# AOT ID: ['0_forward']
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_4/inductor_cache/5s/c5sni7dzheaodogr5chdb3cizynndekqs4ajsctpfcvi3r5v37oa.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_0 = async_compile.triton('triton_poi_fused_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=[4096, 256], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: '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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_0', 'mutated_arg_names': [], '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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 2304
    xnumel = 256
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 3
    y1 = (yindex // 3)
    tmp0 = tl.load(in_ptr0 + (x2 + (256*y3)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (3*x2) + (768*y1)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/5b/c5brnjme4e4oybuabwsko4vuljormwjqoawce7jgxo5fbkhzx55r.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_1 = async_compile.triton('triton_poi_fused_1', '''
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=[16, 4096], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: '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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_1', 'mutated_arg_names': [], '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_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 12
    xnumel = 4096
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 3
    y1 = (yindex // 3)
    tmp0 = tl.load(in_ptr0 + (x2 + (4096*y3)), ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (3*x2) + (12288*y1)), tmp0, ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/fj/cfjptm75idloolbk3j5sjagpcx6yh5k5tf4b3erhrl5ujmlrsvpm.py
# Topologically Sorted Source Nodes: [x, x_2], Original ATen: [aten.convolution, aten.native_layer_norm]
# Source node to ATen node mapping:
#   x => convolution
#   x_2 => add, add_1, clone, mul, mul_1, rsqrt, sub, var_mean
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [16, 16], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %clone : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%permute,), kwargs = {memory_format: torch.contiguous_format})
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%clone, [2]), kwargs = {correction: 0, keepdim: True})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clone, %getitem_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %rsqrt), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %primals_4), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_5), kwargs = {})
triton_per_fused_convolution_native_layer_norm_2 = async_compile.triton('triton_per_fused_convolution_native_layer_norm_2', '''
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.persistent_reduction(
    size_hints=[64, 1024],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32', 8: '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, 3, 4, 5, 6, 7, 8), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_convolution_native_layer_norm_2', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': True, 'num_load': 4, 'num_reduction': 4, '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}
)
@triton.jit
def triton_per_fused_convolution_native_layer_norm_2(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel):
    xnumel = 64
    XBLOCK: tl.constexpr = 1
    rnumel = 768
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (r1 + (768*x0)), rmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + (r1), rmask, eviction_policy='evict_last', other=0.0)
    tmp26 = tl.load(in_ptr1 + (r1), rmask, eviction_policy='evict_last', other=0.0)
    tmp28 = tl.load(in_ptr2 + (r1), rmask, eviction_policy='evict_last', other=0.0)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = tl.where(rmask, tmp3, 0)
    tmp6 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp8 = tl.where(rmask, tmp6, 0)
    tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp8, 0))
    tmp10 = tl.full([1], 768, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp3 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [RBLOCK])
    tmp17 = tl.where(rmask, tmp15, 0)
    tmp18 = triton_helpers.promote_to_tensor(tl.sum(tmp17, 0))
    tmp19 = 768.0
    tmp20 = tmp18 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tmp24 = tmp2 - tmp12
    tmp25 = tmp24 * tmp23
    tmp27 = tmp25 * tmp26
    tmp29 = tmp27 + tmp28
    tl.store(in_out_ptr0 + (r1 + (768*x0)), tmp2, rmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (x0), tmp23, None)
    tl.store(out_ptr1 + (r1 + (768*x0)), tmp29, rmask)
    tl.store(out_ptr0 + (x0), tmp12, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (768, 3, 16, 16), (768, 256, 16, 1))
    assert_size_stride(primals_2, (768, ), (1, ))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (768, ), (1, ))
    assert_size_stride(primals_5, (768, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((768, 3, 16, 16), (768, 1, 48, 3), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        stream0 = get_raw_stream(0)
        triton_poi_fused_0.run(primals_1, buf0, 2304, 256, grid=grid(2304, 256), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((4, 3, 64, 64), (12288, 1, 192, 3), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_1.run(primals_3, buf1, 12, 4096, grid=grid(12, 4096), stream=stream0)
        del primals_3
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, buf0, stride=(16, 16), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 768, 4, 4), (12288, 1, 3072, 768))
        buf3 = buf2; del buf2  # reuse
        buf4 = empty_strided_cuda((4, 16, 1), (16, 1, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 16, 1), (16, 1, 64), torch.float32)
        buf7 = reinterpret_tensor(buf5, (4, 16, 1), (16, 1, 1), 0); del buf5  # reuse
        buf8 = empty_strided_cuda((4, 16, 768), (12288, 768, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x, x_2], Original ATen: [aten.convolution, aten.native_layer_norm]
        triton_per_fused_convolution_native_layer_norm_2.run(buf3, buf7, primals_2, primals_4, primals_5, buf4, buf8, 64, 768, grid=grid(64), stream=stream0)
        del primals_2
        del primals_5
    return (buf8, buf0, buf1, primals_4, buf3, buf4, buf7, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((768, 3, 16, 16), (768, 256, 16, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 3, 64, 64), (12288, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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
from typing import Optional


