# AOT ID: ['0_inference']
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/cz/ccz75kr63vqusrmgpms64x6b3esgmjev377qdx5r2v35pm2wuvrn.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%expand, %expand_1, %arg2_1, %arg3_1], -1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_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=[256], 
    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_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_cat_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x2 = (xindex // 64)
    x3 = (xindex // 16)
    x4 = xindex
    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 + ((4*x2) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + ((4*x2) + ((-4) + x0)), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 12, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr2 + ((4*x3) + ((-8) + x0)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp16 = tmp0 >= tmp12
    tmp17 = tl.full([1], 16, tl.int64)
    tmp18 = tmp0 < tmp17
    tmp19 = tl.load(in_ptr3 + ((4*x3) + ((-12) + x0)), tmp16 & xmask, eviction_policy='evict_last', other=0.0)
    tmp20 = tl.where(tmp14, tmp15, tmp19)
    tmp21 = tl.where(tmp9, tmp10, tmp20)
    tmp22 = tl.where(tmp4, tmp5, tmp21)
    tl.store(out_ptr0 + (x4), tmp22, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    assert_size_stride(arg2_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg3_1, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(arg0_1, arg1_1, arg2_1, arg3_1, buf0, 256, grid=grid(256), stream=stream0)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
    return (buf0, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg3_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_1, arg3_1])
    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
from typing import Optional
import torch.nn as nn


class FeatureAssembler(nn.Module):

    def __init__(self, T: 'int', embed_static: 'Optional[FeatureEmbedder]'=
        None, embed_dynamic: 'Optional[FeatureEmbedder]'=None) ->None:
        super().__init__()
        self.T = T
        self.embeddings = nn.ModuleDict({'embed_static': embed_static,
            'embed_dynamic': embed_dynamic})

    def forward(self, feat_static_cat: 'torch.Tensor', feat_static_real:
        'torch.Tensor', feat_dynamic_cat: 'torch.Tensor', feat_dynamic_real:
        'torch.Tensor') ->torch.Tensor:
        processed_features = [self.process_static_cat(feat_static_cat),
            self.process_static_real(feat_static_real), self.
            process_dynamic_cat(feat_dynamic_cat), self.
            process_dynamic_real(feat_dynamic_real)]
        return torch.cat(processed_features, dim=-1)

    def process_static_cat(self, feature: 'torch.Tensor') ->torch.Tensor:
        if self.embeddings['embed_static'] is not None:
            feature = self.embeddings['embed_static'](feature)
        return feature.unsqueeze(1).expand(-1, self.T, -1).float()

    def process_dynamic_cat(self, feature: 'torch.Tensor') ->torch.Tensor:
        if self.embeddings['embed_dynamic'] is None:
            return feature.float()
        else:
            return self.embeddings['embed_dynamic'](feature)

    def process_static_real(self, feature: 'torch.Tensor') ->torch.Tensor:
        return feature.unsqueeze(1).expand(-1, self.T, -1)

    def process_dynamic_real(self, feature: 'torch.Tensor') ->torch.Tensor:
        return feature


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


def get_init_inputs():
    return [[], {'T': 4}]

import torch
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 typing import Optional
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_cat_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x2 = xindex // 64
    x3 = xindex // 16
    x4 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x2 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (4 * x2 + (-4 + x0)), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 12, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr2 + (4 * x3 + (-8 + x0)), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tmp0 >= tmp12
    tl.full([1], 16, tl.int64)
    tmp19 = tl.load(in_ptr3 + (4 * x3 + (-12 + x0)), tmp16 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp20 = tl.where(tmp14, tmp15, tmp19)
    tmp21 = tl.where(tmp9, tmp10, tmp20)
    tmp22 = tl.where(tmp4, tmp5, tmp21)
    tl.store(out_ptr0 + x4, tmp22, xmask)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    assert_size_stride(arg2_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg3_1, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(256)](arg0_1, arg1_1, arg2_1, arg3_1,
            buf0, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
    return buf0,


class FeatureAssemblerNew(nn.Module):

    def __init__(self, T: 'int', embed_static: 'Optional[FeatureEmbedder]'=
        None, embed_dynamic: 'Optional[FeatureEmbedder]'=None) ->None:
        super().__init__()
        self.T = T
        self.embeddings = nn.ModuleDict({'embed_static': embed_static,
            'embed_dynamic': embed_dynamic})

    def process_static_cat(self, feature: 'torch.Tensor') ->torch.Tensor:
        if self.embeddings['embed_static'] is not None:
            feature = self.embeddings['embed_static'](feature)
        return feature.unsqueeze(1).expand(-1, self.T, -1).float()

    def process_dynamic_cat(self, feature: 'torch.Tensor') ->torch.Tensor:
        if self.embeddings['embed_dynamic'] is None:
            return feature.float()
        else:
            return self.embeddings['embed_dynamic'](feature)

    def process_static_real(self, feature: 'torch.Tensor') ->torch.Tensor:
        return feature.unsqueeze(1).expand(-1, self.T, -1)

    def process_dynamic_real(self, feature: 'torch.Tensor') ->torch.Tensor:
        return feature

    def forward(self, input_0, input_1, input_2, input_3):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        arg3_1 = input_3
        output = call([arg0_1, arg1_1, arg2_1, arg3_1])
        return output[0]
