# 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_1/inductor_cache/na/cnak6v36bwpgvwoplicu6iqan6mtuzm37prkdglarz33d6ftf5ev.py
# Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv1d => convolution
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1], [2], [2], False, [0], 1), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_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=[128], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, '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},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 96
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 6) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/25/c254wu6xcta5qkwqoh6zxsylo4hl72c5ne4lzeafhps64ivdeyed.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   out => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_3, %mul], 1), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_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=[128], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '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, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, '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},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4) % 8
    x0 = xindex % 4
    x2 = (xindex // 32)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (4*x1) + (16*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + (x0 + (6*((-4) + x1)) + (24*x2)), tmp6 & xmask, other=0.0)
    tmp10 = libdevice.tanh(tmp9)
    tmp11 = tl.load(in_ptr2 + (x0 + (6*((-4) + x1)) + (24*x2)), tmp6 & xmask, other=0.0)
    tmp12 = tl.sigmoid(tmp11)
    tmp13 = tmp10 * tmp12
    tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
    tmp15 = tl.where(tmp6, tmp13, tmp14)
    tmp16 = tl.where(tmp4, tmp5, tmp15)
    tl.store(out_ptr0 + (x3), tmp16, 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, 2), (8, 2, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 2), (8, 2, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,), padding=(2,), dilation=(2,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 6), (24, 6, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(buf1, primals_2, 96, grid=grid(96), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [conv1d_1], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(primals_3, primals_4, stride=(1,), padding=(2,), dilation=(2,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 6), (24, 6, 1))
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [conv1d_1], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_0.run(buf3, primals_5, 96, grid=grid(96), stream=stream0)
        del primals_5
        buf4 = empty_strided_cuda((4, 8, 4), (32, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(primals_3, buf1, buf3, buf4, 128, grid=grid(128), stream=stream0)
    return (buf4, primals_1, primals_3, primals_4, buf1, buf3, )


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, 2), (8, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 2), (8, 2, 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
from torch import nn
from torch.nn import functional as F


class CausalConv1d(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size=2, dilation=2):
        super(CausalConv1d, self).__init__()
        self.padding = dilation
        self.causal_conv = nn.Conv1d(in_channels, out_channels, kernel_size,
            padding=self.padding, dilation=dilation)

    def forward(self, minibatch):
        return self.causal_conv(minibatch)[:, :, :-self.padding]


class DenseBlock(nn.Module):

    def __init__(self, in_channels, filters, dilation=2):
        super(DenseBlock, self).__init__()
        self.causal_conv1 = CausalConv1d(in_channels, filters, dilation=
            dilation)
        self.causal_conv2 = CausalConv1d(in_channels, filters, dilation=
            dilation)

    def forward(self, minibatch):
        tanh = F.tanh(self.causal_conv1(minibatch))
        sig = F.sigmoid(self.causal_conv2(minibatch))
        out = torch.cat([minibatch, tanh * sig], dim=1)
        return out


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'filters': 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.triton_helpers import libdevice
from torch import 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_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 96
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 6 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 8
    x0 = xindex % 4
    x2 = xindex // 32
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 6 * (-4 + x1) + 24 * x2), tmp6 & xmask,
        other=0.0)
    tmp10 = libdevice.tanh(tmp9)
    tmp11 = tl.load(in_ptr2 + (x0 + 6 * (-4 + x1) + 24 * x2), tmp6 & xmask,
        other=0.0)
    tmp12 = tl.sigmoid(tmp11)
    tmp13 = tmp10 * tmp12
    tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
    tmp15 = tl.where(tmp6, tmp13, tmp14)
    tmp16 = tl.where(tmp4, tmp5, tmp15)
    tl.store(out_ptr0 + x3, tmp16, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 2), (8, 2, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 2), (8, 2, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,),
            padding=(2,), dilation=(2,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 6), (24, 6, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(96)](buf1, primals_2, 96,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(primals_3, primals_4, stride=(1,),
            padding=(2,), dilation=(2,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 6), (24, 6, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_0[grid(96)](buf3, primals_5, 96,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((4, 8, 4), (32, 4, 1), torch.float32)
        triton_poi_fused_cat_1[grid(128)](primals_3, buf1, buf3, buf4, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
    return buf4, primals_1, primals_3, primals_4, buf1, buf3


class CausalConv1d(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size=2, dilation=2):
        super(CausalConv1d, self).__init__()
        self.padding = dilation
        self.causal_conv = nn.Conv1d(in_channels, out_channels, kernel_size,
            padding=self.padding, dilation=dilation)

    def forward(self, minibatch):
        return self.causal_conv(minibatch)[:, :, :-self.padding]


class DenseBlockNew(nn.Module):

    def __init__(self, in_channels, filters, dilation=2):
        super(DenseBlockNew, self).__init__()
        self.causal_conv1 = CausalConv1d(in_channels, filters, dilation=
            dilation)
        self.causal_conv2 = CausalConv1d(in_channels, filters, dilation=
            dilation)

    def forward(self, input_0):
        primals_1 = self.causal_conv1.causal_conv.weight
        primals_2 = self.causal_conv1.causal_conv.bias
        primals_4 = self.causal_conv2.causal_conv.weight
        primals_5 = self.causal_conv2.causal_conv.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
