# 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/um/cum65j23qchrjf5dndblqgbw6zomhgwfj2obfidtgy7b5j3zwklm.py
# Topologically Sorted Source Nodes: [attn_1], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   attn_1 => amax, exp, sub
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%squeeze, [1], 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 = {})
triton_poi_fused__softmax_0 = async_compile.triton('triton_poi_fused__softmax_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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__softmax_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__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/wk/cwk2wao7opapqbjj7klnqrd6tgist3ts3nc5veryzhzstwpx7d4l.py
# Topologically Sorted Source Nodes: [attn_1], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   attn_1 => div, sum_1
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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__softmax_1', '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__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/zd/czdeq2ohbgubcyeps2ukquvfhigxtyega57i24ketclusfgmyedi.py
# Topologically Sorted Source Nodes: [weightedContext_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   weightedContext_1 => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%squeeze_1, %primals_2], 1), kwargs = {})
triton_poi_fused_cat_2 = async_compile.triton('triton_poi_fused_cat_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.pointwise(
    size_hints=[32], 
    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_cat_2', '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_cat_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = (xindex // 8)
    x2 = 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*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + ((4*x1) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x2), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/nd/cndmyjzjevhkuc7c6qt54venjnegfpn7mgzqrw5yus7yt5u3qqaj.py
# Topologically Sorted Source Nodes: [weightedContext_3], Original ATen: [aten.tanh]
# Source node to ATen node mapping:
#   weightedContext_3 => tanh
# Graph fragment:
#   %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%mm_1,), kwargs = {})
triton_poi_fused_tanh_3 = async_compile.triton('triton_poi_fused_tanh_3', '''
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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_tanh_3', 'mutated_arg_names': ['in_out_ptr0'], '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_tanh_3(in_out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = libdevice.tanh(tmp0)
    tl.store(in_out_ptr0 + (x0), tmp1, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 8), (8, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm]
        extern_kernels.mm(primals_2, reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf0)
        del primals_3
        buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [bmm], Original ATen: [aten.bmm]
        extern_kernels.bmm(primals_1, reinterpret_tensor(buf0, (4, 4, 1), (4, 1, 1), 0), out=buf1)
        buf2 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [attn_1], Original ATen: [aten._softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__softmax_0.run(buf1, buf2, 16, grid=grid(16), stream=stream0)
        buf3 = reinterpret_tensor(buf1, (4, 4), (4, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [attn_1], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_1.run(buf2, buf3, 16, grid=grid(16), stream=stream0)
        buf4 = reinterpret_tensor(buf2, (4, 1, 4), (4, 4, 1), 0); del buf2  # reuse
        # Topologically Sorted Source Nodes: [bmm_1], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf3, (4, 1, 4), (4, 4, 1), 0), primals_1, out=buf4)
        buf5 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [weightedContext_1], Original ATen: [aten.cat]
        triton_poi_fused_cat_2.run(buf4, primals_2, buf5, 32, grid=grid(32), stream=stream0)
        buf6 = reinterpret_tensor(buf4, (4, 4), (4, 1), 0); del buf4  # reuse
        # Topologically Sorted Source Nodes: [weightedContext_2], Original ATen: [aten.mm]
        extern_kernels.mm(buf5, reinterpret_tensor(primals_4, (8, 4), (1, 8), 0), out=buf6)
        buf7 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [weightedContext_3], Original ATen: [aten.tanh]
        triton_poi_fused_tanh_3.run(buf7, 16, grid=grid(16), stream=stream0)
    return (buf7, buf3, primals_2, buf3, buf5, buf7, primals_4, reinterpret_tensor(primals_1, (4, 4, 4), (16, 1, 4), 0), )


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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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
import torch.cuda


def aeq(*args):
    base = args[0]
    for a in args[1:]:
        assert a == base, str(args)


class Bottle(nn.Module):

    def forward(self, input):
        if len(input.size()) <= 2:
            return super(Bottle, self).forward(input)
        size = input.size()[:2]
        out = super(Bottle, self).forward(input.view(size[0] * size[1], -1))
        return out.contiguous().view(size[0], size[1], -1)


class BottleLinear(Bottle, nn.Linear):
    pass


class GlobalAttention(nn.Module):
    """
    Luong Attention.

