from torch.nn import Module
import torch
import torch.nn.functional as F
import torch.nn
from torch.nn.modules.module import Module


class AffineGridGen(Module):

    def __init__(self, out_h=240, out_w=240, out_ch=3, use_cuda=True):
        super(AffineGridGen, self).__init__()
        self.out_h = out_h
        self.out_w = out_w
        self.out_ch = out_ch

    def forward(self, theta):
        b = theta.size()[0]
        if not theta.size() == (b, 2, 3):
            theta = theta.view(-1, 2, 3)
        theta = theta.contiguous()
        batch_size = theta.size()[0]
        out_size = torch.Size((batch_size, self.out_ch, self.out_h, self.out_w)
            )
        return F.affine_grid(theta, out_size)


def get_inputs():
    return [torch.rand([4, 2, 3])]


def get_init_inputs():
    return [[], {}]

import torch
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.nn import Module
import torch.nn
from torch.nn.modules.module import Module
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_affine_grid_generator_0(out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 172800
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 3
    x1 = xindex // 3 % 240
    x2 = xindex // 720
    x5 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 1, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = x1
    tmp4 = tmp3.to(tl.float32)
    tmp5 = 120.0
    tmp6 = tmp4 < tmp5
    tmp7 = 0.008333333333333333
    tmp8 = tmp4 * tmp7
    tmp9 = -0.9958333333333333
    tmp10 = tmp8 + tmp9
    tmp11 = 239 + -1 * x1
    tmp12 = tmp11.to(tl.float32)
    tmp13 = tmp12 * tmp7
    tmp14 = 0.9958333333333333
    tmp15 = tmp14 - tmp13
    tmp16 = tl.where(tmp6, tmp10, tmp15)
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp2, tmp16, tmp17)
    tmp19 = -1 + x0
    tmp20 = tl.full([1], 0, tl.int64)
    tmp21 = tmp19 >= tmp20
    tmp22 = tmp19 < tmp1
    tmp23 = tmp21 & tmp22
    tmp24 = x2
    tmp25 = tmp24.to(tl.float32)
    tmp26 = tmp25 < tmp5
    tmp27 = tmp25 * tmp7
    tmp28 = tmp27 + tmp9
    tmp29 = 239 + -1 * x2
    tmp30 = tmp29.to(tl.float32)
    tmp31 = tmp30 * tmp7
    tmp32 = tmp14 - tmp31
    tmp33 = tl.where(tmp26, tmp28, tmp32)
    tmp34 = tl.full(tmp33.shape, 0.0, tmp33.dtype)
    tmp35 = tl.where(tmp23, tmp33, tmp34)
    tmp36 = tmp18 + tmp35
    tmp37 = -2 + x0
    tmp38 = tmp37 >= tmp20
    tmp39 = 1.0
    tmp40 = tl.full(tmp39.shape, 0.0, tmp39.dtype)
    tmp41 = tl.where(tmp38, tmp39, tmp40)
    tmp42 = tmp36 + tmp41
    tl.store(out_ptr0 + x5, tmp42, xmask)


@triton.jit
def triton_poi_fused_affine_grid_generator_1(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 2 % 57600
    x0 = xindex % 2
    x2 = xindex // 115200
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + 3 * x1, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (3 * x0 + 6 * x2), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 3 * x1), None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 3 * x0 + 6 * x2), None, eviction_policy=
        'evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 3 * x1), None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + 3 * x0 + 6 * x2), None, eviction_policy=
        'evict_last')
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tl.store(out_ptr0 + x3, tmp10, None)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 2, 3), (6, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((240, 240, 3), (720, 3, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_affine_grid_generator_0[grid(172800)](buf1, 172800,
            XBLOCK=512, num_warps=8, num_stages=1)
        buf2 = empty_strided_cuda((4, 57600, 2), (115200, 2, 1), torch.float32)
        triton_poi_fused_affine_grid_generator_1[grid(460800)](buf1, arg0_1,
            buf2, 460800, XBLOCK=1024, num_warps=4, num_stages=1)
        del arg0_1
        del buf1
    return reinterpret_tensor(buf2, (4, 240, 240, 2), (115200, 480, 2, 1), 0),


class AffineGridGenNew(Module):

    def __init__(self, out_h=240, out_w=240, out_ch=3, use_cuda=True):
        super(AffineGridGenNew, self).__init__()
        self.out_h = out_h
        self.out_w = out_w
        self.out_ch = out_ch

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]
