_hidden, n_actions * n_policies)
        self.n_policies = n_policies
        self.n_actions = n_actions

    def forward(self, input_0, input_1):
        primals_1 = self.linear.weight
        primals_2 = self.linear.bias
        primals_4 = self.linear2.weight
        primals_5 = self.linear2.bias
        primals_3 = input_0
        primals_6 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]


Measured latency_ms: 0.026457