# 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/2y/c2y2fwx75kg4audbbgzukvwhfwdw3z7iptxcsdk2hai4j3ue6ztz.py
# Topologically Sorted Source Nodes: [cat, add], Original ATen: [aten.cat, aten.add]
# Source node to ATen node mapping:
#   add => add
#   cat => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%permute, %permute_1], 2), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%cat, %slice_1), 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=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '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), 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, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = (xindex // 8) % 16
    x2 = (xindex // 128)
    x4 = xindex % 128
    tmp19 = tl.load(in_ptr3 + (x4), xmask, eviction_policy='evict_last')
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x1 + (16*x0) + (64*x2)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + (x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 8, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tl.load(in_ptr2 + (x1 + (16*((-4) + x0)) + (64*x2)), tmp10 & xmask, eviction_policy='evict_last', other=0.0)
    tmp14 = tl.load(in_ptr1 + ((-4) + x0), tmp10 & xmask, eviction_policy='evict_last', other=0.0)
    tmp15 = tmp13 + tmp14
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp10, tmp15, tmp16)
    tmp18 = tl.where(tmp4, tmp9, tmp17)
    tmp20 = tmp18 + tmp19
    tl.store(out_ptr0 + (x0 + (8*x2) + (32*x1)), tmp20, 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, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (196, 1, 8), (8, 8, 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_3, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(primals_4, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        buf2 = empty_strided_cuda((16, 4, 8), (32, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat, add], Original ATen: [aten.cat, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_cat_0.run(buf0, primals_2, buf1, primals_5, buf2, 512, grid=grid(512), stream=stream0)
        del buf0
        del buf1
        del primals_2
        del primals_5
    return (buf2, primals_1, primals_3, primals_4, )


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, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((196, 1, 8), (8, 8, 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 math
import torch
from torch import Tensor
import torch.nn as nn


class PositionalEncoding(nn.Module):
    """
    Learnable position embeddings

    Args:
        pe_type (str): type of position embeddings,
            which is chosen from ['fully_learnable', 'sinusoidal']
        d_model (int): embed dim (required).
        max_len (int): max. length of the incoming sequence (default=5000).
    Examples:
        >>> pos_encoder = PositionalEncoding(d_model, max_len=100)
    """

    def __init__(self, pe_type: 'str', d_model: 'int', max_len: 'int'=5000):
        super(PositionalEncoding, self).__init__()
        if pe_type == 'fully_learnable':
            self.pe = nn.parameter.Parameter(torch.randn(max_len, 1, d_model))
        elif pe_type == 'sinusoidal':
            pe = torch.zeros(max_len, d_model)
            position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
            div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-
                math.log(10000.0) / d_model))
            pe[:, 0::2] = torch.sin(position * div_term)
            pe[:, 1::2] = torch.cos(position * div_term)
            pe = pe.unsqueeze(0).transpose(0, 1)
            self.register_buffer('pe', pe)
        else:
            raise RuntimeError(
                'PE type should be fully_learnable/sinusoidal, not {}'.
                format(pe_type))

    def forward(self, x: 'Tensor') ->Tensor:
        """Inputs of forward function
        Args:
            x (Tensor): the sequence fed to the positional encoder model [L, N, C]
        Returns:
            output (Tensor): position embeddings [L, N, C]
        """
        return x + self.pe[:x.size(0)]


class SimpleEncoder(nn.Module):

    def __init__(self, feature_dim, encoder_dim, image_size=14):
        super(SimpleEncoder, self).__init__()
        self.linear = nn.Conv2d(feature_dim, encoder_dim, kernel_size=1)
        self.positional_encoding = PositionalEncoding('fully_learnable', 2 *
            encoder_dim, image_size ** 2)

    def forward(self, x1, x2):
        return self.positional_encoding(torch.cat([self.linear(x1).flatten(
            start_dim=2).permute(2, 0, 1), self.linear(x2).flatten(
            start_dim=2).permute(2, 0, 1)], dim=2))


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


def get_init_inputs():
    return [[], {'feature_dim': 4, 'encoder_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
import math
from torch import Tensor
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_add_cat_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8 % 16
    x2 = xindex // 128
    x4 = xindex % 128
    tmp19 = tl.load(in_ptr3 + x4, xmask, eviction_policy='evict_last')
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x1 + 16 * x0 + 64 * x2), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + x0, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp13 = tl.load(in_ptr2 + (x1 + 16 * (-4 + x0) + 64 * x2), tmp10 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp14 = tl.load(in_ptr1 + (-4 + x0), tmp10 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp15 = tmp13 + tmp14
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp10, tmp15, tmp16)
    tmp18 = tl.where(tmp4, tmp9, tmp17)
    tmp20 = tmp18 + tmp19
    tl.store(out_ptr0 + (x0 + 8 * x2 + 32 * x1), tmp20, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (196, 1, 8), (8, 8, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = extern_kernels.convolution(primals_4, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        buf2 = empty_strided_cuda((16, 4, 8), (32, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_cat_0[grid(512)](buf0, primals_2, buf1,
            primals_5, buf2, 512, XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
        del buf1
        del primals_2
        del primals_5
    return buf2, primals_1, primals_3, primals_4


class PositionalEncoding(nn.Module):
    """
    Learnable position embeddings

    Args:
        pe_type (str): type of position embeddings,
            which is chosen from ['fully_learnable', 'sinusoidal']
        d_model (int): embed dim (required).
        max_len (int): max. length of the incoming sequence (default=5000).
    Examples:
        >>> pos_encoder = PositionalEncoding(d_model, max_len=100)
    """

    def __init__(self, pe_type: 'str', d_model: 'int', max_len: 'int'=5000):
        super(PositionalEncoding, self).__init__()
        if pe_type == 'fully_learnable':
            self.pe = nn.parameter.Parameter(torch.randn(max_len, 1, d_model))
        elif pe_type == 'sinusoidal':
            pe = torch.zeros(max_len, d_model)
            position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
            div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-
                math.log(10000.0) / d_model))
            pe[:, 0::2] = torch.sin(position * div_term)
            pe[:, 1::2] = torch.cos(position * div_term)
            pe = pe.unsqueeze(0).transpose(0, 1)
            self.register_buffer('pe', pe)
        else:
            raise RuntimeError(
                'PE type should be fully_learnable/sinusoidal, not {}'.
                format(pe_type))

    def forward(self, x: 'Tensor') ->Tensor:
        """Inputs of forward function
        Args:
            x (Tensor): the sequence fed to the positional encoder model [L, N, C]
        Returns:
            output (Tensor): position embeddings [L, N, C]
        """
        return x + self.pe[:x.size(0)]


class SimpleEncoderNew(nn.Module):

    def __init__(self, feature_dim, encoder_dim, image_size=14):
        super(SimpleEncoderNew, self).__init__()
        self.linear = nn.Conv2d(feature_dim, encoder_dim, kernel_size=1)
        self.positional_encoding = PositionalEncoding('fully_learnable', 2 *
            encoder_dim, image_size ** 2)

    def forward(self, input_0, input_1):
        primals_1 = self.linear.weight
        primals_2 = self.linear.bias
        primals_5 = self.positional_encoding.pe
        primals_3 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
