from torch.nn import Module
import torch
from torch.nn.modules import Module
from torch.nn.functional import softmax
from torch.nn import Linear


def neginf(dtype):
    """
    Return a representable finite 
    number near -inf for a dtype.
    """
    if dtype is torch.float16:
        return -65504
    else:
        return -1e+20


class Attention(Module):
    """
    Luong style general attention from 
    https://arxiv.org/pdf/1508.04025.pdf.
    """

    def __init__(self, hidden_size):
        super().__init__()
        self.project = Linear(in_features=hidden_size, out_features=
            hidden_size, bias=False)
        self.combine = Linear(in_features=hidden_size * 2, out_features=
            hidden_size, bias=False)

    def forward(self, decoder_output, hidden_state, encoder_outputs,
        attn_mask=None):
        """
        Applies attention by creating the weighted 
        context vector. Implementation is based on 
        `IBM/pytorch-seq2seq`.
        """
        hidden_state = self.project(hidden_state)
        hidden_state = hidden_state.transpose(0, 1)
        encoder_outputs_t = encoder_outputs.transpose(1, 2)
        attn_scores = torch.bmm(hidden_state, encoder_outputs_t)
        if attn_mask is not None:
            attn_scores = attn_scores.squeeze(1)
            attn_scores.masked_fill_(attn_mask, neginf(attn_scores.dtype))
            attn_scores = attn_scores.unsqueeze(1)
        attn_weights = softmax(attn_scores, dim=-1)
        attn_applied = torch.bmm(attn_weights, encoder_outputs)
        stacked = torch.cat([decoder_output, attn_applied], dim=-1)
        outputs = self.combine(stacked)
        return outputs, attn_weights


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


def get_init_inputs():
    return [[], {'hidden_size': 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 math as tl_math
from torch.nn import Module
from torch.nn.modules import Module
from torch.nn import Linear
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 = 64
    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 = 64
    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 = 128
    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)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_5, (4, 8), (8, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 4), (4, 16, 1), 
            0), reinterpret_tensor(primals_3, (4, 4, 4), (16, 1, 4), 0),
            out=buf1)
        buf2 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(64)](buf1, buf2, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf3 = buf1
        del buf1
        triton_poi_fused__softmax_1[grid(64)](buf2, buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf4 = buf2
        del buf2
        extern_kernels.bmm(buf3, primals_3, out=buf4)
        buf5 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        triton_poi_fused_cat_2[grid(128)](primals_4, buf4, buf5, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_4
        buf6 = reinterpret_tensor(buf4, (16, 4), (4, 1), 0)
        del buf4
        extern_kernels.mm(reinterpret_tensor(buf5, (16, 8), (8, 1), 0),
            reinterpret_tensor(primals_5, (8, 4), (1, 8), 0), out=buf6)
    return reinterpret_tensor(buf6, (4, 4, 4), (16, 4, 1), 0
        ), buf3, reinterpret_tensor(primals_2, (16, 4), (4, 1), 0
        ), reinterpret_tensor(primals_3, (4, 4, 4), (16, 1, 4), 0
        ), buf3, reinterpret_tensor(buf5, (16, 8), (8, 1), 0), primals_5


def neginf(dtype):
    """
    Return a representable finite 
    number near -inf for a dtype.
    """
    if dtype is torch.float16:
        return -65504
    else:
        return -1e+20


class AttentionNew(Module):
    """
    Luong style general attention from 
    https://arxiv.org/pdf/1508.04025.pdf.
    """

    def __init__(self, hidden_size):
        super().__init__()
        self.project = Linear(in_features=hidden_size, out_features=
            hidden_size, bias=False)
        self.combine = Linear(in_features=hidden_size * 2, out_features=
            hidden_size, bias=False)

    def forward(self, input_0, input_1, input_2):
        primals_1 = self.project.weight
        primals_5 = self.combine.weight
        primals_2 = input_0
        primals_3 = input_1
        primals_4 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0], output[1]
