# 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_1/inductor_cache/k4/ck4uqdauescu563i3mamosknsgtg7rlubztsotcvu25p3bzh6dj3.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=[512, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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 = 288
    xnumel = 16
    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 + (16*y3)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (y0 + (3*x2) + (48*y1)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/az/cazvf33aclbntgyixs3zlm6bdzs672xtry2xl6pc3l3sjzwrnks5.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=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_1/inductor_cache/p7/cp7xhyvlynylpekafk6gzhcdpvxf7en7s4sd52qpmat5xcuhai7e.py
# Topologically Sorted Source Nodes: [embedding, layer_norm], Original ATen: [aten.convolution, aten.native_layer_norm]
# Source node to ATen node mapping:
#   embedding => convolution
#   layer_norm => 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, [4, 4], [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, [3]), 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_red_fused_convolution_native_layer_norm_2 = async_compile.triton('triton_red_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.reduction(
    size_hints=[1024, 128],
    reduction_hint=ReductionHint.DEFAULT,
    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=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_red_fused_convolution_native_layer_norm_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 2, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_red_fused_convolution_native_layer_norm_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr2, out_ptr3, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 1024
    rnumel = 96
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    tmp4_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp4_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp4_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    x2 = xindex % 16
    x3 = (xindex // 16) % 16
    x4 = (xindex // 256)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_out_ptr0 + (r1 + (96*x0)), rmask & xmask, eviction_policy='evict_first', other=0.0)
        tmp1 = tl.load(in_ptr0 + (r1), rmask, eviction_policy='evict_last', other=0.0)
        tmp2 = tmp0 + tmp1
        tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
        tmp4_mean_next, tmp4_m2_next, tmp4_weight_next = triton_helpers.welford_reduce(
            tmp3, tmp4_mean, tmp4_m2, tmp4_weight, roffset == 0
        )
        tmp4_mean = tl.where(rmask & xmask, tmp4_mean_next, tmp4_mean)
        tmp4_m2 = tl.where(rmask & xmask, tmp4_m2_next, tmp4_m2)
        tmp4_weight = tl.where(rmask & xmask, tmp4_weight_next, tmp4_weight)
        tl.store(in_out_ptr0 + (r1 + (96*x0)), tmp2, rmask & xmask)
    tmp4_tmp, tmp5_tmp, tmp6_tmp = triton_helpers.welford(
        tmp4_mean, tmp4_m2, tmp4_weight, 1
    )
    tmp4 = tmp4_tmp[:, None]
    tmp5 = tmp5_tmp[:, None]
    tmp6 = tmp6_tmp[:, None]
    tl.store(out_ptr0 + (x3 + (16*x2) + (256*x4)), tmp4, xmask)
    tmp7 = 96.0
    tmp8 = tmp5 / tmp7
    tmp9 = 1e-05
    tmp10 = tmp8 + tmp9
    tmp11 = libdevice.rsqrt(tmp10)
    tl.store(out_ptr2 + (x3 + (16*x2) + (256*x4)), tmp11, xmask)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp12 = tl.load(in_out_ptr0 + (r1 + (96*x0)), rmask & xmask, eviction_policy='evict_first', other=0.0)
        tmp15 = tl.load(in_ptr1 + (r1), rmask, eviction_policy='evict_last', other=0.0)
        tmp17 = tl.load(in_ptr2 + (r1), rmask, eviction_policy='evict_last', other=0.0)
        tmp13 = tmp12 - tmp4
        tmp14 = tmp13 * tmp11
        tmp16 = tmp14 * tmp15
        tmp18 = tmp16 + tmp17
        tl.store(out_ptr3 + (r1 + (96*x0)), tmp18, rmask & xmask)
''', 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, (96, 3, 4, 4), (48, 16, 4, 1))
    assert_size_stride(primals_2, (96, ), (1, ))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (96, ), (1, ))
    assert_size_stride(primals_5, (96, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((96, 3, 4, 4), (48, 1, 12, 3), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        stream0 = get_raw_stream(0)
        triton_poi_fused_0.run(primals_1, buf0, 288, 16, grid=grid(288, 16), 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: [embedding], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, buf0, stride=(4, 4), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 96, 16, 16), (24576, 1, 1536, 96))
        buf3 = buf2; del buf2  # reuse
        buf4 = empty_strided_cuda((4, 16, 16, 1), (256, 1, 16, 16), torch.float32)
        buf7 = empty_strided_cuda((4, 16, 16, 1), (256, 1, 16, 16), torch.float32)
        buf8 = empty_strided_cuda((4, 16, 16, 96), (24576, 1536, 96, 1), torch.float32)
        # Topologically Sorted Source Nodes: [embedding, layer_norm], Original ATen: [aten.convolution, aten.native_layer_norm]
        triton_red_fused_convolution_native_layer_norm_2.run(buf3, primals_2, primals_4, primals_5, buf4, buf7, buf8, 1024, 96, grid=grid(1024), stream=stream0)
        del primals_2
        del primals_5
    return (reinterpret_tensor(buf8, (4, 96, 16, 16), (24576, 1, 1536, 96), 0), 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((96, 3, 4, 4), (48, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((96, ), (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((96, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((96, ), (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


