# 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_8/inductor_cache/af/caf7neijm3kbkzae765spedgco2ojidwgdae5r4fritcjagme5n2.py
# Topologically Sorted Source Nodes: [x_rgb, out], Original ATen: [aten.cat, aten.flip]
# Source node to ATen node mapping:
#   out => rev
#   x_rgb => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%getitem, %getitem_1, %getitem_2], -3), kwargs = {})
#   %rev : [num_users=1] = call_function[target=torch.ops.prims.rev.default](args = (%cat, [1]), kwargs = {})
triton_poi_fused_cat_flip_0 = async_compile.triton('triton_poi_fused_cat_flip_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_cat_flip_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_cat_flip_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
    x1 = (xindex // 16) % 3
    x0 = xindex % 16
    x2 = (xindex // 48)
    x3 = xindex
    tmp0 = 2 + ((-1)*x1)
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (64*x2)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 2, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 3, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), tmp11 & xmask, eviction_policy='evict_last', other=0.0)
    tmp15 = tl.where(tmp9, tmp10, tmp14)
    tmp16 = tl.where(tmp4, tmp5, tmp15)
    tl.store(out_ptr0 + (x3), tmp16, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_rgb, out], Original ATen: [aten.cat, aten.flip]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_flip_0.run(arg0_1, buf0, 192, grid=grid(192), stream=stream0)
        del arg0_1
    return (buf0, )


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)
    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)

import torch
import torch.nn as nn


def bgr_to_rgb(image: 'torch.Tensor') ->torch.Tensor:
    """Convert a BGR image to RGB.

    See :class:`~kornia.color.BgrToRgb` for details.

    Args:
        image (torch.Tensor): BGR Image to be converted to RGB.

    Returns:
        torch.Tensor: RGB version of the image.
    """
    if not torch.is_tensor(image):
        raise TypeError('Input type is not a torch.Tensor. Got {}'.format(
            type(image)))
    if len(image.shape) < 3 or image.shape[-3] != 3:
        raise ValueError('Input size must have a shape of (*, 3, H, W).Got {}'
            .format(image.shape))
    out: 'torch.Tensor' = image.flip(-3)
    return out


def rgb_to_bgr(image: 'torch.Tensor') ->torch.Tensor:
    """Convert a RGB image to BGR.

    See :class:`~kornia.color.RgbToBgr` for details.

    Args:
        image (torch.Tensor): RGB Image to be converted to BGR.

    Returns:
        torch.Tensor: BGR version of the image.
    """
    return bgr_to_rgb(image)


def rgba_to_rgb(image: 'torch.Tensor') ->torch.Tensor:
    """Convert image from RGBA to RGB.

    See :class:`~kornia.color.RgbaToRgb` for details.

    Args:
        image (torch.Tensor): RGBA Image to be converted to RGB.

    Returns:
        torch.Tensor: RGB version of the image.
    """
    if not torch.is_tensor(image):
        raise TypeError(f'Input type is not a torch.Tensor. Got {type(image)}')
    if len(image.shape) < 3 or image.shape[-3] != 4:
        raise ValueError(
            f'Input size must have a shape of (*, 3, H, W).Got {image.shape}')
    r, g, b, a = torch.chunk(image, image.shape[-3], dim=-3)
    a_one = torch.tensor(1.0) - a
    a_one * r + a * r
    a_one * g + a * g
    a_one * b + a * b
    return torch.cat([r, g, b], dim=-3)


def rgba_to_bgr(image: 'torch.Tensor') ->torch.Tensor:
    """Convert image from RGBA to BGR.

    See :class:`~kornia.color.RgbaToBgr` for details.

    Args:
        image (torch.Tensor): RGBA Image to be converted to BGR.

    Returns:
        torch.Tensor: BGR version of the image.
    """
    if not torch.is_tensor(image):
        raise TypeError(f'Input type is not a torch.Tensor. Got {type(image)}')
    if len(image.shape) < 3 or image.shape[-3] != 4:
        raise ValueError(
            f'Input size must have a shape of (*, 3, H, W).Got {image.shape}')
    x_rgb: 'torch.Tensor' = rgba_to_rgb(image)
    return rgb_to_bgr(x_rgb)


class RgbaToBgr(nn.Module):
    """Convert image from RGBA to BGR.

