# 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/w7/cw7kmlo7vngjrzz2fl4nrd6rnf7o2266le56li6smcptk7jnvzpz.py
# Topologically Sorted Source Nodes: [diag], Original ATen: [aten.diag_embed]
# Source node to ATen node mapping:
#   diag => eq, full_default, iota, where
# Graph fragment:
#   %iota : [num_users=2] = call_function[target=torch.ops.prims.iota.default](args = (4,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Tensor](args = (%iota, %unsqueeze_1), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%eq, %permute, %full_default), kwargs = {})
triton_poi_fused_diag_embed_0 = async_compile.triton('triton_poi_fused_diag_embed_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=[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_diag_embed_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_diag_embed_0(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 % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp0 = x0
    tmp1 = x1
    tmp2 = tmp0 == tmp1
    tmp4 = 0.0
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tl.store(out_ptr0 + (x2), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/tc/ctcagp37ljugm52zu6ckorigrppqo67voefe2f2odg5r6hyllhyu.py
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d => convolution
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_4, %view_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_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: '*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_convolution_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, ), (1, ))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [diag], Original ATen: [aten.diag_embed]
        stream0 = get_raw_stream(0)
        triton_poi_fused_diag_embed_0.run(primals_1, buf0, 16, grid=grid(16), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mm], Original ATen: [aten.mm]
        extern_kernels.mm(buf0, reinterpret_tensor(primals_2, (4, 64), (64, 1), 0), out=buf1)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(primals_4, reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0), stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 1, 1), (4, 1, 1, 1))
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf3, primals_3, 16, grid=grid(16), stream=stream0)
        del primals_3
    return (buf3, primals_4, reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(buf0, (4, 4), (1, 4), 0), reinterpret_tensor(primals_2, (64, 4), (1, 64), 0), )


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, ), (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)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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 math
import torch
import numpy as np
import torch.nn as nn
import torch.utils.data
import torch.nn.functional as F
import scipy.sparse as sparse
from torch.nn.modules.utils import _pair


class SparseConv2d(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, k, rho_init,
        rho_maximum, mu, stride=1, padding=0, bias=True):
        super(SparseConv2d, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = _pair(kernel_size)
        self.stride = _pair(stride)
        self.padding = _pair(padding)
        self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels,
            *self.kernel_size))
        self.v = nn.Parameter(torch.Tensor(out_channels))
        if bias:
            self.bias = nn.Parameter(torch.Tensor(out_channels))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()
        self.k = k
        self.rho_init = rho_init
        self.rho = rho_init
        self.rho_maximum = rho_maximum
        self.mu = mu
        self.y1 = np.zeros([out_channels, 1])
        self.y2 = np.zeros([out_channels, 1])
        self.z1 = np.zeros([out_channels, 1])
        self.z2 = np.zeros([out_channels, 1])
        self.v_np = np.zeros([out_channels, 1])
        self.P = sparse.csc_matrix(np.eye(self.out_channels))
        self.q = np.zeros([self.out_channels, 1])
        self.E = sparse.csc_matrix(np.vstack([np.eye(self.out_channels), np
            .ones([self.out_channels, 1]).transpose()]))
        self.l = np.vstack([np.zeros([self.out_channels, 1]), self.k * np.
            ones([1, 1])])
        self.u = np.vstack([np.ones([self.out_channels, 1]), self.k * np.
            ones([1, 1])])

    def reset_parameters(self):
        stdv = math.sqrt(2.0 / sum(self.weight.size()))
        self.weight.data.normal_(0, stdv)
        if self.bias is not None:
            self.bias.data.zero_()
        v_shape = self.v.data.numpy().shape
        np_v = np.ones(v_shape)
        self.v.data = torch.from_numpy(np_v).float()

    def forward(self, input):
        return F.conv2d(input, torch.diag(self.v).mm(self.weight.view(self.
            out_channels, self.in_channels * self.kernel_size[0] * self.
            kernel_size[1])).view_as(self.weight), self.bias, self.stride,
            self.padding)


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 4, 'k':
        4, 'rho_init': 4, 'rho_maximum': 4, 'mu': 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 math
import numpy as np
import torch.nn as nn
import torch.utils.data
import scipy.sparse as sparse
from torch.nn.modules.utils import _pair
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_diag_embed_0(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 % 4
    x1 = xindex // 4
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp0 = x0
    tmp1 = x1
    tmp2 = tmp0 == tmp1
    tmp4 = 0.0
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tl.store(out_ptr0 + x2, tmp5, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4,), (1,))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_diag_embed_0[grid(16)](primals_1, buf0, 16, XBLOCK
            =16, num_warps=1, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 64), (64, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(primals_2, (4, 64), (64,
            1), 0), out=buf1)
        buf2 = extern_kernels.convolution(primals_4, reinterpret_tensor(
            buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0), stride=(1, 1), padding=
            (0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0
            ), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 1, 1), (4, 1, 1, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_1[grid(16)](buf3, primals_3, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_3
    return buf3, primals_4, reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16,
        4, 1), 0), reinterpret_tensor(buf0, (4, 4), (1, 4), 0
        ), reinterpret_tensor(primals_2, (64, 4), (1, 64), 0)


class SparseConv2dNew(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, k, rho_init,
        rho_maximum, mu, stride=1, padding=0, bias=True):
        super(SparseConv2dNew, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = _pair(kernel_size)
        self.stride = _pair(stride)
        self.padding = _pair(padding)
        self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels,
            *self.kernel_size))
        self.v = nn.Parameter(torch.Tensor(out_channels))
        if bias:
            self.bias = nn.Parameter(torch.Tensor(out_channels))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()
        self.k = k
        self.rho_init = rho_init
        self.rho = rho_init
        self.rho_maximum = rho_maximum
        self.mu = mu
        self.y1 = np.zeros([out_channels, 1])
        self.y2 = np.zeros([out_channels, 1])
        self.z1 = np.zeros([out_channels, 1])
        self.z2 = np.zeros([out_channels, 1])
        self.v_np = np.zeros([out_channels, 1])
        self.P = sparse.csc_matrix(np.eye(self.out_channels))
        self.q = np.zeros([self.out_channels, 1])
        self.E = sparse.csc_matrix(np.vstack([np.eye(self.out_channels), np
            .ones([self.out_channels, 1]).transpose()]))
        self.l = np.vstack([np.zeros([self.out_channels, 1]), self.k * np.
            ones([1, 1])])
        self.u = np.vstack([np.ones([self.out_channels, 1]), self.k * np.
            ones([1, 1])])

    def reset_parameters(self):
        stdv = math.sqrt(2.0 / sum(self.weight.size()))
        self.weight.data.normal_(0, stdv)
        if self.bias is not None:
            self.bias.data.zero_()
        v_shape = self.v.data.numpy().shape
        np_v = np.ones(v_shape)
        self.v.data = torch.from_numpy(np_v).float()

    def forward(self, input_0):
        primals_2 = self.weight
        primals_1 = self.v
        primals_3 = self.bias
        primals_4 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
