# 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_6/inductor_cache/el/cel2ruypk2j6vym2z6ulvhjdkbq7qlspyf4cxbfr54apd5tjoehc.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%where, %where_1], 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=[256], 
    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': 4, '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 = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16) % 4
    x0 = xindex % 16
    x2 = (xindex // 64)
    x3 = xindex
    tmp8 = tl.load(in_ptr1 + (0))
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
    tmp19 = tl.load(in_ptr2 + (0))
    tmp20 = tl.broadcast_to(tmp19, [XBLOCK])
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (16*x1) + (64*x2)), tmp4 & xmask, other=0.0)
    tmp6 = 0.0
    tmp7 = tmp5 > tmp6
    tmp10 = tmp9 * tmp5
    tmp11 = tl.where(tmp7, tmp5, tmp10)
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp4, tmp11, tmp12)
    tmp14 = tmp0 >= tmp3
    tmp15 = tl.full([1], 4, tl.int64)
    tmp16 = tmp0 < tmp15
    tmp17 = tl.load(in_ptr0 + (32 + x0 + (16*((-2) + x1)) + (64*x2)), tmp14 & xmask, other=0.0)
    tmp18 = tmp17 > tmp6
    tmp21 = tmp20 * tmp17
    tmp22 = tl.where(tmp18, tmp17, tmp21)
    tmp23 = tl.full(tmp22.shape, 0.0, tmp22.dtype)
    tmp24 = tl.where(tmp14, tmp22, tmp23)
    tmp25 = tl.where(tmp4, tmp13, tmp24)
    tl.store(out_ptr0 + (x3), tmp25, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, ), (1, ))
    assert_size_stride(primals_3, (1, ), (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: [cat], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(primals_1, primals_2, primals_3, buf0, 256, grid=grid(256), stream=stream0)
        del primals_2
        del primals_3
    return (buf0, reinterpret_tensor(primals_1, (4, 2, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_1, (4, 2, 4, 4), (64, 16, 4, 1), 32), )


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((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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 cPReLU(nn.Module):

    def __init__(self, complex_axis=1):
        super(cPReLU, self).__init__()
        self.r_prelu = nn.PReLU()
        self.i_prelu = nn.PReLU()
        self.complex_axis = complex_axis

    def forward(self, inputs):
        real, imag = torch.chunk(inputs, 2, self.complex_axis)
        real = self.r_prelu(real)
        imag = self.i_prelu(imag)
        return torch.cat([real, imag], self.complex_axis)


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


def get_init_inputs():
    return [[], {}]

import torch
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_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 4
    x0 = xindex % 16
    x2 = xindex // 64
    x3 = xindex
    tmp8 = tl.load(in_ptr1 + 0)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
    tmp19 = tl.load(in_ptr2 + 0)
    tmp20 = tl.broadcast_to(tmp19, [XBLOCK])
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = 0.0
    tmp7 = tmp5 > tmp6
    tmp10 = tmp9 * tmp5
    tmp11 = tl.where(tmp7, tmp5, tmp10)
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp4, tmp11, tmp12)
    tmp14 = tmp0 >= tmp3
    tl.full([1], 4, tl.int64)
    tmp17 = tl.load(in_ptr0 + (32 + x0 + 16 * (-2 + x1) + 64 * x2), tmp14 &
        xmask, other=0.0)
    tmp18 = tmp17 > tmp6
    tmp21 = tmp20 * tmp17
    tmp22 = tl.where(tmp18, tmp17, tmp21)
    tmp23 = tl.full(tmp22.shape, 0.0, tmp22.dtype)
    tmp24 = tl.where(tmp14, tmp22, tmp23)
    tmp25 = tl.where(tmp4, tmp13, tmp24)
    tl.store(out_ptr0 + x3, tmp25, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1,), (1,))
    assert_size_stride(primals_3, (1,), (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_cat_0[grid(256)](primals_1, primals_2, primals_3,
            buf0, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        del primals_3
    return buf0, reinterpret_tensor(primals_1, (4, 2, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(primals_1, (4, 2, 4, 4), (64, 16, 4, 1), 32)


class cPReLUNew(nn.Module):

    def __init__(self, complex_axis=1):
        super(cPReLUNew, self).__init__()
        self.r_prelu = nn.PReLU()
        self.i_prelu = nn.PReLU()
        self.complex_axis = complex_axis

    def forward(self, input_0):
        primals_2 = self.r_prelu.weight
        primals_3 = self.i_prelu.weight
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