def bchw_to_bhwc(input: 'torch.Tensor') ->torch.Tensor:
    """
    Permutes a tensor to the shape [batch size, height, width, channels]
    :param input: (torch.Tensor) Input tensor of the shape [batch size, height, width, channels]
    :return: (torch.Tensor) Output tensor of the shape [batch size, height, width, channels]
    """
    return input.permute(0, 2, 3, 1)


def bhwc_to_bchw(input: 'torch.Tensor') ->torch.Tensor:
    """
    Permutes a tensor to the shape [batch size, channels, height, width]
    :param input: (torch.Tensor) Input tensor of the shape [batch size, height, width, channels]
    :return: (torch.Tensor) Output tensor of the shape [batch size, channels, height, width]
    """
    return input.permute(0, 3, 1, 2)


class PatchEmbedding(nn.Module):
    """
    Module embeds a given image into patch embeddings.
    """

    def __init__(self, in_channels: 'int'=3, out_channels: 'int'=96,
        patch_size: 'int'=4) ->None:
        """
        Constructor method
        :param in_channels: (int) Number of input channels
        :param out_channels: (int) Number of output channels
        :param patch_size: (int) Patch size to be utilized
        :param image_size: (int) Image size to be used
        """
        super(PatchEmbedding, self).__init__()
        self.out_channels: 'int' = out_channels
        self.linear_embedding: 'nn.Module' = nn.Conv2d(in_channels=
            in_channels, out_channels=out_channels, kernel_size=(patch_size,
            patch_size), stride=(patch_size, patch_size))
        self.normalization: 'nn.Module' = nn.LayerNorm(normalized_shape=
            out_channels)

    def forward(self, input: 'torch.Tensor') ->torch.Tensor:
        """
        Forward pass transforms a given batch of images into a patch embedding
        :param input: (torch.Tensor) Input images of the shape [batch size, in channels, height, width]
        :return: (torch.Tensor) Patch embedding of the shape [batch size, patches + 1, out channels]
        """
        embedding: 'torch.Tensor' = self.linear_embedding(input)
        embedding: 'torch.Tensor' = bhwc_to_bchw(self.normalization(
            bchw_to_bhwc(embedding)))
        return embedding


