# 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_8/inductor_cache/an/cankwq3o4lhc2dnogcgqlnauehioeeknxoi7ick6tzq3ht2xroxz.py
# Topologically Sorted Source Nodes: [mul, outputs, outputs_1], Original ATen: [aten.mul, aten.add, aten.leaky_relu]
# Source node to ATen node mapping:
#   mul => mul
#   outputs => add
#   outputs_1 => gt, mul_1, where
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %primals_2), kwargs = {})
#   %add : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %primals_3), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%add, 0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 0.2), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %add, %mul_1), kwargs = {})
triton_poi_fused_add_leaky_relu_mul_0 = async_compile.triton('triton_poi_fused_add_leaky_relu_mul_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=[16], 
    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_add_leaky_relu_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_add_leaky_relu_mul_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    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 = tl.load(in_ptr1 + (x0), xmask)
    tmp3 = tl.load(in_ptr2 + (x0), xmask)
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = 0.0
    tmp6 = tmp4 > tmp5
    tmp7 = 0.2
    tmp8 = tmp4 * tmp7
    tmp9 = tl.where(tmp6, tmp4, tmp8)
    tl.store(out_ptr0 + (x0), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/2m/c2mt7vvxypcyg4roj4r3bns7fqblbouce2ybektelf7rsc62boym.py
# Topologically Sorted Source Nodes: [outputs_2], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   outputs_2 => convolution
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%unsqueeze, %primals_4, %primals_5, [1], [1], [1], False, [0], 1), kwargs = {})
triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_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=[16], 
    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_convolution_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_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4, 3), (12, 3, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, outputs, outputs_1], Original ATen: [aten.mul, aten.add, aten.leaky_relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_leaky_relu_mul_0.run(primals_1, primals_2, primals_3, buf0, 16, grid=grid(16), stream=stream0)
        del primals_1
        del primals_2
        del primals_3
        # Topologically Sorted Source Nodes: [outputs_2], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(reinterpret_tensor(buf0, (1, 4, 4), (0, 4, 1), 0), primals_4, stride=(1,), padding=(1,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf1, (1, 4, 4), (16, 4, 1))
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [outputs_2], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf2, primals_5, 16, grid=grid(16), stream=stream0)
        del primals_5
    return (reinterpret_tensor(buf2, (4, 4), (4, 1), 0), primals_4, reinterpret_tensor(buf0, (1, 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
    primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 3), (12, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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


class BaseModule(torch.nn.Module):

    def __init__(self):
        super(BaseModule, self).__init__()

    @property
    def nparams(self):
        return sum(p.numel() for p in self.parameters() if p.requires_grad)


class Conv1dWithInitialization(BaseModule):

    def __init__(self, **kwargs):
        super(Conv1dWithInitialization, self).__init__()
        self.conv1d = torch.nn.Conv1d(**kwargs)
        torch.nn.init.orthogonal_(self.conv1d.weight.data, gain=1)

    def forward(self, x):
        return self.conv1d(x)


class FeatureWiseAffine(BaseModule):

    def __init__(self):
        super(FeatureWiseAffine, self).__init__()

    def forward(self, x, scale, shift):
        outputs = scale * x + shift
        return outputs


class BasicModulationBlock(BaseModule):
    """
    Linear modulation part of UBlock, represented by sequence of the following layers:
        - Feature-wise Affine
        - LReLU
        - 3x1 Conv
    """

    def __init__(self, n_channels, dilation):
        super(BasicModulationBlock, self).__init__()
        self.featurewise_affine = FeatureWiseAffine()
        self.leaky_relu = torch.nn.LeakyReLU(0.2)
        self.convolution = Conv1dWithInitialization(in_channels=n_channels,
            out_channels=n_channels, kernel_size=3, stride=1, padding=
            dilation, dilation=dilation)

    def forward(self, x, scale, shift):
        outputs = self.featurewise_affine(x, scale, shift)
        outputs = self.leaky_relu(outputs)
        outputs = self.convolution(outputs)
        return outputs


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


def get_init_inputs():
    return [[], {'n_channels': 4, 'dilation': 1}]

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
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_poi_fused_add_leaky_relu_mul_0(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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 = tl.load(in_ptr1 + x0, xmask)
    tmp3 = tl.load(in_ptr2 + x0, xmask)
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = 0.0
    tmp6 = tmp4 > tmp5
    tmp7 = 0.2
    tmp8 = tmp4 * tmp7
    tmp9 = tl.where(tmp6, tmp4, tmp8)
    tl.store(out_ptr0 + x0, tmp9, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4, 3), (12, 3, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_leaky_relu_mul_0[grid(16)](primals_1,
            primals_2, primals_3, buf0, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        del primals_1
        del primals_2
        del primals_3
        buf1 = extern_kernels.convolution(reinterpret_tensor(buf0, (1, 4, 4
            ), (0, 4, 1), 0), primals_4, stride=(1,), padding=(1,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf1, (1, 4, 4), (16, 4, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_1[grid(16)](buf2, primals_5, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_5
    return reinterpret_tensor(buf2, (4, 4), (4, 1), 0
        ), primals_4, reinterpret_tensor(buf0, (1, 4, 4), (16, 4, 1), 0)


class BaseModule(torch.nn.Module):

    def __init__(self):
        super(BaseModule, self).__init__()

    @property
    def nparams(self):
        return sum(p.numel() for p in self.parameters() if p.requires_grad)


class Conv1dWithInitialization(BaseModule):

    def __init__(self, **kwargs):
        super(Conv1dWithInitialization, self).__init__()
        self.conv1d = torch.nn.Conv1d(**kwargs)
        torch.nn.init.orthogonal_(self.conv1d.weight.data, gain=1)

    def forward(self, x):
        return self.conv1d(x)


class FeatureWiseAffine(BaseModule):

    def __init__(self):
        super(FeatureWiseAffine, self).__init__()

    def forward(self, x, scale, shift):
        outputs = scale * x + shift
        return outputs


class BasicModulationBlockNew(BaseModule):
    """
    Linear modulation part of UBlock, represented by sequence of the following layers:
        - Feature-wise Affine
        - LReLU
        - 3x1 Conv
    """

    def __init__(self, n_channels, dilation):
        super(BasicModulationBlockNew, self).__init__()
        self.featurewise_affine = FeatureWiseAffine()
        self.leaky_relu = torch.nn.LeakyReLU(0.2)
        self.convolution = Conv1dWithInitialization(in_channels=n_channels,
            out_channels=n_channels, kernel_size=3, stride=1, padding=
            dilation, dilation=dilation)

    def forward(self, input_0, input_1, input_2):
        primals_4 = self.convolution.conv1d.weight
        primals_5 = self.convolution.conv1d.bias
        primals_1 = input_0
        primals_2 = input_1
        primals_3 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
