# 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_1/inductor_cache/hh/chhyjiz5lbcafmexef23hsyfqgr7k4jea5qsudb5jrcrbticzmnh.py
# Topologically Sorted Source Nodes: [conv2d, i_t, conv2d_1, f_t, conv2d_2, o_t, mul, conv2d_3, tanh, mul_1, c_t, tanh_1, h_t], Original ATen: [aten.convolution, aten.sigmoid, aten.mul, aten.tanh, aten.add]
# Source node to ATen node mapping:
#   c_t => add
#   conv2d => convolution
#   conv2d_1 => convolution_1
#   conv2d_2 => convolution_2
#   conv2d_3 => convolution_3
#   f_t => sigmoid_1
#   h_t => mul_2
#   i_t => sigmoid
#   mul => mul
#   mul_1 => mul_1
#   o_t => sigmoid_2
#   tanh => tanh
#   tanh_1 => tanh_1
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%convolution,), kwargs = {})
#   %convolution_1 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_4, %primals_5, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %sigmoid_1 : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%convolution_1,), kwargs = {})
#   %convolution_2 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_6, %primals_7, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %sigmoid_2 : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%convolution_2,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_1, %primals_8), kwargs = {})
#   %convolution_3 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_9, %primals_10, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%convolution_3,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %tanh), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %tanh_1 : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%add,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_2, %tanh_1), kwargs = {})
triton_poi_fused_add_convolution_mul_sigmoid_tanh_0 = async_compile.triton('triton_poi_fused_add_convolution_mul_sigmoid_tanh_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '*fp32', 9: '*fp32', 10: '*fp32', 11: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_mul_sigmoid_tanh_0', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1', 'in_out_ptr2', 'in_out_ptr3'], 'no_x_dim': False, 'num_load': 9, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_sigmoid_tanh_0(in_out_ptr0, in_out_ptr1, in_out_ptr2, in_out_ptr3, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 4) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr1 + (x3), xmask)
    tmp4 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_out_ptr2 + (x3), xmask)
    tmp7 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_out_ptr3 + (x3), xmask)
    tmp10 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr4 + (x3), xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp8 = tmp6 + tmp7
    tmp11 = tmp9 + tmp10
    tmp12 = tl.sigmoid(tmp5)
    tmp14 = tmp12 * tmp13
    tmp15 = tl.sigmoid(tmp2)
    tmp16 = libdevice.tanh(tmp11)
    tmp17 = tmp15 * tmp16
    tmp18 = tmp14 + tmp17
    tmp19 = tl.sigmoid(tmp8)
    tmp20 = libdevice.tanh(tmp18)
    tmp21 = tmp19 * tmp20
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
    tl.store(in_out_ptr1 + (x3), tmp5, xmask)
    tl.store(in_out_ptr2 + (x3), tmp8, xmask)
    tl.store(in_out_ptr3 + (x3), tmp11, xmask)
    tl.store(out_ptr0 + (x3), tmp18, xmask)
    tl.store(out_ptr1 + (x3), tmp21, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 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, ), (1, ))
    assert_size_stride(primals_8, (4, 4, 2, 2), (16, 4, 2, 1))
    assert_size_stride(primals_9, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_10, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv2d], 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=1, bias=None)
        assert_size_stride(buf0, (4, 4, 2, 2), (16, 4, 2, 1))
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(primals_3, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 2, 2), (16, 4, 2, 1))
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(primals_3, primals_6, 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, 2, 2), (16, 4, 2, 1))
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(primals_3, primals_9, 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, 2, 2), (16, 4, 2, 1))
        buf1 = buf0; del buf0  # reuse
        buf3 = buf2; del buf2  # reuse
        buf5 = buf4; del buf4  # reuse
        buf7 = buf6; del buf6  # reuse
        buf8 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        buf9 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d, i_t, conv2d_1, f_t, conv2d_2, o_t, mul, conv2d_3, tanh, mul_1, c_t, tanh_1, h_t], Original ATen: [aten.convolution, aten.sigmoid, aten.mul, aten.tanh, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_convolution_mul_sigmoid_tanh_0.run(buf1, buf3, buf5, buf7, primals_2, primals_5, primals_7, primals_10, primals_8, buf8, buf9, 64, grid=grid(64), stream=stream0)
        del primals_10
        del primals_2
        del primals_5
        del primals_7
    return (buf9, buf8, primals_1, primals_3, primals_4, primals_6, primals_8, primals_9, buf1, buf3, 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((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 3, 3), (36, 9, 3, 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, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 4, 2, 2), (16, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = 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, primals_8, primals_9, primals_10])
    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
import torch.nn as nn


class ConvLSTMCell(nn.Module):
    """
    Implementation of the Basic ConvLSTM.
    No peephole connection, no forget gate.

