# 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/yr/cyrsfiqcqep5ianv6lfxy43inavacsrots2hnf6qyokhutlu5ocy.py
# Topologically Sorted Source Nodes: [h_prev], Original ATen: [aten.new_zeros]
# Source node to ATen node mapping:
#   h_prev => full_default
# Graph fragment:
#   %full_default : [num_users=4] = call_function[target=torch.ops.aten.full.default](args = ([4, 4, 4, 4], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
triton_poi_fused_new_zeros_0 = async_compile.triton('triton_poi_fused_new_zeros_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_new_zeros_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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_new_zeros_0(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 = 0.0
    tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/qv/cqvqoxzqwduraftvkpveor26twbksdy4eroiqe5gkhhcqhmp4fly.py
# Topologically Sorted Source Nodes: [combined], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   combined => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %full_default], 1), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_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=[512], 
    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_cat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_1(in_ptr0, 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 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = 0.0
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp6, tmp9, tmp10)
    tmp12 = tl.where(tmp4, tmp5, tmp11)
    tl.store(out_ptr0 + (x3), tmp12, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/cx/ccxv3djgp4azaupyxj6k27iatx6fdzs4dd65zqaoeei7no4esmab.py
# Topologically Sorted Source Nodes: [conv2d, combined_conv], Original ATen: [aten.convolution, aten.sigmoid]
# Source node to ATen node mapping:
#   combined_conv => sigmoid
#   conv2d => convolution
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%cat, %primals_2, %primals_3, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_sigmoid_2 = async_compile.triton('triton_poi_fused_convolution_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=[512], 
    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_sigmoid_2', '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_sigmoid_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 16) % 8
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tl.store(in_out_ptr0 + (x3), tmp3, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/nn/cnn7cjpchplaegvrmuuug4x3kbotklqczshw3hbasnx2ijtf4yn3.py
# Topologically Sorted Source Nodes: [conv2d_1, conv2d_2, mul, add, h_, sub, mul_1, mul_2, h_cur], Original ATen: [aten.convolution, aten.mul, aten.add, aten.tanh, aten.rsub]
# Source node to ATen node mapping:
#   add => add
#   conv2d_1 => convolution_1
#   conv2d_2 => convolution_2
#   h_ => tanh
#   h_cur => add_1
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   sub => sub
# Graph fragment:
#   %convolution_1 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_1, %primals_4, %primals_5, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %convolution_2 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%full_default, %primals_6, %primals_7, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%getitem_1, %convolution_2), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_1, %mul), kwargs = {})
#   %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%add,), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %getitem), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %tanh), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%getitem, %full_default), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %mul_2), kwargs = {})
triton_poi_fused_add_convolution_mul_rsub_tanh_3 = async_compile.triton('triton_poi_fused_add_convolution_mul_rsub_tanh_3', '''
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_convolution_mul_rsub_tanh_3', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 6, '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_convolution_mul_rsub_tanh_3(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, 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
    x4 = xindex
    x1 = (xindex // 16) % 4
    x2 = (xindex // 64)
    x3 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + (x4), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr1 + (x4), xmask)
    tmp4 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr2 + (x3 + (128*x2)), xmask)
    tmp9 = tl.load(in_ptr2 + (64 + x3 + (128*x2)), xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp7 = 1.0
    tmp8 = tmp7 - tmp6
    tmp10 = tmp9 * tmp5
    tmp11 = tmp2 + tmp10
    tmp12 = libdevice.tanh(tmp11)
    tmp13 = tmp8 * tmp12
    tmp14 = 0.0
    tmp15 = tmp6 * tmp14
    tmp16 = tmp13 + tmp15
    tl.store(in_out_ptr0 + (x4), tmp2, xmask)
    tl.store(in_out_ptr1 + (x4), tmp5, xmask)
    tl.store(out_ptr0 + (x4), tmp8, xmask)
    tl.store(out_ptr1 + (x4), tmp16, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (8, 8, 3, 3), (72, 9, 3, 1))
    assert_size_stride(primals_3, (8, ), (1, ))
    assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h_prev], Original ATen: [aten.new_zeros]
        stream0 = get_raw_stream(0)
        triton_poi_fused_new_zeros_0.run(buf0, 256, grid=grid(256), stream=stream0)
        buf1 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [combined], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(primals_1, buf1, 512, grid=grid(512), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 8, 4, 4), (128, 16, 4, 1))
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [conv2d, combined_conv], Original ATen: [aten.convolution, aten.sigmoid]
        triton_poi_fused_convolution_sigmoid_2.run(buf3, primals_3, 512, grid=grid(512), stream=stream0)
        del primals_3
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(primals_1, primals_4, 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))
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf0, primals_6, stride=(1, 1), padding=(1, 1), 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))
        buf5 = buf4; del buf4  # reuse
        buf7 = buf6; del buf6  # reuse
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_1, conv2d_2, mul, add, h_, sub, mul_1, mul_2, h_cur], Original ATen: [aten.convolution, aten.mul, aten.add, aten.tanh, aten.rsub]
        triton_poi_fused_add_convolution_mul_rsub_tanh_3.run(buf5, buf7, primals_5, primals_7, buf3, buf8, buf9, 256, grid=grid(256), stream=stream0)
        del primals_5
        del primals_7
    return (buf9, primals_1, primals_2, primals_4, primals_6, buf0, buf1, buf3, reinterpret_tensor(buf3, (4, 4, 4, 4), (128, 16, 4, 1), 64), buf5, buf7, buf8, )


