# 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_4/inductor_cache/by/cbyov6dryaqjwmmslffo5d7emxcqphfyezaudbzliyrapdu3kwho.py
# Topologically Sorted Source Nodes: [VperW_diff, ge_1, Better_j_than_i], Original ATen: [aten.sub, aten.ge, aten.mul]
# Source node to ATen node mapping:
#   Better_j_than_i => mul
#   VperW_diff => sub
#   ge_1 => ge_1
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%unsqueeze, %unsqueeze_1), kwargs = {})
#   %ge_1 : [num_users=1] = call_function[target=torch.ops.aten.ge.Scalar](args = (%sub, 0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%ge_1, 1.0), kwargs = {})
triton_poi_fused_ge_mul_sub_0 = async_compile.triton('triton_poi_fused_ge_mul_sub_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=[64], 
    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_ge_mul_sub_0', 'mutated_arg_names': [], '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_ge_mul_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = (xindex // 16)
    x3 = (xindex // 4)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (x3), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = 0.0
    tmp4 = tmp2 >= tmp3
    tmp5 = tmp4.to(tl.float32)
    tmp6 = 1.0
    tmp7 = tmp5 * tmp6
    tl.store(out_ptr0 + (x4), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/7k/c7kgo6bkzv3gvnwofunppraug5x44tpcbfj4os2obs6d7djs6z2q.py
# Topologically Sorted Source Nodes: [sub_1, Satisfying_Constr, mul_1], Original ATen: [aten.rsub, aten.ge, aten.mul]
# Source node to ATen node mapping:
#   Satisfying_Constr => ge_2
#   mul_1 => mul_1
#   sub_1 => sub_1
# Graph fragment:
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (4, %squeeze), kwargs = {})
#   %ge_2 : [num_users=1] = call_function[target=torch.ops.aten.ge.Scalar](args = (%sub_1, 0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%ge_2, %arg1_1), kwargs = {})
triton_poi_fused_ge_mul_rsub_1 = async_compile.triton('triton_poi_fused_ge_mul_rsub_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_ge_mul_rsub_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_ge_mul_rsub_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
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp6 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 4.0
    tmp2 = tmp1 - tmp0
    tmp3 = 0.0
    tmp4 = tmp2 >= tmp3
    tmp5 = tmp4.to(tl.float32)
    tmp7 = tmp5 * tmp6
    tl.store(in_out_ptr0 + (x0), tmp7, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [VperW_diff, ge_1, Better_j_than_i], Original ATen: [aten.sub, aten.ge, aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_ge_mul_sub_0.run(arg0_1, buf0, 64, grid=grid(64), stream=stream0)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [VperW_diff, ge_1, Better_j_than_i, matmul], Original ATen: [aten.sub, aten.ge, aten.mul, aten.bmm]
        extern_kernels.bmm(buf0, reinterpret_tensor(arg1_1, (4, 4, 1), (4, 1, 1), 0), out=buf1)
        del buf0
        buf2 = reinterpret_tensor(buf1, (4, 4), (4, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [sub_1, Satisfying_Constr, mul_1], Original ATen: [aten.rsub, aten.ge, aten.mul]
        triton_poi_fused_ge_mul_rsub_1.run(buf2, arg1_1, 16, grid=grid(16), stream=stream0)
        del arg1_1
    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, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_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)

from torch.nn import Module
import torch
from torch.nn.modules.module import Module


class AllocatingLayer(Module):
    """The actor NN base its output for the case of full CSI  on a continuous relaxation of the problem. Specifically it gives
    a value for every user. This layer will start allocating to the most valuable bw until no more resources are available for 
    the least valuable users
    """

    def __init__(self, Resource):
        super(AllocatingLayer, self).__init__()
        self.W = Resource

    def forward(self, values, weights):
        batchSize, Kusers = values.shape
        assert list(weights.size()) == [batchSize, Kusers] and (values >= 0
            ).all()
        VperW_diff = values.unsqueeze(dim=1).detach() - values.unsqueeze(dim=2
            ).detach()
        assert list(VperW_diff.shape) == [batchSize, Kusers, Kusers]
        Better_j_than_i = 1.0 * (VperW_diff >= 0)
        Satisfying_Constr = self.W - torch.matmul(Better_j_than_i, weights.
            unsqueeze(dim=2)).squeeze() >= 0
        assert list(Satisfying_Constr.shape) == [batchSize, Kusers]
        return Satisfying_Constr * weights


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


def get_init_inputs():
    return [[], {'Resource': 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
from torch.nn import Module
from torch.nn.modules.module import Module
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_ge_mul_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex // 16
    x3 = xindex // 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp1 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = 0.0
    tmp4 = tmp2 >= tmp3
    tmp5 = tmp4.to(tl.float32)
    tmp6 = 1.0
    tmp7 = tmp5 * tmp6
    tl.store(out_ptr0 + x4, tmp7, xmask)


@triton.jit
def triton_poi_fused_ge_mul_rsub_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
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp6 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 4.0
    tmp2 = tmp1 - tmp0
    tmp3 = 0.0
    tmp4 = tmp2 >= tmp3
    tmp5 = tmp4.to(tl.float32)
    tmp7 = tmp5 * tmp6
    tl.store(in_out_ptr0 + x0, tmp7, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_ge_mul_sub_0[grid(64)](arg0_1, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(buf0, reinterpret_tensor(arg1_1, (4, 4, 1), (4, 
            1, 1), 0), out=buf1)
        del buf0
        buf2 = reinterpret_tensor(buf1, (4, 4), (4, 1), 0)
        del buf1
        triton_poi_fused_ge_mul_rsub_1[grid(16)](buf2, arg1_1, 16, XBLOCK=
            16, num_warps=1, num_stages=1)
        del arg1_1
    return buf2,


class AllocatingLayerNew(Module):
    """The actor NN base its output for the case of full CSI  on a continuous relaxation of the problem. Specifically it gives
    a value for every user. This layer will start allocating to the most valuable bw until no more resources are available for 
    the least valuable users
    """

    def __init__(self, Resource):
        super(AllocatingLayerNew, self).__init__()
        self.W = Resource

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]
