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
import torch.nn as nn
from torch.autograd import Variable
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_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 48
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 12
    x1 = xindex // 12
    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
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tl.full([1], 12, tl.int64)
    tmp14 = tl.load(in_ptr2 + (4 * x1 + (-8 + x0)), tmp11 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp15 = tl.where(tmp9, tmp10, tmp14)
    tmp16 = tl.where(tmp4, tmp5, tmp15)
    tl.store(out_ptr0 + x2, tmp16, xmask)


@triton.jit
def triton_poi_fused_cat_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
    x0 = xindex % 4
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tl.store(out_ptr0 + (x0 + 8 * x1), tmp0, xmask)


@triton.jit
def triton_poi_fused__log_softmax_2(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
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused__log_softmax_3(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')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp2 + tmp4
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp5 + tmp7
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp0 - tmp12
    tl.store(out_ptr0 + x2, tmp13, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (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, 12), (12, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 12), (12, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 8), (8, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 12), (12, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(48)](primals_1, primals_2, primals_3,
            buf0, 48, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_1
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf0, reinterpret_tensor(primals_4,
            (12, 4), (1, 12), 0), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf4 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        buf2 = reinterpret_tensor(buf4, (4, 4), (8, 1), 4)
        extern_kernels.addmm(primals_7, buf0, reinterpret_tensor(primals_6,
            (12, 4), (1, 12), 0), alpha=1, beta=1, out=buf2)
        del primals_6
        del primals_7
        buf3 = reinterpret_tensor(buf4, (4, 4), (8, 1), 0)
        triton_poi_fused_cat_1[grid(16)](buf1, buf3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_9, buf4, reinterpret_tensor(primals_8,
            (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf5)
        del primals_9
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__log_softmax_2[grid(16)](buf5, buf6, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf7 = buf5
        del buf5
        triton_poi_fused__log_softmax_3[grid(16)](buf6, buf7, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf6
    return buf7, buf1, buf0, buf4, buf7, primals_8


class RNNNew(nn.Module):

    def __init__(self, input_size, hidden_size, output_size, all_categories,
        n_categories, all_letters, n_letters):
        super(RNNNew, self).__init__()
        self.hidden_size = hidden_size
        self.all_categories = all_categories
        self.n_categories = n_categories
        self.all_letters = all_letters
        self.n_letters = n_letters
        self.i2h = nn.Linear(n_categories + input_size + hidden_size,
            hidden_size)
        self.i2o = nn.Linear(n_categories + input_size + hidden_size,
            output_size)
        self.o2o = nn.Linear(hidden_size + output_size, output_size)
        self.dropout = nn.Dropout(0.1)
        self.softmax = nn.LogSoftmax(dim=1)

    def init_hidden(self):
        return Variable(torch.zeros(1, self.hidden_size))

    @staticmethod
    def gen_input_tensor(all_letters, n_letters, line):
        tensor = torch.zeros(len(line), 1, n_letters)
        for li in range(len(line)):
            letter = line[li]
            tensor[li][0][all_letters.find(letter)] = 1
        return tensor

    @staticmethod
    def gen_category_tensor(all_categories, n_categories, category):
        li = all_categories.index(category)
        tensor = torch.zeros(1, n_categories)
        tensor[0][li] = 1
        return tensor

    def sample(self, category, start_letter='A'):
        category_tensor = Variable(self.gen_category_tensor(self.
            all_categories, self.n_categories, category))
        input_tensor = Variable(self.gen_input_tensor(self.all_letters,
            self.n_letters, start_letter))
        hidden = self.init_hidden()
        output_name = start_letter
        max_length = 20
        for i in range(max_length):
            output, hidden = self.forward(category_tensor, input_tensor[0],
                hidden)
            _topv, topi = output.data.topk(1)
            topi = topi[0][0]
            if topi == self.n_letters - 1:
                break
            else:
                letter = self.all_letters[topi]
                output_name += letter
            input_tensor = Variable(self.gen_input_tensor(self.all_letters,
                self.n_letters, letter))
        return output_name

    def samples(self, category, start_letters='ABC'):
        for start_letter in start_letters:
            yield self.sample(category, start_letter)

    def forward(self, input_0, input_1, input_2):
        primals_4 = self.i2h.weight
        primals_5 = self.i2h.bias
        primals_6 = self.i2o.weight
        primals_7 = self.i2o.bias
        primals_8 = self.o2o.weight
        primals_9 = self.o2o.bias
        primals_1 = input_0
        primals_2 = input_1
        primals_3 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0], output[1]


Measured latency_ms: 0.081057