import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.spectral_norm import spectral_norm


def build_norm_layer(norm_type, param=None, num_feats=None):
    if norm_type == 'bnorm':
        return nn.BatchNorm1d(num_feats)
    elif norm_type == 'snorm':
        spectral_norm(param)
        return None
    elif norm_type is None:
        return None
    else:
        raise TypeError('Unrecognized norm type: ', norm_type)


class ResARModule(nn.Module):

    def __init__(self, ninp, fmaps, res_fmaps, kwidth, dilation, bias=True,
        norm_type=None, act=None):
        super().__init__()
        self.dil_conv = nn.Conv1d(ninp, fmaps, kwidth, dilation=dilation,
            bias=bias)
        if act is not None:
            self.act = getattr(nn, act)()
        else:
            self.act = nn.PReLU(fmaps, init=0)
        self.dil_norm = build_norm_layer(norm_type, self.dil_conv, fmaps)
        self.kwidth = kwidth
        self.dilation = dilation
        self.conv_1x1_skip = nn.Conv1d(fmaps, ninp, 1, bias=bias)
        self.conv_1x1_skip_norm = build_norm_layer(norm_type, self.
            conv_1x1_skip, ninp)
        self.conv_1x1_res = nn.Conv1d(fmaps, res_fmaps, 1, bias=bias)
        self.conv_1x1_res_norm = build_norm_layer(norm_type, self.
            conv_1x1_res, res_fmaps)

    def forward_norm(self, x, norm_layer):
        if norm_layer is not None:
            return norm_layer(x)
        else:
            return x

    def forward(self, x):
        kw__1 = self.kwidth - 1
        P = kw__1 + kw__1 * (self.dilation - 1)
        x_p = F.pad(x, (P, 0))
        h = self.dil_conv(x_p)
        h = self.forward_norm(h, self.dil_norm)
        h = self.act(h)
        a = h
        h = self.conv_1x1_skip(h)
        h = self.forward_norm(h, self.conv_1x1_skip_norm)
        y = x + h
        sh = self.conv_1x1_res(a)
        sh = self.forward_norm(sh, self.conv_1x1_res_norm)
        return y, sh


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


def get_init_inputs():
    return [[], {'ninp': 4, 'fmaps': 4, 'res_fmaps': 4, 'kwidth': 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
import torch.nn as nn
from torch.nn.utils.spectral_norm import spectral_norm
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_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 28
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 7
    x1 = xindex // 7
    x2 = xindex
    tmp0 = -3 + x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.load(in_ptr0 + (-3 + x0 + 4 * x1), tmp2 & xmask, other=0.0)
    tl.store(out_ptr0 + x2, tmp3, xmask)


@triton.jit
def triton_poi_fused__prelu_kernel_convolution_1(in_out_ptr0, in_ptr0,
    in_ptr1, out_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
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp6 = tmp5 * tmp2
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + x2, tmp2, xmask)
    tl.store(out_ptr0 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused_add_2(in_out_ptr0, in_ptr0, in_ptr1, 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_ptr0 + x2, xmask)
    tmp1 = tl.load(in_out_ptr0 + x2, xmask)
    tmp2 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_3(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, primals_6,
        primals_7, primals_8) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_8, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 7), (7, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(28)](primals_1, buf0, 28,
            XBLOCK=32, num_warps=1, num_stages=1)
        buf1 = extern_kernels.convolution(reinterpret_tensor(buf0, (1, 4, 7
            ), (0, 7, 1), 0), primals_2, stride=(1,), padding=(0,),
            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
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__prelu_kernel_convolution_1[grid(16)](buf2,
            primals_3, primals_4, buf3, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        del primals_3
        buf4 = extern_kernels.convolution(reinterpret_tensor(buf3, (1, 4, 4
            ), (0, 4, 1), 0), primals_5, stride=(1,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf4, (1, 4, 4), (16, 4, 1))
        buf5 = reinterpret_tensor(buf4, (4, 4), (4, 1), 0)
        del buf4
        triton_poi_fused_add_2[grid(16)](buf5, primals_1, primals_6, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_1
        del primals_6
        buf6 = extern_kernels.convolution(reinterpret_tensor(buf3, (1, 4, 4
            ), (0, 4, 1), 0), primals_7, stride=(1,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf6, (1, 4, 4), (16, 4, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_3[grid(16)](buf7, primals_8, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_8
    return buf5, reinterpret_tensor(buf7, (4, 4), (4, 1), 0
        ), primals_2, primals_4, primals_5, primals_7, reinterpret_tensor(buf0,
        (1, 4, 7), (28, 7, 1), 0), buf2, reinterpret_tensor(buf3, (1, 4, 4),
        (16, 4, 1), 0)


def build_norm_layer(norm_type, param=None, num_feats=None):
    if norm_type == 'bnorm':
        return nn.BatchNorm1d(num_feats)
    elif norm_type == 'snorm':
        spectral_norm(param)
        return None
    elif norm_type is None:
        return None
    else:
        raise TypeError('Unrecognized norm type: ', norm_type)


class ResARModuleNew(nn.Module):

    def __init__(self, ninp, fmaps, res_fmaps, kwidth, dilation, bias=True,
        norm_type=None, act=None):
        super().__init__()
        self.dil_conv = nn.Conv1d(ninp, fmaps, kwidth, dilation=dilation,
            bias=bias)
        if act is not None:
            self.act = getattr(nn, act)()
        else:
            self.act = nn.PReLU(fmaps, init=0)
        self.dil_norm = build_norm_layer(norm_type, self.dil_conv, fmaps)
        self.kwidth = kwidth
        self.dilation = dilation
        self.conv_1x1_skip = nn.Conv1d(fmaps, ninp, 1, bias=bias)
        self.conv_1x1_skip_norm = build_norm_layer(norm_type, self.
            conv_1x1_skip, ninp)
        self.conv_1x1_res = nn.Conv1d(fmaps, res_fmaps, 1, bias=bias)
        self.conv_1x1_res_norm = build_norm_layer(norm_type, self.
            conv_1x1_res, res_fmaps)

    def forward_norm(self, x, norm_layer):
        if norm_layer is not None:
            return norm_layer(x)
        else:
            return x

    def forward(self, input_0):
        primals_2 = self.dil_conv.weight
        primals_3 = self.dil_conv.bias
        primals_4 = self.act.weight
        primals_5 = self.conv_1x1_skip.weight
        primals_6 = self.conv_1x1_skip.bias
        primals_7 = self.conv_1x1_res.weight
        primals_8 = self.conv_1x1_res.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0], output[1]
