# 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/3u/c3ub52l73zdv4klgqzgxmtzrzxvztuyczv2jksnvrjr7erq7guxd.py
# Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   linear_1 => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_0 = async_compile.triton('triton_poi_fused_clone_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, 4], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_clone_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 64
    xnumel = 4
    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
    y0 = yindex % 16
    y1 = (yindex // 16)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (16*x2) + (64*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/2r/c2rfym7x5d6gfe43hgp23gbkfnsow7ttgn2hby3xsrg6fgja77us.py
# Topologically Sorted Source Nodes: [add, tanh], Original ATen: [aten.add, aten.tanh]
# Source node to ATen node mapping:
#   add => add
#   tanh => tanh
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %unsqueeze), kwargs = {})
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add,), kwargs = {})
triton_poi_fused_add_tanh_1 = async_compile.triton('triton_poi_fused_add_tanh_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=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_tanh_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_tanh_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 131072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 512
    x2 = (xindex // 8192) % 4
    x3 = (xindex // 32768)
    x4 = xindex % 8192
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (512*x2)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x4 + (8192*x3)), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tl.store(out_ptr0 + (x5), tmp3, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/g2/cg266bzrczdgrg4t2bizcm563ududawebjvz37s7yg2panbw22xg.py
# Topologically Sorted Source Nodes: [att_1], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   att_1 => amax, div, exp, sub, sum_1
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%squeeze, [2], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%squeeze, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [2], True), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_per_fused__softmax_2 = async_compile.triton('triton_per_fused__softmax_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.persistent_reduction(
    size_hints=[16, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__softmax_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 2, '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}
)
@triton.jit
def triton_per_fused__softmax_2(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (16*x0)), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float("-inf"))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tmp5 = tmp0 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.where(xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = tmp6 / tmp10
    tl.store(out_ptr2 + (r1 + (16*x0)), tmp11, 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, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 512), (512, 1))
    assert_size_stride(primals_3, (512, 512), (512, 1))
    assert_size_stride(primals_4, (512, 4), (4, 1))
    assert_size_stride(primals_5, (1, 512), (512, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm]
        extern_kernels.mm(primals_2, reinterpret_tensor(primals_3, (512, 512), (1, 512), 0), out=buf0)
        buf1 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.clone]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clone_0.run(primals_1, buf1, 64, 4, grid=grid(64, 4), stream=stream0)
        buf2 = empty_strided_cuda((64, 512), (512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 512), (1, 4), 0), out=buf2)
        del primals_4
        buf3 = empty_strided_cuda((4, 4, 16, 512), (32768, 8192, 512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [add, tanh], Original ATen: [aten.add, aten.tanh]
        triton_poi_fused_add_tanh_1.run(buf0, buf2, buf3, 131072, grid=grid(131072), stream=stream0)
        del buf0
        del buf2
        buf4 = empty_strided_cuda((256, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf3, (256, 512), (512, 1), 0), reinterpret_tensor(primals_5, (512, 1), (1, 512), 0), out=buf4)
        buf7 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [att_1], Original ATen: [aten._softmax]
        triton_per_fused__softmax_2.run(buf4, buf7, 16, 16, grid=grid(16), stream=stream0)
        del buf4
        buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.bmm]
        extern_kernels.bmm(buf7, reinterpret_tensor(primals_1, (4, 16, 4), (64, 1, 16), 0), out=buf8)
    return (buf8, primals_2, reinterpret_tensor(buf1, (64, 4), (4, 1), 0), buf3, buf7, reinterpret_tensor(primals_1, (4, 4, 16), (64, 16, 1), 0), primals_5, primals_3, )