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, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((8, 8, 3, 3), (72, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
    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 torch
from torch import nn as nn
import torch.nn.functional as F


def one_param(m):
    """First parameter in `m`"""
    return next(m.parameters())


class ConvGRUCell(nn.Module):

    def __init__(self, input_dim, hidden_dim, kernel_size=(3, 3), bias=True,
        activation=F.tanh, batchnorm=False):
        """
        Initialize ConvGRU cell.
        Parameters
        ----------
        input_dim: int
            Number of channels of input tensor.
        hidden_dim: int
            Number of channels of hidden state.
        kernel_size: (int, int)
            Size of the convolutional kernel.
        bias: bool
            Whether or not to add the bias.
        """
        super().__init__()
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.kernel_size = kernel_size if isinstance(kernel_size, (tuple, list)
            ) else [kernel_size] * 2
        self.padding = self.kernel_size[0] // 2, self.kernel_size[1] // 2
        self.bias = bias
        self.activation = activation
        self.batchnorm = batchnorm
        self.conv_zr = nn.Conv2d(in_channels=self.input_dim + self.
            hidden_dim, out_channels=2 * self.hidden_dim, kernel_size=self.
            kernel_size, padding=self.padding, bias=self.bias)
        self.conv_h1 = nn.Conv2d(in_channels=self.input_dim, out_channels=
            self.hidden_dim, kernel_size=self.kernel_size, padding=self.
            padding, bias=self.bias)
        self.conv_h2 = nn.Conv2d(in_channels=self.hidden_dim, out_channels=
            self.hidden_dim, kernel_size=self.kernel_size, padding=self.
            padding, bias=self.bias)
        self.reset_parameters()

    def forward(self, input, h_prev=None):
        if h_prev is None:
            h_prev = self.init_hidden(input)
        combined = torch.cat((input, h_prev), dim=1)
        combined_conv = F.sigmoid(self.conv_zr(combined))
        z, r = torch.split(combined_conv, self.hidden_dim, dim=1)
        h_ = self.activation(self.conv_h1(input) + r * self.conv_h2(h_prev))
        h_cur = (1 - z) * h_ + z * h_prev
        return h_cur

    def init_hidden(self, input):
        bs, _ch, h, w = input.shape
        return one_param(self).new_zeros(bs, self.hidden_dim, h, w)

    def reset_parameters(self):
        nn.init.xavier_uniform_(self.conv_zr.weight, gain=nn.init.
            calculate_gain('tanh'))
        self.conv_zr.bias.data.zero_()
        nn.init.xavier_uniform_(self.conv_h1.weight, gain=nn.init.
            calculate_gain('tanh'))
        self.conv_h1.bias.data.zero_()
        nn.init.xavier_uniform_(self.conv_h2.weight, gain=nn.init.
            calculate_gain('tanh'))
        self.conv_h2.bias.data.zero_()
        if self.batchnorm:
            self.bn1.reset_parameters()
            self.bn2.reset_parameters()


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


def get_init_inputs():
    return [[], {'input_dim': 4, 'hidden_dim': 4}]

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
from torch import nn as nn
import torch.nn.functional as F
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_new_zeros_0(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 = 0.0
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, 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 = 0.0
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp6, tmp9, tmp10)
    tmp12 = tl.where(tmp4, tmp5, tmp11)
    tl.store(out_ptr0 + x3, tmp12, xmask)


@triton.jit
def triton_poi_fused_convolution_sigmoid_2(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 8
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tl.store(in_out_ptr0 + x3, tmp3, xmask)


