# 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/w7/cw7nwm4phyff5u2mxvg4wffuk3qkl3kcrshhf3qydzhb7zd4zl75.py
# Topologically Sorted Source Nodes: [contiguous_1], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   contiguous_1 => clone_1
# Graph fragment:
#   %clone_1 : [num_users=3] = call_function[target=torch.ops.aten.clone.default](args = (%permute_2,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_0 = async_compile.triton('triton_poi_fused_clone_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=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_clone_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_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/fh/cfhxlck7uzhxtofknhjghf2xokzgxovbt22nsyc7lfq6lggpmsc6.py
# Topologically Sorted Source Nodes: [out_2], Original ATen: [aten.neg]
# Source node to ATen node mapping:
#   out_2 => neg
# Graph fragment:
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%_cdist_forward,), kwargs = {})
triton_poi_fused_neg_1 = async_compile.triton('triton_poi_fused_neg_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_neg_1', 'mutated_arg_names': ['in_out_ptr0'], '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_neg_1(in_out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = -tmp0
    tl.store(in_out_ptr0 + (x0), tmp1, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/xb/cxb4lxq2tbmyzyffeyfgkeb34canjnomidn7ybre2qjgrajgfrs5.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.neg]
# Source node to ATen node mapping:
#   out => neg_1
# Graph fragment:
#   %neg_1 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%_cdist_forward_1,), kwargs = {})
triton_poi_fused_neg_2 = async_compile.triton('triton_poi_fused_neg_2', '''
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=[16], 
    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_neg_2', '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_neg_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    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 = -tmp0
    tl.store(out_ptr0 + (x0), tmp1, 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, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [contiguous_1], Original ATen: [aten.clone]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clone_0.run(primals_2, buf0, 256, grid=grid(256), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [cdist], Original ATen: [aten._cdist_forward]
        buf1 = torch.ops.aten._cdist_forward.default(reinterpret_tensor(primals_1, (4, 64), (64, 1), 0), buf0, 1.0, None)
        buf2 = buf1
        del buf1
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [out_2], Original ATen: [aten.neg]
        triton_poi_fused_neg_1.run(buf3, 16, grid=grid(16), stream=stream0)
        # Topologically Sorted Source Nodes: [cdist_1], Original ATen: [aten._cdist_forward]
        buf4 = torch.ops.aten._cdist_forward.default(reinterpret_tensor(primals_1, (4, 64), (64, 1), 0), buf0, 2.0, None)
        buf5 = buf4
        del buf4
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.neg]
        triton_poi_fused_neg_2.run(buf5, buf6, 16, grid=grid(16), stream=stream0)
    return (buf3, buf6, reinterpret_tensor(primals_1, (4, 64), (64, 1), 0), buf0, buf5, )


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)
    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


def adder2d_function(X, W, stride=1, padding=0, groups=1):
    n_filters, _d_filter, h_filter, w_filter = W.size()
    n_x, _d_x, h_x, w_x = X.size()
    h_out = (h_x - h_filter + 2 * padding) / stride + 1
    w_out = (w_x - w_filter + 2 * padding) / stride + 1
    h_out, w_out = int(h_out), int(w_out)
    if groups == 1:
        X_col = torch.nn.functional.unfold(X.view(1, -1, h_x, w_x),
            h_filter, dilation=1, padding=padding, stride=stride).view(n_x,
            -1, h_out * w_out)
        X_col = X_col.permute(1, 2, 0).contiguous().view(X_col.size(1), -1)
        W_col = W.view(n_filters, -1)
        out_2 = -torch.cdist(W_col, X_col.transpose(0, 1).contiguous(), 1)
        out_2 = out_2.detach()
        out = -torch.cdist(W_col, X_col.transpose(0, 1).contiguous(), 2)
        out.data = out_2.data
        out = out.view(n_filters, h_out, w_out, n_x)
        out = out.permute(3, 0, 1, 2).contiguous()
        return out
    else:
        torch.Tensor(n_x, n_filters, h_out, w_out)
        outs = []
        for g in range(groups):
            X_part = X[:, g, ...].unsqueeze(1)
            W_part = W[g, ...].unsqueeze(0)
            X_col = torch.nn.functional.unfold(X_part.view(1, -1, h_x, w_x),
                h_filter, dilation=1, padding=padding, stride=stride).view(n_x,
                -1, h_out * w_out)
            X_col = X_col.permute(1, 2, 0).contiguous().view(X_col.size(1), -1)
            W_col = W_part.view(1, -1)
            out_2 = -torch.cdist(W_col, X_col.transpose(0, 1).contiguous(), 1)
            out_2 = out_2.detach()
            out = -torch.cdist(W_col, X_col.transpose(0, 1).contiguous(), 2)
            out.data = out_2.data
            out = out.view(1, h_out, w_out, n_x)
            out = out.permute(3, 0, 1, 2).contiguous()
            outs.append(out)
        return torch.cat(outs, dim=1)


class adder2d(nn.Module):