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, 512), (512, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((512, 512), (512, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((512, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1, 512), (512, 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 constant_init(module, val, bias=0):
    nn.init.constant_(module.weight, val)
    if hasattr(module, 'bias') and module.bias is not None:
        nn.init.constant_(module.bias, bias)


def kaiming_init(module, a=0, is_rnn=False, mode='fan_in', nonlinearity=
    'leaky_relu', bias=0, distribution='normal'):
    assert distribution in ['uniform', 'normal']
    if distribution == 'uniform':
        if is_rnn:
            for name, param in module.named_parameters():
                if 'bias' in name:
                    nn.init.constant_(param, bias)
                elif 'weight' in name:
                    nn.init.kaiming_uniform_(param, a=a, mode=mode,
                        nonlinearity=nonlinearity)
        else:
            nn.init.kaiming_uniform_(module.weight, a=a, mode=mode,
                nonlinearity=nonlinearity)
    elif is_rnn:
        for name, param in module.named_parameters():
            if 'bias' in name:
                nn.init.constant_(param, bias)
            elif 'weight' in name:
                nn.init.kaiming_normal_(param, a=a, mode=mode, nonlinearity
                    =nonlinearity)
    else:
        nn.init.kaiming_normal_(module.weight, a=a, mode=mode, nonlinearity
            =nonlinearity)
    if not is_rnn and hasattr(module, 'bias') and module.bias is not None:
        nn.init.constant_(module.bias, bias)


def xavier_init(module, gain=1, bias=0, distribution='normal'):
    assert distribution in ['uniform', 'normal']
    if distribution == 'uniform':
        nn.init.xavier_uniform_(module.weight, gain=gain)
    else:
        nn.init.xavier_normal_(module.weight, gain=gain)
    if hasattr(module, 'bias') and module.bias is not None:
        nn.init.constant_(module.bias, bias)


def init_weights(modules):
    for m in modules:
        if isinstance(m, nn.Conv2d):
            kaiming_init(m)
        elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
            constant_init(m, 1)
        elif isinstance(m, nn.Linear):
            xavier_init(m)
        elif isinstance(m, (nn.LSTM, nn.LSTMCell)):
            kaiming_init(m, is_rnn=True)


class PVABlock(nn.Module):

    def __init__(self, num_steps, in_channels, embedding_channels=512,
        inner_channels=512):
        super(PVABlock, self).__init__()
        self.num_steps = num_steps
        self.in_channels = in_channels
        self.inner_channels = inner_channels
        self.embedding_channels = embedding_channels
        self.order_embeddings = nn.Parameter(torch.randn(self.num_steps,
            self.embedding_channels), requires_grad=True)
        self.v_linear = nn.Linear(self.in_channels, self.inner_channels,
            bias=False)
        self.o_linear = nn.Linear(self.embedding_channels, self.
            inner_channels, bias=False)
        self.e_linear = nn.Linear(self.inner_channels, 1, bias=False)
        init_weights(self.modules())

    def forward(self, x):
        b, c, h, w = x.size()
        x = x.reshape(b, c, h * w).permute(0, 2, 1)
        o_out = self.o_linear(self.order_embeddings).view(1, self.num_steps,
            1, self.inner_channels)
        v_out = self.v_linear(x).unsqueeze(1)
        att = self.e_linear(torch.tanh(o_out + v_out)).squeeze(3)
        att = torch.softmax(att, dim=2)
        out = torch.bmm(att, x)
        return out


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


def get_init_inputs():
    return [[], {'num_steps': 4, 'in_channels': 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
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_clone_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 4
    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
    y0 = yindex % 16
    y1 = yindex // 16
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 16 * x2 + 64 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_tanh_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 512
    x2 = xindex // 8192 % 4
    x3 = xindex // 32768
    x4 = xindex % 8192
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 512 * x2), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + (x4 + 8192 * x3), None, eviction_policy=
        'evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tl.store(out_ptr0 + x5, tmp3, None)


