import torch
import torch.nn as nn


def pair(t):
    """
    Parameters
    ----------
    t: tuple[int] or int
    """
    return t if isinstance(t, tuple) else (t, t)


class PatchEmbedding(nn.Module):
    """

    Parameters
    ----------
    img_size: int
        Image Size
    patch_size: int
        Patch Size
    in_channels: int
        Number of input channels in the image
    embedding_dim: int
        Number of linear projection output channels
    norm_layer: nn.Module,
        Normalization layer, Default is `nn.LayerNorm`

    """

    def __init__(self, img_size, patch_size, in_channels, embedding_dim,
        norm_layer=nn.LayerNorm):
        super(PatchEmbedding, self).__init__()
        self.img_size = pair(img_size)
        self.patch_size = pair(patch_size)
        self.patch_resolution = [self.img_size[0] // self.patch_size[0], 
            self.img_size[1] // self.patch_size[1]]
        self.proj = nn.Conv2d(in_channels=in_channels, out_channels=
            embedding_dim, kernel_size=patch_size, stride=patch_size)
        self.norm = norm_layer(embedding_dim)

    def forward(self, x):
        """

        Parameters
        ----------
        x:torch.Tensor
            Input tensor

        Returns
        ----------
        torch.Tensor
            Returns output tensor by applying convolution operation with same `kernel_size` and `stride` on input tensor.

        """
        _B, _C, H, W = x.shape
        assert H == self.img_size[0] and W == self.img_size[1
            ], f'Input Image Size {H}*{W} doesnt match model {self.img_size[0]}*{self.img_size[1]}'
        x = self.proj(x).flatten(2).transpose(1, 2)
        x = self.norm(x)
        return x


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


def get_init_inputs():
    return [[], {'img_size': 4, 'patch_size': 4, 'in_channels': 4,
        'embedding_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
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_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_1(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp23, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(4, 
            4), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(16)](buf1, primals_3, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32)
        buf3 = empty_strided_cuda((4, 1, 1), (1, 4, 4), torch.float32)
        triton_poi_fused_native_layer_norm_1[grid(4)](buf1, buf2, buf3, 4,
            XBLOCK=4, num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((4, 1, 4), (4, 1, 1), torch.float32)
        triton_poi_fused_native_layer_norm_2[grid(16)](buf1, buf2, buf3,
            primals_4, primals_5, buf4, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        del buf2
        del buf3
        del primals_5
    return buf4, primals_1, primals_2, primals_4, buf1


def pair(t):
    """
    Parameters
    ----------
    t: tuple[int] or int
    """
    return t if isinstance(t, tuple) else (t, t)


class PatchEmbeddingNew(nn.Module):
    """

    Parameters
    ----------
    img_size: int
        Image Size
    patch_size: int
        Patch Size
    in_channels: int
        Number of input channels in the image
    embedding_dim: int
        Number of linear projection output channels
    norm_layer: nn.Module,
        Normalization layer, Default is `nn.LayerNorm`

    """

    def __init__(self, img_size, patch_size, in_channels, embedding_dim,
        norm_layer=nn.LayerNorm):
        super(PatchEmbeddingNew, self).__init__()
        self.img_size = pair(img_size)
        self.patch_size = pair(patch_size)
        self.patch_resolution = [self.img_size[0] // self.patch_size[0], 
            self.img_size[1] // self.patch_size[1]]
        self.proj = nn.Conv2d(in_channels=in_channels, out_channels=
            embedding_dim, kernel_size=patch_size, stride=patch_size)
        self.norm = norm_layer(embedding_dim)

    def forward(self, input_0):
        primals_1 = self.proj.weight
        primals_3 = self.proj.bias
        primals_4 = self.norm.weight
        primals_5 = self.norm.bias
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