class PatchEmbed(nn.Module):

    def __init__(self, img_size: 'int'=224, patch_size: 'int'=16, stride:
        'int'=None, in_channels: 'int'=3, embed_dim: 'int'=768, multi_conv:
        'bool'=False, norm_layer: 'Optional'=nn.LayerNorm):
        super(PatchEmbed, self).__init__()
        assert img_size % patch_size == 0, 'Argument `img_size` should be factor of argument `patch_size`'
        self.grid_size = img_size // patch_size
        self.patch_size = patch_size
        self.num_patches = self.grid_size ** 2
        if stride is None:
            stride = patch_size
        if multi_conv:
            if patch_size == 12:
                self.proj = nn.Sequential(nn.Conv2d(in_channels=in_channels,
                    out_channels=embed_dim // 4, kernel_size=7, stride=4,
                    padding=3), nn.ReLU(inplace=True), nn.Conv2d(
                    in_channels=embed_dim // 4, out_channels=embed_dim // 2,
                    kernel_size=3, stride=3), nn.ReLU(inplace=True), nn.
                    Conv2d(in_channels=embed_dim // 2, out_channels=
                    embed_dim, kernel_size=3, stride=1, padding=1))
            elif patch_size == 16:
                self.proj = nn.Sequential(nn.Conv2d(in_channels, embed_dim //
                    4, kernel_size=7, stride=4, padding=3), nn.ReLU(inplace
                    =True), nn.Conv2d(in_channels=embed_dim // 4,
                    out_channels=embed_dim // 2, kernel_size=3, stride=2,
                    padding=1), nn.ReLU(inplace=True), nn.Conv2d(
                    in_channels=embed_dim // 2, out_channels=embed_dim,
                    kernel_size=3, stride=2, padding=1))
        else:
            self.proj = nn.Conv2d(in_channels=in_channels, out_channels=
                embed_dim, kernel_size=patch_size, stride=stride)
        if norm_layer is not None:
            self.norm = norm_layer(embed_dim)
        else:
            self.norm = norm_layer

    def forward(self, x):
        x = self.proj(x)
        x = x.flatten(start_dim=2).transpose(1, 2)
        if self.norm is not None:
            x = self.norm(x)
        return x


def get_inputs():
    return [torch.rand([4, 3, 64, 64])]


def get_init_inputs():
    return [[], {}]

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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from typing import Optional
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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 2304
    xnumel = 256
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 3
    y1 = yindex // 3
    tmp0 = tl.load(in_ptr0 + (x2 + 256 * y3), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + 3 * x2 + 768 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 12
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 3
    y1 = yindex // 3
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 3 * x2 + 12288 * y1), tmp0, ymask)