    ConvLSTM:
        x - input
        h - hidden representation
        c - memory cell
        f - forget gate
        o - output gate

    Reference:Convolutional LSTM Network: A Machine Learning Approach for Precipitation
    Nowcasting
    """

    def __init__(self, input_channels, hidden_channels, kernel_size):
        super(ConvLSTMCell, self).__init__()
        self.input_channels = input_channels
        self.hidden_channels = hidden_channels
        self.kernel_size = kernel_size
        self.num_features = 4
        self.padding = int((kernel_size - 1) / 2)
        self.W_i = nn.Conv2d(self.input_channels, self.hidden_channels,
            self.kernel_size, 1, self.padding, bias=True)
        self.W_f = nn.Conv2d(self.input_channels, self.hidden_channels,
            self.kernel_size, 1, self.padding, bias=True)
        self.W_o = nn.Conv2d(self.input_channels, self.hidden_channels,
            self.kernel_size, 1, self.padding, bias=True)
        self.W_c = nn.Conv2d(self.input_channels, self.hidden_channels,
            self.kernel_size, 1, self.padding, bias=True)
        self.reset_parameters()

    def forward(self, inputs, c):
        i_t = torch.sigmoid(self.W_i(inputs))
        f_t = torch.sigmoid(self.W_f(inputs))
        o_t = torch.sigmoid(self.W_o(inputs))
        c_t = f_t * c + i_t * torch.tanh(self.W_c(inputs))
        h_t = o_t * torch.tanh(c_t)
        return h_t, c_t

    def reset_parameters(self):
        self.W_i.reset_parameters()
        self.W_f.reset_parameters()
        self.W_o.reset_parameters()
        self.W_c.reset_parameters()


def get_inputs():
    return [torch.rand([4, 4, 3, 3]), torch.rand([4, 4, 2, 2])]


def get_init_inputs():
    return [[], {'input_channels': 4, 'hidden_channels': 4, 'kernel_size': 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
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


@triton.jit
def triton_poi_fused_add_convolution_mul_sigmoid_tanh_0(in_out_ptr0,
    in_out_ptr1, in_out_ptr2, in_out_ptr3, in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr1 + x3, xmask)
    tmp4 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_out_ptr2 + x3, xmask)
    tmp7 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_out_ptr3 + x3, xmask)
    tmp10 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr4 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp8 = tmp6 + tmp7
    tmp11 = tmp9 + tmp10
    tmp12 = tl.sigmoid(tmp5)
    tmp14 = tmp12 * tmp13
    tmp15 = tl.sigmoid(tmp2)
    tmp16 = libdevice.tanh(tmp11)
    tmp17 = tmp15 * tmp16
    tmp18 = tmp14 + tmp17
    tmp19 = tl.sigmoid(tmp8)
    tmp20 = libdevice.tanh(tmp18)
    tmp21 = tmp19 * tmp20
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(in_out_ptr1 + x3, tmp5, xmask)
    tl.store(in_out_ptr2 + x3, tmp8, xmask)
    tl.store(in_out_ptr3 + x3, tmp11, xmask)
    tl.store(out_ptr0 + x3, tmp18, xmask)
    tl.store(out_ptr1 + x3, tmp21, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 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,), (1,))
    assert_size_stride(primals_8, (4, 4, 2, 2), (16, 4, 2, 1))
    assert_size_stride(primals_9, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_10, (4,), (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=1, bias=None)
        assert_size_stride(buf0, (4, 4, 2, 2), (16, 4, 2, 1))
        buf2 = extern_kernels.convolution(primals_3, primals_4, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 2, 2), (16, 4, 2, 1))
        buf4 = extern_kernels.convolution(primals_3, primals_6, 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, 2, 2), (16, 4, 2, 1))
        buf6 = extern_kernels.convolution(primals_3, primals_9, 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, 2, 2), (16, 4, 2, 1))
        buf1 = buf0
        del buf0
        buf3 = buf2
        del buf2
        buf5 = buf4
        del buf4
        buf7 = buf6
        del buf6
        buf8 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        buf9 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_convolution_mul_sigmoid_tanh_0[grid(64)](buf1,
            buf3, buf5, buf7, primals_2, primals_5, primals_7, primals_10,
            primals_8, buf8, buf9, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_10
        del primals_2
        del primals_5
        del primals_7
    return (buf9, buf8, primals_1, primals_3, primals_4, primals_6,
        primals_8, primals_9, buf1, buf3, buf5, buf7, buf8)


class ConvLSTMCellNew(nn.Module):
    """
    Implementation of the Basic ConvLSTM.
    No peephole connection, no forget gate.

    ConvLSTM:
        x - input
        h - hidden representation
        c - memory cell
        f - forget gate
        o - output gate

    Reference:Convolutional LSTM Network: A Machine Learning Approach for Precipitation
    Nowcasting
    """

    def __init__(self, input_channels, hidden_channels, kernel_size):
        super(ConvLSTMCellNew, self).__init__()
        self.input_channels = input_channels
        self.hidden_channels = hidden_channels
        self.kernel_size = kernel_size
        self.num_features = 4
        self.padding = int((kernel_size - 1) / 2)
        self.W_i = nn.Conv2d(self.input_channels, self.hidden_channels,
            self.kernel_size, 1, self.padding, bias=True)
        self.W_f = nn.Conv2d(self.input_channels, self.hidden_channels,
            self.kernel_size, 1, self.padding, bias=True)
        self.W_o = nn.Conv2d(self.input_channels, self.hidden_channels,
            self.kernel_size, 1, self.padding, bias=True)
        self.W_c = nn.Conv2d(self.input_channels, self.hidden_channels,
            self.kernel_size, 1, self.padding, bias=True)
        self.reset_parameters()

    def reset_parameters(self):
        self.W_i.reset_parameters()
        self.W_f.reset_parameters()
        self.W_o.reset_parameters()
        self.W_c.reset_parameters()

    def forward(self, input_0, input_1):
        primals_1 = self.W_i.weight
        primals_2 = self.W_i.bias
        primals_4 = self.W_f.weight
        primals_5 = self.W_f.bias
        primals_6 = self.W_o.weight
        primals_7 = self.W_o.bias
        primals_9 = self.W_c.weight
        primals_10 = self.W_c.bias
        primals_3 = input_0
        primals_8 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9, primals_10])
        return output[0], output[1]
