# 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/cy/ccy7yru2zdy7ixr5e4xphx2c7ryepqm2xt3yzpntkputvlz2y3ua.py
# Topologically Sorted Source Nodes: [getitem_2], Original ATen: [aten.lift_fresh]
# Source node to ATen node mapping:
#   getitem_2 => full_default
# Graph fragment:
#   %full_default : [num_users=2] = call_function[target=torch.ops.aten.full.default](args = ([1], 0), kwargs = {dtype: torch.int64, layout: torch.strided, device: cuda:0, pin_memory: False})
triton_poi_fused_lift_fresh_0 = async_compile.triton('triton_poi_fused_lift_fresh_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=[1], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 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': {1: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=(1,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_lift_fresh_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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_lift_fresh_0(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    tmp0 = tl.full([1], 0, tl.int64)
    tl.store(out_ptr0 + (tl.full([XBLOCK], 0, tl.int32)), tmp0, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ge/cgeru34cvt6nnywd3qmie5evkuyzszcwjdamafkyhj6upvphaydq.py
# Topologically Sorted Source Nodes: [getitem_3], Original ATen: [aten.lift_fresh]
# Source node to ATen node mapping:
#   getitem_3 => full_default_1
# Graph fragment:
#   %full_default_1 : [num_users=2] = call_function[target=torch.ops.aten.full.default](args = ([1], -1), kwargs = {dtype: torch.int64, layout: torch.strided, device: cuda:0, pin_memory: False})
triton_poi_fused_lift_fresh_1 = async_compile.triton('triton_poi_fused_lift_fresh_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=[1], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 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': {1: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=(1,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_lift_fresh_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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_lift_fresh_1(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    tmp0 = tl.full([1], -1, tl.int64)
    tl.store(out_ptr0 + (tl.full([XBLOCK], 0, tl.int32)), tmp0, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ub/cubbhho3d4olluhydb6hq6avsqsjszp5c44cbvmmno7salnl32jt.py
# Topologically Sorted Source Nodes: [link_mat_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   link_mat_1 => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%index, %sub_1, %sub_2], 1), kwargs = {})
triton_poi_fused_cat_2 = async_compile.triton('triton_poi_fused_cat_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 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_cat_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, '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_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 240
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = (xindex // 12) % 5
    x0 = xindex % 3
    x3 = (xindex // 60)
    x4 = xindex % 12
    x5 = xindex
    tmp0 = x2
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + (x4 + (48*x3)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 - tmp6
    tmp8 = tl.sigmoid(tmp7)
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp4, tmp8, tmp9)
    tmp11 = tmp0 >= tmp3
    tmp12 = tl.full([1], 4, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr0 + (x0), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr1 + (12 + x4 + (12*((-1) + x2)) + (48*x3)), tmp14 & xmask, other=0.0)
    tmp17 = tmp15 - tmp16
    tmp18 = tl.sigmoid(tmp17)
    tmp19 = tl.load(in_ptr1 + (x4 + (12*((-1) + x2)) + (48*x3)), tmp14 & xmask, other=0.0)
    tmp20 = tmp15 - tmp19
    tmp21 = tl.sigmoid(tmp20)
    tmp22 = tmp18 - tmp21
    tmp23 = tl.full(tmp22.shape, 0.0, tmp22.dtype)
    tmp24 = tl.where(tmp14, tmp22, tmp23)
    tmp25 = tmp0 >= tmp12
    tmp26 = tl.full([1], 5, tl.int64)
    tmp27 = tmp0 < tmp26
    tmp28 = tl.load(in_ptr0 + (x0), tmp25 & xmask, eviction_policy='evict_last', other=0.0)
    tmp29 = tl.load(in_ptr1 + (36 + x4 + (48*x3)), tmp25 & xmask, eviction_policy='evict_last', other=0.0)
    tmp30 = tmp28 - tmp29
    tmp31 = tl.sigmoid(tmp30)
    tmp32 = 1.0
    tmp33 = tmp32 - tmp31
    tmp34 = tl.full(tmp33.shape, 0.0, tmp33.dtype)
    tmp35 = tl.where(tmp25, tmp33, tmp34)
    tmp36 = tl.where(tmp14, tmp24, tmp35)
    tmp37 = tl.where(tmp4, tmp10, tmp36)
    tl.store(out_ptr0 + (x5), tmp37, 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, (3, ), (1, ))
    assert_size_stride(primals_2, (4, 4, 4, 3), (48, 12, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [getitem_2], Original ATen: [aten.lift_fresh]
        stream0 = get_raw_stream(0)
        triton_poi_fused_lift_fresh_0.run(buf0, 1, grid=grid(1), stream=stream0)
        buf1 = empty_strided_cuda((1, ), (1, ), torch.int64)
        # Topologically Sorted Source Nodes: [getitem_3], Original ATen: [aten.lift_fresh]
        triton_poi_fused_lift_fresh_1.run(buf1, 1, grid=grid(1), stream=stream0)
        buf2 = empty_strided_cuda((4, 5, 4, 3), (60, 12, 3, 1), torch.float32)
        # Topologically Sorted Source Nodes: [link_mat_1], Original ATen: [aten.cat]
        triton_poi_fused_cat_2.run(primals_1, primals_2, buf2, 240, grid=grid(240), stream=stream0)
    return (buf2, primals_1, primals_2, buf0, buf1, )