@triton.jit
def triton_poi_fused_add_convolution_mul_rsub_tanh_3(in_out_ptr0,
    in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, 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
    x4 = xindex
    x1 = xindex // 16 % 4
    x2 = xindex // 64
    x3 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr1 + x4, xmask)
    tmp4 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr2 + (x3 + 128 * x2), xmask)
    tmp9 = tl.load(in_ptr2 + (64 + x3 + 128 * x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp7 = 1.0
    tmp8 = tmp7 - tmp6
    tmp10 = tmp9 * tmp5
    tmp11 = tmp2 + tmp10
    tmp12 = libdevice.tanh(tmp11)
    tmp13 = tmp8 * tmp12
    tmp14 = 0.0
    tmp15 = tmp6 * tmp14
    tmp16 = tmp13 + tmp15
    tl.store(in_out_ptr0 + x4, tmp2, xmask)
    tl.store(in_out_ptr1 + x4, tmp5, xmask)
    tl.store(out_ptr0 + x4, tmp8, xmask)
    tl.store(out_ptr1 + x4, tmp16, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (8, 8, 3, 3), (72, 9, 3, 1))
    assert_size_stride(primals_3, (8,), (1,))
    assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_new_zeros_0[grid(256)](buf0, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        triton_poi_fused_cat_1[grid(512)](primals_1, buf1, 512, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf2 = extern_kernels.convolution(buf1, primals_2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 8, 4, 4), (128, 16, 4, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_sigmoid_2[grid(512)](buf3, primals_3, 
            512, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
        buf4 = extern_kernels.convolution(primals_1, primals_4, 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))
        buf6 = extern_kernels.convolution(buf0, primals_6, stride=(1, 1),
            padding=(1, 1), 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))
        buf5 = buf4
        del buf4
        buf7 = buf6
        del buf6
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_convolution_mul_rsub_tanh_3[grid(256)](buf5,
            buf7, primals_5, primals_7, buf3, buf8, buf9, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_5
        del primals_7
    return (buf9, primals_1, primals_2, primals_4, primals_6, buf0, buf1,
        buf3, reinterpret_tensor(buf3, (4, 4, 4, 4), (128, 16, 4, 1), 64),
        buf5, buf7, buf8)


def one_param(m):
    """First parameter in `m`"""
    return next(m.parameters())


class ConvGRUCellNew(nn.Module):

    def __init__(self, input_dim, hidden_dim, kernel_size=(3, 3), bias=True,
        activation=F.tanh, batchnorm=False):
        """
        Initialize ConvGRU cell.
        Parameters
        ----------
        input_dim: int
            Number of channels of input tensor.
        hidden_dim: int
            Number of channels of hidden state.
        kernel_size: (int, int)
            Size of the convolutional kernel.
        bias: bool
            Whether or not to add the bias.
        """
        super().__init__()
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.kernel_size = kernel_size if isinstance(kernel_size, (tuple, list)
            ) else [kernel_size] * 2
        self.padding = self.kernel_size[0] // 2, self.kernel_size[1] // 2
        self.bias = bias
        self.activation = activation
        self.batchnorm = batchnorm
        self.conv_zr = nn.Conv2d(in_channels=self.input_dim + self.
            hidden_dim, out_channels=2 * self.hidden_dim, kernel_size=self.
            kernel_size, padding=self.padding, bias=self.bias)
        self.conv_h1 = nn.Conv2d(in_channels=self.input_dim, out_channels=
            self.hidden_dim, kernel_size=self.kernel_size, padding=self.
            padding, bias=self.bias)
        self.conv_h2 = nn.Conv2d(in_channels=self.hidden_dim, out_channels=
            self.hidden_dim, kernel_size=self.kernel_size, padding=self.
            padding, bias=self.bias)
        self.reset_parameters()

    def init_hidden(self, input):
        bs, _ch, h, w = input.shape
        return one_param(self).new_zeros(bs, self.hidden_dim, h, w)

    def reset_parameters(self):
        nn.init.xavier_uniform_(self.conv_zr.weight, gain=nn.init.
            calculate_gain('tanh'))
        self.conv_zr.bias.data.zero_()
        nn.init.xavier_uniform_(self.conv_h1.weight, gain=nn.init.
            calculate_gain('tanh'))
        self.conv_h1.bias.data.zero_()
        nn.init.xavier_uniform_(self.conv_h2.weight, gain=nn.init.
            calculate_gain('tanh'))
        self.conv_h2.bias.data.zero_()
        if self.batchnorm:
            self.bn1.reset_parameters()
            self.bn2.reset_parameters()

    def forward(self, input_0):
        primals_2 = self.conv_zr.weight
        primals_3 = self.conv_zr.bias
        primals_4 = self.conv_h1.weight
        primals_5 = self.conv_h1.bias
        primals_6 = self.conv_h2.weight
        primals_7 = self.conv_h2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
