# 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_7/inductor_cache/n5/cn53kutdhlbunayahmqxnq32y7m3jg2zlxxjfj3hzzpb5jsrqgdk.py
# Topologically Sorted Source Nodes: [embeddings, embeddings_1], Original ATen: [aten.cat, aten.add]
# Source node to ATen node mapping:
#   embeddings => cat
#   embeddings_1 => add
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%expand, %expand_1, %permute], 1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%cat, %primals_6), kwargs = {})
triton_poi_fused_add_cat_0 = async_compile.triton('triton_poi_fused_add_cat_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_add_cat_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 48
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4) % 3
    x0 = xindex % 4
    x2 = (xindex // 12)
    x3 = xindex % 12
    x4 = xindex
    tmp21 = tl.load(in_ptr4 + (x3), xmask, eviction_policy='evict_last')
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 2, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 3, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tl.load(in_ptr2 + (x0 + (4*x2)), tmp11 & xmask, eviction_policy='evict_last', other=0.0)
    tmp15 = tl.load(in_ptr3 + (x0), tmp11 & xmask, eviction_policy='evict_last', other=0.0)
    tmp16 = tmp14 + tmp15
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp11, tmp16, tmp17)
    tmp19 = tl.where(tmp9, tmp10, tmp18)
    tmp20 = tl.where(tmp4, tmp5, tmp19)
    tmp22 = tmp20 + tmp21
    tl.store(out_ptr0 + (x4), tmp22, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = 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, (1, 1, 4), (4, 4, 1))
    assert_size_stride(primals_5, (1, 1, 4), (4, 4, 1))
    assert_size_stride(primals_6, (1, 3, 4), (12, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        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 = empty_strided_cuda((4, 3, 4), (12, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [embeddings, embeddings_1], Original ATen: [aten.cat, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_cat_0.run(primals_4, primals_5, buf0, primals_3, primals_6, buf1, 48, grid=grid(48), stream=stream0)
        del buf0
        del primals_3
        del primals_4
        del primals_5
        del primals_6
    return (buf1, primals_1, primals_2, )


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((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, 1, 4), (4, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1, 1, 4), (4, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((1, 3, 4), (12, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6])
    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)

from _paritybench_helpers import _mock_config
import collections
import torch
from torch import nn
import torch.utils.checkpoint
import collections.abc


def to_2tuple(x):
    if isinstance(x, collections.abc.Iterable):
        return x
    return x, x


class PatchEmbeddings(nn.Module):
    """
    Image to Patch Embedding.

    """

    def __init__(self, image_size=224, patch_size=16, num_channels=3,
        embed_dim=768):
        super().__init__()
        image_size = to_2tuple(image_size)
        patch_size = to_2tuple(patch_size)
        num_patches = image_size[1] // patch_size[1] * (image_size[0] //
            patch_size[0])
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_patches = num_patches
        self.projection = nn.Conv2d(num_channels, embed_dim, kernel_size=
            patch_size, stride=patch_size)

    def forward(self, pixel_values):
        _batch_size, _num_channels, height, width = pixel_values.shape
        if height != self.image_size[0] or width != self.image_size[1]:
            raise ValueError(
                f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})."
                )
        x = self.projection(pixel_values).flatten(2).transpose(1, 2)
        return x


class DeiTEmbeddings(nn.Module):
    """
    Construct the CLS token, distillation token, position and patch embeddings.

