# 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_1/inductor_cache/ch/cchqa5hdpbx7rsa6qui2lc3gc2ibrjiz764rtewh4vyywumhvxek.py
# Topologically Sorted Source Nodes: [sub, sigma, mul, weight, weight_1], Original ATen: [aten.sub, aten.softplus, aten.mul, aten.add, aten.linalg_vector_norm]
# Source node to ATen node mapping:
#   mul => mul
#   sigma => exp, gt, log1p, where
#   sub => sub
#   weight => add
#   weight_1 => pow_1, sum_1
# Graph fragment:
#   %sub : [num_users=3] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_2, 4), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %log1p : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%exp,), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sub, 20), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %sub, %log1p), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%where, %randn), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %primals_1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%add, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [1], True), kwargs = {})
triton_poi_fused_add_linalg_vector_norm_mul_softplus_sub_0 = async_compile.triton('triton_poi_fused_add_linalg_vector_norm_mul_softplus_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=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_linalg_vector_norm_mul_softplus_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 12, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_add_linalg_vector_norm_mul_softplus_sub_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr2 + (4*x0), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr2 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr2 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp37 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp43 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp45 = tl.load(in_ptr2 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp1 = 4.0
    tmp2 = tmp0 - tmp1
    tmp3 = 20.0
    tmp4 = tmp2 > tmp3
    tmp5 = tl_math.exp(tmp2)
    tmp6 = libdevice.log1p(tmp5)
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp9 = tmp7 * tmp8
    tmp11 = tmp9 + tmp10
    tmp12 = tmp11 * tmp11
    tmp14 = tmp13 - tmp1
    tmp15 = tmp14 > tmp3
    tmp16 = tl_math.exp(tmp14)
    tmp17 = libdevice.log1p(tmp16)
    tmp18 = tl.where(tmp15, tmp14, tmp17)
    tmp20 = tmp18 * tmp19
    tmp22 = tmp20 + tmp21
    tmp23 = tmp22 * tmp22
    tmp24 = tmp12 + tmp23
    tmp26 = tmp25 - tmp1
    tmp27 = tmp26 > tmp3
    tmp28 = tl_math.exp(tmp26)
    tmp29 = libdevice.log1p(tmp28)
    tmp30 = tl.where(tmp27, tmp26, tmp29)
    tmp32 = tmp30 * tmp31
    tmp34 = tmp32 + tmp33
    tmp35 = tmp34 * tmp34
    tmp36 = tmp24 + tmp35
    tmp38 = tmp37 - tmp1
    tmp39 = tmp38 > tmp3
    tmp40 = tl_math.exp(tmp38)
    tmp41 = libdevice.log1p(tmp40)
    tmp42 = tl.where(tmp39, tmp38, tmp41)
    tmp44 = tmp42 * tmp43
    tmp46 = tmp44 + tmp45
    tmp47 = tmp46 * tmp46
    tmp48 = tmp36 + tmp47
    tl.store(out_ptr0 + (x0), tmp48, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/iy/ciyoam77rqlmo46awlouw6beqi42jrkvczuvflcc5gyko2m7oxvf.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.div]
# Source node to ATen node mapping:
#   x => div_1
# Graph fragment:
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_3, %expand_1), kwargs = {})
triton_poi_fused_div_1 = async_compile.triton('triton_poi_fused_div_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_div_1(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
    x3 = xindex
    x0 = xindex % 16
    x2 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-12
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp0 / tmp14
    tl.store(out_ptr0 + (x3), tmp15, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/cu/ccudamigdjuge5lrohb5jywlfmakjinu7tfdwxtxrzyowiviiboc.py
