# 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_7/inductor_cache/an/canp6h4p2trr3uwsmtpvsjsbg5wrhkcmqmukfeuwnmtqzjkixg4z.py
# Topologically Sorted Source Nodes: [_weight_norm], Original ATen: [aten._weight_norm_interface]
# Source node to ATen node mapping:
#   _weight_norm => div, mul, pow_1, pow_2, sum_1
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_2, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [1, 2], True), kwargs = {})
#   %pow_2 : [num_users=2] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_1, 0.5), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_1, %pow_2), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %div), kwargs = {})
triton_per_fused__weight_norm_interface_0 = async_compile.triton('triton_per_fused__weight_norm_interface_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=[1, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: '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': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=(4,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__weight_norm_interface_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused__weight_norm_interface_0(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 12
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = rindex < rnumel
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), rmask, other=0.0)
    tmp7 = tl.load(in_ptr1 + (0))
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(rmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tmp6 = libdevice.sqrt(tmp5)
    tmp9 = tmp8 / tmp6
    tmp10 = tmp0 * tmp9
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp6, None)
    tl.store(out_ptr0 + (tl.broadcast_to(r0, [XBLOCK, RBLOCK])), tmp10, rmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/m6/cm62btibdetu2yykomgzbrrz7bekg7iumgpvvvyncxf36vanuhwz.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_1 => relu
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%squeeze,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_relu_threshold_backward_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=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: '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_relu_threshold_backward_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_relu_threshold_backward_1(in_out_ptr0, in_ptr0, 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_out_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr0 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp6 = 0.0
    tmp7 = tmp5 <= tmp6
    tl.store(in_out_ptr0 + (x0), tmp5, xmask)
    tl.store(out_ptr0 + (x0), tmp7, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (1, 1, 1), (1, 1, 1))
    assert_size_stride(primals_2, (1, 4, 3), (12, 3, 1))
    assert_size_stride(primals_3, (1, ), (1, ))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 1, 1), (1, 1, 1), torch.float32)
        buf1 = buf0; del buf0  # reuse
        buf2 = empty_strided_cuda((1, 4, 3), (12, 3, 1), torch.float32)
        # Topologically Sorted Source Nodes: [_weight_norm], Original ATen: [aten._weight_norm_interface]
        stream0 = get_raw_stream(0)
        triton_per_fused__weight_norm_interface_0.run(buf1, primals_2, primals_1, buf2, 1, 12, grid=grid(1), stream=stream0)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(reinterpret_tensor(primals_4, (1, 4, 4), (16, 4, 1), 0), buf2, stride=(1,), padding=(1,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf3, (1, 1, 4), (4, 4, 1))
        buf4 = reinterpret_tensor(buf3, (1, 4), (4, 1), 0); del buf3  # reuse
        buf5 = empty_strided_cuda((1, 4), (4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_1.run(buf4, primals_3, buf5, 4, grid=grid(4), stream=stream0)
        del primals_3
    return (buf4, buf2, primals_1, primals_2, buf1, buf2, reinterpret_tensor(primals_4, (1, 4, 4), (16, 4, 1), 0), buf5, )


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((1, 1, 1), (1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1, 4, 3), (12, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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.cuda
from torch.nn import functional as F
from torch import nn
import torch.distributed
from torch.cuda.amp import autocast as autocast
import torch.utils.data
import torch.optim


class ConvNorm(torch.nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
        padding=None, dilation=1, bias=True, w_init_gain='linear'):
        super().__init__()
        if padding is None:
            assert kernel_size % 2 == 1
            padding = int(dilation * (kernel_size - 1) / 2)
        self.conv = torch.nn.Conv1d(in_channels, out_channels, kernel_size=
            kernel_size, stride=stride, padding=padding, dilation=dilation,
            bias=bias)
        torch.nn.init.xavier_uniform_(self.conv.weight, gain=torch.nn.init.
            calculate_gain(w_init_gain))

    def forward(self, signal):
        conv_signal = self.conv(signal)
        return conv_signal


class BottleneckLayerLayer(nn.Module):

    def __init__(self, in_dim, reduction_factor, norm='weightnorm',
        non_linearity='relu', use_pconv=False):
        super(BottleneckLayerLayer, self).__init__()
        self.reduction_factor = reduction_factor
        reduced_dim = int(in_dim / reduction_factor)
        self.out_dim = reduced_dim
        if self.reduction_factor > 1:
            fn = ConvNorm(in_dim, reduced_dim, kernel_size=3)
            if norm == 'weightnorm':
                fn = torch.nn.utils.weight_norm(fn.conv, name='weight')
            elif norm == 'instancenorm':
                fn = nn.Sequential(fn, nn.InstanceNorm1d(reduced_dim,
                    affine=True))
            self.projection_fn = fn
            self.non_linearity = non_linearity

    def forward(self, x):
        if self.reduction_factor > 1:
            x = self.projection_fn(x)
            if self.non_linearity == 'relu':
                x = F.relu(x)
            elif self.non_linearity == 'leakyrelu':
                x = F.leaky_relu(x)
        return x


