# 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_7/inductor_cache/32/c32ppse2vdmak5is2nuwq2vbmvddtxyrdxkeqrxyec7bhptha7aa.py
# Topologically Sorted Source Nodes: [cam_points], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   cam_points => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_2,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_0 = async_compile.triton('triton_poi_fused_clone_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 48
    x1 = (xindex // 48)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (64*x1)), xmask)
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/p3/cp33b3l57w5jlxxjkbyxuiiw6erdw3sxbh2kc6ydjd74u3ubmxdx.py
# Topologically Sorted Source Nodes: [sub, pix_coords_3], Original ATen: [aten.sub, aten.mul]
# Source node to ATen node mapping:
#   pix_coords_3 => mul
#   sub => sub
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%permute_16, 0.5), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, 2), kwargs = {})
triton_poi_fused_mul_sub_1 = async_compile.triton('triton_poi_fused_mul_sub_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=[128], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_sub_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_mul_sub_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16) % 2
    x0 = xindex % 16
    x2 = (xindex // 32)
    x3 = xindex % 32
    x4 = xindex
    tmp7 = tl.load(in_ptr0 + (x0 + (48*x2)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (32 + x0 + (48*x2)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (16 + x0 + (48*x2)), xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr0 + (x3 + (48*x2)), xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 1, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp3 = tmp1 == tmp1
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = tmp1 == tmp4
    tmp6 = tmp4 == tmp4
    tmp9 = 1e-07
    tmp10 = tmp8 + tmp9
    tmp11 = tmp7 / tmp10
    tmp12 = 0.3333333333333333
    tmp13 = tmp11 * tmp12
    tmp14 = tl.where(tmp6, tmp13, tmp11)
    tmp16 = tmp15 / tmp10
    tmp17 = tl.where(tmp5, tmp13, tmp16)
    tmp18 = tl.where(tmp5, tmp14, tmp17)
    tmp19 = tmp18 * tmp12
    tmp20 = tl.where(tmp3, tmp19, tmp18)
    tmp21 = tmp0 == tmp4
    tmp23 = tmp22 / tmp10
    tmp24 = tl.where(tmp21, tmp13, tmp23)
    tmp25 = tl.where(tmp21, tmp14, tmp24)
    tmp26 = tl.where(tmp2, tmp19, tmp25)
    tmp27 = tl.where(tmp2, tmp20, tmp26)
    tmp28 = 0.5
    tmp29 = tmp27 - tmp28
    tmp30 = 2.0
    tmp31 = tmp29 * tmp30
    tl.store(out_ptr0 + (x4), tmp31, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 3, 4, 4), (48, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(arg0_1, (16, 4, 4), (16, 4, 1), 0), out=buf0)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cam_points], Original ATen: [aten.clone]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clone_0.run(buf0, buf1, 192, grid=grid(192), stream=stream0)
        del buf0
        buf2 = empty_strided_cuda((12, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cam_points], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf1, (12, 4, 4), (16, 4, 1), 0), reinterpret_tensor(arg2_1, (12, 4, 4), (16, 4, 1), 0), out=buf2)
        del arg2_1
        del buf1
        buf3 = empty_strided_cuda((4, 4, 4, 2), (32, 4, 1, 16), torch.float32)
        # Topologically Sorted Source Nodes: [sub, pix_coords_3], Original ATen: [aten.sub, aten.mul]
        triton_poi_fused_mul_sub_1.run(buf2, buf3, 128, grid=grid(128), stream=stream0)
        del buf2
    return (buf3, )