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((3, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 3), (48, 12, 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
from torch import nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed


class LogisticCumulativeLink(nn.Module):
    """
    Converts a single number to the proportional odds of belonging to a class.
    Parameters
    ----------
    num_classes : int
        Number of ordered classes to partition the odds into.
    init_cutpoints : str (default='ordered')
        How to initialize the cutpoints of the model. Valid values are
        - ordered : cutpoints are initialized to halfway between each class.
        - random : cutpoints are initialized with random values.
    """

    def __init__(self, num_classes: 'int', init_cutpoints: 'str'='ordered'
        ) ->None:
        assert num_classes > 2, 'Only use this model if you have 3 or more classes'
        super().__init__()
        self.num_classes = num_classes
        self.init_cutpoints = init_cutpoints
        if init_cutpoints == 'ordered':
            num_cutpoints = self.num_classes - 1
            cutpoints = torch.arange(num_cutpoints).float() - num_cutpoints / 2
            self.cutpoints = nn.Parameter(cutpoints)
        elif init_cutpoints == 'random':
            cutpoints = torch.rand(self.num_classes - 1).sort()[0]
            self.cutpoints = nn.Parameter(cutpoints)
        else:
            raise ValueError(
                f'{init_cutpoints} is not a valid init_cutpoints type')

    def forward(self, X: 'torch.Tensor') ->torch.Tensor:
        """
        Equation (11) from
        "On the consistency of ordinal regression methods", Pedregosa et. al.
        """
        sigmoids = torch.sigmoid(self.cutpoints - X)
        link_mat = sigmoids[:, 1:] - sigmoids[:, :-1]
        link_mat = torch.cat((sigmoids[:, [0]], link_mat, 1 - sigmoids[:, [
            -1]]), dim=1)
        return link_mat


def get_inputs():
    return [torch.rand([4, 4, 4, 3])]


def get_init_inputs():
    return [[], {'num_classes': 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
from torch import nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
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_lift_fresh_0(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    tmp0 = tl.full([1], 0, tl.int64)
    tl.store(out_ptr0 + tl.full([XBLOCK], 0, tl.int32), tmp0, None)


@triton.jit
def triton_poi_fused_lift_fresh_1(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    tmp0 = tl.full([1], -1, tl.int64)
    tl.store(out_ptr0 + tl.full([XBLOCK], 0, tl.int32), tmp0, None)


