import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed


class SEModule(nn.Module):

    def __init__(self, channels, dw_conv):
        super().__init__()
        ks = 1
        pad = (ks - 1) // 2
        self.fc1 = nn.Conv2d(channels, channels, kernel_size=ks, padding=
            pad, groups=channels if dw_conv else 1, bias=False)

    def forward(self, x):
        x = self.fc1(x)
        return x


class TAM(nn.Module):

    def __init__(self, duration, channels, dw_conv=True, blending_frames=3,
        blending_method='sum'):
        super().__init__()
        self.blending_frames = blending_frames
        self.blending_method = blending_method
        if blending_frames == 3:
            self.prev_se = SEModule(channels, dw_conv)
            self.next_se = SEModule(channels, dw_conv)
            self.curr_se = SEModule(channels, dw_conv)
        else:
            self.blending_layers = nn.ModuleList([SEModule(channels,
                dw_conv) for _ in range(blending_frames)])
        self.relu = nn.ReLU(inplace=True)
        self.duration = duration

    def name(self):
        return 'TAM-b{}-{}'.format(self.blending_frames, self.blending_method)

    def forward(self, x):
        if self.blending_frames == 3:
            prev_x = self.prev_se(x)
            curr_x = self.curr_se(x)
            next_x = self.next_se(x)
            prev_x = prev_x.view((-1, self.duration) + prev_x.size()[1:])
            curr_x = curr_x.view((-1, self.duration) + curr_x.size()[1:])
            next_x = next_x.view((-1, self.duration) + next_x.size()[1:])
            prev_x = F.pad(prev_x, (0, 0, 0, 0, 0, 0, 1, 0))[:, :-1, ...]
            next_x = F.pad(next_x, (0, 0, 0, 0, 0, 0, 0, 1))[:, 1:, ...]
            out = torch.stack([prev_x, curr_x, next_x], dim=0)
        else:
            xs = [se(x) for se in self.blending_layers]
            xs = [x.view((-1, self.duration) + x.size()[1:]) for x in xs]
            shifted_xs = []
            for i in range(self.blending_frames):
                shift = i - self.blending_frames // 2
                x_temp = xs[i]
                _n, t, _c, _h, _w = x_temp.shape
                start_index = 0 if shift < 0 else shift
                end_index = t if shift < 0 else t + shift
                padding = None
                if shift < 0:
                    padding = 0, 0, 0, 0, 0, 0, abs(shift), 0
                elif shift > 0:
                    padding = 0, 0, 0, 0, 0, 0, 0, shift
                shifted_xs.append(F.pad(x_temp, padding)[:, start_index:
                    end_index, ...] if padding is not None else x_temp)
            out = torch.stack(shifted_xs, dim=0)
        if self.blending_method == 'sum':
            out = torch.sum(out, dim=0)
        elif self.blending_method == 'max':
            out, _ = torch.max(out, dim=0)
        else:
            raise ValueError('Blending method %s not supported' % self.
                blending_method)
        out = self.relu(out)
        _n, t, _c, _h, _w = out.shape
        out = out.view((-1,) + out.size()[2:])
        return out


