# 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/ko/ckoyg3wc5qizxkiab7mlwvvzc5aw7hphof45bkxr5zq27h74ryn4.py
# Topologically Sorted Source Nodes: [x_6, x_7], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   x_6 => convolution_1
#   x_7 => gt, mul, where
# Graph fragment:
#   %convolution_1 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%view_1, %primals_4, %primals_5, [1], [12], [1], False, [0], 1), kwargs = {})
#   %gt : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_1, 0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_1, 0.2), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %convolution_1, %mul), kwargs = {})
triton_poi_fused_convolution_leaky_relu_0 = async_compile.triton('triton_poi_fused_convolution_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_convolution_leaky_relu_0', 'mutated_arg_names': [], '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_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 4) % 4
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x3), tmp4, xmask)
    tl.store(out_ptr1 + (x3), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/kh/ckhhky3clmsuriq2rzr7ybgt25y63c26q3wxei5xrryybvs5ubhe.py
# Topologically Sorted Source Nodes: [shortcut, x_8, x_9, add], Original ATen: [aten.convolution, aten.leaky_relu, aten.add]
# Source node to ATen node mapping:
#   add => add
#   shortcut => convolution
#   x_8 => convolution_2
#   x_9 => gt_1, mul_1, where_1
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%view_1, %primals_2, %primals_3, [1], [0], [1], False, [0], 1), kwargs = {})
#   %convolution_2 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%where, %primals_6, %primals_7, [1], [12], [1], False, [0], 1), kwargs = {})
#   %gt_1 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_2, 0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_2, 0.2), kwargs = {})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %convolution_2, %mul_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%where_1, %convolution), kwargs = {})
triton_poi_fused_add_convolution_leaky_relu_1 = async_compile.triton('triton_poi_fused_add_convolution_leaky_relu_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*i1', 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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_leaky_relu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, '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_convolution_leaky_relu_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 4) % 4
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp9 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp10 = tmp8 + tmp9
    tmp11 = tmp7 + tmp10
    tl.store(out_ptr0 + (x3), tmp4, xmask)
    tl.store(in_out_ptr0 + (x3), tmp11, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (4, 1, 1), (1, 1, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 1, 25), (25, 25, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, 4, 25), (100, 25, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [shortcut], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4, 1, 4), (4, 4, 1), 0), primals_2, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4), (16, 4, 1))
        # Topologically Sorted Source Nodes: [x_6], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4, 1, 4), (4, 4, 1), 0), primals_4, stride=(1,), padding=(12,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4), (16, 4, 1))
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_6, x_7], Original ATen: [aten.convolution, aten.leaky_relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_leaky_relu_0.run(buf1, primals_5, buf2, buf3, 64, grid=grid(64), stream=stream0)
        del buf1
        del primals_5
        # Topologically Sorted Source Nodes: [x_8], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1,), padding=(12,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf4, (4, 4, 4), (16, 4, 1))
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        buf6 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [shortcut, x_8, x_9, add], Original ATen: [aten.convolution, aten.leaky_relu, aten.add]
        triton_poi_fused_add_convolution_leaky_relu_1.run(buf6, buf4, primals_7, primals_3, buf5, 64, grid=grid(64), stream=stream0)
        del buf4
        del primals_3
        del primals_7
    return (buf6, primals_2, primals_4, primals_6, reinterpret_tensor(primals_1, (4, 1, 4), (4, 1, 1), 0), buf2, buf3, 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((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 1, 1), (1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 1, 25), (25, 25, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 25), (100, 25, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
    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 PixelShuffle1d(nn.Module):

    def __init__(self, upscale_factor):
        super().__init__()
        self.upscale_factor = upscale_factor

    def forward(self, x):
        batch_size = x.shape[0]
        short_channel_len = x.shape[1]
        short_width = x.shape[2]
        long_channel_len = short_channel_len // self.upscale_factor
        long_width = self.upscale_factor * short_width
        x = x.contiguous().view([batch_size, self.upscale_factor,
            long_channel_len, short_width])
        x = x.permute(0, 2, 3, 1).contiguous()
        x = x.view(batch_size, long_channel_len, long_width)
        return x


class SamePaddingConv1d(nn.Module):

    def __init__(self, in_dim, out_dim, kernel_size):
        super().__init__()
        self.conv = nn.Conv1d(in_dim, out_dim, kernel_size, padding=int((
            kernel_size - 1) / 2))

