# AOT ID: ['0_inference']
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/qe/cqeeqbdhzjgqgez7ulct4iu2pihhnqy35eqv4vlirc2jwtjqhqtd.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.replication_pad2d]
# Source node to ATen node mapping:
#   x => _unsafe_index, _unsafe_index_1
# Graph fragment:
#   %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%arg0_1, [None, None, %clamp_max, None]), kwargs = {})
#   %_unsafe_index_1 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %clamp_max_1]), kwargs = {})
triton_poi_fused_replication_pad2d_0 = async_compile.triton('triton_poi_fused_replication_pad2d_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: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_replication_pad2d_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_replication_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = (xindex // 6) % 6
    x2 = (xindex // 36)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + ((4*((3) * ((3) <= (((0) * ((0) >= ((-1) + x1)) + ((-1) + x1) * (((-1) + x1) > (0))))) + (((0) * ((0) >= ((-1) + x1)) + ((-1) + x1) * (((-1) + x1) > (0)))) * ((((0) * ((0) >= ((-1) + x1)) + ((-1) + x1) * (((-1) + x1) > (0)))) < (3)))) + (16*x2) + ((3) * ((3) <= (((0) * ((0) >= ((-1) + x0)) + ((-1) + x0) * (((-1) + x0) > (0))))) + (((0) * ((0) >= ((-1) + x0)) + ((-1) + x0) * (((-1) + x0) > (0)))) * ((((0) * ((0) >= ((-1) + x0)) + ((-1) + x0) * (((-1) + x0) > (0)))) < (3)))), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x3), tmp0, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(arg1_1, (1, 1, 1, 4), (4, 4, 4, 1))
    assert_size_stride(arg2_1, (1, 1, 4, 1), (4, 4, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 6, 6), (36, 36, 6, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.replication_pad2d]
        stream0 = get_raw_stream(0)
        triton_poi_fused_replication_pad2d_0.run(arg0_1, buf0, 144, grid=grid(144), stream=stream0)
        del arg0_1
        # Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.replication_pad2d, aten.convolution]
        buf1 = extern_kernels.convolution(buf0, arg1_1, stride=(1, 2), padding=(0, 3), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 1, 6, 8), (48, 48, 8, 1))
        del arg1_1
        del buf0
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, arg2_1, stride=(2, 1), padding=(3, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 1, 8, 8), (64, 64, 8, 1))
        del arg2_1
        del buf1
    return (buf2, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 1, 4, 4), (16, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((1, 1, 1, 4), (4, 4, 4, 1), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((1, 1, 4, 1), (4, 4, 1, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_1])
    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 as th
import torch.utils.data


class BilinearUpsampler(th.nn.Module):

    def __init__(self, scale=2, channels=1):
        super(BilinearUpsampler, self).__init__()
        ksize = 2 * scale
        total_pad = ksize - scale // 2
        if scale % 2 == 1:
            ksize += 1
        self.pad = th.nn.ReplicationPad2d((1, 1, 1, 1))
        self.us_x = th.nn.ConvTranspose2d(channels, channels, (1, ksize),
            stride=(1, scale), padding=(0, total_pad), groups=channels,
            bias=False)
        self.us_y = th.nn.ConvTranspose2d(channels, channels, (ksize, 1),
            stride=(scale, 1), padding=(total_pad, 0), groups=channels,
            bias=False)
        k_idx = th.arange(0, ksize) + 0.5
        k_coord = k_idx / scale - ksize * 0.5 / scale
        k_weight = th.clamp(1.0 - th.abs(k_coord), min=0)
        for c in range(channels):
            self.us_x.weight.data[c, 0, 0, :].copy_(k_weight)
            self.us_y.weight.data[c, 0, :, 0].copy_(k_weight)
        for p in self.parameters():
            p.requires_grad = False

    def forward(self, x):
        x = self.pad(x)
        x = self.us_x(x)
        x = self.us_y(x)
        return x


def get_inputs():
    return [torch.rand([4, 1, 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 as th
import torch.utils.data
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_replication_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = xindex // 6 % 6
    x2 = xindex // 36
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4 * (3 * (3 <= 0 * (0 >= -1 + x1) + (-1 + x1) *
        (-1 + x1 > 0)) + (0 * (0 >= -1 + x1) + (-1 + x1) * (-1 + x1 > 0)) *
        (0 * (0 >= -1 + x1) + (-1 + x1) * (-1 + x1 > 0) < 3)) + 16 * x2 + (
        3 * (3 <= 0 * (0 >= -1 + x0) + (-1 + x0) * (-1 + x0 > 0)) + (0 * (0 >=
        -1 + x0) + (-1 + x0) * (-1 + x0 > 0)) * (0 * (0 >= -1 + x0) + (-1 +
        x0) * (-1 + x0 > 0) < 3))), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(arg1_1, (1, 1, 1, 4), (4, 4, 4, 1))
    assert_size_stride(arg2_1, (1, 1, 4, 1), (4, 4, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 6, 6), (36, 36, 6, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_replication_pad2d_0[grid(144)](arg0_1, buf0, 144,
            XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
        buf1 = extern_kernels.convolution(buf0, arg1_1, stride=(1, 2),
            padding=(0, 3), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 1, 6, 8), (48, 48, 8, 1))
        del arg1_1
        del buf0
        buf2 = extern_kernels.convolution(buf1, arg2_1, stride=(2, 1),
            padding=(3, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 1, 8, 8), (64, 64, 8, 1))
        del arg2_1
        del buf1
    return buf2,


class BilinearUpsamplerNew(th.nn.Module):

    def __init__(self, scale=2, channels=1):
        super(BilinearUpsamplerNew, self).__init__()
        ksize = 2 * scale
        total_pad = ksize - scale // 2
        if scale % 2 == 1:
            ksize += 1
        self.pad = th.nn.ReplicationPad2d((1, 1, 1, 1))
        self.us_x = th.nn.ConvTranspose2d(channels, channels, (1, ksize),
            stride=(1, scale), padding=(0, total_pad), groups=channels,
            bias=False)
        self.us_y = th.nn.ConvTranspose2d(channels, channels, (ksize, 1),
            stride=(scale, 1), padding=(total_pad, 0), groups=channels,
            bias=False)
        k_idx = th.arange(0, ksize) + 0.5
        k_coord = k_idx / scale - ksize * 0.5 / scale
        k_weight = th.clamp(1.0 - th.abs(k_coord), min=0)
        for c in range(channels):
            self.us_x.weight.data[c, 0, 0, :].copy_(k_weight)
            self.us_y.weight.data[c, 0, :, 0].copy_(k_weight)
        for p in self.parameters():
            p.requires_grad = False

    def forward(self, input_0):
        arg1_1 = self.us_x.weight
        arg2_1 = self.us_y.weight
        arg0_1 = input_0
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]
