# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/7u/c7uhkvgwxg2by2iylt2a32rce6cpfi4djnsc523l63tuabi6td67.py
# Topologically Sorted Source Nodes: [y], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   y => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%convolution, %primals_4], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    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
    tmp11 = tl.full([1], 8, tl.int64)
    tmp12 = tmp0 < tmp11
    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)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/32/c32v7egt4mupqssam3gmac2qgv3ujprjybthsgweflmot256qqw7.py
# Topologically Sorted Source Nodes: [i], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   i => convolution_1
# Graph fragment:
#   %convolution_1 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%cat, %primals_5, %primals_6, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/4y/c4y524dityt5y7bit2dd7suaguohzd3ocgsyslukjc5qd6obcjsc.py
# Topologically Sorted Source Nodes: [ci, cf, mul, hardtanh, mul_1, cc, co, hardtanh_1, ch], Original ATen: [aten.sigmoid, aten.mul, aten.hardtanh, aten.add]
# Source node to ATen node mapping:
#   cc => add
#   cf => sigmoid_1
#   ch => mul_2
#   ci => sigmoid
#   co => sigmoid_2
#   hardtanh => clamp_max, clamp_min
#   hardtanh_1 => clamp_max_1, clamp_min_1
#   mul => mul
#   mul_1 => mul_1
# Graph fragment:
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%convolution_3,), kwargs = {})
#   %sigmoid_1 : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%convolution_4,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_1, %primals_10), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%convolution_5, 0.0), kwargs = {})
#   %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 6.0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %clamp_max), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %sigmoid_2 : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%convolution_6,), kwargs = {})
#   %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add, 0.0), kwargs = {})
#   %clamp_max_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_1, 6.0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_2, %clamp_max_1), kwargs = {})
triton_poi_fused_add_hardtanh_mul_sigmoid_2 = async_compile.triton('triton_poi_fused_add_hardtanh_mul_sigmoid_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_hardtanh_mul_sigmoid_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        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)
        # Topologically Sorted Source Nodes: [y], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(buf0, primals_2, primals_4, buf1, 512, grid=grid(512), stream=stream0)
        del primals_2
        del primals_4
        # Topologically Sorted Source Nodes: [i], Original ATen: [aten.convolution]
        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  # reuse
        # Topologically Sorted Source Nodes: [i], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf3, primals_6, 256, grid=grid(256), stream=stream0)
        del primals_6
        # Topologically Sorted Source Nodes: [b], Original ATen: [aten.convolution]
        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))
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        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))
        # Topologically Sorted Source Nodes: [conv2d_4], Original ATen: [aten.convolution]
        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))
        # Topologically Sorted Source Nodes: [conv2d_5], Original ATen: [aten.convolution]
        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))
        # Topologically Sorted Source Nodes: [conv2d_6], Original ATen: [aten.convolution]
        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  # reuse
        buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [ci, cf, mul, hardtanh, mul_1, cc, co, hardtanh_1, ch], Original ATen: [aten.sigmoid, aten.mul, aten.hardtanh, aten.add]
        triton_poi_fused_add_hardtanh_mul_sigmoid_2.run(buf6, primals_10, buf5, buf7, buf9, buf8, buf10, 256, grid=grid(256), stream=stream0)
    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, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 1, 3, 3), (9, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 8, 1, 1), (8, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, 1, 3, 3), (9, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: 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 print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import logging
import torch
import torch.nn as nn
from torch.autograd import Variable


class BottleneckLSTMCell(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(BottleneckLSTMCell, 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 forward(self, x, h, c):
        """
        Arguments:
            x : input tensor
            h : hidden state tensor
            c : cell state tensor
        Returns:
            output tensor after LSTM cell
        """
        x = self.W(x)
        y = torch.cat((x, h), 1)
        i = self.Wy(y)
        b = self.Wi(i)
        ci = torch.sigmoid(self.Wbi(b))
        cf = torch.sigmoid(self.Wbf(b))
        cc = cf * c + ci * self.relu(self.Wbc(b))
        co = torch.sigmoid(self.Wbo(b))
        ch = co * self.relu(cc)
        return ch, cc

    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 get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand(
        [4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'input_channels': 4, 'hidden_channels': 4, 'device': 0}]

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]
