# 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/3w/c3wk4dew6hre2j5pafyhvw4d4ejdth475l2pb4uou3ln3hu2ijdf.py
# Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.constant_pad_nd, aten.replication_pad2d]
# Source node to ATen node mapping:
#   x => constant_pad_nd
#   x_1 => _unsafe_index, _unsafe_index_1
# Graph fragment:
#   %constant_pad_nd : [num_users=1] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%primals_1, [0, 0, 1, 0], 0.0), kwargs = {})
#   %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%constant_pad_nd, [None, None, %clamp_max, None]), kwargs = {})
#   %_unsafe_index_1 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %clamp_max_1]), kwargs = {})
triton_poi_fused_constant_pad_nd_replication_pad2d_0 = async_compile.triton('triton_poi_fused_constant_pad_nd_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=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_constant_pad_nd_replication_pad2d_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_constant_pad_nd_replication_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 672
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 6) % 7
    x0 = xindex % 6
    x2 = (xindex // 42)
    x3 = xindex
    tmp0 = (-1) + ((4) * ((4) <= (((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)))) < (4)))
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.load(in_ptr0 + ((-4) + (4*((4) * ((4) <= (((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)))) < (4)))) + (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)))), tmp2 & xmask, eviction_policy='evict_last', other=0.0)
    tl.store(out_ptr0 + (x3), tmp3, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/sc/cscl36nzv3adkfvzo3nbv2knix4cx3n454oapm6d6qnldfqqu4es.py
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.leaky_relu, aten.leaky_relu_backward]
# Source node to ATen node mapping:
#   x_3 => gt, mul, where
# Graph fragment:
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution, 0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 0.1), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %convolution, %mul), kwargs = {})
#   %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%where, 0), kwargs = {})
triton_poi_fused_leaky_relu_leaky_relu_backward_1 = async_compile.triton('triton_poi_fused_leaky_relu_leaky_relu_backward_1', '''
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=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i1', 2: '*fp32', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_leaky_relu_leaky_relu_backward_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_leaky_relu_leaky_relu_backward_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 320
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.1
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tmp6 = tmp5 > tmp1
    tl.store(out_ptr0 + (x0), tmp6, xmask)
    tl.store(out_ptr1 + (x0), tmp5, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 7, 6), (168, 42, 6, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.constant_pad_nd, aten.replication_pad2d]
        stream0 = get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_replication_pad2d_0.run(primals_1, buf0, 672, grid=grid(672), stream=stream0)
        del primals_1
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 5, 4), (80, 20, 4, 1))
        buf2 = empty_strided_cuda((4, 4, 5, 4), (80, 20, 4, 1), torch.bool)
        buf3 = empty_strided_cuda((4, 4, 5, 4), (80, 20, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.leaky_relu, aten.leaky_relu_backward]
        triton_poi_fused_leaky_relu_leaky_relu_backward_1.run(buf1, buf2, buf3, 320, grid=grid(320), stream=stream0)
        del buf1
    return (reinterpret_tensor(buf3, (4, 4, 4, 4), (80, 20, 4, 1), 0), primals_2, buf0, buf2, )


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, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2])
    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 crop(nn.Module):

    def __init__(self):
        super().__init__()

    def forward(self, x):
        N, C, H, W = x.shape
        x = x[0:N, 0:C, 0:H - 1, 0:W]
        return x


class Conv(nn.Module):

    def __init__(self, in_channels, out_channels, bias=False, blind=True):
        super().__init__()
        self.blind = blind
        if blind:
            self.shift_down = nn.ZeroPad2d((0, 0, 1, 0))
            self.crop = crop()
        self.replicate = nn.ReplicationPad2d(1)
        self.conv = nn.Conv2d(in_channels, out_channels, 3, bias=bias)
        self.relu = nn.LeakyReLU(0.1, inplace=True)

    def forward(self, x):
        if self.blind:
            x = self.shift_down(x)
        x = self.replicate(x)
        x = self.conv(x)
        x = self.relu(x)
        if self.blind:
            x = self.crop(x)
        return x


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 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_constant_pad_nd_replication_pad2d_0(in_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 672
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 6 % 7
    x0 = xindex % 6
    x2 = xindex // 42
    x3 = xindex
    tmp0 = -1 + (4 * (4 <= 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) < 4))
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.load(in_ptr0 + (-4 + 4 * (4 * (4 <= 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) < 4)) + 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))), tmp2 & xmask,
        eviction_policy='evict_last', other=0.0)
    tl.store(out_ptr0 + x3, tmp3, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_leaky_relu_backward_1(in_ptr0, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 320
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.1
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tmp6 = tmp5 > tmp1
    tl.store(out_ptr0 + x0, tmp6, xmask)
    tl.store(out_ptr1 + x0, tmp5, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 7, 6), (168, 42, 6, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_replication_pad2d_0[grid(672)](
            primals_1, buf0, 672, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 5, 4), (80, 20, 4, 1))
        buf2 = empty_strided_cuda((4, 4, 5, 4), (80, 20, 4, 1), torch.bool)
        buf3 = empty_strided_cuda((4, 4, 5, 4), (80, 20, 4, 1), torch.float32)
        triton_poi_fused_leaky_relu_leaky_relu_backward_1[grid(320)](buf1,
            buf2, buf3, 320, XBLOCK=256, num_warps=4, num_stages=1)
        del buf1
    return reinterpret_tensor(buf3, (4, 4, 4, 4), (80, 20, 4, 1), 0
        ), primals_2, buf0, buf2


class crop(nn.Module):

    def __init__(self):
        super().__init__()

    def forward(self, x):
        N, C, H, W = x.shape
        x = x[0:N, 0:C, 0:H - 1, 0:W]
        return x


class ConvNew(nn.Module):

    def __init__(self, in_channels, out_channels, bias=False, blind=True):
        super().__init__()
        self.blind = blind
        if blind:
            self.shift_down = nn.ZeroPad2d((0, 0, 1, 0))
            self.crop = crop()
        self.replicate = nn.ReplicationPad2d(1)
        self.conv = nn.Conv2d(in_channels, out_channels, 3, bias=bias)
        self.relu = nn.LeakyReLU(0.1, inplace=True)

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