    Global attention takes a matrix and a query vector. It
    then computes a parameterized convex combination of the matrix
    based on the input query.


        H_1 H_2 H_3 ... H_n
          q   q   q       q
            |  |   |       |
              \\ |   |      /
                      .....
                  \\   |  /
                      a

    Constructs a unit mapping.
    $$(H_1 + H_n, q) => (a)$$
    Where H is of `batch x n x dim` and q is of `batch x dim`.

    Loung Attention (dotprod):
    $$	anh(W_2 [(softmax((W_1 q + b_1) H) H), q] + b_2)$$.:

    Bahdanau Attention (mlp):
    $$c = \\sum_{j=1}^{SeqLength}_jh_j$$.
    The Alignment-function $$a$$ computes an alignment as:
    $$a_j = softmax(v_a^T 	anh(W_a q + U_a h_j) )$$.

    """

    def __init__(self, dim, coverage=False, attn_type='dotprod'):
        super(GlobalAttention, self).__init__()
        self.dim = dim
        self.attn_type = attn_type
        assert self.attn_type in ['dotprod', 'mlp'
            ], 'Please select a valid attention type.'
        if self.attn_type == 'dotprod':
            self.linear_in = nn.Linear(dim, dim, bias=False)
            self.linear_out = nn.Linear(dim * 2, dim, bias=False)
        elif self.attn_type == 'mlp':
            self.linear_context = BottleLinear(dim, dim, bias=False)
            self.linear_query = nn.Linear(dim, dim, bias=False)
            self.v = BottleLinear(dim, 1, bias=False)
        self.sm = nn.Softmax()
        self.tanh = nn.Tanh()
        self.mask = None
        if coverage:
            self.linear_cover = nn.Linear(1, dim, bias=False)

    def applyMask(self, mask):
        self.mask = mask

    def forward(self, input, context, coverage=None):
        """
        input (FloatTensor): batch x dim
        context (FloatTensor): batch x sourceL x dim
        coverage (FloatTensor): batch x sourceL
        """
        batch, sourceL, dim = context.size()
        batch_, dim_ = input.size()
        aeq(batch, batch_)
        aeq(dim, dim_)
        aeq(self.dim, dim)
        if coverage is not None:
            batch_, sourceL_ = coverage.size()
            aeq(batch, batch_)
            aeq(sourceL, sourceL_)
        if self.mask is not None:
            beam_, batch_, sourceL_ = self.mask.size()
            aeq(batch, batch_ * beam_)
            aeq(sourceL, sourceL_)
        if coverage:
            context += self.linear_cover(coverage.view(-1).unsqueeze(1)
                ).view_as(context)
            context = self.tanh(context)
        if self.attn_type == 'dotprod':
            targetT = self.linear_in(input).unsqueeze(2)
            attn = torch.bmm(context, targetT).squeeze(2)
        elif self.attn_type == 'mlp':
            wq = self.linear_query(input).unsqueeze(1)
            uh = self.linear_context(context.contiguous())
            wquh = uh + wq.expand_as(uh)
            wquh = self.tanh(wquh)
            attn = self.v(wquh.contiguous()).squeeze()
        if self.mask is not None:
            attn.data.masked_fill_(self.mask, -float('inf'))
        attn = self.sm(attn)
        attn3 = attn.view(attn.size(0), 1, attn.size(1))
        weightedContext = torch.bmm(attn3, context).squeeze(1)
        if self.attn_type == 'dotprod':
            weightedContext = torch.cat((weightedContext, input), 1)
            weightedContext = self.linear_out(weightedContext)
            weightedContext = self.tanh(weightedContext)
        batch_, sourceL_ = attn.size()
        aeq(batch, batch_)
        aeq(sourceL, sourceL_)
        batch_, dim_ = weightedContext.size()
        aeq(batch, batch_)
        aeq(dim, dim_)
        return weightedContext, attn


