# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/ps/cpsodef265fez7yctn2j3nynklbbmfuxn7bi57zsgzlanh73sk5f.py
# Topologically Sorted Source Nodes: [affine_grid], Original ATen: [aten.affine_grid_generator]
# Source node to ATen node mapping:
#   affine_grid => add_3, constant_pad_nd_2, full_default
# Graph fragment:
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([1, 1, 1], 1), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %constant_pad_nd_2 : [num_users=1] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%full_default, [2, 0], 0.0), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %constant_pad_nd_2), kwargs = {})
triton_poi_fused_affine_grid_generator_0 = async_compile.triton('triton_poi_fused_affine_grid_generator_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_affine_grid_generator_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/72/c72ko7bwrnxcz33klyfokecvp4hnpe3scztiorsnr3founy7q6m3.py
# Topologically Sorted Source Nodes: [affine_grid], Original ATen: [aten.affine_grid_generator]
# Source node to ATen node mapping:
#   affine_grid => mul_4, sum_1
# Graph fragment:
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_2, %unsqueeze), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_4, [-2]), kwargs = {})
triton_poi_fused_affine_grid_generator_1 = async_compile.triton('triton_poi_fused_affine_grid_generator_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_affine_grid_generator_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_affine_grid_generator_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 460800
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = 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)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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)
        # Topologically Sorted Source Nodes: [affine_grid], Original ATen: [aten.affine_grid_generator]
        stream0 = get_raw_stream(0)
        triton_poi_fused_affine_grid_generator_0.run(buf1, 172800, grid=grid(172800), stream=stream0)
        buf2 = empty_strided_cuda((4, 57600, 2), (115200, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [affine_grid], Original ATen: [aten.affine_grid_generator]
        triton_poi_fused_affine_grid_generator_1.run(buf1, arg0_1, buf2, 460800, grid=grid(460800), stream=stream0)
        del arg0_1
        del buf1
    return (reinterpret_tensor(buf2, (4, 240, 240, 2), (115200, 480, 2, 1), 0), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 2, 3), (6, 3, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

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]
