# 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_8/inductor_cache/bd/cbdohqvf5ovewdd5y6dtctx4lewezgg3jimtio6amaolbcczwafo.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%convolution, %convolution_1, %convolution_2, %convolution_3], 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: '*fp32', 5: '*fp32', 6: '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_cat_0', '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_cat_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, 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
    tmp6 = tl.load(in_ptr1 + (0))
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp0 = x1
    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 + (x0 + (16*x2)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp8 = tmp5 + tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp4, tmp8, tmp9)
    tmp11 = tmp0 >= tmp3
    tmp12 = tl.full([1], 2, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr2 + (x0 + (16*x2)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp16 = tmp15 + tmp7
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp14, tmp16, tmp17)
    tmp19 = tmp0 >= tmp12
    tmp20 = tl.full([1], 3, tl.int64)
    tmp21 = tmp0 < tmp20
    tmp22 = tmp19 & tmp21
    tmp23 = tl.load(in_ptr3 + (x0 + (16*x2)), tmp22 & xmask, eviction_policy='evict_last', other=0.0)
    tmp24 = tmp23 + tmp7
    tmp25 = tl.full(tmp24.shape, 0.0, tmp24.dtype)
    tmp26 = tl.where(tmp22, tmp24, tmp25)
    tmp27 = tmp0 >= tmp20
    tmp28 = tl.full([1], 4, tl.int64)
    tmp29 = tmp0 < tmp28
    tmp30 = tl.load(in_ptr4 + (x0 + (16*x2)), tmp27 & xmask, eviction_policy='evict_last', other=0.0)
    tmp31 = tmp30 + tmp7
    tmp32 = tl.full(tmp31.shape, 0.0, tmp31.dtype)
    tmp33 = tl.where(tmp27, tmp31, tmp32)
    tmp34 = tl.where(tmp22, tmp26, tmp33)
    tmp35 = tl.where(tmp14, tmp18, tmp34)
    tmp36 = tl.where(tmp4, tmp10, tmp35)
    tl.store(out_ptr0 + (x3), tmp36, 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, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_3, (1, ), (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, (4, 1, 4, 4), (64, 0, 4, 1), 0), primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 1, 4, 4), (16, 16, 4, 1))
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 0, 4, 1), 16), primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 1, 4, 4), (16, 16, 4, 1))
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 0, 4, 1), 32), 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, 1, 4, 4), (16, 16, 4, 1))
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 0, 4, 1), 48), primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 1, 4, 4), (16, 16, 4, 1))
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(buf0, primals_3, buf1, buf2, buf3, buf4, 256, grid=grid(256), stream=stream0)
        del buf0
        del buf1
        del buf2
        del buf3
        del primals_3
    return (buf4, primals_2, reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 16, 4, 1), 16), reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 16, 4, 1), 32), reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 16, 4, 1), 48), )


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, 3, 3), (9, 9, 3, 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
import torch.utils.data


class depthwise_clipseg_conv(nn.Module):

    def __init__(self):
        super(depthwise_clipseg_conv, self).__init__()
        self.depthwise = nn.Conv2d(1, 1, kernel_size=3, padding=1)

    def depthwise_clipseg(self, x, channels):
        x = torch.cat([self.depthwise(x[:, i].unsqueeze(1)) for i in range(
            channels)], dim=1)
        return x

    def forward(self, x):
        channels = x.shape[1]
        out = self.depthwise_clipseg(x, channels)
        return out


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


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

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
import torch.utils.data
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, in_ptr3, in_ptr4,
    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
    tmp6 = tl.load(in_ptr1 + 0)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x2), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp8 = tmp5 + tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp4, tmp8, tmp9)
    tmp11 = tmp0 >= tmp3
    tmp12 = tl.full([1], 2, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr2 + (x0 + 16 * x2), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tmp15 + tmp7
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp14, tmp16, tmp17)
    tmp19 = tmp0 >= tmp12
    tmp20 = tl.full([1], 3, tl.int64)
    tmp21 = tmp0 < tmp20
    tmp22 = tmp19 & tmp21
    tmp23 = tl.load(in_ptr3 + (x0 + 16 * x2), tmp22 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp24 = tmp23 + tmp7
    tmp25 = tl.full(tmp24.shape, 0.0, tmp24.dtype)
    tmp26 = tl.where(tmp22, tmp24, tmp25)
    tmp27 = tmp0 >= tmp20
    tl.full([1], 4, tl.int64)
    tmp30 = tl.load(in_ptr4 + (x0 + 16 * x2), tmp27 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp31 = tmp30 + tmp7
    tmp32 = tl.full(tmp31.shape, 0.0, tmp31.dtype)
    tmp33 = tl.where(tmp27, tmp31, tmp32)
    tmp34 = tl.where(tmp22, tmp26, tmp33)
    tmp35 = tl.where(tmp14, tmp18, tmp34)
    tmp36 = tl.where(tmp4, tmp10, tmp35)
    tl.store(out_ptr0 + x3, tmp36, 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, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_3, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4,
            1, 4, 4), (64, 0, 4, 1), 0), primals_2, stride=(1, 1), padding=
            (1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0
            ), groups=1, bias=None)
        assert_size_stride(buf0, (4, 1, 4, 4), (16, 16, 4, 1))
        buf1 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4,
            1, 4, 4), (64, 0, 4, 1), 16), primals_2, stride=(1, 1), padding
            =(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 
            0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 1, 4, 4), (16, 16, 4, 1))
        buf2 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4,
            1, 4, 4), (64, 0, 4, 1), 32), 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, 1, 4, 4), (16, 16, 4, 1))
        buf3 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4,
            1, 4, 4), (64, 0, 4, 1), 48), primals_2, stride=(1, 1), padding
            =(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 
            0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 1, 4, 4), (16, 16, 4, 1))
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(256)](buf0, primals_3, buf1, buf2, buf3,
            buf4, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf0
        del buf1
        del buf2
        del buf3
        del primals_3
    return buf4, primals_2, reinterpret_tensor(primals_1, (4, 1, 4, 4), (64,
        16, 4, 1), 0), reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 16,
        4, 1), 16), reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 16, 4,
        1), 32), reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 16, 4, 1), 48
        )


class depthwise_clipseg_convNew(nn.Module):

    def __init__(self):
        super(depthwise_clipseg_convNew, self).__init__()
        self.depthwise = nn.Conv2d(1, 1, kernel_size=3, padding=1)

    def depthwise_clipseg(self, x, channels):
        x = torch.cat([self.depthwise(x[:, i].unsqueeze(1)) for i in range(
            channels)], dim=1)
        return x

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