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 torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import numpy as np
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_div_mul_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp16 = tl.load(in_ptr1 + x3, xmask)
    tmp17 = tl.load(in_ptr1 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp19 = tl.load(in_ptr1 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp22 = tl.load(in_ptr1 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp25 = tl.load(in_ptr1 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-12
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp0 / tmp14
    tmp18 = tmp17 * tmp17
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = libdevice.sqrt(tmp27)
    tmp29 = triton_helpers.maximum(tmp28, tmp13)
    tmp30 = tmp16 / tmp29
    tmp31 = tmp15 * tmp30
    tl.store(out_ptr0 + x3, tmp31, xmask)


@triton.jit
def triton_per_fused_add_log_mean_mul_rsub_sigmoid_sum_1(in_ptr0, in_ptr1,
    out_ptr0, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r3 = rindex
    r0 = rindex % 16
    r1 = rindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + r3, None)
    tmp1 = tl.load(in_ptr1 + (r0 + 64 * r1), None, eviction_policy='evict_last'
        )
    tmp2 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None, eviction_policy=
        'evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tl.sigmoid(tmp7)
    tmp9 = tl_math.log(tmp8)
    tmp10 = tmp0 * tmp9
    tmp11 = 1.0
    tmp12 = tmp11 - tmp0
    tmp13 = tmp11 - tmp8
    tmp14 = tl_math.log(tmp13)
    tmp15 = tmp12 * tmp14
    tmp16 = tmp10 + tmp15
    tmp17 = tl.broadcast_to(tmp16, [RBLOCK])
    tmp19 = triton_helpers.promote_to_tensor(tl.sum(tmp17, 0))
    tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp19, None)


@triton.jit
def triton_per_fused_add_log_mean_mul_neg_rsub_sigmoid_sum_2(in_out_ptr0,
    in_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp1 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp3 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp5 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp12 = tl.load(in_out_ptr0 + 0)
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, 1])
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = tl.sigmoid(tmp6)
    tmp8 = tl_math.log(tmp7)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.sum(tmp9, 1)[:, None]
    tmp14 = 256.0
    tmp15 = tmp13 / tmp14
    tmp16 = -tmp15
    tmp17 = 64.0
    tmp18 = tmp11 / tmp17
    tmp19 = -tmp18
    tmp20 = 4.0
    tmp21 = tmp19 * tmp20
    tmp22 = tmp16 + tmp21
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp22, None)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1, arg4_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg3_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg4_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_div_mul_0[grid(256)](arg0_1, arg1_1, buf0, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused_add_log_mean_mul_rsub_sigmoid_sum_1[grid(1)](arg2_1,
            buf0, buf1, 1, 256, num_warps=2, num_stages=1)
        del arg2_1
        buf2 = buf0
        del buf0
        triton_poi_fused_div_mul_0[grid(256)](arg3_1, arg4_1, buf2, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg3_1
        del arg4_1
        buf4 = buf1
        del buf1
        triton_per_fused_add_log_mean_mul_neg_rsub_sigmoid_sum_2[grid(1)](buf4,
            buf2, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del buf2
    return buf4,


class SplitterNew(torch.nn.Module):
    """
    An implementation of "Splitter: Learning Node Representations that Capture Multiple Social Contexts" (WWW 2019).
    Paper: http://epasto.org/papers/www2019splitter.pdf
    """

    def __init__(self, dimensions, lambd, base_node_count, node_count, device):
        """
        Splitter set up.
        :param dimensions: Dimension of embedding vectors
        :param lambd: Parameter that determine how much personas spread from original embedding
        :param base_node_count: Number of nodes in the source graph.
        :param node_count: Number of nodes in the persona graph.
        :param device: Device which torch use
        """
        super(SplitterNew, self).__init__()
        self.dimensions = dimensions
        self.lambd = lambd
        self.base_node_count = base_node_count
        self.node_count = node_count
        self.device = device

    def create_weights(self):
        """
        Creating weights for embedding.
        """
        self.base_node_embedding = torch.nn.Embedding(self.base_node_count,
            self.dimensions, padding_idx=0)
        self.node_embedding = torch.nn.Embedding(self.node_count, self.
            dimensions, padding_idx=0)

    def initialize_weights(self, base_node_embedding, mapping, str2idx):
        """
        Using the base embedding and the persona mapping for initializing the embedding matrices.
        :param base_node_embedding: Node embedding of the source graph.
        :param mapping: Mapping of personas to nodes.
        :param str2idx: Mapping string of original network to index in original network
        """
        persona_embedding = np.array([base_node_embedding[str2idx[
            original_node]] for node, original_node in mapping.items()])
        self.node_embedding.weight.data = torch.nn.Parameter(torch.Tensor(
            persona_embedding))
        self.base_node_embedding.weight.data = torch.nn.Parameter(torch.
            Tensor(base_node_embedding), requires_grad=False)

    def calculate_main_loss(self, node_f, feature_f, targets):
        """
        Calculating the main loss which is used to learning based on persona random walkers
        It will be act likes centrifugal force from the base embedding
        :param node_f: Embedding vectors of source nodes
        :param feature_f: Embedding vectors of target nodes to predict
        :param targets: Boolean vector whether negative samples or not
        """
        node_f = torch.nn.functional.normalize(node_f, p=2, dim=1)
        feature_f = torch.nn.functional.normalize(feature_f, p=2, dim=1)
        scores = torch.sum(node_f * feature_f, dim=1)
        scores = torch.sigmoid(scores)
        main_loss = targets * torch.log(scores) + (1 - targets) * torch.log(
            1 - scores)
        main_loss = -torch.mean(main_loss)
        return main_loss

    def calculate_regularization(self, source_f, original_f):
        """
         Calculating the main loss which is used to learning based on persona random walkers
         It will be act likes centripetal force from the base embedding
         :param source_f: Embedding vectors of source nodes
         :param original_f: Embedding vectors of base embedding of source nodes
         """
        source_f = torch.nn.functional.normalize(source_f, p=2, dim=1)
        original_f = torch.nn.functional.normalize(original_f, p=2, dim=1)
        scores = torch.sum(source_f * original_f, dim=1)
        scores = torch.sigmoid(scores)
        regularization_loss = -torch.mean(torch.log(scores))
        return regularization_loss

    def forward(self, input_0, input_1, input_2, input_3, input_4):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        arg3_1 = input_3
        arg4_1 = input_4
        output = call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1])
        return output[0]


Measured latency_ms: 0.051599