import torch
import torch.utils.data
import torch.nn
import torch.optim


class UpsampleCBR(torch.nn.Sequential):

    def __init__(self, input_channels, output_channels, count=1, num_flat=0):
        layers = []
        for i in range(count):
            if i == 0:
                inch = input_channels
            else:
                inch = output_channels
            layers += [torch.nn.ConvTranspose2d(inch, output_channels,
                kernel_size=4, stride=2, padding=1), torch.nn.BatchNorm2d(
                output_channels), torch.nn.ReLU()]
            for i in range(num_flat):
                layers += [torch.nn.Conv2d(output_channels, output_channels,
                    kernel_size=3, stride=1, padding=1), torch.nn.
                    BatchNorm2d(output_channels), torch.nn.ReLU()]
        super(UpsampleCBR, self).__init__(*layers)


class CmapPafHeadAttention(torch.nn.Module):

    def __init__(self, input_channels, cmap_channels, paf_channels,
        upsample_channels=256, num_upsample=0, num_flat=0):
        super(CmapPafHeadAttention, self).__init__()
        self.cmap_up = UpsampleCBR(input_channels, upsample_channels,
            num_upsample, num_flat)
        self.paf_up = UpsampleCBR(input_channels, upsample_channels,
            num_upsample, num_flat)
        self.cmap_att = torch.nn.Conv2d(upsample_channels,
            upsample_channels, kernel_size=3, stride=1, padding=1)
        self.paf_att = torch.nn.Conv2d(upsample_channels, upsample_channels,
            kernel_size=3, stride=1, padding=1)
        self.cmap_conv = torch.nn.Conv2d(upsample_channels, cmap_channels,
            kernel_size=1, stride=1, padding=0)
        self.paf_conv = torch.nn.Conv2d(upsample_channels, paf_channels,
            kernel_size=1, stride=1, padding=0)

    def forward(self, x):
        xc = self.cmap_up(x)
        ac = torch.sigmoid(self.cmap_att(xc))
        xp = self.paf_up(x)
        ap = torch.tanh(self.paf_att(xp))
        return self.cmap_conv(xc * ac), self.paf_conv(xp * ap)


def get_inputs():
    return [torch.rand([4, 256, 64, 64])]


def get_init_inputs():
    return [[], {'input_channels': 4, 'cmap_channels': 4, 'paf_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.triton_helpers import libdevice
import torch.utils.data
import torch.nn
import torch.optim
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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = yindex // 256
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), None, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 256 * x2 + 1048576 * y1), tmp0, None)


@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
        ) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = yindex // 256
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 256 * x2 + 2304 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_convolution_mul_sigmoid_tanh_2(in_out_ptr0,
    in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr1 + x2, None)
    tmp4 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr2 + x2, None)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp7 = tl.sigmoid(tmp5)
    tmp8 = tmp6 * tmp7
    tmp9 = libdevice.tanh(tmp2)
    tmp10 = tmp6 * tmp9
    tl.store(in_out_ptr0 + x2, tmp2, None)
    tl.store(in_out_ptr1 + x2, tmp5, None)
    tl.store(out_ptr0 + x2, tmp8, None)
    tl.store(out_ptr1 + x2, tmp10, None)