    """

    def __init__(self, config):
        super().__init__()
        self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
        self.distillation_token = nn.Parameter(torch.zeros(1, 1, config.
            hidden_size))
        self.patch_embeddings = PatchEmbeddings(image_size=config.
            image_size, patch_size=config.patch_size, num_channels=config.
            num_channels, embed_dim=config.hidden_size)
        num_patches = self.patch_embeddings.num_patches
        self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches +
            2, config.hidden_size))
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, pixel_values):
        batch_size = pixel_values.shape[0]
        embeddings = self.patch_embeddings(pixel_values)
        cls_tokens = self.cls_token.expand(batch_size, -1, -1)
        distillation_tokens = self.distillation_token.expand(batch_size, -1, -1
            )
        embeddings = torch.cat((cls_tokens, distillation_tokens, embeddings
            ), dim=1)
        embeddings = embeddings + self.position_embeddings
        embeddings = self.dropout(embeddings)
        return embeddings


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


def get_init_inputs():
    return [[], {'config': _mock_config(hidden_size=4, image_size=4,
        patch_size=4, num_channels=4, hidden_dropout_prob=0.5)}]

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
import collections
from torch import nn
import torch.utils.checkpoint
import collections.abc
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_add_cat_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 48
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 3
    x0 = xindex % 4
    x2 = xindex // 12
    x3 = xindex % 12
    x4 = xindex
    tmp21 = tl.load(in_ptr4 + x3, xmask, eviction_policy='evict_last')
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + x0, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 2, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + x0, tmp9 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tl.full([1], 3, tl.int64)
    tmp14 = tl.load(in_ptr2 + (x0 + 4 * x2), tmp11 & xmask, eviction_policy
        ='evict_last', other=0.0)
    tmp15 = tl.load(in_ptr3 + x0, tmp11 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp16 = tmp14 + tmp15
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp11, tmp16, tmp17)
    tmp19 = tl.where(tmp9, tmp10, tmp18)
    tmp20 = tl.where(tmp4, tmp5, tmp19)
    tmp22 = tmp20 + tmp21
    tl.store(out_ptr0 + x4, tmp22, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = 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, (1, 1, 4), (4, 4, 1))
    assert_size_stride(primals_5, (1, 1, 4), (4, 4, 1))
    assert_size_stride(primals_6, (1, 3, 4), (12, 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 = empty_strided_cuda((4, 3, 4), (12, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_cat_0[grid(48)](primals_4, primals_5, buf0,
            primals_3, primals_6, buf1, 48, XBLOCK=64, num_warps=1,
            num_stages=1)
        del buf0
        del primals_3
        del primals_4
        del primals_5
        del primals_6
    return buf1, primals_1, primals_2


def to_2tuple(x):
    if isinstance(x, collections.abc.Iterable):
        return x
    return x, x


class PatchEmbeddings(nn.Module):
    """
    Image to Patch Embedding.

    """

    def __init__(self, image_size=224, patch_size=16, num_channels=3,
        embed_dim=768):
        super().__init__()
        image_size = to_2tuple(image_size)
        patch_size = to_2tuple(patch_size)
        num_patches = image_size[1] // patch_size[1] * (image_size[0] //
            patch_size[0])
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_patches = num_patches
        self.projection = nn.Conv2d(num_channels, embed_dim, kernel_size=
            patch_size, stride=patch_size)

    def forward(self, pixel_values):
        _batch_size, _num_channels, height, width = pixel_values.shape
        if height != self.image_size[0] or width != self.image_size[1]:
            raise ValueError(
                f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})."
                )
        x = self.projection(pixel_values).flatten(2).transpose(1, 2)
        return x


class DeiTEmbeddingsNew(nn.Module):
    """
    Construct the CLS token, distillation token, position and patch embeddings.

    """

    def __init__(self, config):
        super().__init__()
        self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
        self.distillation_token = nn.Parameter(torch.zeros(1, 1, config.
            hidden_size))
        self.patch_embeddings = PatchEmbeddings(image_size=config.
            image_size, patch_size=config.patch_size, num_channels=config.
            num_channels, embed_dim=config.hidden_size)
        num_patches = self.patch_embeddings.num_patches
        self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches +
            2, config.hidden_size))
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, input_0):
        primals_4 = self.cls_token
        primals_5 = self.distillation_token
        primals_6 = self.position_embeddings
        primals_1 = self.patch_embeddings.projection.weight
        primals_3 = self.patch_embeddings.projection.bias
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