    Remove an alpha channel from BGR image.

    returns:
        torch.Tensor: BGR version of the image.

    shape:
        - image: :math:`(*, 4, H, W)`
        - output: :math:`(*, 3, H, W)`

    Examples::

        >>> input = torch.rand(2, 4, 4, 5)
        >>> rgba = kornia.color.RgbaToBgr()
        >>> output = rgba(input)  # 2x3x4x5
    """

    def __init__(self) ->None:
        super(RgbaToBgr, self).__init__()

    def forward(self, image: 'torch.Tensor') ->torch.Tensor:
        return rgba_to_bgr(image)


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


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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_cat_flip_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
    x1 = xindex // 16 % 3
    x0 = xindex % 16
    x2 = xindex // 48
    x3 = xindex
    tmp0 = 2 + -1 * x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 64 * x2), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 2, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tl.full([1], 3, tl.int64)
    tmp14 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), tmp11 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp15 = tl.where(tmp9, tmp10, tmp14)
    tmp16 = tl.where(tmp4, tmp5, tmp15)
    tl.store(out_ptr0 + x3, tmp16, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_flip_0[grid(192)](arg0_1, buf0, 192, XBLOCK=
            256, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


def bgr_to_rgb(image: 'torch.Tensor') ->torch.Tensor:
    """Convert a BGR image to RGB.

    See :class:`~kornia.color.BgrToRgb` for details.

    Args:
        image (torch.Tensor): BGR Image to be converted to RGB.

    Returns:
        torch.Tensor: RGB version of the image.
    """
    if not torch.is_tensor(image):
        raise TypeError('Input type is not a torch.Tensor. Got {}'.format(
            type(image)))
    if len(image.shape) < 3 or image.shape[-3] != 3:
        raise ValueError('Input size must have a shape of (*, 3, H, W).Got {}'
            .format(image.shape))
    out: 'torch.Tensor' = image.flip(-3)
    return out


def rgb_to_bgr(image: 'torch.Tensor') ->torch.Tensor:
    """Convert a RGB image to BGR.

    See :class:`~kornia.color.RgbToBgr` for details.

    Args:
        image (torch.Tensor): RGB Image to be converted to BGR.

    Returns:
        torch.Tensor: BGR version of the image.
    """
    return bgr_to_rgb(image)


def rgba_to_rgb(image: 'torch.Tensor') ->torch.Tensor:
    """Convert image from RGBA to RGB.

    See :class:`~kornia.color.RgbaToRgb` for details.

    Args:
        image (torch.Tensor): RGBA Image to be converted to RGB.

    Returns:
        torch.Tensor: RGB version of the image.
    """
    if not torch.is_tensor(image):
        raise TypeError(f'Input type is not a torch.Tensor. Got {type(image)}')
    if len(image.shape) < 3 or image.shape[-3] != 4:
        raise ValueError(
            f'Input size must have a shape of (*, 3, H, W).Got {image.shape}')
    r, g, b, a = torch.chunk(image, image.shape[-3], dim=-3)
    a_one = torch.tensor(1.0) - a
    a_one * r + a * r
    a_one * g + a * g
    a_one * b + a * b
    return torch.cat([r, g, b], dim=-3)


def rgba_to_bgr(image: 'torch.Tensor') ->torch.Tensor:
    """Convert image from RGBA to BGR.

    See :class:`~kornia.color.RgbaToBgr` for details.

    Args:
        image (torch.Tensor): RGBA Image to be converted to BGR.

    Returns:
        torch.Tensor: BGR version of the image.
    """
    if not torch.is_tensor(image):
        raise TypeError(f'Input type is not a torch.Tensor. Got {type(image)}')
    if len(image.shape) < 3 or image.shape[-3] != 4:
        raise ValueError(
            f'Input size must have a shape of (*, 3, H, W).Got {image.shape}')
    x_rgb: 'torch.Tensor' = rgba_to_rgb(image)
    return rgb_to_bgr(x_rgb)


class RgbaToBgrNew(nn.Module):
    """Convert image from RGBA to BGR.

    Remove an alpha channel from BGR image.

    returns:
        torch.Tensor: BGR version of the image.

    shape:
        - image: :math:`(*, 4, H, W)`
        - output: :math:`(*, 3, H, W)`

    Examples::

        >>> input = torch.rand(2, 4, 4, 5)
        >>> rgba = kornia.color.RgbaToBgr()
        >>> output = rgba(input)  # 2x3x4x5
    """

    def __init__(self) ->None:
        super(RgbaToBgrNew, self).__init__()

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