# Topologically Sorted Source Nodes: [sub, sigma, mul, weight, weight_1], Original ATen: [aten.sub, aten.softplus, aten.mul, aten.add, aten.div]
# Source node to ATen node mapping:
#   mul => mul
#   sigma => exp, gt, log1p, where
#   sub => sub
#   weight => add
#   weight_1 => div
# Graph fragment:
#   %sub : [num_users=3] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_2, 4), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %log1p : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%exp,), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sub, 20), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %sub, %log1p), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%where, %randn), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %primals_1), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add, %expand), kwargs = {})
triton_poi_fused_add_div_mul_softplus_sub_2 = async_compile.triton('triton_poi_fused_add_div_mul_softplus_sub_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: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_mul_softplus_sub_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_add_div_mul_softplus_sub_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp8 = tl.load(in_ptr1 + (x2), xmask)
    tmp10 = tl.load(in_ptr2 + (x2), xmask)
    tmp12 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp1 = 4.0
    tmp2 = tmp0 - tmp1
    tmp3 = 20.0
    tmp4 = tmp2 > tmp3
    tmp5 = tl_math.exp(tmp2)
    tmp6 = libdevice.log1p(tmp5)
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp9 = tmp7 * tmp8
    tmp11 = tmp9 + tmp10
    tmp13 = libdevice.sqrt(tmp12)
    tmp14 = 1e-12
    tmp15 = triton_helpers.maximum(tmp13, tmp14)
    tmp16 = tmp11 / tmp15
    tl.store(out_ptr0 + (x2), tmp16, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/ra/cras443tkv6z6hwlmatoqwgpcamttsk37ugx7fpwtni3jqua6ejo.py
# Topologically Sorted Source Nodes: [score_1], Original ATen: [aten.div]
# Source node to ATen node mapping:
#   score_1 => div_2
# Graph fragment:
#   %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%view_1, 0.05), kwargs = {})
triton_poi_fused_div_3 = async_compile.triton('triton_poi_fused_div_3', '''
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: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_div_3(in_out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    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 = 20.0
    tmp2 = tmp0 * tmp1
    tl.store(in_out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [randn_like], Original ATen: [aten.randn_like]
        buf0 = torch.ops.aten.randn.default([4, 4], dtype=torch.float32, device=device(type='cuda', index=0), pin_memory=False)
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        # Topologically Sorted Source Nodes: [sub, sigma, mul, weight, weight_1], Original ATen: [aten.sub, aten.softplus, aten.mul, aten.add, aten.linalg_vector_norm]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_linalg_vector_norm_mul_softplus_sub_0.run(primals_2, buf1, primals_1, buf2, 4, grid=grid(4), stream=stream0)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.div]
        triton_poi_fused_div_1.run(primals_3, buf3, 256, grid=grid(256), stream=stream0)
        del primals_3
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sub, sigma, mul, weight, weight_1], Original ATen: [aten.sub, aten.softplus, aten.mul, aten.add, aten.div]
        triton_poi_fused_add_div_mul_softplus_sub_2.run(primals_2, buf1, primals_1, buf2, buf4, 16, grid=grid(16), stream=stream0)
        del buf2
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [score], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0), reinterpret_tensor(buf4, (4, 4), (1, 4), 0), out=buf5)
        del buf4
        buf6 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf5  # reuse
        # Topologically Sorted Source Nodes: [score_1], Original ATen: [aten.div]
        triton_poi_fused_div_3.run(buf6, 256, grid=grid(256), stream=stream0)
    return (buf6, primals_1, primals_2, buf1, reinterpret_tensor(buf3, (64, 4), (4, 1), 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, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = 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])
    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 torch.nn import functional as F