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


def get_init_inputs():
    return [[], {'duration': 4, 'channels': 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 import triton_helpers
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
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_relu_sum_threshold_backward_0(in_out_ptr0, in_ptr0,
    in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 64
    x2 = xindex
    tmp0 = tl.full([1], 0, tl.int64)
    tmp2 = tl.full([1], 1, tl.int64)
    tmp3 = tmp0 < tmp2
    tmp4 = -1 + x1
    tmp5 = tmp4 >= tmp0
    tmp6 = tmp5 & tmp3
    tmp7 = tl.load(in_ptr0 + (-64 + x2), tmp6 & xmask, other=0.0)
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp3, tmp7, tmp8)
    tmp10 = tmp0 >= tmp2
    tmp11 = tl.full([1], 2, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tmp10 & tmp12
    tmp14 = tl.load(in_out_ptr0 + x2, tmp13 & xmask, other=0.0)
    tmp15 = tmp0 >= tmp11
    tl.full([1], 3, tl.int64)
    tmp18 = 1 + x1
    tmp19 = tl.full([1], 4, tl.int64)
    tmp20 = tmp18 < tmp19
    tmp21 = tmp20 & tmp15
    tmp22 = tl.load(in_ptr1 + (64 + x2), tmp21 & xmask, other=0.0)
    tmp23 = tl.full(tmp22.shape, 0.0, tmp22.dtype)
    tmp24 = tl.where(tmp15, tmp22, tmp23)
    tmp25 = tl.where(tmp13, tmp14, tmp24)
    tmp26 = tl.where(tmp3, tmp9, tmp25)
    tmp28 = tmp2 < tmp2
    tmp29 = tmp5 & tmp28
    tmp30 = tl.load(in_ptr0 + (-64 + x2), tmp29 & xmask, other=0.0)
    tmp31 = tl.full(tmp30.shape, 0.0, tmp30.dtype)
    tmp32 = tl.where(tmp28, tmp30, tmp31)
    tmp33 = tmp2 >= tmp2
    tmp34 = tmp2 < tmp11
    tmp35 = tmp33 & tmp34
    tmp36 = tl.load(in_out_ptr0 + x2, tmp35 & xmask, other=0.0)
    tmp37 = tmp2 >= tmp11
    tmp39 = tmp20 & tmp37
    tmp40 = tl.load(in_ptr1 + (64 + x2), tmp39 & xmask, other=0.0)
    tmp41 = tl.full(tmp40.shape, 0.0, tmp40.dtype)
    tmp42 = tl.where(tmp37, tmp40, tmp41)
    tmp43 = tl.where(tmp35, tmp36, tmp42)
    tmp44 = tl.where(tmp28, tmp32, tmp43)
    tmp45 = tmp26 + tmp44
    tmp47 = tmp11 < tmp2
    tmp48 = tmp5 & tmp47
    tmp49 = tl.load(in_ptr0 + (-64 + x2), tmp48 & xmask, other=0.0)
    tmp50 = tl.full(tmp49.shape, 0.0, tmp49.dtype)
    tmp51 = tl.where(tmp47, tmp49, tmp50)
    tmp52 = tmp11 >= tmp2
    tmp53 = tmp11 < tmp11
    tmp54 = tmp52 & tmp53
    tmp55 = tl.load(in_out_ptr0 + x2, tmp54 & xmask, other=0.0)
    tmp56 = tmp11 >= tmp11
    tmp58 = tmp20 & tmp56
    tmp59 = tl.load(in_ptr1 + (64 + x2), tmp58 & xmask, other=0.0)
    tmp60 = tl.full(tmp59.shape, 0.0, tmp59.dtype)
    tmp61 = tl.where(tmp56, tmp59, tmp60)
    tmp62 = tl.where(tmp54, tmp55, tmp61)
    tmp63 = tl.where(tmp47, tmp51, tmp62)
    tmp64 = tmp45 + tmp63
    tmp65 = tl.full([1], 0, tl.int32)
    tmp66 = triton_helpers.maximum(tmp65, tmp64)
    tmp67 = 0.0
    tmp68 = tmp66 <= tmp67
    tl.store(out_ptr0 + x2, tmp68, xmask)
    tl.store(out_ptr1 + x2, tmp66, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 1, 1), (1, 1, 1, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 1, 1, 1), (1, 1, 1, 1))
    assert_size_stride(primals_4, (4, 1, 1, 1), (1, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = extern_kernels.convolution(primals_2, primals_3, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        buf2 = extern_kernels.convolution(primals_2, primals_4, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
        buf3 = reinterpret_tensor(buf1, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0
            )
        del buf1
        buf4 = empty_strided_cuda((1, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.bool)
        buf5 = empty_strided_cuda((1, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_relu_sum_threshold_backward_0[grid(256)](buf3,
            buf0, buf2, buf4, buf5, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
        del buf2
        del buf3
    return reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), primals_1, primals_2, primals_3, primals_4, buf4


class SEModule(nn.Module):

    def __init__(self, channels, dw_conv):
        super().__init__()
        ks = 1
        pad = (ks - 1) // 2
        self.fc1 = nn.Conv2d(channels, channels, kernel_size=ks, padding=
            pad, groups=channels if dw_conv else 1, bias=False)

    def forward(self, x):
        x = self.fc1(x)
        return x


class TAMNew(nn.Module):

    def __init__(self, duration, channels, dw_conv=True, blending_frames=3,
        blending_method='sum'):
        super().__init__()
        self.blending_frames = blending_frames
        self.blending_method = blending_method
        if blending_frames == 3:
            self.prev_se = SEModule(channels, dw_conv)
            self.next_se = SEModule(channels, dw_conv)
            self.curr_se = SEModule(channels, dw_conv)
        else:
            self.blending_layers = nn.ModuleList([SEModule(channels,
                dw_conv) for _ in range(blending_frames)])
        self.relu = nn.ReLU(inplace=True)
        self.duration = duration

    def name(self):
        return 'TAM-b{}-{}'.format(self.blending_frames, self.blending_method)

    def forward(self, input_0):
        primals_1 = self.prev_se.fc1.weight
        primals_3 = self.next_se.fc1.weight
        primals_4 = self.curr_se.fc1.weight
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
