# 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_7/inductor_cache/75/c75kmue6qd44hzaqpn7rrmrp5xm7szydl3qiglyxzw3rfrnp6sdi.py
# Topologically Sorted Source Nodes: [output], Original ATen: [aten.stack]
# Source node to ATen node mapping:
#   output => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%unsqueeze_4, %unsqueeze_5], -1), kwargs = {})
triton_poi_fused_stack_0 = async_compile.triton('triton_poi_fused_stack_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=[8], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_stack_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_stack_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = (xindex // 2)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x1), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + (x1), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.load(in_ptr2 + (x1), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp9 = tl.load(in_ptr3 + (x1), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tmp8 + tmp9
    tmp11 = tmp7 - tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp4, tmp11, tmp12)
    tmp14 = tmp0 >= tmp3
    tmp15 = tl.full([1], 2, tl.int64)
    tmp16 = tmp0 < tmp15
    tmp17 = tl.load(in_ptr4 + (x1), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp18 = tl.load(in_ptr1 + (x1), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp19 = tmp17 + tmp18
    tmp20 = tl.load(in_ptr5 + (x1), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp21 = tl.load(in_ptr3 + (x1), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp22 = tmp20 + tmp21
    tmp23 = tmp19 + tmp22
    tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype)
    tmp25 = tl.where(tmp14, tmp23, tmp24)
    tmp26 = tl.where(tmp4, tmp13, tmp25)
    tl.store(out_ptr0 + (x2), tmp26, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = 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, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (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(reinterpret_tensor(primals_1, (1, 4, 4, 4), (0, 64, 16, 4), 0), primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (1, 4, 1, 1), (4, 1, 1, 1))
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(reinterpret_tensor(primals_1, (1, 4, 4, 4), (0, 64, 16, 4), 1), primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (1, 4, 1, 1), (4, 1, 1, 1))
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(reinterpret_tensor(primals_1, (1, 4, 4, 4), (0, 64, 16, 4), 1), primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (1, 4, 1, 1), (4, 1, 1, 1))
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(reinterpret_tensor(primals_1, (1, 4, 4, 4), (0, 64, 16, 4), 0), primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (1, 4, 1, 1), (4, 1, 1, 1))
        buf4 = empty_strided_cuda((4, 1, 1, 2), (2, 2, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten.stack]
        stream0 = get_raw_stream(0)
        triton_poi_fused_stack_0.run(buf0, primals_3, buf1, primals_5, buf2, buf3, buf4, 8, grid=grid(8), stream=stream0)
        del buf0
        del buf1
        del buf2
        del buf3
        del primals_3
        del primals_5
    return (buf4, primals_2, primals_4, reinterpret_tensor(primals_1, (1, 4, 4, 4), (256, 64, 16, 4), 0), reinterpret_tensor(primals_1, (1, 4, 4, 4), (256, 64, 16, 4), 1), )


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, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((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, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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 ComplexConv2d(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        padding=0, dilation=1, groups=1, bias=True, **kwargs):
        super().__init__()
        self.conv_re = nn.Conv2d(in_channels, out_channels, kernel_size,
            stride=stride, padding=padding, dilation=dilation, groups=
            groups, bias=bias, **kwargs)
        self.conv_im = nn.Conv2d(in_channels, out_channels, kernel_size,
            stride=stride, padding=padding, dilation=dilation, groups=
            groups, bias=bias, **kwargs)

    def forward(self, x):
        real = self.conv_re(x[..., 0]) - self.conv_im(x[..., 1])
        imaginary = self.conv_re(x[..., 1]) + self.conv_im(x[..., 0])
        output = torch.stack((real, imaginary), dim=-1)
        return output


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_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
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_stack_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = xindex // 2
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + x1, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp6 = tl.load(in_ptr1 + x1, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.load(in_ptr2 + x1, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp9 = tl.load(in_ptr3 + x1, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp10 = tmp8 + tmp9
    tmp11 = tmp7 - tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp4, tmp11, tmp12)
    tmp14 = tmp0 >= tmp3
    tl.full([1], 2, tl.int64)
    tmp17 = tl.load(in_ptr4 + x1, tmp14 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp18 = tl.load(in_ptr1 + x1, tmp14 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp19 = tmp17 + tmp18
    tmp20 = tl.load(in_ptr5 + x1, tmp14 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp21 = tl.load(in_ptr3 + x1, tmp14 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp22 = tmp20 + tmp21
    tmp23 = tmp19 + tmp22
    tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype)
    tmp25 = tl.where(tmp14, tmp23, tmp24)
    tmp26 = tl.where(tmp4, tmp13, tmp25)
    tl.store(out_ptr0 + x2, tmp26, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = 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, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_1, (1,
            4, 4, 4), (0, 64, 16, 4), 0), primals_2, stride=(1, 1), padding
            =(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 
            0), groups=1, bias=None)
        assert_size_stride(buf0, (1, 4, 1, 1), (4, 1, 1, 1))
        buf1 = extern_kernels.convolution(reinterpret_tensor(primals_1, (1,
            4, 4, 4), (0, 64, 16, 4), 1), primals_4, stride=(1, 1), padding
            =(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 
            0), groups=1, bias=None)
        assert_size_stride(buf1, (1, 4, 1, 1), (4, 1, 1, 1))
        buf2 = extern_kernels.convolution(reinterpret_tensor(primals_1, (1,
            4, 4, 4), (0, 64, 16, 4), 1), primals_2, stride=(1, 1), padding
            =(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 
            0), groups=1, bias=None)
        assert_size_stride(buf2, (1, 4, 1, 1), (4, 1, 1, 1))
        buf3 = extern_kernels.convolution(reinterpret_tensor(primals_1, (1,
            4, 4, 4), (0, 64, 16, 4), 0), primals_4, stride=(1, 1), padding
            =(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 
            0), groups=1, bias=None)
        assert_size_stride(buf3, (1, 4, 1, 1), (4, 1, 1, 1))
        buf4 = empty_strided_cuda((4, 1, 1, 2), (2, 2, 2, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_stack_0[grid(8)](buf0, primals_3, buf1, primals_5,
            buf2, buf3, buf4, 8, XBLOCK=8, num_warps=1, num_stages=1)
        del buf0
        del buf1
        del buf2
        del buf3
        del primals_3
        del primals_5
    return buf4, primals_2, primals_4, reinterpret_tensor(primals_1, (1, 4,
        4, 4), (256, 64, 16, 4), 0), reinterpret_tensor(primals_1, (1, 4, 4,
        4), (256, 64, 16, 4), 1)


class ComplexConv2dNew(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        padding=0, dilation=1, groups=1, bias=True, **kwargs):
        super().__init__()
        self.conv_re = nn.Conv2d(in_channels, out_channels, kernel_size,
            stride=stride, padding=padding, dilation=dilation, groups=
            groups, bias=bias, **kwargs)
        self.conv_im = nn.Conv2d(in_channels, out_channels, kernel_size,
            stride=stride, padding=padding, dilation=dilation, groups=
            groups, bias=bias, **kwargs)

    def forward(self, input_0):
        primals_1 = self.conv_re.weight
        primals_3 = self.conv_re.bias
        primals_2 = self.conv_im.weight
        primals_5 = self.conv_im.bias
        primals_4 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