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


class UpConv1d(nn.Module):

    def __init__(self, in_dim, out_dim, scale_factor, kernel_size):
        super().__init__()
        self.pixel_shuffer = PixelShuffle1d(scale_factor)
        self.conv = SamePaddingConv1d(in_dim // scale_factor, out_dim,
            kernel_size)

    def forward(self, x):
        x = self.pixel_shuffer(x)
        return self.conv(x)


class UpsampleBlock(nn.Module):

    def __init__(self, in_dim, out_dim):
        super().__init__()
        self.deconv = UpConv1d(in_dim, out_dim, scale_factor=4, kernel_size=1)
        self.conv_1 = UpConv1d(in_dim, out_dim, scale_factor=4, kernel_size=25)
        self.conv_2 = nn.Conv1d(out_dim, out_dim, 25, padding=12)
        self.LReLU_1 = nn.LeakyReLU(0.2)
        self.LReLU_2 = nn.LeakyReLU(0.2)

    def forward(self, input):
        shortcut = self.deconv(input)
        x = input
        x = self.conv_1(x)
        x = self.LReLU_1(x)
        x = self.conv_2(x)
        x = self.LReLU_2(x)
        return x + shortcut


def get_inputs():
    return [torch.rand([4, 4, 1, 1])]


def get_init_inputs():
    return [[], {'in_dim': 4, 'out_dim': 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
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_convolution_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, xmask)
    tl.store(out_ptr1 + x3, tmp7, xmask)


@triton.jit
def triton_poi_fused_add_convolution_leaky_relu_1(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_out_ptr0 + x3, xmask)
    tmp9 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp10 = tmp8 + tmp9
    tmp11 = tmp7 + tmp10
    tl.store(out_ptr0 + x3, tmp4, xmask)
    tl.store(in_out_ptr0 + x3, tmp11, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (4, 1, 1), (1, 1, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 1, 25), (25, 25, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 25), (100, 25, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4,
            1, 4), (4, 4, 1), 0), primals_2, stride=(1,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf0, (4, 4, 4), (16, 4, 1))
        buf1 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4,
            1, 4), (4, 4, 1), 0), primals_4, stride=(1,), padding=(12,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf1, (4, 4, 4), (16, 4, 1))
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_leaky_relu_0[grid(64)](buf1, primals_5,
            buf2, buf3, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf1
        del primals_5
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1,),
            padding=(12,), dilation=(1,), transposed=False, output_padding=
            (0,), groups=1, bias=None)
        assert_size_stride(buf4, (4, 4, 4), (16, 4, 1))
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        buf6 = buf0
        del buf0
        triton_poi_fused_add_convolution_leaky_relu_1[grid(64)](buf6, buf4,
            primals_7, primals_3, buf5, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del buf4
        del primals_3
        del primals_7
    return buf6, primals_2, primals_4, primals_6, reinterpret_tensor(primals_1,
        (4, 1, 4), (4, 1, 1), 0), buf2, buf3, buf5


class PixelShuffle1d(nn.Module):

    def __init__(self, upscale_factor):
        super().__init__()
        self.upscale_factor = upscale_factor

    def forward(self, x):
        batch_size = x.shape[0]
        short_channel_len = x.shape[1]
        short_width = x.shape[2]
        long_channel_len = short_channel_len // self.upscale_factor
        long_width = self.upscale_factor * short_width
        x = x.contiguous().view([batch_size, self.upscale_factor,
            long_channel_len, short_width])
        x = x.permute(0, 2, 3, 1).contiguous()
        x = x.view(batch_size, long_channel_len, long_width)
        return x


class SamePaddingConv1d(nn.Module):

    def __init__(self, in_dim, out_dim, kernel_size):
        super().__init__()
        self.conv = nn.Conv1d(in_dim, out_dim, kernel_size, padding=int((
            kernel_size - 1) / 2))

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


class UpConv1d(nn.Module):

    def __init__(self, in_dim, out_dim, scale_factor, kernel_size):
        super().__init__()
        self.pixel_shuffer = PixelShuffle1d(scale_factor)
        self.conv = SamePaddingConv1d(in_dim // scale_factor, out_dim,
            kernel_size)

    def forward(self, x):
        x = self.pixel_shuffer(x)
        return self.conv(x)


class UpsampleBlockNew(nn.Module):

    def __init__(self, in_dim, out_dim):
        super().__init__()
        self.deconv = UpConv1d(in_dim, out_dim, scale_factor=4, kernel_size=1)
        self.conv_1 = UpConv1d(in_dim, out_dim, scale_factor=4, kernel_size=25)
        self.conv_2 = nn.Conv1d(out_dim, out_dim, 25, padding=12)
        self.LReLU_1 = nn.LeakyReLU(0.2)
        self.LReLU_2 = nn.LeakyReLU(0.2)

    def forward(self, input_0):
        primals_2 = self.deconv.conv.conv.weight
        primals_3 = self.deconv.conv.conv.bias
        primals_4 = self.conv_1.conv.conv.weight
        primals_5 = self.conv_1.conv.conv.bias
        primals_6 = self.conv_2.weight
        primals_7 = self.conv_2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