@triton.jit
def triton_poi_fused_convolution_3(in_ptr0, in_ptr1, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    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]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16384 * y1), ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 4096 * y3), tmp2, ymask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 256, 64, 64), (1048576, 4096, 64, 1))
    assert_size_stride(primals_2, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_3, (256,), (1,))
    assert_size_stride(primals_4, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_5, (256,), (1,))
    assert_size_stride(primals_6, (4, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 256, 1, 1), (256, 1, 1, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 256, 64, 64), (1048576, 1, 16384, 256
            ), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(1024, 4096)](primals_1, buf0, 1024, 4096,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
            torch.float32)
        triton_poi_fused_1[grid(65536, 9)](primals_2, buf1, 65536, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
            torch.float32)
        triton_poi_fused_1[grid(65536, 9)](primals_4, buf2, 65536, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_4
        buf3 = extern_kernels.convolution(buf0, buf1, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 256, 64, 64), (1048576, 1, 16384, 256))
        buf5 = extern_kernels.convolution(buf0, buf2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 256, 64, 64), (1048576, 1, 16384, 256))
        buf6 = buf5
        del buf5
        buf4 = buf3
        del buf3
        buf7 = empty_strided_cuda((4, 256, 64, 64), (1048576, 1, 16384, 256
            ), torch.float32)
        buf10 = empty_strided_cuda((4, 256, 64, 64), (1048576, 1, 16384, 
            256), torch.float32)
        triton_poi_fused_convolution_mul_sigmoid_tanh_2[grid(4194304)](buf6,
            buf4, primals_5, primals_3, buf0, buf7, buf10, 4194304, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_3
        del primals_5
        buf8 = extern_kernels.convolution(buf7, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 4, 64, 64), (16384, 1, 256, 4))
        buf9 = empty_strided_cuda((4, 4, 64, 64), (16384, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_convolution_3[grid(16, 4096)](buf8, primals_7,
            buf9, 16, 4096, XBLOCK=32, YBLOCK=16, num_warps=4, num_stages=1)
        del primals_7
        buf11 = extern_kernels.convolution(buf10, primals_8, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 4, 64, 64), (16384, 1, 256, 4))
        buf12 = reinterpret_tensor(buf8, (4, 4, 64, 64), (16384, 4096, 64, 
            1), 0)
        del buf8
        triton_poi_fused_convolution_3[grid(16, 4096)](buf11, primals_9,
            buf12, 16, 4096, XBLOCK=32, YBLOCK=16, num_warps=4, num_stages=1)
        del buf11
        del primals_9
    return (buf9, buf12, buf0, buf1, buf2, primals_6, primals_8, buf4, buf6,
        buf7, buf10)


class UpsampleCBR(torch.nn.Sequential):

    def __init__(self, input_channels, output_channels, count=1, num_flat=0):
        layers = []
        for i in range(count):
            if i == 0:
                inch = input_channels
            else:
                inch = output_channels
            layers += [torch.nn.ConvTranspose2d(inch, output_channels,
                kernel_size=4, stride=2, padding=1), torch.nn.BatchNorm2d(
                output_channels), torch.nn.ReLU()]
            for i in range(num_flat):
                layers += [torch.nn.Conv2d(output_channels, output_channels,
                    kernel_size=3, stride=1, padding=1), torch.nn.
                    BatchNorm2d(output_channels), torch.nn.ReLU()]
        super(UpsampleCBR, self).__init__(*layers)


class CmapPafHeadAttentionNew(torch.nn.Module):

    def __init__(self, input_channels, cmap_channels, paf_channels,
        upsample_channels=256, num_upsample=0, num_flat=0):
        super(CmapPafHeadAttentionNew, self).__init__()
        self.cmap_up = UpsampleCBR(input_channels, upsample_channels,
            num_upsample, num_flat)
        self.paf_up = UpsampleCBR(input_channels, upsample_channels,
            num_upsample, num_flat)
        self.cmap_att = torch.nn.Conv2d(upsample_channels,
            upsample_channels, kernel_size=3, stride=1, padding=1)
        self.paf_att = torch.nn.Conv2d(upsample_channels, upsample_channels,
            kernel_size=3, stride=1, padding=1)
        self.cmap_conv = torch.nn.Conv2d(upsample_channels, cmap_channels,
            kernel_size=1, stride=1, padding=0)
        self.paf_conv = torch.nn.Conv2d(upsample_channels, paf_channels,
            kernel_size=1, stride=1, padding=0)

    def forward(self, input_0):
        primals_2 = self.cmap_att.weight
        primals_3 = self.cmap_att.bias
        primals_4 = self.paf_att.weight
        primals_5 = self.paf_att.bias
        primals_6 = self.cmap_conv.weight
        primals_7 = self.cmap_conv.bias
        primals_8 = self.paf_conv.weight
        primals_9 = self.paf_conv.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0], output[1]