@triton.jit
def triton_per_fused_convolution_native_layer_norm_2(in_out_ptr0,
    in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel
    ):
    XBLOCK: tl.constexpr = 1
    rnumel = 768
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (r1 + 768 * x0), rmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + r1, rmask, eviction_policy='evict_last', other=0.0
        )
    tmp26 = tl.load(in_ptr1 + r1, rmask, eviction_policy='evict_last',
        other=0.0)
    tmp28 = tl.load(in_ptr2 + r1, rmask, eviction_policy='evict_last',
        other=0.0)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tl.where(rmask, tmp3, 0)
    tmp6 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp8 = tl.where(rmask, tmp6, 0)
    tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp8, 0))
    tmp10 = tl.full([1], 768, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp3 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [RBLOCK])
    tmp17 = tl.where(rmask, tmp15, 0)
    tmp18 = triton_helpers.promote_to_tensor(tl.sum(tmp17, 0))
    tmp19 = 768.0
    tmp20 = tmp18 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tmp24 = tmp2 - tmp12
    tmp25 = tmp24 * tmp23
    tmp27 = tmp25 * tmp26
    tmp29 = tmp27 + tmp28
    tl.store(in_out_ptr0 + (r1 + 768 * x0), tmp2, rmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x0, tmp23, None)
    tl.store(out_ptr1 + (r1 + 768 * x0), tmp29, rmask)
    tl.store(out_ptr0 + x0, tmp12, None)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (768, 3, 16, 16), (768, 256, 16, 1))
    assert_size_stride(primals_2, (768,), (1,))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (768,), (1,))
    assert_size_stride(primals_5, (768,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((768, 3, 16, 16), (768, 1, 48, 3), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(2304, 256)](primals_1, buf0, 2304, 256,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 3, 64, 64), (12288, 1, 192, 3), torch
            .float32)
        triton_poi_fused_1[grid(12, 4096)](primals_3, buf1, 12, 4096,
            XBLOCK=64, YBLOCK=16, num_warps=4, num_stages=1)
        del primals_3
        buf2 = extern_kernels.convolution(buf1, buf0, stride=(16, 16),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 768, 4, 4), (12288, 1, 3072, 768))
        buf3 = buf2
        del buf2
        buf4 = empty_strided_cuda((4, 16, 1), (16, 1, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 16, 1), (16, 1, 64), torch.float32)
        buf7 = reinterpret_tensor(buf5, (4, 16, 1), (16, 1, 1), 0)
        del buf5
        buf8 = empty_strided_cuda((4, 16, 768), (12288, 768, 1), torch.float32)
        triton_per_fused_convolution_native_layer_norm_2[grid(64)](buf3,
            buf7, primals_2, primals_4, primals_5, buf4, buf8, 64, 768,
            num_warps=8, num_stages=1)
        del primals_2
        del primals_5
    return buf8, buf0, buf1, primals_4, buf3, buf4, buf7


class PatchEmbedNew(nn.Module):

    def __init__(self, img_size: 'int'=224, patch_size: 'int'=16, stride:
        'int'=None, in_channels: 'int'=3, embed_dim: 'int'=768, multi_conv:
        'bool'=False, norm_layer: 'Optional'=nn.LayerNorm):
        super(PatchEmbedNew, self).__init__()
        assert img_size % patch_size == 0, 'Argument `img_size` should be factor of argument `patch_size`'
        self.grid_size = img_size // patch_size
        self.patch_size = patch_size
        self.num_patches = self.grid_size ** 2
        if stride is None:
            stride = patch_size
        if multi_conv:
            if patch_size == 12:
                self.proj = nn.Sequential(nn.Conv2d(in_channels=in_channels,
                    out_channels=embed_dim // 4, kernel_size=7, stride=4,
                    padding=3), nn.ReLU(inplace=True), nn.Conv2d(
                    in_channels=embed_dim // 4, out_channels=embed_dim // 2,
                    kernel_size=3, stride=3), nn.ReLU(inplace=True), nn.
                    Conv2d(in_channels=embed_dim // 2, out_channels=
                    embed_dim, kernel_size=3, stride=1, padding=1))
            elif patch_size == 16:
                self.proj = nn.Sequential(nn.Conv2d(in_channels, embed_dim //
                    4, kernel_size=7, stride=4, padding=3), nn.ReLU(inplace
                    =True), nn.Conv2d(in_channels=embed_dim // 4,
                    out_channels=embed_dim // 2, kernel_size=3, stride=2,
                    padding=1), nn.ReLU(inplace=True), nn.Conv2d(
                    in_channels=embed_dim // 2, out_channels=embed_dim,
                    kernel_size=3, stride=2, padding=1))
        else:
            self.proj = nn.Conv2d(in_channels=in_channels, out_channels=
                embed_dim, kernel_size=patch_size, stride=stride)
        if norm_layer is not None:
            self.norm = norm_layer(embed_dim)
        else:
            self.norm = norm_layer

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