# 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_7/inductor_cache/ji/cjihl3zqz5nxocpettdxcvssgg2odvnzztzwoiffs5q3ddeizdyv.py
# Topologically Sorted Source Nodes: [input_1, input_2, input_3, input_4, input_5, input_6, input_7, input_8, input_9, input_10], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   input_1 => gt, mul, where
#   input_10 => gt_9, mul_9, where_9
#   input_2 => gt_1, mul_1, where_1
#   input_3 => gt_2, mul_2, where_2
#   input_4 => gt_3, mul_3, where_3
#   input_5 => gt_4, mul_4, where_4
#   input_6 => gt_5, mul_5, where_5
#   input_7 => gt_6, mul_6, where_6
#   input_8 => gt_7, mul_7, where_7
#   input_9 => gt_8, mul_8, where_8
# Graph fragment:
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%arg0_1, 0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, 0.01), kwargs = {})
#   %where : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt, %arg0_1, %mul), kwargs = {})
#   %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%where, 0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%where, 0.01), kwargs = {})
#   %where_1 : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %where, %mul_1), kwargs = {})
#   %gt_2 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%where_1, 0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%where_1, 0.01), kwargs = {})
#   %where_2 : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %where_1, %mul_2), kwargs = {})
#   %gt_3 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%where_2, 0), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%where_2, 0.01), kwargs = {})
#   %where_3 : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt_3, %where_2, %mul_3), kwargs = {})
#   %gt_4 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%where_3, 0), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%where_3, 0.01), kwargs = {})
#   %where_4 : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt_4, %where_3, %mul_4), kwargs = {})
#   %gt_5 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%where_4, 0), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%where_4, 0.01), kwargs = {})
#   %where_5 : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt_5, %where_4, %mul_5), kwargs = {})
#   %gt_6 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%where_5, 0), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%where_5, 0.01), kwargs = {})
#   %where_6 : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt_6, %where_5, %mul_6), kwargs = {})
#   %gt_7 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%where_6, 0), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%where_6, 0.01), kwargs = {})
#   %where_7 : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt_7, %where_6, %mul_7), kwargs = {})
#   %gt_8 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%where_7, 0), kwargs = {})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%where_7, 0.01), kwargs = {})
#   %where_8 : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt_8, %where_7, %mul_8), kwargs = {})
#   %gt_9 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%where_8, 0), kwargs = {})
#   %mul_9 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%where_8, 0.01), kwargs = {})
#   %where_9 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_9, %where_8, %mul_9), kwargs = {})
#   %copy_ : [num_users=1] = call_function[target=torch.ops.aten.copy_.default](args = (%arg0_1, %where_9), kwargs = {})
triton_poi_fused_leaky_relu_0 = async_compile.triton('triton_poi_fused_leaky_relu_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: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_leaky_relu_0', 'mutated_arg_names': ['in_ptr0', 'out_ptr3'], 'no_x_dim': False, 'num_load': 1, '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_leaky_relu_0(in_ptr0, out_ptr3, 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_ptr0 + (x0), xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.01
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tmp6 = tmp5 > tmp1
    tmp7 = tmp5 * tmp3
    tmp8 = tl.where(tmp6, tmp5, tmp7)
    tmp9 = tmp8 > tmp1
    tmp10 = tmp8 * tmp3
    tmp11 = tl.where(tmp9, tmp8, tmp10)
    tmp12 = tmp11 > tmp1
    tmp13 = tmp11 * tmp3
    tmp14 = tl.where(tmp12, tmp11, tmp13)
    tmp15 = tmp14 > tmp1
    tmp16 = tmp14 * tmp3
    tmp17 = tl.where(tmp15, tmp14, tmp16)
    tmp18 = tmp17 > tmp1
    tmp19 = tmp17 * tmp3
    tmp20 = tl.where(tmp18, tmp17, tmp19)
    tmp21 = tmp20 > tmp1
    tmp22 = tmp20 * tmp3
    tmp23 = tl.where(tmp21, tmp20, tmp22)
    tmp24 = tmp23 > tmp1
    tmp25 = tmp23 * tmp3
    tmp26 = tl.where(tmp24, tmp23, tmp25)
    tmp27 = tmp26 > tmp1
    tmp28 = tmp26 * tmp3
    tmp29 = tl.where(tmp27, tmp26, tmp28)
    tmp30 = tmp29 > tmp1
    tmp31 = tmp29 * tmp3
    tmp32 = tl.where(tmp30, tmp29, tmp31)
    tl.store(out_ptr3 + (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, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [input_1, input_2, input_3, input_4, input_5, input_6, input_7, input_8, input_9, input_10], Original ATen: [aten.leaky_relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_leaky_relu_0.run(arg0_1, arg0_1, 256, grid=grid(256), stream=stream0)
    return (arg0_1, )


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)
    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


class leakyrelu(nn.Module):

    def __init__(self, layer=10, channels=32):
        super(leakyrelu, self).__init__()
        layers = []
        for i in range(layer):
            layers.append(nn.LeakyReLU(inplace=True))
        self.layers = nn.Sequential(*layers)

    def forward(self, x):
        return self.layers(x)


def get_inputs():
    return [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


@triton.jit
def triton_poi_fused_leaky_relu_0(in_ptr0, out_ptr3, 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_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.01
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tmp6 = tmp5 > tmp1
    tmp7 = tmp5 * tmp3
    tmp8 = tl.where(tmp6, tmp5, tmp7)
    tmp9 = tmp8 > tmp1
    tmp10 = tmp8 * tmp3
    tmp11 = tl.where(tmp9, tmp8, tmp10)
    tmp12 = tmp11 > tmp1
    tmp13 = tmp11 * tmp3
    tmp14 = tl.where(tmp12, tmp11, tmp13)
    tmp15 = tmp14 > tmp1
    tmp16 = tmp14 * tmp3
    tmp17 = tl.where(tmp15, tmp14, tmp16)
    tmp18 = tmp17 > tmp1
    tmp19 = tmp17 * tmp3
    tmp20 = tl.where(tmp18, tmp17, tmp19)
    tmp21 = tmp20 > tmp1
    tmp22 = tmp20 * tmp3
    tmp23 = tl.where(tmp21, tmp20, tmp22)
    tmp24 = tmp23 > tmp1
    tmp25 = tmp23 * tmp3
    tmp26 = tl.where(tmp24, tmp23, tmp25)
    tmp27 = tmp26 > tmp1
    tmp28 = tmp26 * tmp3
    tmp29 = tl.where(tmp27, tmp26, tmp28)
    tmp30 = tmp29 > tmp1
    tmp31 = tmp29 * tmp3
    tmp32 = tl.where(tmp30, tmp29, tmp31)
    tl.store(out_ptr3 + x0, tmp32, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        get_raw_stream(0)
        triton_poi_fused_leaky_relu_0[grid(256)](arg0_1, arg0_1, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
    return arg0_1,


class leakyreluNew(nn.Module):

    def __init__(self, layer=10, channels=32):
        super(leakyreluNew, self).__init__()
        layers = []
        for i in range(layer):
            layers.append(nn.LeakyReLU(inplace=True))
        self.layers = nn.Sequential(*layers)

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