import torch
import torch.nn as nn
import torch.nn.functional as F
from math import sqrt
from torch import optim as optim


class PatchMerging(nn.Module):
    """Patch Merging Layer.

    Args:
        input_resolution (tuple[int]): Resolution of input feature.
        dim (int): Number of input channels.
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
        self.norm = norm_layer(4 * dim)

    def forward(self, x):
        """ Forward function.
        Args:
            x: Input feature, tensor size (B, H*W, C).
            H, W: Spatial resolution of the input feature.
        """
        B, L, C = x.shape
        H = int(sqrt(L))
        W = H
        x = x.view(B, H, W, C)
        pad_input = H % 2 == 1 or W % 2 == 1
        if pad_input:
            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
        x0 = x[:, 0::2, 0::2, :]
        x1 = x[:, 1::2, 0::2, :]
        x2 = x[:, 0::2, 1::2, :]
        x3 = x[:, 1::2, 1::2, :]
        x = torch.cat([x0, x1, x2, x3], -1)
        x = x.view(B, -1, 4 * C)
        x = self.norm(x)
        x = self.reduction(x)
        return x

    def extra_repr(self) ->str:
        return f'input_resolution={self.input_resolution}, dim={self.dim}'

    def flops(self):
        H, W = self.input_resolution
        flops = H * W * self.dim
        flops += H // 2 * (W // 2) * 4 * self.dim * 2 * self.dim
        return flops


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


def get_init_inputs():
    return [[], {'input_resolution': 4, 'dim': 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._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from torch import optim as optim
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_per_fused_cat_native_layer_norm_0(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 4
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp46 = tl.load(in_ptr1 + r1, None, eviction_policy='evict_last')
    tmp48 = tl.load(in_ptr2 + r1, None, eviction_policy='evict_last')
    tmp0 = r1
    tl.full([1, 1], 0, tl.int64)
    tmp3 = tl.full([1, 1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (16 * x0 + r1), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1, 1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr0 + (8 + 16 * x0 + (-4 + r1)), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1, 1], 12, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr0 + (4 + 16 * x0 + (-8 + r1)), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tmp0 >= tmp12
    tl.full([1, 1], 16, tl.int64)
    tmp19 = tl.load(in_ptr0 + (12 + 16 * x0 + (-12 + r1)), tmp16 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp20 = tl.where(tmp14, tmp15, tmp19)
    tmp21 = tl.where(tmp9, tmp10, tmp20)
    tmp22 = tl.where(tmp4, tmp5, tmp21)
    tmp23 = tl.broadcast_to(tmp22, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp23, 0)
    tmp26 = tl.broadcast_to(tmp23, [XBLOCK, RBLOCK])
    tmp28 = tl.where(xmask, tmp26, 0)
    tmp29 = tl.sum(tmp28, 1)[:, None]
    tmp30 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp31 = tmp30.to(tl.float32)
    tmp32 = tmp29 / tmp31
    tmp33 = tmp23 - tmp32
    tmp34 = tmp33 * tmp33
    tmp35 = tl.broadcast_to(tmp34, [XBLOCK, RBLOCK])
    tmp37 = tl.where(xmask, tmp35, 0)
    tmp38 = tl.sum(tmp37, 1)[:, None]
    tmp39 = 16.0
    tmp40 = tmp38 / tmp39
    tmp41 = 1e-05
    tmp42 = tmp40 + tmp41
    tmp43 = libdevice.rsqrt(tmp42)
    tmp44 = tmp22 - tmp32
    tmp45 = tmp44 * tmp43
    tmp47 = tmp45 * tmp46
    tmp49 = tmp47 + tmp48
    tl.store(out_ptr0 + (r1 + 16 * x0), tmp22, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp43, xmask)
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp49, xmask)
    tl.store(out_ptr1 + x0, tmp32, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (16,), (1,))
    assert_size_stride(primals_3, (16,), (1,))
    assert_size_stride(primals_4, (8, 16), (16, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 1, 16), (16, 16, 16, 1), torch.float32
            )
        buf1 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32)
        buf4 = reinterpret_tensor(buf2, (4, 1, 1), (1, 1, 1), 0)
        del buf2
        buf5 = empty_strided_cuda((4, 1, 16), (16, 16, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_cat_native_layer_norm_0[grid(4)](buf4, primals_1,
            primals_2, primals_3, buf0, buf1, buf5, 4, 16, XBLOCK=1,
            num_warps=2, num_stages=1)
        del primals_1
        del primals_2
        del primals_3
        buf6 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf5, (4, 16), (16, 1), 0),
            reinterpret_tensor(primals_4, (16, 8), (1, 16), 0), out=buf6)
    return reinterpret_tensor(buf6, (4, 1, 8), (8, 8, 1), 0
        ), buf0, buf1, buf4, reinterpret_tensor(buf5, (4, 16), (16, 1), 0
        ), primals_4


class PatchMergingNew(nn.Module):
    """Patch Merging Layer.

    Args:
        input_resolution (tuple[int]): Resolution of input feature.
        dim (int): Number of input channels.
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
        self.norm = norm_layer(4 * dim)

    def extra_repr(self) ->str:
        return f'input_resolution={self.input_resolution}, dim={self.dim}'

    def flops(self):
        H, W = self.input_resolution
        flops = H * W * self.dim
        flops += H // 2 * (W // 2) * 4 * self.dim * 2 * self.dim
        return flops

    def forward(self, input_0):
        primals_4 = self.reduction.weight
        primals_2 = self.norm.weight
        primals_3 = self.norm.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
