# 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_6/inductor_cache/rt/crt24dzchwgllhruf5jsdkgnfxquif3uquthvco4ygm56zu4c7hv.py
# Topologically Sorted Source Nodes: [input_1, view], Original ATen: [aten.constant_pad_nd, aten.view]
# Source node to ATen node mapping:
#   input_1 => constant_pad_nd
#   view => view
# Graph fragment:
#   %constant_pad_nd : [num_users=1] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%arg0_1, [0, 0, 0, 0], 0.0), kwargs = {})
#   %view : [num_users=1] = call_function[target=torch.ops.aten.reshape.default](args = (%permute, [4, -1, 4, 4, 4]), kwargs = {})
triton_poi_fused_constant_pad_nd_view_0 = async_compile.triton('triton_poi_fused_constant_pad_nd_view_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_constant_pad_nd_view_0', 'mutated_arg_names': ['in_out_ptr0'], '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_constant_pad_nd_view_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tl.store(in_out_ptr0 + (x0), tmp0, 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, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 1, 4, 4, 4), (64, 16, 16, 4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [input_1, view], Original ATen: [aten.constant_pad_nd, aten.view]
        stream0 = get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_view_0.run(buf1, arg0_1, 256, grid=grid(256), stream=stream0)
        del arg0_1
    return (buf1, )


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
import torch.nn.functional as F
from typing import Tuple
from typing import Union
from typing import Optional
from torch.nn.modules.utils import _pair


def _extract_tensor_patchesnd(input: 'torch.Tensor', window_sizes:
    'Tuple[int, ...]', strides: 'Tuple[int, ...]') ->torch.Tensor:
    batch_size, num_channels = input.size()[:2]
    dims = range(2, input.dim())
    for dim, patch_size, stride in zip(dims, window_sizes, strides):
        input = input.unfold(dim, patch_size, stride)
    input = input.permute(0, *dims, 1, *[(dim + len(dims)) for dim in dims]
        ).contiguous()
    return input.view(batch_size, -1, num_channels, *window_sizes)


def extract_tensor_patches(input: 'torch.Tensor', window_size:
    'Union[int, Tuple[int, int]]', stride: 'Union[int, Tuple[int, int]]'=1,
    padding: 'Union[int, Tuple[int, int]]'=0) ->torch.Tensor:
    """Function that extract patches from tensors and stack them.

    See :class:`~kornia.contrib.ExtractTensorPatches` for details.
    """
    if not torch.is_tensor(input):
        raise TypeError('Input input type is not a torch.Tensor. Got {}'.
            format(type(input)))
    if not len(input.shape) == 4:
        raise ValueError('Invalid input shape, we expect BxCxHxW. Got: {}'.
            format(input.shape))
    if padding:
        pad_vert, pad_horz = _pair(padding)
        input = F.pad(input, [pad_horz, pad_horz, pad_vert, pad_vert])
    return _extract_tensor_patchesnd(input, _pair(window_size), _pair(stride))


class ExtractTensorPatches(nn.Module):
    """Module that extract patches from tensors and stack them.

    In the simplest case, the output value of the operator with input size
    :math:`(B, C, H, W)` is :math:`(B, N, C, H_{out}, W_{out})`.

    where
      - :math:`B` is the batch size.
      - :math:`N` denotes the total number of extracted patches stacked in
      - :math:`C` denotes the number of input channels.
      - :math:`H`, :math:`W` the input height and width of the input in pixels.
      - :math:`H_{out}`, :math:`W_{out}` denote to denote to the patch size
        defined in the function signature.
        left-right and top-bottom order.

    * :attr:`window_size` is the size of the sliding window and controls the
      shape of the output tensor and defines the shape of the output patch.
    * :attr:`stride` controls the stride to apply to the sliding window and
      regulates the overlapping between the extracted patches.
    * :attr:`padding` controls the amount of implicit zeros-paddings on both
      sizes at each dimension.

    The parameters :attr:`window_size`, :attr:`stride` and :attr:`padding` can
    be either:

        - a single ``int`` -- in which case the same value is used for the
          height and width dimension.
        - a ``tuple`` of two ints -- in which case, the first `int` is used for
          the height dimension, and the second `int` for the width dimension.

    Arguments:
        window_size (Union[int, Tuple[int, int]]): the size of the sliding
          window and the output patch size.
        stride (Optional[Union[int, Tuple[int, int]]]): stride of the
          sliding window. Default is 1.
        padding (Optional[Union[int, Tuple[int, int]]]): Zero-padding added to
          both side of the input. Default is 0.

    Shape:
        - Input: :math:`(B, C, H, W)`
        - Output: :math:`(B, N, C, H_{out}, W_{out})`

    Returns:
        torch.Tensor: the tensor with the extracted patches.

