# 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/ng/cngqivfvvl2dq67u2ui7jcj3osrhwji3l6tjo2vukmtvgnfcg6nv.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%arg0_1, %pow_2, %pow_3, %pow_4], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_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_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16) % 16
    x0 = xindex % 16
    x2 = (xindex // 256)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (16*x1) + (64*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr0 + (x0 + (16*((-4) + x1)) + (64*x2)), tmp9 & xmask, other=0.0)
    tmp11 = tmp10 * tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp9, tmp11, tmp12)
    tmp14 = tmp0 >= tmp7
    tmp15 = tl.full([1], 12, tl.int64)
    tmp16 = tmp0 < tmp15
    tmp17 = tmp14 & tmp16
    tmp18 = tl.load(in_ptr0 + (x0 + (16*((-8) + x1)) + (64*x2)), tmp17 & xmask, other=0.0)
    tmp19 = tmp18 * tmp18
    tmp20 = tmp19 * tmp18
    tmp21 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
    tmp22 = tl.where(tmp17, tmp20, tmp21)
    tmp23 = tmp0 >= tmp15
    tmp24 = tl.full([1], 16, tl.int64)
    tmp25 = tmp0 < tmp24
    tmp26 = tl.load(in_ptr0 + (x0 + (16*((-12) + x1)) + (64*x2)), tmp23 & xmask, other=0.0)
    tmp27 = tmp26 * tmp26
    tmp28 = tmp27 * tmp27
    tmp29 = tl.full(tmp28.shape, 0.0, tmp28.dtype)
    tmp30 = tl.where(tmp23, tmp28, tmp29)
    tmp31 = tl.where(tmp17, tmp22, tmp30)
    tmp32 = tl.where(tmp9, tmp13, tmp31)
    tmp33 = tl.where(tmp4, tmp5, tmp32)
    tl.store(out_ptr0 + (x3), tmp33, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16, 4, 4), (256, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(arg0_1, buf0, 1024, grid=grid(1024), stream=stream0)
        del arg0_1
    return (buf0, )


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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_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.nn as nn
from warnings import warn


class MonomialNN(nn.Module):
    """A network that expands its input to a given list of monomials.

    Its output shape will be (n_samples, n_input_units * n_degrees)

    :param degrees: max degree to be included, or a list of degrees that will be used
    :type degrees: int or list[int] or tuple[int]
    """

    def __init__(self, degrees):
        super(MonomialNN, self).__init__()
        if isinstance(degrees, int):
            degrees = [d for d in range(1, degrees + 1)]
        self.degrees = tuple(degrees)
        if len(self.degrees) == 0:
            raise ValueError('No degrees used, check `degrees` argument again')
        if 0 in degrees:
            warn(
                'One of the degrees is 0 which might introduce redundant features'
                )
        if len(set(self.degrees)) < len(self.degrees):
            warn(f'Duplicate degrees found: {self.degrees}')

    def forward(self, x):
        return torch.cat([(x ** d) for d in self.degrees], dim=1)

    def __repr__(self):
        return f'{self.__class__.__name__}(degrees={self.degrees})'

    def __str__(self):
        return self.__repr__()


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


def get_init_inputs():
    return [[], {'degrees': 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
from warnings import warn
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_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 16
    x0 = xindex % 16
    x2 = xindex // 256
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr0 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp9 & xmask,
        other=0.0)
    tmp11 = tmp10 * tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp9, tmp11, tmp12)
    tmp14 = tmp0 >= tmp7
    tmp15 = tl.full([1], 12, tl.int64)
    tmp16 = tmp0 < tmp15
    tmp17 = tmp14 & tmp16
    tmp18 = tl.load(in_ptr0 + (x0 + 16 * (-8 + x1) + 64 * x2), tmp17 &
        xmask, other=0.0)
    tmp19 = tmp18 * tmp18
    tmp20 = tmp19 * tmp18
    tmp21 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
    tmp22 = tl.where(tmp17, tmp20, tmp21)
    tmp23 = tmp0 >= tmp15
    tl.full([1], 16, tl.int64)
    tmp26 = tl.load(in_ptr0 + (x0 + 16 * (-12 + x1) + 64 * x2), tmp23 &
        xmask, other=0.0)
    tmp27 = tmp26 * tmp26
    tmp28 = tmp27 * tmp27
    tmp29 = tl.full(tmp28.shape, 0.0, tmp28.dtype)
    tmp30 = tl.where(tmp23, tmp28, tmp29)
    tmp31 = tl.where(tmp17, tmp22, tmp30)
    tmp32 = tl.where(tmp9, tmp13, tmp31)
    tmp33 = tl.where(tmp4, tmp5, tmp32)
    tl.store(out_ptr0 + x3, tmp33, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16, 4, 4), (256, 16, 4, 1), torch.float32
            )
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(1024)](arg0_1, buf0, 1024, XBLOCK=128,
            num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class MonomialNNNew(nn.Module):
    """A network that expands its input to a given list of monomials.

    Its output shape will be (n_samples, n_input_units * n_degrees)

    :param degrees: max degree to be included, or a list of degrees that will be used
    :type degrees: int or list[int] or tuple[int]
    """

    def __init__(self, degrees):
        super(MonomialNNNew, self).__init__()
        if isinstance(degrees, int):
            degrees = [d for d in range(1, degrees + 1)]
        self.degrees = tuple(degrees)
        if len(self.degrees) == 0:
            raise ValueError('No degrees used, check `degrees` argument again')
        if 0 in degrees:
            warn(
                'One of the degrees is 0 which might introduce redundant features'
                )
        if len(set(self.degrees)) < len(self.degrees):
            warn(f'Duplicate degrees found: {self.degrees}')

    def __repr__(self):
        return f'{self.__class__.__name__}(degrees={self.degrees})'

    def __str__(self):
        return self.__repr__()

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