class StochasticClassifier(nn.Module):

    def __init__(self, num_features, num_classes, temp=0.05):
        super().__init__()
        self.mu = nn.Parameter(0.01 * torch.randn(num_classes, num_features))
        self.sigma = nn.Parameter(torch.zeros(num_classes, num_features))
        self.temp = temp

    def forward(self, x, stochastic=True):
        mu = self.mu
        sigma = self.sigma
        if stochastic:
            sigma = F.softplus(sigma - 4)
            weight = sigma * torch.randn_like(mu) + mu
        else:
            weight = mu
        weight = F.normalize(weight, p=2, dim=1)
        x = F.normalize(x, p=2, dim=1)
        score = F.linear(x, weight)
        score = score / self.temp
        return score


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


def get_init_inputs():
    return [[], {'num_features': 4, 'num_classes': 4}]

import torch
from torch import device
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._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
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_add_linalg_vector_norm_mul_softplus_sub_0(in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr2 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp19 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp21 = tl.load(in_ptr2 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp25 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp31 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp33 = tl.load(in_ptr2 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp37 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp43 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp45 = tl.load(in_ptr2 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp1 = 4.0
    tmp2 = tmp0 - tmp1
    tmp3 = 20.0
    tmp4 = tmp2 > tmp3
    tmp5 = tl_math.exp(tmp2)
    tmp6 = libdevice.log1p(tmp5)
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp9 = tmp7 * tmp8
    tmp11 = tmp9 + tmp10
    tmp12 = tmp11 * tmp11
    tmp14 = tmp13 - tmp1
    tmp15 = tmp14 > tmp3
    tmp16 = tl_math.exp(tmp14)
    tmp17 = libdevice.log1p(tmp16)
    tmp18 = tl.where(tmp15, tmp14, tmp17)
    tmp20 = tmp18 * tmp19
    tmp22 = tmp20 + tmp21
    tmp23 = tmp22 * tmp22
    tmp24 = tmp12 + tmp23
    tmp26 = tmp25 - tmp1
    tmp27 = tmp26 > tmp3
    tmp28 = tl_math.exp(tmp26)
    tmp29 = libdevice.log1p(tmp28)
    tmp30 = tl.where(tmp27, tmp26, tmp29)
    tmp32 = tmp30 * tmp31
    tmp34 = tmp32 + tmp33
    tmp35 = tmp34 * tmp34
    tmp36 = tmp24 + tmp35
    tmp38 = tmp37 - tmp1
    tmp39 = tmp38 > tmp3
    tmp40 = tl_math.exp(tmp38)
    tmp41 = libdevice.log1p(tmp40)
    tmp42 = tl.where(tmp39, tmp38, tmp41)
    tmp44 = tmp42 * tmp43
    tmp46 = tmp44 + tmp45
    tmp47 = tmp46 * tmp46
    tmp48 = tmp36 + tmp47
    tl.store(out_ptr0 + x0, tmp48, xmask)


@triton.jit
def triton_poi_fused_div_1(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
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-12
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp0 / tmp14
    tl.store(out_ptr0 + x3, tmp15, xmask)


@triton.jit
def triton_poi_fused_add_div_mul_softplus_sub_2(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp8 = tl.load(in_ptr1 + x2, xmask)
    tmp10 = tl.load(in_ptr2 + x2, xmask)
    tmp12 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp1 = 4.0
    tmp2 = tmp0 - tmp1
    tmp3 = 20.0
    tmp4 = tmp2 > tmp3
    tmp5 = tl_math.exp(tmp2)
    tmp6 = libdevice.log1p(tmp5)
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp9 = tmp7 * tmp8
    tmp11 = tmp9 + tmp10
    tmp13 = libdevice.sqrt(tmp12)
    tmp14 = 1e-12
    tmp15 = triton_helpers.maximum(tmp13, tmp14)
    tmp16 = tmp11 / tmp15
    tl.store(out_ptr0 + x2, tmp16, xmask)


@triton.jit
def triton_poi_fused_div_3(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    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 = 20.0
    tmp2 = tmp0 * tmp1
    tl.store(in_out_ptr0 + x0, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = torch.ops.aten.randn.default([4, 4], dtype=torch.float32,
            device=device(type='cuda', index=0), pin_memory=False)
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_linalg_vector_norm_mul_softplus_sub_0[grid(4)](
            primals_2, buf1, primals_1, buf2, 4, XBLOCK=4, num_warps=1,
            num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_div_1[grid(256)](primals_3, buf3, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_3
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_add_div_mul_softplus_sub_2[grid(16)](primals_2,
            buf1, primals_1, buf2, buf4, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        del buf2
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0),
            reinterpret_tensor(buf4, (4, 4), (1, 4), 0), out=buf5)
        del buf4
        buf6 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf5
        triton_poi_fused_div_3[grid(256)](buf6, 256, XBLOCK=256, num_warps=
            4, num_stages=1)
    return buf6, primals_1, primals_2, buf1, reinterpret_tensor(buf3, (64, 
        4), (4, 1), 0)


class StochasticClassifierNew(nn.Module):

    def __init__(self, num_features, num_classes, temp=0.05):
        super().__init__()
        self.mu = nn.Parameter(0.01 * torch.randn(num_classes, num_features))
        self.sigma = nn.Parameter(torch.zeros(num_classes, num_features))
        self.temp = temp

    def forward(self, input_0):
        primals_1 = self.mu
        primals_2 = self.sigma
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