    Examples:
        >>> input = torch.arange(9.).view(1, 1, 3, 3)
        >>> patches = extract_tensor_patches(input, (2, 3))
        >>> input
        tensor([[[[0., 1., 2.],
                  [3., 4., 5.],
                  [6., 7., 8.]]]])
        >>> patches[:, -1]
        tensor([[[[3., 4., 5.],
                  [6., 7., 8.]]]])
    """

    def __init__(self, window_size: 'Union[int, Tuple[int, int]]', stride:
        'Optional[Union[int, Tuple[int, int]]]'=1, padding:
        'Optional[Union[int, Tuple[int, int]]]'=0) ->None:
        super(ExtractTensorPatches, self).__init__()
        self.window_size: 'Tuple[int, int]' = _pair(window_size)
        self.stride: 'Tuple[int, int]' = _pair(stride)
        self.padding: 'Tuple[int, int]' = _pair(padding)

    def forward(self, input: 'torch.Tensor') ->torch.Tensor:
        return extract_tensor_patches(input, self.window_size, stride=self.
            stride, padding=self.padding)


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


def get_init_inputs():
    return [[], {'window_size': 4}]

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
import torch.nn.functional as F
from typing import Tuple
from typing import Union
from typing import Optional
from torch.nn.modules.utils import _pair
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_constant_pad_nd_view_0(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tl.store(in_out_ptr0 + x0, tmp0, 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, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 1, 4, 4, 4), (64, 16, 16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_view_0[grid(256)](buf1, arg0_1, 
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
    return buf1,


def _extract_tensor_patchesnd(input: 'torch.Tensor', window_sizes:
    'Tuple[int, ...]', strides: 'Tuple[int, ...]') ->torch.Tensor:
    batch_size, num_channels = input.size()[:2]
    dims = range(2, input.dim())
    for dim, patch_size, stride in zip(dims, window_sizes, strides):
        input = input.unfold(dim, patch_size, stride)
    input = input.permute(0, *dims, 1, *[(dim + len(dims)) for dim in dims]
        ).contiguous()
    return input.view(batch_size, -1, num_channels, *window_sizes)


def extract_tensor_patches(input: 'torch.Tensor', window_size:
    'Union[int, Tuple[int, int]]', stride: 'Union[int, Tuple[int, int]]'=1,
    padding: 'Union[int, Tuple[int, int]]'=0) ->torch.Tensor:
    """Function that extract patches from tensors and stack them.

    See :class:`~kornia.contrib.ExtractTensorPatches` for details.
    """
    if not torch.is_tensor(input):
        raise TypeError('Input input type is not a torch.Tensor. Got {}'.
            format(type(input)))
    if not len(input.shape) == 4:
        raise ValueError('Invalid input shape, we expect BxCxHxW. Got: {}'.
            format(input.shape))
    if padding:
        pad_vert, pad_horz = _pair(padding)
        input = F.pad(input, [pad_horz, pad_horz, pad_vert, pad_vert])
    return _extract_tensor_patchesnd(input, _pair(window_size), _pair(stride))


class ExtractTensorPatchesNew(nn.Module):
    """Module that extract patches from tensors and stack them.

    In the simplest case, the output value of the operator with input size
    :math:`(B, C, H, W)` is :math:`(B, N, C, H_{out}, W_{out})`.

    where
      - :math:`B` is the batch size.
      - :math:`N` denotes the total number of extracted patches stacked in
      - :math:`C` denotes the number of input channels.
      - :math:`H`, :math:`W` the input height and width of the input in pixels.
      - :math:`H_{out}`, :math:`W_{out}` denote to denote to the patch size
        defined in the function signature.
        left-right and top-bottom order.

    * :attr:`window_size` is the size of the sliding window and controls the
      shape of the output tensor and defines the shape of the output patch.
    * :attr:`stride` controls the stride to apply to the sliding window and
      regulates the overlapping between the extracted patches.
    * :attr:`padding` controls the amount of implicit zeros-paddings on both
      sizes at each dimension.

    The parameters :attr:`window_size`, :attr:`stride` and :attr:`padding` can
    be either:

        - a single ``int`` -- in which case the same value is used for the
          height and width dimension.
        - a ``tuple`` of two ints -- in which case, the first `int` is used for
          the height dimension, and the second `int` for the width dimension.

    Arguments:
        window_size (Union[int, Tuple[int, int]]): the size of the sliding
          window and the output patch size.
        stride (Optional[Union[int, Tuple[int, int]]]): stride of the
          sliding window. Default is 1.
        padding (Optional[Union[int, Tuple[int, int]]]): Zero-padding added to
          both side of the input. Default is 0.

    Shape:
        - Input: :math:`(B, C, H, W)`
        - Output: :math:`(B, N, C, H_{out}, W_{out})`

    Returns:
        torch.Tensor: the tensor with the extracted patches.

    Examples:
        >>> input = torch.arange(9.).view(1, 1, 3, 3)
        >>> patches = extract_tensor_patches(input, (2, 3))
        >>> input
        tensor([[[[0., 1., 2.],
                  [3., 4., 5.],
                  [6., 7., 8.]]]])
        >>> patches[:, -1]
        tensor([[[[3., 4., 5.],
                  [6., 7., 8.]]]])
    """

    def __init__(self, window_size: 'Union[int, Tuple[int, int]]', stride:
        'Optional[Union[int, Tuple[int, int]]]'=1, padding:
        'Optional[Union[int, Tuple[int, int]]]'=0) ->None:
        super(ExtractTensorPatchesNew, self).__init__()
        self.window_size: 'Tuple[int, int]' = _pair(window_size)
        self.stride: 'Tuple[int, int]' = _pair(stride)
        self.padding: 'Tuple[int, int]' = _pair(padding)

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