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 torch.nn as nn
from torch.autograd import Variable
from math import sqrt as sqrt
from itertools import product as product
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, 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 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp6 & xmask,
        other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 128 * x2), tmp6 & xmask,
        other=0.0)
    tmp10 = tl.load(in_ptr2 + (-4 + x1), tmp6 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tmp9 + tmp10
    tmp12 = tl.sigmoid(tmp11)
    tmp13 = tl.load(in_ptr3 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp6 & xmask,
        other=0.0)
    tmp14 = tmp12 * tmp13
    tmp15 = tl.full(tmp14.shape, 0.0, tmp14.dtype)
    tmp16 = tl.where(tmp6, tmp14, tmp15)
    tmp17 = tl.where(tmp4, tmp5, tmp16)
    tl.store(out_ptr0 + x3, tmp17, xmask)


@triton.jit
def triton_poi_fused_add_convolution_mul_rsub_sigmoid_tanh_2(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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
    x2 = xindex // 64
    x4 = xindex % 64
    x1 = xindex // 16 % 4
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (64 + x4 + 128 * x2), xmask)
    tmp1 = tl.load(in_ptr1 + (4 + x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_out_ptr0 + x3, xmask)
    tmp5 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr3 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tmp6 = tmp4 + tmp5
    tmp8 = tmp3 * tmp7
    tmp9 = 1.0
    tmp10 = tmp9 - tmp3
    tmp11 = libdevice.tanh(tmp6)
    tmp12 = tmp10 * tmp11
    tmp13 = tmp8 + tmp12
    tl.store(out_ptr0 + x3, tmp3, xmask)
    tl.store(in_out_ptr0 + x3, tmp6, xmask)
    tl.store(out_ptr1 + x3, tmp13, xmask)


@triton.jit
def triton_poi_fused_sigmoid_sigmoid_backward_3(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
    x2 = xindex // 64
    x3 = xindex % 64
    x1 = xindex // 16 % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + 128 * x2), xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tmp4 = 1.0
    tmp5 = tmp4 - tmp3
    tmp6 = tmp3 * tmp5
    tl.store(out_ptr0 + x4, tmp6, 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (8, 8, 3, 3), (72, 9, 3, 1))
    assert_size_stride(primals_4, (8,), (1,))
    assert_size_stride(primals_5, (4, 8, 3, 3), (72, 9, 3, 1))
    assert_size_stride(primals_6, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(512)](primals_2, primals_1, buf0, 512,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 8, 4, 4), (128, 16, 4, 1))
        buf3 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        triton_poi_fused_cat_1[grid(512)](primals_2, buf1, primals_4,
            primals_1, buf3, 512, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf4 = extern_kernels.convolution(buf3, primals_5, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 4, 4, 4), (64, 16, 4, 1))
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf5 = buf4
        del buf4
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_convolution_mul_rsub_sigmoid_tanh_2[grid(256)](
            buf5, buf1, primals_4, primals_6, primals_1, buf2, buf6, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_6
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_sigmoid_sigmoid_backward_3[grid(256)](buf1,
            primals_4, buf7, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf1
        del primals_4
    return buf6, primals_1, primals_3, primals_5, buf0, buf2, buf3, buf5, buf7


class CGRU_cellNew(nn.Module):
    """Initialize a basic Conv GRU cell.
    Args:
      filter_size: int that is the height and width of the filters
      num_features: int thats the num of channels of the states, like hidden_size
    """

    def __init__(self, input_chans, filter_size, num_features):
        super(CGRU_cellNew, self).__init__()
        self.input_chans = input_chans
        self.filter_size = filter_size
        self.num_features = num_features
        self.padding = int((filter_size - 1) / 2)
        self.ConvGates = nn.Conv2d(self.input_chans + self.num_features, 2 *
            self.num_features, 3, padding=self.padding)
        self.Conv_ct = nn.Conv2d(self.input_chans + self.num_features, self
            .num_features, 3, padding=self.padding)

    def init_hidden(self, input):
        feature_size = input.size()[-2:]
        return Variable(torch.zeros(input.size(0), self.num_features,
            feature_size[0], feature_size[1]))

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


Measured latency_ms: 0.081861