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 logging
import torch.nn as nn
from torch.autograd import Variable
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 8
    x0 = xindex % 16
    x2 = xindex // 128
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tl.load(in_ptr1 + x1, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp13 = tl.load(in_ptr2 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp10 &
        xmask, other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + x3, tmp14, xmask)


@triton.jit
def triton_poi_fused_convolution_1(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
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_hardtanh_mul_sigmoid_2(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, out_ptr1, 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)
    tmp4 = tl.load(in_ptr2 + x0, xmask)
    tmp6 = tl.load(in_ptr3 + x0, xmask)
    tmp13 = tl.load(in_ptr4 + x0, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp5 = tl.sigmoid(tmp4)
    tmp7 = 0.0
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = 6.0
    tmp10 = triton_helpers.minimum(tmp8, tmp9)
    tmp11 = tmp5 * tmp10
    tmp12 = tmp3 + tmp11
    tmp14 = tl.sigmoid(tmp13)
    tmp15 = triton_helpers.maximum(tmp12, tmp7)
    tmp16 = triton_helpers.minimum(tmp15, tmp9)
    tmp17 = tmp14 * tmp16
    tl.store(out_ptr0 + x0, tmp12, xmask)
    tl.store(out_ptr1 + x0, tmp17, 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
        ) = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 3, 3), (9, 9, 3, 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4, 8, 1, 1), (8, 1, 1, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_8, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_9, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_10, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_11, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_12, (4, 4, 1, 1), (4, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(512)](buf0, primals_2, primals_4, buf1,
            512, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        del primals_4
        buf2 = extern_kernels.convolution(buf1, primals_5, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_1[grid(256)](buf3, primals_6, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_6
        buf4 = extern_kernels.convolution(buf3, primals_7, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf4, (4, 4, 4, 4), (64, 16, 4, 1))
        buf5 = extern_kernels.convolution(buf4, primals_8, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 4, 4), (64, 16, 4, 1))
        buf6 = extern_kernels.convolution(buf4, primals_9, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
        buf7 = extern_kernels.convolution(buf4, primals_11, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 4, 4, 4), (64, 16, 4, 1))
        buf9 = extern_kernels.convolution(buf4, primals_12, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf9, (4, 4, 4, 4), (64, 16, 4, 1))
        buf8 = buf0
        del buf0
        buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_hardtanh_mul_sigmoid_2[grid(256)](buf6,
            primals_10, buf5, buf7, buf9, buf8, buf10, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
    return (buf10, buf8, primals_1, primals_3, primals_5, primals_7,
        primals_8, primals_9, primals_10, primals_11, primals_12, buf1,
        buf3, buf4, buf5, buf6, buf7, buf8, buf9)


class BottleneckLSTMCellNew(nn.Module):
    """ Creates a LSTM layer cell
    Arguments:
        input_channels : variable used to contain value of number of channels in input
        hidden_channels : variable used to contain value of number of channels in the hidden state of LSTM cell
    """

    def __init__(self, input_channels, hidden_channels, device):
        super(BottleneckLSTMCellNew, self).__init__()
        assert hidden_channels % 2 == 0
        self.device = device
        self.input_channels = int(input_channels)
        self.hidden_channels = int(hidden_channels)
        self.num_features = 4
        self.W = nn.Conv2d(in_channels=self.input_channels, out_channels=
            self.input_channels, kernel_size=3, groups=self.input_channels,
            stride=1, padding=1)
        self.Wy = nn.Conv2d(int(self.input_channels + self.hidden_channels),
            self.hidden_channels, kernel_size=1)
        self.Wi = nn.Conv2d(self.hidden_channels, self.hidden_channels, 3, 
            1, 1, groups=self.hidden_channels, bias=False)
        self.Wbi = nn.Conv2d(self.hidden_channels, self.hidden_channels, 1,
            1, 0, bias=False)
        self.Wbf = nn.Conv2d(self.hidden_channels, self.hidden_channels, 1,
            1, 0, bias=False)
        self.Wbc = nn.Conv2d(self.hidden_channels, self.hidden_channels, 1,
            1, 0, bias=False)
        self.Wbo = nn.Conv2d(self.hidden_channels, self.hidden_channels, 1,
            1, 0, bias=False)
        self.relu = nn.ReLU6()
        logging.info('Initializing weights of lstm')
        self._initialize_weights()

    def _initialize_weights(self):
        """
        Returns:
            initialized weights of the model
        """
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.xavier_uniform_(m.weight)
                if m.bias is not None:
                    m.bias.data.zero_()
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def init_hidden(self, batch_size, hidden, shape):
        """
        Arguments:
            batch_size : an int variable having value of batch size while training
            hidden : an int variable having value of number of channels in hidden state
            shape : an array containing shape of the hidden and cell state
            device : CPU or GPU id string device
        Returns:
            cell state and hidden state
        """
        if str(self.device) == 'cpu':
            return Variable(torch.zeros(batch_size, hidden, shape[0], shape[1])
                ), Variable(torch.zeros(batch_size, hidden, shape[0], shape[1])
                )
        else:
            return Variable(torch.zeros(batch_size, hidden, shape[0], shape[1])
                ), Variable(torch.zeros(batch_size, hidden, shape[0], shape[1])
                )

    def forward(self, input_0, input_1, input_2):
        primals_1 = self.W.weight
        primals_2 = self.W.bias
        primals_5 = self.Wy.weight
        primals_6 = self.Wy.bias
        primals_7 = self.Wi.weight
        primals_8 = self.Wbi.weight
        primals_9 = self.Wbf.weight
        primals_11 = self.Wbc.weight
        primals_12 = self.Wbo.weight
        primals_3 = input_0
        primals_4 = input_1
        primals_10 = input_2
        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])
        return output[0], output[1]


Measured latency_ms: 0.015081