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


def get_init_inputs():
    return [[], {'in_dim': 4, 'reduction_factor': 4}]

import torch
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
import torch.cuda
from torch import nn
import torch.distributed
from torch.cuda.amp import autocast as autocast
import torch.utils.data
import torch.optim
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__weight_norm_interface_0(in_out_ptr0, in_ptr0, in_ptr1,
    out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    rnumel = 12
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, rmask, other=0.0)
    tmp7 = tl.load(in_ptr1 + 0)
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(rmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tmp6 = libdevice.sqrt(tmp5)
    tmp9 = tmp8 / tmp6
    tmp10 = tmp0 * tmp9
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp6, None)
    tl.store(out_ptr0 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp10, rmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
    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_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp6 = 0.0
    tmp7 = tmp5 <= tmp6
    tl.store(in_out_ptr0 + x0, tmp5, xmask)
    tl.store(out_ptr0 + x0, tmp7, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (1, 1, 1), (1, 1, 1))
    assert_size_stride(primals_2, (1, 4, 3), (12, 3, 1))
    assert_size_stride(primals_3, (1,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 1, 1), (1, 1, 1), torch.float32)
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((1, 4, 3), (12, 3, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused__weight_norm_interface_0[grid(1)](buf1, primals_2,
            primals_1, buf2, 1, 12, XBLOCK=1, num_warps=2, num_stages=1)
        buf3 = extern_kernels.convolution(reinterpret_tensor(primals_4, (1,
            4, 4), (16, 4, 1), 0), buf2, stride=(1,), padding=(1,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf3, (1, 1, 4), (4, 4, 1))
        buf4 = reinterpret_tensor(buf3, (1, 4), (4, 1), 0)
        del buf3
        buf5 = empty_strided_cuda((1, 4), (4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(4)](buf4, primals_3,
            buf5, 4, XBLOCK=4, num_warps=1, num_stages=1)
        del primals_3
    return buf4, buf2, primals_1, primals_2, buf1, buf2, reinterpret_tensor(
        primals_4, (1, 4, 4), (16, 4, 1), 0), buf5


class ConvNorm(torch.nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
        padding=None, dilation=1, bias=True, w_init_gain='linear'):
        super().__init__()
        if padding is None:
            assert kernel_size % 2 == 1
            padding = int(dilation * (kernel_size - 1) / 2)
        self.conv = torch.nn.Conv1d(in_channels, out_channels, kernel_size=
            kernel_size, stride=stride, padding=padding, dilation=dilation,
            bias=bias)
        torch.nn.init.xavier_uniform_(self.conv.weight, gain=torch.nn.init.
            calculate_gain(w_init_gain))

    def forward(self, signal):
        conv_signal = self.conv(signal)
        return conv_signal


class BottleneckLayerLayerNew(nn.Module):

    def __init__(self, in_dim, reduction_factor, norm='weightnorm',
        non_linearity='relu', use_pconv=False):
        super(BottleneckLayerLayerNew, self).__init__()
        self.reduction_factor = reduction_factor
        reduced_dim = int(in_dim / reduction_factor)
        self.out_dim = reduced_dim
        if self.reduction_factor > 1:
            fn = ConvNorm(in_dim, reduced_dim, kernel_size=3)
            if norm == 'weightnorm':
                fn = torch.nn.utils.weight_norm(fn.conv, name='weight')
            elif norm == 'instancenorm':
                fn = nn.Sequential(fn, nn.InstanceNorm1d(reduced_dim,
                    affine=True))
            self.projection_fn = fn
            self.non_linearity = non_linearity

    def forward(self, input_0):
        primals_3 = self.projection_fn.bias
        primals_1 = self.projection_fn.weight_g
        primals_2 = self.projection_fn.weight_v
        primals_4 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
