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]
