# 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/y6/cy6wrdk7w3v6irqdpgj5h7c5osop4flxcgmudzw6yf7soycxgc4a.py
# Topologically Sorted Source Nodes: [max_1, min_1, nval, range_1, gt, eps_mat, range_2, x_1], Original ATen: [aten.max, aten.min, aten.sub, aten.gt, aten.add, aten.where, aten.div]
# Source node to ATen node mapping:
#   eps_mat => full_default
#   gt => gt
#   max_1 => max_1
#   min_1 => min_1
#   nval => sub
#   range_1 => sub_1
#   range_2 => where
#   x_1 => div
# Graph fragment:
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%view, 1), kwargs = {})
#   %min_1 : [num_users=1] = call_function[target=torch.ops.aten.min.dim](args = (%view, 1), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view, %view_2), kwargs = {})
#   %sub_1 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_1, %view_2), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%sub_1, 1e-09), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4, 1], 9.999999717180685e-10), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %sub_1, %full_default), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, %where), kwargs = {})
triton_per_fused_add_div_gt_max_min_sub_where_0 = async_compile.triton('triton_per_fused_add_div_gt_max_min_sub_where_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_gt_max_min_sub_where_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 2, '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_gt_max_min_sub_where_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, float("-inf"))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tmp6 = tl.where(xmask, tmp1, float("inf"))
    tmp7 = triton_helpers.min2(tmp6, 1)[:, None]
    tmp8 = tmp0 - tmp7
    tmp9 = tmp4 - tmp7
    tmp10 = 1e-09
    tmp11 = tmp9 > tmp10
    tmp12 = 9.999999717180685e-10
    tmp13 = tl.where(tmp11, tmp9, tmp12)
    tmp14 = tmp8 / tmp13
    tl.store(out_ptr2 + (r1 + (16*x0)), tmp14, 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: [max_1, min_1, nval, range_1, gt, eps_mat, range_2, x_1], Original ATen: [aten.max, aten.min, aten.sub, aten.gt, aten.add, aten.where, aten.div]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_div_gt_max_min_sub_where_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 RangeNorm2D(nn.Module):
    """
        This will normalize a saliency map to range from 0 to 1 via linear range 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
        
        Parameters:
            full_norm:     This forces the values to range completely from 0 to 1. 
    """

    def __init__(self, full_norm=True, eps=1e-09):
        super(RangeNorm2D, self).__init__()
        self.full_norm = full_norm
        self.eps = eps

    def forward(self, x):
        """
            Input: 
                x:     A Torch Tensor image with shape [batch size x height x width] e.g. [64,7,7]
                       All values should be real positive (i.e. >= 0).  
            Return:
                x:     x Normalized by dividing by either the min value or the range between max and min. 
                       Each max/min is computed for each batch item.  
        """
        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)
        xmax = x.max(dim=1)[0].reshape(s0, 1)
        if self.full_norm:
            xmin = x.min(dim=1)[0].reshape(s0, 1)
            nval = x - xmin
            range = xmax - xmin
        else:
            nval = x
            range = xmax
        """
            prevent divide by zero by setting zero to a small number
            
            Simply adding eps does not work will in this case. So we use torch.where to set a minimum value. 
        """
        eps_mat = torch.zeros_like(range) + self.eps
        range = torch.where(range > self.eps, range, eps_mat)
        x = nval / range
        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 import triton_helpers
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_gt_max_min_sub_where_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, float('-inf'))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tmp6 = tl.where(xmask, tmp1, float('inf'))
    tmp7 = triton_helpers.min2(tmp6, 1)[:, None]
    tmp8 = tmp0 - tmp7
    tmp9 = tmp4 - tmp7
    tmp10 = 1e-09
    tmp11 = tmp9 > tmp10
    tmp12 = 9.999999717180685e-10
    tmp13 = tl.where(tmp11, tmp9, tmp12)
    tmp14 = tmp8 / tmp13
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp14, 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_gt_max_min_sub_where_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 RangeNorm2DNew(nn.Module):
    """
        This will normalize a saliency map to range from 0 to 1 via linear range 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
        
        Parameters:
            full_norm:     This forces the values to range completely from 0 to 1. 
    """

    def __init__(self, full_norm=True, eps=1e-09):
        super(RangeNorm2DNew, self).__init__()
        self.full_norm = full_norm
        self.eps = eps

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