@triton.jit
def triton_poi_fused_cat_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 240
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 12 % 5
    x0 = xindex % 3
    x3 = xindex // 60
    x4 = xindex % 12
    x5 = xindex
    tmp0 = x2
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + x0, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp6 = tl.load(in_ptr1 + (x4 + 48 * x3), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp7 = tmp5 - tmp6
    tmp8 = tl.sigmoid(tmp7)
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp4, tmp8, tmp9)
    tmp11 = tmp0 >= tmp3
    tmp12 = tl.full([1], 4, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr0 + x0, tmp14 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp16 = tl.load(in_ptr1 + (12 + x4 + 12 * (-1 + x2) + 48 * x3), tmp14 &
        xmask, other=0.0)
    tmp17 = tmp15 - tmp16
    tmp18 = tl.sigmoid(tmp17)
    tmp19 = tl.load(in_ptr1 + (x4 + 12 * (-1 + x2) + 48 * x3), tmp14 &
        xmask, other=0.0)
    tmp20 = tmp15 - tmp19
    tmp21 = tl.sigmoid(tmp20)
    tmp22 = tmp18 - tmp21
    tmp23 = tl.full(tmp22.shape, 0.0, tmp22.dtype)
    tmp24 = tl.where(tmp14, tmp22, tmp23)
    tmp25 = tmp0 >= tmp12
    tl.full([1], 5, tl.int64)
    tmp28 = tl.load(in_ptr0 + x0, tmp25 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp29 = tl.load(in_ptr1 + (36 + x4 + 48 * x3), tmp25 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp30 = tmp28 - tmp29
    tmp31 = tl.sigmoid(tmp30)
    tmp32 = 1.0
    tmp33 = tmp32 - tmp31
    tmp34 = tl.full(tmp33.shape, 0.0, tmp33.dtype)
    tmp35 = tl.where(tmp25, tmp33, tmp34)
    tmp36 = tl.where(tmp14, tmp24, tmp35)
    tmp37 = tl.where(tmp4, tmp10, tmp36)
    tl.store(out_ptr0 + x5, tmp37, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (3,), (1,))
    assert_size_stride(primals_2, (4, 4, 4, 3), (48, 12, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1,), (1,), torch.int64)
        get_raw_stream(0)
        triton_poi_fused_lift_fresh_0[grid(1)](buf0, 1, XBLOCK=1, num_warps
            =1, num_stages=1)
        buf1 = empty_strided_cuda((1,), (1,), torch.int64)
        triton_poi_fused_lift_fresh_1[grid(1)](buf1, 1, XBLOCK=1, num_warps
            =1, num_stages=1)
        buf2 = empty_strided_cuda((4, 5, 4, 3), (60, 12, 3, 1), torch.float32)
        triton_poi_fused_cat_2[grid(240)](primals_1, primals_2, buf2, 240,
            XBLOCK=128, num_warps=4, num_stages=1)
    return buf2, primals_1, primals_2, buf0, buf1


class LogisticCumulativeLinkNew(nn.Module):
    """
    Converts a single number to the proportional odds of belonging to a class.
    Parameters
    ----------
    num_classes : int
        Number of ordered classes to partition the odds into.
    init_cutpoints : str (default='ordered')
        How to initialize the cutpoints of the model. Valid values are
        - ordered : cutpoints are initialized to halfway between each class.
        - random : cutpoints are initialized with random values.
    """

    def __init__(self, num_classes: 'int', init_cutpoints: 'str'='ordered'
        ) ->None:
        assert num_classes > 2, 'Only use this model if you have 3 or more classes'
        super().__init__()
        self.num_classes = num_classes
        self.init_cutpoints = init_cutpoints
        if init_cutpoints == 'ordered':
            num_cutpoints = self.num_classes - 1
            cutpoints = torch.arange(num_cutpoints).float() - num_cutpoints / 2
            self.cutpoints = nn.Parameter(cutpoints)
        elif init_cutpoints == 'random':
            cutpoints = torch.rand(self.num_classes - 1).sort()[0]
            self.cutpoints = nn.Parameter(cutpoints)
        else:
            raise ValueError(
                f'{init_cutpoints} is not a valid init_cutpoints type')

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