# 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/hs/chscdfxmjzi45ww3p3zlataqi7a3rmlzbfqcxc7ufsuk4zmwtg4v.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 = ([%unsqueeze, %unsqueeze_1, %unsqueeze_2, %unsqueeze_3], 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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 12, '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, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16) % 4
    x0 = xindex % 16
    x2 = (xindex // 64)
    x3 = xindex
    tmp0 = x1
    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 + (64*x2)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + (x0 + (16*x2)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 - tmp6
    tmp8 = tl.load(in_ptr2 + (x0 + (16*x2)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp9 = tmp7 / tmp8
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tmp13 = tl.full([1], 2, tl.int64)
    tmp14 = tmp0 < tmp13
    tmp15 = tmp12 & tmp14
    tmp16 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), tmp15 & xmask, eviction_policy='evict_last', other=0.0)
    tmp17 = tl.load(in_ptr1 + (64 + x0 + (16*x2)), tmp15 & xmask, eviction_policy='evict_last', other=0.0)
    tmp18 = tmp16 - tmp17
    tmp19 = tl.load(in_ptr2 + (64 + x0 + (16*x2)), tmp15 & xmask, eviction_policy='evict_last', other=0.0)
    tmp20 = tmp18 / tmp19
    tmp21 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
    tmp22 = tl.where(tmp15, tmp20, tmp21)
    tmp23 = tmp0 >= tmp13
    tmp24 = tl.full([1], 3, tl.int64)
    tmp25 = tmp0 < tmp24
    tmp26 = tmp23 & tmp25
    tmp27 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), tmp26 & xmask, eviction_policy='evict_last', other=0.0)
    tmp28 = tl.load(in_ptr1 + (128 + x0 + (16*x2)), tmp26 & xmask, eviction_policy='evict_last', other=0.0)
    tmp29 = tmp27 - tmp28
    tmp30 = tl.load(in_ptr2 + (128 + x0 + (16*x2)), tmp26 & xmask, eviction_policy='evict_last', other=0.0)
    tmp31 = tmp29 / tmp30
    tmp32 = tl.full(tmp31.shape, 0.0, tmp31.dtype)
    tmp33 = tl.where(tmp26, tmp31, tmp32)
    tmp34 = tmp0 >= tmp24
    tmp35 = tl.full([1], 4, tl.int64)
    tmp36 = tmp0 < tmp35
    tmp37 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), tmp34 & xmask, eviction_policy='evict_last', other=0.0)
    tmp38 = tl.load(in_ptr1 + (192 + x0 + (16*x2)), tmp34 & xmask, eviction_policy='evict_last', other=0.0)
    tmp39 = tmp37 - tmp38
    tmp40 = tl.load(in_ptr2 + (192 + x0 + (16*x2)), tmp34 & xmask, eviction_policy='evict_last', other=0.0)
    tmp41 = tmp39 / tmp40
    tmp42 = tl.full(tmp41.shape, 0.0, tmp41.dtype)
    tmp43 = tl.where(tmp34, tmp41, tmp42)
    tmp44 = tl.where(tmp26, tmp33, tmp43)
    tmp45 = tl.where(tmp15, tmp22, tmp44)
    tmp46 = tl.where(tmp4, tmp11, tmp45)
    tl.store(out_ptr0 + (x3), tmp46, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 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(arg1_1, arg0_1, arg2_1, buf0, 256, grid=grid(256), stream=stream0)
        del arg0_1
        del arg1_1
        del arg2_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)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_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


class std_norm(nn.Module):

    def __init__(self, inverse=False):
        super(std_norm, self).__init__()
        self.inverse = inverse

    def forward(self, x, mean, std):
        out = []
        for i in range(len(mean)):
            if not self.inverse:
                normalized = (x[:, i, :, :] - mean[i]) / std[i]
            else:
                normalized = x[:, i, :, :] * std[i] + mean[i]
            normalized = torch.unsqueeze(normalized, 1)
            out.append(normalized)
        return torch.cat(out, dim=1)


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


@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 4
    x0 = xindex % 16
    x2 = xindex // 64
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 64 * x2), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + (x0 + 16 * x2), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp7 = tmp5 - tmp6
    tmp8 = tl.load(in_ptr2 + (x0 + 16 * x2), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp9 = tmp7 / tmp8
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp0 >= tmp3
    tmp13 = tl.full([1], 2, tl.int64)
    tmp14 = tmp0 < tmp13
    tmp15 = tmp12 & tmp14
    tmp16 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), tmp15 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp17 = tl.load(in_ptr1 + (64 + x0 + 16 * x2), tmp15 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp18 = tmp16 - tmp17
    tmp19 = tl.load(in_ptr2 + (64 + x0 + 16 * x2), tmp15 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp20 = tmp18 / tmp19
    tmp21 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
    tmp22 = tl.where(tmp15, tmp20, tmp21)
    tmp23 = tmp0 >= tmp13
    tmp24 = tl.full([1], 3, tl.int64)
    tmp25 = tmp0 < tmp24
    tmp26 = tmp23 & tmp25
    tmp27 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), tmp26 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp28 = tl.load(in_ptr1 + (128 + x0 + 16 * x2), tmp26 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp29 = tmp27 - tmp28
    tmp30 = tl.load(in_ptr2 + (128 + x0 + 16 * x2), tmp26 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp31 = tmp29 / tmp30
    tmp32 = tl.full(tmp31.shape, 0.0, tmp31.dtype)
    tmp33 = tl.where(tmp26, tmp31, tmp32)
    tmp34 = tmp0 >= tmp24
    tl.full([1], 4, tl.int64)
    tmp37 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), tmp34 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp38 = tl.load(in_ptr1 + (192 + x0 + 16 * x2), tmp34 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp39 = tmp37 - tmp38
    tmp40 = tl.load(in_ptr2 + (192 + x0 + 16 * x2), tmp34 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp41 = tmp39 / tmp40
    tmp42 = tl.full(tmp41.shape, 0.0, tmp41.dtype)
    tmp43 = tl.where(tmp34, tmp41, tmp42)
    tmp44 = tl.where(tmp26, tmp33, tmp43)
    tmp45 = tl.where(tmp15, tmp22, tmp44)
    tmp46 = tl.where(tmp4, tmp11, tmp45)
    tl.store(out_ptr0 + x3, tmp46, xmask)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(256)](arg1_1, arg0_1, arg2_1, buf0, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
    return buf0,


class std_normNew(nn.Module):

    def __init__(self, inverse=False):
        super(std_normNew, self).__init__()
        self.inverse = inverse

    def forward(self, input_0, input_1, input_2):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]
