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
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_threshold_backward_0(in_out_ptr0, in_ptr0,
    out_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 % 2
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_div_log_mul_neg_pow_sub_sum_tanh_1(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
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp2 = tl.load(in_ptr1 + (x0 + 64 * x1), xmask)
    tmp15 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp17 = tl.load(in_ptr1 + (16 + x0 + 64 * x1), xmask)
    tmp27 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp29 = tl.load(in_ptr1 + (32 + x0 + 64 * x1), xmask)
    tmp39 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp41 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask)
    tmp1 = libdevice.tanh(tmp0)
    tmp3 = 4.0
    tmp4 = tmp2 * tmp3
    tmp5 = tmp1 + tmp4
    tmp6 = tmp5 - tmp1
    tmp7 = tmp6 * tmp6
    tmp8 = -tmp7
    tmp9 = 0.03125
    tmp10 = tmp8 * tmp9
    tmp11 = 1.3862943649291992
    tmp12 = tmp10 - tmp11
    tmp13 = 0.9189385332046727
    tmp14 = tmp12 - tmp13
    tmp16 = libdevice.tanh(tmp15)
    tmp18 = tmp17 * tmp3
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 - tmp16
    tmp21 = tmp20 * tmp20
    tmp22 = -tmp21
    tmp23 = tmp22 * tmp9
    tmp24 = tmp23 - tmp11
    tmp25 = tmp24 - tmp13
    tmp26 = tmp14 + tmp25
    tmp28 = libdevice.tanh(tmp27)
    tmp30 = tmp29 * tmp3
    tmp31 = tmp28 + tmp30
    tmp32 = tmp31 - tmp28
    tmp33 = tmp32 * tmp32
    tmp34 = -tmp33
    tmp35 = tmp34 * tmp9
    tmp36 = tmp35 - tmp11
    tmp37 = tmp36 - tmp13
    tmp38 = tmp26 + tmp37
    tmp40 = libdevice.tanh(tmp39)
    tmp42 = tmp41 * tmp3
    tmp43 = tmp40 + tmp42
    tmp44 = tmp43 - tmp40
    tmp45 = tmp44 * tmp44
    tmp46 = -tmp45
    tmp47 = tmp46 * tmp9
    tmp48 = tmp47 - tmp11
    tmp49 = tmp48 - tmp13
    tmp50 = tmp38 + tmp49
    tl.store(out_ptr0 + x2, tmp50, xmask)


@triton.jit
def triton_poi_fused_add_clamp_mul_tanh_2(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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp2 = tl.load(in_ptr1 + x0, xmask)
    tmp1 = libdevice.tanh(tmp0)
    tmp3 = 4.0
    tmp4 = tmp2 * tmp3
    tmp5 = tmp1 + tmp4
    tmp6 = -1.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = 1.0
    tmp9 = triton_helpers.minimum(tmp7, tmp8)
    tl.store(out_ptr0 + x0, tmp9, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (2, 4), (4, 1))
    assert_size_stride(primals_3, (2,), (1,))
    assert_size_stride(primals_4, (4, 2), (2, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 2), (2, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 2), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 2), (32, 8, 2, 1), 0)
        del buf0
        buf8 = empty_strided_cuda((4, 4, 4, 2), (32, 8, 2, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(128)](buf1,
            primals_3, buf8, 128, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 2), (
            2, 1), 0), reinterpret_tensor(primals_4, (2, 4), (1, 2), 0),
            alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf4 = torch.ops.aten.normal_functional.default(buf3)
        buf5 = buf4
        del buf4
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_div_log_mul_neg_pow_sub_sum_tanh_1[grid(64)](buf2,
            buf5, buf6, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf7 = buf3
        del buf3
        triton_poi_fused_add_clamp_mul_tanh_2[grid(256)](buf2, buf5, buf7, 
            256, XBLOCK=128, num_warps=4, num_stages=1)
    return buf6, buf7, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (64, 2), (2, 1), 0
        ), buf2, buf5, primals_4, buf8


class location_networkNew(nn.Module):
    """
    Uses the internal state `h_t` of the core network to
    produce the location coordinates `l_t` for the next
    time step.

    Concretely, feeds the hidden state `h_t` through a fc
    layer followed by a tanh to clamp the output beween
    [-1, 1]. This produces a 2D vector of means used to
    parametrize a two-component Gaussian with a fixed
    variance from which the location coordinates `l_t`
    for the next time step are sampled.

    Hence, the location `l_t` is chosen stochastically
    from a distribution conditioned on an affine
    transformation of the hidden state vector `h_t`.

    Args
    ----
    - input_size: input size of the fc layer.
    - output_size: output size of the fc layer.
    - std: standard deviation of the normal distribution.
    - h_t: the hidden state vector of the core network for
      the current time step `t`.

    Returns
    -------
    - mu: a 2D vector of shape (B, 2).
    - l_t: a 2D vector of shape (B, 2).
    """

    def __init__(self, input_size, output_size, std):
        super().__init__()
        self.std = std
        hid_size = input_size // 2
        self.fc = nn.Linear(input_size, hid_size)
        self.fc_lt = nn.Linear(hid_size, output_size)

    def forward(self, input_0):
        primals_2 = self.fc.weight
        primals_3 = self.fc.bias
        primals_4 = self.fc_lt.weight
        primals_5 = self.fc_lt.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0], output[1]


Measured latency_ms: 0.07185