    def __init__(self, input_channel, output_channel, kernel_size, stride=1,
        padding=0, groups=1, bias=False):
        super(adder2d, self).__init__()
        self.stride = stride
        self.padding = padding
        self.input_channel = input_channel
        self.output_channel = output_channel
        self.kernel_size = kernel_size
        self.groups = groups
        self.adder = torch.nn.Parameter(nn.init.kaiming_normal_(torch.randn
            (output_channel, input_channel // groups, kernel_size,
            kernel_size)))
        self.bias = bias
        if bias:
            self.b = torch.nn.Parameter(nn.init.kaiming_uniform_(torch.
                zeros(output_channel)))

    def forward(self, x):
        output = adder2d_function(x, self.adder, self.stride, self.padding,
            self.groups)
        if self.bias:
            output += self.b.unsqueeze(0).unsqueeze(2).unsqueeze(3)
        return output

    def extra_repr(self):
        return (
            f"{'v2'.upper()}, {self.input_channel}, {self.output_channel}, " +
            f'kenrel_size={self.kernel_size}, stride={self.stride}, padding={self.padding}, groups={self.groups}, bias={self.bias}'
            )


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


def get_init_inputs():
    return [[], {'input_channel': 4, 'output_channel': 4, 'kernel_size': 4}]

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_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_neg_1(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = -tmp0
    tl.store(in_out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_poi_fused_neg_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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 = -tmp0
    tl.store(out_ptr0 + x0, tmp1, 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, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(256)](primals_2, buf0, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_2
        buf1 = torch.ops.aten._cdist_forward.default(reinterpret_tensor(
            primals_1, (4, 64), (64, 1), 0), buf0, 1.0, None)
        buf2 = buf1
        del buf1
        buf3 = buf2
        del buf2
        triton_poi_fused_neg_1[grid(16)](buf3, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        buf4 = torch.ops.aten._cdist_forward.default(reinterpret_tensor(
            primals_1, (4, 64), (64, 1), 0), buf0, 2.0, None)
        buf5 = buf4
        del buf4
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_neg_2[grid(16)](buf5, buf6, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
    return buf3, buf6, reinterpret_tensor(primals_1, (4, 64), (64, 1), 0
        ), buf0, buf5


def adder2d_function(X, W, stride=1, padding=0, groups=1):
    n_filters, _d_filter, h_filter, w_filter = W.size()
    n_x, _d_x, h_x, w_x = X.size()
    h_out = (h_x - h_filter + 2 * padding) / stride + 1
    w_out = (w_x - w_filter + 2 * padding) / stride + 1
    h_out, w_out = int(h_out), int(w_out)
    if groups == 1:
        X_col = torch.nn.functional.unfold(X.view(1, -1, h_x, w_x),
            h_filter, dilation=1, padding=padding, stride=stride).view(n_x,
            -1, h_out * w_out)
        X_col = X_col.permute(1, 2, 0).contiguous().view(X_col.size(1), -1)
        W_col = W.view(n_filters, -1)
        out_2 = -torch.cdist(W_col, X_col.transpose(0, 1).contiguous(), 1)
        out_2 = out_2.detach()
        out = -torch.cdist(W_col, X_col.transpose(0, 1).contiguous(), 2)
        out.data = out_2.data
        out = out.view(n_filters, h_out, w_out, n_x)
        out = out.permute(3, 0, 1, 2).contiguous()
        return out
    else:
        torch.Tensor(n_x, n_filters, h_out, w_out)
        outs = []
        for g in range(groups):
            X_part = X[:, g, ...].unsqueeze(1)
            W_part = W[g, ...].unsqueeze(0)
            X_col = torch.nn.functional.unfold(X_part.view(1, -1, h_x, w_x),
                h_filter, dilation=1, padding=padding, stride=stride).view(n_x,
                -1, h_out * w_out)
            X_col = X_col.permute(1, 2, 0).contiguous().view(X_col.size(1), -1)
            W_col = W_part.view(1, -1)
            out_2 = -torch.cdist(W_col, X_col.transpose(0, 1).contiguous(), 1)
            out_2 = out_2.detach()
            out = -torch.cdist(W_col, X_col.transpose(0, 1).contiguous(), 2)
            out.data = out_2.data
            out = out.view(1, h_out, w_out, n_x)
            out = out.permute(3, 0, 1, 2).contiguous()
            outs.append(out)
        return torch.cat(outs, dim=1)


class adder2dNew(nn.Module):

    def __init__(self, input_channel, output_channel, kernel_size, stride=1,
        padding=0, groups=1, bias=False):
        super(adder2dNew, self).__init__()
        self.stride = stride
        self.padding = padding
        self.input_channel = input_channel
        self.output_channel = output_channel
        self.kernel_size = kernel_size
        self.groups = groups
        self.adder = torch.nn.Parameter(nn.init.kaiming_normal_(torch.randn
            (output_channel, input_channel // groups, kernel_size,
            kernel_size)))
        self.bias = bias
        if bias:
            self.b = torch.nn.Parameter(nn.init.kaiming_uniform_(torch.
                zeros(output_channel)))

    def extra_repr(self):
        return (
            f"{'v2'.upper()}, {self.input_channel}, {self.output_channel}, " +
            f'kenrel_size={self.kernel_size}, stride={self.stride}, padding={self.padding}, groups={self.groups}, bias={self.bias}'
            )

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