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.triton_helpers import libdevice
import numpy as np
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_add_mul_sigmoid_sub_tanh_0(in_out_ptr0, in_out_ptr1,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7,
    in_ptr8, 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_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_out_ptr1 + x2, xmask)
    tmp9 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr4 + x2, xmask)
    tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr6 + x2, xmask)
    tmp17 = tl.load(in_ptr7 + x2, xmask)
    tmp21 = tl.load(in_ptr8 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = tl.sigmoid(tmp6)
    tmp10 = tmp8 + tmp9
    tmp13 = tmp11 + tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = tl.sigmoid(tmp14)
    tmp18 = tmp7 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = libdevice.tanh(tmp19)
    tmp22 = tmp21 - tmp20
    tmp23 = tmp15 * tmp22
    tmp24 = tmp20 + tmp23
    tl.store(in_out_ptr0 + x2, tmp7, xmask)
    tl.store(in_out_ptr1 + x2, tmp15, xmask)
    tl.store(out_ptr0 + x2, tmp24, 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, primals_14) = 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, 4), (64, 16, 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 4), (4, 1))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (4, 4), (4, 1))
    assert_size_stride(primals_12, (4,), (1,))
    assert_size_stride(primals_13, (4, 4), (4, 1))
    assert_size_stride(primals_14, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_6, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf2)
        del primals_7
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_6, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_9, (4, 4), (1, 4), 0), out=buf3)
        del primals_9
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_12, reinterpret_tensor(primals_3, (64,
            4), (4, 1), 0), reinterpret_tensor(primals_11, (4, 4), (1, 4), 
            0), alpha=1, beta=1, out=buf4)
        del primals_11
        del primals_12
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_14, reinterpret_tensor(primals_6, (64,
            4), (4, 1), 0), reinterpret_tensor(primals_13, (4, 4), (1, 4), 
            0), alpha=1, beta=1, out=buf5)
        del primals_13
        del primals_14
        buf6 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        buf7 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf2
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_sigmoid_sub_tanh_0[grid(256)](buf6, buf7,
            primals_2, buf1, primals_5, primals_8, buf3, primals_10, buf4,
            buf5, primals_6, buf8, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf1
        del buf3
        del primals_10
        del primals_2
        del primals_5
        del primals_8
    return buf8, primals_6, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf4, buf5, buf6, buf7


class CustomGruCellNew(nn.Module):
    """
    A forward only GRU cell.
    Input should be: (sequence length x batch size x input_size).
    The output is the output of the final forward call.
    It's not clear if it would be possible to use the output from each cell in a Plan
    because of the assumptions of 2D tensors in backprop.
    """

    def __init__(self, input_size, hidden_size, bias=True):
        super(CustomGruCellNew, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.fc_ir = nn.Linear(input_size, hidden_size, bias=bias)
        self.fc_hr = nn.Linear(hidden_size, hidden_size, bias=bias)
        self.fc_iz = nn.Linear(input_size, hidden_size, bias=bias)
        self.fc_hz = nn.Linear(hidden_size, hidden_size, bias=bias)
        self.fc_in = nn.Linear(input_size, hidden_size, bias=bias)
        self.fc_hn = nn.Linear(hidden_size, hidden_size, bias=bias)
        self.init_parameters()

    def init_parameters(self):
        std = 1.0 / np.sqrt(self.hidden_size)
        for w in self.parameters():
            w.data.uniform_(-std, std)

    def forward(self, input_0, input_1):
        primals_1 = self.fc_ir.weight
        primals_2 = self.fc_ir.bias
        primals_4 = self.fc_hr.weight
        primals_5 = self.fc_hr.bias
        primals_7 = self.fc_iz.weight
        primals_8 = self.fc_iz.bias
        primals_9 = self.fc_hz.weight
        primals_10 = self.fc_hz.bias
        primals_11 = self.fc_in.weight
        primals_12 = self.fc_in.bias
        primals_13 = self.fc_hn.weight
        primals_14 = self.fc_hn.bias
        primals_3 = input_0
        primals_6 = input_1
        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, primals_14])
        return output[0]


Measured latency_ms: 0.014972