# 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_1/inductor_cache/7p/c7pozh4btr2mbqngpjlc7jjcl3rvmhvmosru4rguy6eryt5zjot2.py
# Topologically Sorted Source Nodes: [m, sub, s, sqrt, mul, truediv, erf, add, x_1], Original ATen: [aten.mean, aten.sub, aten.std, aten.sqrt, aten.mul, aten.div, aten.erf, aten.add]
# Source node to ATen node mapping:
#   add => add
#   erf => erf
#   m => mean
#   mul => mul
#   s => var
#   sqrt => full_default
#   sub => sub
#   truediv => div
#   x_1 => mul_1
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%view, [1]), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view, %view_1), kwargs = {})
#   %var : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%view, [1]), kwargs = {correction: 1.0})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 1.4142135381698608), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_2, %full_default), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, %mul), kwargs = {})
#   %erf : [num_users=1] = call_function[target=torch.ops.aten.erf.default](args = (%div,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%erf, 1.0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 0.5), kwargs = {})
triton_per_fused_add_div_erf_mean_mul_sqrt_std_sub_0 = async_compile.triton('triton_per_fused_add_div_erf_mean_mul_sqrt_std_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.persistent_reduction(
    size_hints=[4, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: '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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_erf_mean_mul_sqrt_std_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 4, '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}
)
@triton.jit
def triton_per_fused_add_div_erf_mean_mul_sqrt_std_sub_0(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (16*x0)), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp6 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp1 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 16.0
    tmp20 = tmp4 / tmp19
    tmp21 = tmp0 - tmp20
    tmp22 = 15.0
    tmp23 = tmp18 / tmp22
    tmp24 = libdevice.sqrt(tmp23)
    tmp25 = 1.4142135381698608
    tmp26 = tmp24 * tmp25
    tmp27 = tmp21 / tmp26
    tmp28 = libdevice.erf(tmp27)
    tmp29 = 1.0
    tmp30 = tmp28 + tmp29
    tmp31 = 0.5
    tmp32 = tmp30 * tmp31
    tl.store(out_ptr2 + (r1 + (16*x0)), tmp32, 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), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf4 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [m, sub, s, sqrt, mul, truediv, erf, add, x_1], Original ATen: [aten.mean, aten.sub, aten.std, aten.sqrt, aten.mul, aten.div, aten.erf, aten.add]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_div_erf_mean_mul_sqrt_std_sub_0.run(arg0_1, buf4, 4, 16, grid=grid(4), stream=stream0)
        del arg0_1
    return (reinterpret_tensor(buf4, (4, 4, 4), (16, 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
    arg0_1 = rand_strided((4, 4, 4), (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
import torch.autograd


class GaussNorm2D(nn.Module):
    """
        This will normalize a saliency map to range from 0 to 1 via normal cumulative distribution function. 
        
        Input and output will be a 3D tensor of size [batch size x height x width]. 
        
        Input can be any real valued number (supported by hardware)
        Output will range from 0 to 1
        
        Notes:
        
        (1) GammaNorm2D will produce slightly better results
            The sum ROAR/KAR will improve from 1.44 to 1.45 for FastCAM using GradCAM.
        (2) This method is a bit less expensive than GammaNorm2D.
    """

    def __init__(self, const_mean=None, const_std=None):
        super(GaussNorm2D, self).__init__()
        assert isinstance(const_mean, float) or const_mean is None
        assert isinstance(const_std, float) or const_std is None
        self.const_mean = const_mean
        self.const_std = const_std

    def forward(self, x):
        """
            Input: 
                x:     A Torch Tensor image with shape [batch size x height x width] e.g. [64,7,7]
            Return:
                x:     x Normalized by computing mean and std over each individual batch item and squashed with a 
                       Normal/Gaussian CDF.  
        """
        assert torch.is_tensor(x), 'Input must be a Torch Tensor'
        assert len(x.size()
            ) == 3, 'Input should be sizes [batch size x height x width]'
        s0 = x.size()[0]
        s1 = x.size()[1]
        s2 = x.size()[2]
        x = x.reshape(s0, s1 * s2)
        """
            Compute Mean
        """
        if self.const_mean is None:
            m = x.mean(dim=1)
            m = m.reshape(m.size()[0], 1)
        else:
            m = self.const_mean
        """
            Compute Standard Deviation
        """
        if self.const_std is None:
            s = x.std(dim=1)
            s = s.reshape(s.size()[0], 1)
        else:
            s = self.const_std
        """
            The normal cumulative distribution function is used to squash the values from within the range of 0 to 1
        """
        x = 0.5 * (1.0 + torch.erf((x - m) / (s * torch.sqrt(torch.tensor(
            2.0)))))
        x = x.reshape(s0, s1, s2)
        return x


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


def get_init_inputs():
    return [[], {}]

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._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.autograd
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_per_fused_add_div_erf_mean_mul_sqrt_std_sub_0(in_ptr0, out_ptr2,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp6 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp1 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 16.0
    tmp20 = tmp4 / tmp19
    tmp21 = tmp0 - tmp20
    tmp22 = 15.0
    tmp23 = tmp18 / tmp22
    tmp24 = libdevice.sqrt(tmp23)
    tmp25 = 1.4142135381698608
    tmp26 = tmp24 * tmp25
    tmp27 = tmp21 / tmp26
    tmp28 = libdevice.erf(tmp27)
    tmp29 = 1.0
    tmp30 = tmp28 + tmp29
    tmp31 = 0.5
    tmp32 = tmp30 * tmp31
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp32, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf4 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_add_div_erf_mean_mul_sqrt_std_sub_0[grid(4)](arg0_1,
            buf4, 4, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
    return reinterpret_tensor(buf4, (4, 4, 4), (16, 4, 1), 0),


class GaussNorm2DNew(nn.Module):
    """
        This will normalize a saliency map to range from 0 to 1 via normal cumulative distribution function. 
        
        Input and output will be a 3D tensor of size [batch size x height x width]. 
        
        Input can be any real valued number (supported by hardware)
        Output will range from 0 to 1
        
        Notes:
        
        (1) GammaNorm2D will produce slightly better results
            The sum ROAR/KAR will improve from 1.44 to 1.45 for FastCAM using GradCAM.
        (2) This method is a bit less expensive than GammaNorm2D.
    """

    def __init__(self, const_mean=None, const_std=None):
        super(GaussNorm2DNew, self).__init__()
        assert isinstance(const_mean, float) or const_mean is None
        assert isinstance(const_std, float) or const_std is None
        self.const_mean = const_mean
        self.const_std = const_std

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