import torch
import torch.nn as nn


class Conv1d(nn.Conv1d):
    """
		Convolution 1d
		Args:
			x: (N, T, C_in)
		Returns:
			y: (N, T, C_out)
	"""

    def __init__(self, in_channels, out_channels, kernel_size,
        activation_fn=None, drop_rate=0.0, stride=1, padding='same',
        dilation=1, groups=1, bias=True, ln=False):
        if padding == 'same':
            padding = kernel_size // 2 * dilation
            self.even_kernel = not bool(kernel_size % 2)
        super(Conv1d, self).__init__(in_channels, out_channels, kernel_size,
            stride=stride, padding=padding, dilation=dilation, groups=
            groups, bias=bias)
        self.activation_fn = activation_fn(inplace=True
            ) if activation_fn is not None else None
        self.drop_out = nn.Dropout(drop_rate) if drop_rate > 0 else None
        self.layer_norm = nn.LayerNorm(out_channels) if ln else None

    def forward(self, x):
        y = x.transpose(1, 2)
        y = super(Conv1d, self).forward(y)
        y = y.transpose(1, 2)
        y = self.layer_norm(y) if self.layer_norm is not None else y
        y = self.activation_fn(y) if self.activation_fn is not None else y
        y = self.drop_out(y) if self.drop_out is not None else y
        y = y[:, :-1, :] if self.even_kernel else y
        return y


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 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_0(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 5 % 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)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(16, 4)](primals_1, buf0, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,),
            padding=(2,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 5), (20, 5, 1))
        del buf0
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_1[grid(80)](buf2, primals_3, 80,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
    return reinterpret_tensor(buf2, (4, 4, 4), (20, 1, 5), 0
        ), primals_2, reinterpret_tensor(primals_1, (4, 4, 4), (16, 1, 4), 0)


class Conv1dNew(nn.Conv1d):
    """
		Convolution 1d
		Args:
			x: (N, T, C_in)
		Returns:
			y: (N, T, C_out)
	"""

    def __init__(self, in_channels, out_channels, kernel_size,
        activation_fn=None, drop_rate=0.0, stride=1, padding='same',
        dilation=1, groups=1, bias=True, ln=False):
        if padding == 'same':
            padding = kernel_size // 2 * dilation
            self.even_kernel = not bool(kernel_size % 2)
        super(Conv1dNew, self).__init__(in_channels, out_channels,
            kernel_size, stride=stride, padding=padding, dilation=dilation,
            groups=groups, bias=bias)
        self.activation_fn = activation_fn(inplace=True
            ) if activation_fn is not None else None
        self.drop_out = nn.Dropout(drop_rate) if drop_rate > 0 else None
        self.layer_norm = nn.LayerNorm(out_channels) if ln else None

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