@triton.jit
def triton_per_fused__softmax_2(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float('-inf'))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tmp5 = tmp0 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.where(xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = tmp6 / tmp10
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp11, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 512), (512, 1))
    assert_size_stride(primals_3, (512, 512), (512, 1))
    assert_size_stride(primals_4, (512, 4), (4, 1))
    assert_size_stride(primals_5, (1, 512), (512, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        extern_kernels.mm(primals_2, reinterpret_tensor(primals_3, (512, 
            512), (1, 512), 0), out=buf0)
        buf1 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(64, 4)](primals_1, buf1, 64, 4,
            XBLOCK=4, YBLOCK=32, num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((64, 512), (512, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 512), (1, 4), 0), out=buf2)
        del primals_4
        buf3 = empty_strided_cuda((4, 4, 16, 512), (32768, 8192, 512, 1),
            torch.float32)
        triton_poi_fused_add_tanh_1[grid(131072)](buf0, buf2, buf3, 131072,
            XBLOCK=1024, num_warps=4, num_stages=1)
        del buf0
        del buf2
        buf4 = empty_strided_cuda((256, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (256, 512), (512, 1), 0),
            reinterpret_tensor(primals_5, (512, 1), (1, 512), 0), out=buf4)
        buf7 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        triton_per_fused__softmax_2[grid(16)](buf4, buf7, 16, 16, XBLOCK=8,
            num_warps=2, num_stages=1)
        del buf4
        buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf7, reinterpret_tensor(primals_1, (4, 16, 4),
            (64, 1, 16), 0), out=buf8)
    return buf8, primals_2, reinterpret_tensor(buf1, (64, 4), (4, 1), 0
        ), buf3, buf7, reinterpret_tensor(primals_1, (4, 4, 16), (64, 16, 1), 0
        ), primals_5, primals_3


def constant_init(module, val, bias=0):
    nn.init.constant_(module.weight, val)
    if hasattr(module, 'bias') and module.bias is not None:
        nn.init.constant_(module.bias, bias)


def kaiming_init(module, a=0, is_rnn=False, mode='fan_in', nonlinearity=
    'leaky_relu', bias=0, distribution='normal'):
    assert distribution in ['uniform', 'normal']
    if distribution == 'uniform':
        if is_rnn:
            for name, param in module.named_parameters():
                if 'bias' in name:
                    nn.init.constant_(param, bias)
                elif 'weight' in name:
                    nn.init.kaiming_uniform_(param, a=a, mode=mode,
                        nonlinearity=nonlinearity)
        else:
            nn.init.kaiming_uniform_(module.weight, a=a, mode=mode,
                nonlinearity=nonlinearity)
    elif is_rnn:
        for name, param in module.named_parameters():
            if 'bias' in name:
                nn.init.constant_(param, bias)
            elif 'weight' in name:
                nn.init.kaiming_normal_(param, a=a, mode=mode, nonlinearity
                    =nonlinearity)
    else:
        nn.init.kaiming_normal_(module.weight, a=a, mode=mode, nonlinearity
            =nonlinearity)
    if not is_rnn and hasattr(module, 'bias') and module.bias is not None:
        nn.init.constant_(module.bias, bias)


def xavier_init(module, gain=1, bias=0, distribution='normal'):
    assert distribution in ['uniform', 'normal']
    if distribution == 'uniform':
        nn.init.xavier_uniform_(module.weight, gain=gain)
    else:
        nn.init.xavier_normal_(module.weight, gain=gain)
    if hasattr(module, 'bias') and module.bias is not None:
        nn.init.constant_(module.bias, bias)


def init_weights(modules):
    for m in modules:
        if isinstance(m, nn.Conv2d):
            kaiming_init(m)
        elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
            constant_init(m, 1)
        elif isinstance(m, nn.Linear):
            xavier_init(m)
        elif isinstance(m, (nn.LSTM, nn.LSTMCell)):
            kaiming_init(m, is_rnn=True)


class PVABlockNew(nn.Module):

    def __init__(self, num_steps, in_channels, embedding_channels=512,
        inner_channels=512):
        super(PVABlockNew, self).__init__()
        self.num_steps = num_steps
        self.in_channels = in_channels
        self.inner_channels = inner_channels
        self.embedding_channels = embedding_channels
        self.order_embeddings = nn.Parameter(torch.randn(self.num_steps,
            self.embedding_channels), requires_grad=True)
        self.v_linear = nn.Linear(self.in_channels, self.inner_channels,
            bias=False)
        self.o_linear = nn.Linear(self.embedding_channels, self.
            inner_channels, bias=False)
        self.e_linear = nn.Linear(self.inner_channels, 1, bias=False)
        init_weights(self.modules())

    def forward(self, input_0):
        primals_2 = self.order_embeddings
        primals_4 = self.v_linear.weight
        primals_3 = self.o_linear.weight
        primals_5 = self.e_linear.weight
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