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, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((4, 3, 4, 4), (48, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_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)

import torch
from torch import nn
from functools import *


class Project3D(nn.Module):
    """Layer which projects 3D points into a camera with intrinsics K and at position T
    """

    def __init__(self, batch_size, height, width, eps=1e-07):
        super(Project3D, self).__init__()
        self.batch_size = batch_size
        self.height = height
        self.width = width
        self.eps = eps

    def forward(self, points, K, T):
        P = torch.matmul(K, T)[:, :3, :]
        cam_points = torch.matmul(P, points)
        pix_coords = cam_points[:, :2, :] / (cam_points[:, 2, :].unsqueeze(
            1) + self.eps)
        pix_coords = pix_coords.view(self.batch_size, 2, self.height, self.
            width)
        pix_coords = pix_coords.permute(0, 2, 3, 1)
        pix_coords[..., 0] /= self.width - 1
        pix_coords[..., 1] /= self.height - 1
        pix_coords = (pix_coords - 0.5) * 2
        return pix_coords


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


def get_init_inputs():
    return [[], {'batch_size': 4, 'height': 4, 'width': 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 import nn
from functools import *
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_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 48
    x1 = xindex // 48
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_mul_sub_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 2
    x0 = xindex % 16
    x2 = xindex // 32
    x3 = xindex % 32
    x4 = xindex
    tmp7 = tl.load(in_ptr0 + (x0 + 48 * x2), xmask, eviction_policy=
        'evict_last')
    tmp8 = tl.load(in_ptr0 + (32 + x0 + 48 * x2), xmask, eviction_policy=
        'evict_last')
    tmp15 = tl.load(in_ptr0 + (16 + x0 + 48 * x2), xmask, eviction_policy=
        'evict_last')
    tmp22 = tl.load(in_ptr0 + (x3 + 48 * x2), xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 1, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp3 = tmp1 == tmp1
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = tmp1 == tmp4
    tmp6 = tmp4 == tmp4
    tmp9 = 1e-07
    tmp10 = tmp8 + tmp9
    tmp11 = tmp7 / tmp10
    tmp12 = 0.3333333333333333
    tmp13 = tmp11 * tmp12
    tmp14 = tl.where(tmp6, tmp13, tmp11)
    tmp16 = tmp15 / tmp10
    tmp17 = tl.where(tmp5, tmp13, tmp16)
    tmp18 = tl.where(tmp5, tmp14, tmp17)
    tmp19 = tmp18 * tmp12
    tmp20 = tl.where(tmp3, tmp19, tmp18)
    tmp21 = tmp0 == tmp4
    tmp23 = tmp22 / tmp10
    tmp24 = tl.where(tmp21, tmp13, tmp23)
    tmp25 = tl.where(tmp21, tmp14, tmp24)
    tmp26 = tl.where(tmp2, tmp19, tmp25)
    tmp27 = tl.where(tmp2, tmp20, tmp26)
    tmp28 = 0.5
    tmp29 = tmp27 - tmp28
    tmp30 = 2.0
    tmp31 = tmp29 * tmp30
    tl.store(out_ptr0 + x4, tmp31, xmask)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 3, 4, 4), (48, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(arg1_1, (16, 4, 4), (16, 4, 1
            ), 0), reinterpret_tensor(arg0_1, (16, 4, 4), (16, 4, 1), 0),
            out=buf0)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(192)](buf0, buf1, 192, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf0
        buf2 = empty_strided_cuda((12, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf1, (12, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(arg2_1, (12, 4, 4), (16, 4, 1), 0), out=buf2
            )
        del arg2_1
        del buf1
        buf3 = empty_strided_cuda((4, 4, 4, 2), (32, 4, 1, 16), torch.float32)
        triton_poi_fused_mul_sub_1[grid(128)](buf2, buf3, 128, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf2
    return buf3,


class Project3DNew(nn.Module):
    """Layer which projects 3D points into a camera with intrinsics K and at position T
    """

    def __init__(self, batch_size, height, width, eps=1e-07):
        super(Project3DNew, self).__init__()
        self.batch_size = batch_size
        self.height = height
        self.width = width
        self.eps = eps

    def forward(self, input_0, input_1, input_2):
        arg2_1 = input_0
        arg0_1 = input_1
        arg1_1 = input_2
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]
