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 typing import Optional
from typing import Tuple
import torch.nn as nn
import torch.nn.functional as F
from typing import Union
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=128, 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.

    Args:
        window_size: the size of the sliding window and the output patch size.
        stride: stride of the sliding window.
        padding: Zero-padding added to both side of the input.

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

    Returns:
        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]


Measured latency_ms: 0.014089