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


def get_init_inputs():
    return [[], {'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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.cuda
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__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_cat_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = 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 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_tanh_3(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = libdevice.tanh(tmp0)
    tl.store(in_out_ptr0 + x0, tmp1, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 8), (8, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_2, reinterpret_tensor(primals_3, (4, 4),
            (1, 4), 0), out=buf0)
        del primals_3
        buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(primals_1, reinterpret_tensor(buf0, (4, 4, 1), (
            4, 1, 1), 0), out=buf1)
        buf2 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(16)](buf1, buf2, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf3 = reinterpret_tensor(buf1, (4, 4), (4, 1), 0)
        del buf1
        triton_poi_fused__softmax_1[grid(16)](buf2, buf3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf4 = reinterpret_tensor(buf2, (4, 1, 4), (4, 4, 1), 0)
        del buf2
        extern_kernels.bmm(reinterpret_tensor(buf3, (4, 1, 4), (4, 4, 1), 0
            ), primals_1, out=buf4)
        buf5 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        triton_poi_fused_cat_2[grid(32)](buf4, primals_2, buf5, 32, XBLOCK=
            32, num_warps=1, num_stages=1)
        buf6 = reinterpret_tensor(buf4, (4, 4), (4, 1), 0)
        del buf4
        extern_kernels.mm(buf5, reinterpret_tensor(primals_4, (8, 4), (1, 8
            ), 0), out=buf6)
        buf7 = buf6
        del buf6
        triton_poi_fused_tanh_3[grid(16)](buf7, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
    return (buf7, buf3, primals_2, buf3, buf5, buf7, primals_4,
        reinterpret_tensor(primals_1, (4, 4, 4), (16, 1, 4), 0))


def aeq(*args):
    base = args[0]
    for a in args[1:]:
        assert a == base, str(args)


class Bottle(nn.Module):

    def forward(self, input):
        if len(input.size()) <= 2:
            return super(Bottle, self).forward(input)
        size = input.size()[:2]
        out = super(Bottle, self).forward(input.view(size[0] * size[1], -1))
        return out.contiguous().view(size[0], size[1], -1)


class BottleLinear(Bottle, nn.Linear):
    pass


class GlobalAttentionNew(nn.Module):
    """
    Luong Attention.

    Global attention takes a matrix and a query vector. It
    then computes a parameterized convex combination of the matrix
    based on the input query.


        H_1 H_2 H_3 ... H_n
          q   q   q       q
            |  |   |       |
              \\ |   |      /
                      .....
                  \\   |  /
                      a

    Constructs a unit mapping.
    $$(H_1 + H_n, q) => (a)$$
    Where H is of `batch x n x dim` and q is of `batch x dim`.

    Loung Attention (dotprod):
    $$	anh(W_2 [(softmax((W_1 q + b_1) H) H), q] + b_2)$$.:

    Bahdanau Attention (mlp):
    $$c = \\sum_{j=1}^{SeqLength}_jh_j$$.
    The Alignment-function $$a$$ computes an alignment as:
    $$a_j = softmax(v_a^T 	anh(W_a q + U_a h_j) )$$.

    """

    def __init__(self, dim, coverage=False, attn_type='dotprod'):
        super(GlobalAttentionNew, self).__init__()
        self.dim = dim
        self.attn_type = attn_type
        assert self.attn_type in ['dotprod', 'mlp'
            ], 'Please select a valid attention type.'
        if self.attn_type == 'dotprod':
            self.linear_in = nn.Linear(dim, dim, bias=False)
            self.linear_out = nn.Linear(dim * 2, dim, bias=False)
        elif self.attn_type == 'mlp':
            self.linear_context = BottleLinear(dim, dim, bias=False)
            self.linear_query = nn.Linear(dim, dim, bias=False)
            self.v = BottleLinear(dim, 1, bias=False)
        self.sm = nn.Softmax()
        self.tanh = nn.Tanh()
        self.mask = None
        if coverage:
            self.linear_cover = nn.Linear(1, dim, bias=False)

    def applyMask(self, mask):
        self.mask = mask

    def forward(self, input_0, input_1):
        primals_2 = self.linear_in.weight
        primals_4 = self.linear_out.weight
        primals_3 = input_0
        primals_1 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0], output[1]