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
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 = 288
    xnumel = 16
    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 + 16 * y3), xmask & ymask, eviction_policy
        ='evict_last')
    tl.store(out_ptr0 + (y0 + 3 * x2 + 48 * 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_red_fused_convolution_native_layer_norm_2(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, out_ptr2, out_ptr3, xnumel, rnumel, XBLOCK:
    tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 1024
    rnumel = 96
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    tmp4_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp4_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp4_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    x2 = xindex % 16
    x3 = xindex // 16 % 16
    x4 = xindex // 256
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_out_ptr0 + (r1 + 96 * x0), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp1 = tl.load(in_ptr0 + r1, rmask, eviction_policy='evict_last',
            other=0.0)
        tmp2 = tmp0 + tmp1
        tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
        tmp4_mean_next, tmp4_m2_next, tmp4_weight_next = (triton_helpers.
            welford_reduce(tmp3, tmp4_mean, tmp4_m2, tmp4_weight, roffset == 0)
            )
        tmp4_mean = tl.where(rmask & xmask, tmp4_mean_next, tmp4_mean)
        tmp4_m2 = tl.where(rmask & xmask, tmp4_m2_next, tmp4_m2)
        tmp4_weight = tl.where(rmask & xmask, tmp4_weight_next, tmp4_weight)
        tl.store(in_out_ptr0 + (r1 + 96 * x0), tmp2, rmask & xmask)
    tmp4_tmp, tmp5_tmp, tmp6_tmp = triton_helpers.welford(tmp4_mean,
        tmp4_m2, tmp4_weight, 1)
    tmp4 = tmp4_tmp[:, None]
    tmp5 = tmp5_tmp[:, None]
    tmp6_tmp[:, None]
    tl.store(out_ptr0 + (x3 + 16 * x2 + 256 * x4), tmp4, xmask)
    tmp7 = 96.0
    tmp8 = tmp5 / tmp7
    tmp9 = 1e-05
    tmp10 = tmp8 + tmp9
    tmp11 = libdevice.rsqrt(tmp10)
    tl.store(out_ptr2 + (x3 + 16 * x2 + 256 * x4), tmp11, xmask)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp12 = tl.load(in_out_ptr0 + (r1 + 96 * x0), rmask & xmask,
            eviction_policy='evict_first', other=0.0)
        tmp15 = tl.load(in_ptr1 + r1, rmask, eviction_policy='evict_last',
            other=0.0)
        tmp17 = tl.load(in_ptr2 + r1, rmask, eviction_policy='evict_last',
            other=0.0)
        tmp13 = tmp12 - tmp4
        tmp14 = tmp13 * tmp11
        tmp16 = tmp14 * tmp15
        tmp18 = tmp16 + tmp17
        tl.store(out_ptr3 + (r1 + 96 * x0), tmp18, rmask & xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (96, 3, 4, 4), (48, 16, 4, 1))
    assert_size_stride(primals_2, (96,), (1,))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (96,), (1,))
    assert_size_stride(primals_5, (96,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((96, 3, 4, 4), (48, 1, 12, 3), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(288, 16)](primals_1, buf0, 288, 16, XBLOCK=
            16, YBLOCK=64, 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=(4, 4),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 96, 16, 16), (24576, 1, 1536, 96))
        buf3 = buf2
        del buf2
        buf4 = empty_strided_cuda((4, 16, 16, 1), (256, 1, 16, 16), torch.
            float32)
        buf7 = empty_strided_cuda((4, 16, 16, 1), (256, 1, 16, 16), torch.
            float32)
        buf8 = empty_strided_cuda((4, 16, 16, 96), (24576, 1536, 96, 1),
            torch.float32)
        triton_red_fused_convolution_native_layer_norm_2[grid(1024)](buf3,
            primals_2, primals_4, primals_5, buf4, buf7, buf8, 1024, 96,
            XBLOCK=16, RBLOCK=128, num_warps=16, num_stages=1)
        del primals_2
        del primals_5
    return reinterpret_tensor(buf8, (4, 96, 16, 16), (24576, 1, 1536, 96), 0
        ), buf0, buf1, primals_4, buf3, buf4, buf7


def bchw_to_bhwc(input: 'torch.Tensor') ->torch.Tensor:
    """
    Permutes a tensor to the shape [batch size, height, width, channels]
    :param input: (torch.Tensor) Input tensor of the shape [batch size, height, width, channels]
    :return: (torch.Tensor) Output tensor of the shape [batch size, height, width, channels]
    """
    return input.permute(0, 2, 3, 1)


def bhwc_to_bchw(input: 'torch.Tensor') ->torch.Tensor:
    """
    Permutes a tensor to the shape [batch size, channels, height, width]
    :param input: (torch.Tensor) Input tensor of the shape [batch size, height, width, channels]
    :return: (torch.Tensor) Output tensor of the shape [batch size, channels, height, width]
    """
    return input.permute(0, 3, 1, 2)


class PatchEmbeddingNew(nn.Module):
    """
    Module embeds a given image into patch embeddings.
    """

    def __init__(self, in_channels: 'int'=3, out_channels: 'int'=96,
        patch_size: 'int'=4) ->None:
        """
        Constructor method
        :param in_channels: (int) Number of input channels
        :param out_channels: (int) Number of output channels
        :param patch_size: (int) Patch size to be utilized
        :param image_size: (int) Image size to be used
        """
        super(PatchEmbeddingNew, self).__init__()
        self.out_channels: 'int' = out_channels
        self.linear_embedding: 'nn.Module' = nn.Conv2d(in_channels=
            in_channels, out_channels=out_channels, kernel_size=(patch_size,
            patch_size), stride=(patch_size, patch_size))
        self.normalization: 'nn.Module' = nn.LayerNorm(normalized_shape=
            out_channels)

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