from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import json
import os
import tensorflow as tf
from embedding_as_service.text.xlnet.models import modeling


def _get_initializer(FLAGS):
  """Get variable intializer."""
  if FLAGS.init == "uniform":
    initializer = tf.initializers.random_uniform(
        minval=-FLAGS.init_range,
        maxval=FLAGS.init_range,
        seed=None)
  elif FLAGS.init == "normal":
    initializer = tf.initializers.random_normal(
        stddev=FLAGS.init_std,
        seed=None)
  else:
    raise ValueError("Initializer {} not supported".format(FLAGS.init))
  return initializer


class XLNetConfig(object):
  """XLNetConfig contains hyperparameters that are specific to a model checkpoint;
  i.e., these hyperparameters should be the same between
  pretraining and finetuning.
  The following hyperparameters are defined:
    n_layer: int, the number of layers.
    d_model: int, the hidden size.
    n_head: int, the number of attention heads.
    d_head: int, the dimension size of each attention head.
    d_inner: int, the hidden size in feed-forward layers.
    ff_activation: str, "relu" or "gelu".
    untie_r: bool, whether to untie the biases in attention.
    n_token: int, the vocab size.
  """

  def __init__(self, FLAGS=None, json_path=None):
    """Constructing an XLNetConfig.
    One of FLAGS or json_path should be provided."""

    assert FLAGS is not None or json_path is not None

    self.keys = ["n_layer", "d_model", "n_head", "d_head", "d_inner",
                 "ff_activation", "untie_r", "n_token"]

    if FLAGS is not None:
      self.init_from_flags(FLAGS)

    if json_path is not None:
      self.init_from_json(json_path)

  def init_from_flags(self, FLAGS):
    for key in self.keys:
      setattr(self, key, getattr(FLAGS, key))

  def init_from_json(self, json_path):
    with tf.gfile.Open(json_path) as f:
      json_data = json.load(f)
      for key in self.keys:
        setattr(self, key, json_data[key])

  def to_json(self, json_path):
    """Save XLNetConfig to a json file."""
    json_data = {}
    for key in self.keys:
      json_data[key] = getattr(self, key)

    json_dir = os.path.dirname(json_path)
    if not tf.gfile.Exists(json_dir):
      tf.gfile.MakeDirs(json_dir)
    with tf.gfile.Open(json_path, "w") as f:
      json.dump(json_data, f, indent=4, sort_keys=True)


def create_run_config(is_training, is_finetune, FLAGS):
  kwargs = dict(
      is_training=is_training,
      use_tpu=FLAGS.use_tpu,
      use_bfloat16=FLAGS.use_bfloat16,
      dropout=FLAGS.dropout,
      dropatt=FLAGS.dropatt,
      init=FLAGS.init,
      init_range=FLAGS.init_range,
      init_std=FLAGS.init_std,
      clamp_len=FLAGS.clamp_len)

  if not is_finetune:
    kwargs.update(dict(
        mem_len=FLAGS.mem_len,
        reuse_len=FLAGS.reuse_len,
        bi_data=FLAGS.bi_data,
        clamp_len=FLAGS.clamp_len,
        same_length=FLAGS.same_length))

  return RunConfig(**kwargs)


class RunConfig(object):
  """RunConfig contains hyperparameters that could be different
  between pretraining and finetuning.
  These hyperparameters can also be changed from run to run.
  We store them separately from XLNetConfig for flexibility.
  """

  def __init__(self, is_training, use_tpu, use_bfloat16, dropout, dropatt,
               init="normal", init_range=0.1, init_std=0.02, mem_len=None,
               reuse_len=None, bi_data=False, clamp_len=-1, same_length=False):
    """
    Args:
      is_training: bool, whether in training mode.
      use_tpu: bool, whether TPUs are used.
      use_bfloat16: bool, use bfloat16 instead of float32.
      dropout: float, dropout rate.
      dropatt: float, dropout rate on attention probabilities.
      init: str, the initialization scheme, either "normal" or "uniform".
      init_range: float, initialize the parameters with a uniform distribution
        in [-init_range, init_range]. Only effective when init="uniform".
      init_std: float, initialize the parameters with a normal distribution
        with mean 0 and stddev init_std. Only effective when init="normal".
      mem_len: int, the number of tokens to cache.
      reuse_len: int, the number of tokens in the currect batch to be cached
        and reused in the future.
      bi_data: bool, whether to use bidirectional input pipeline.
        Usually set to True during pretraining and False during finetuning.
      clamp_len: int, clamp all relative distances larger than clamp_len.
        -1 means no clamping.
      same_length: bool, whether to use the same attention length for each token.
    """

    self.init = init
    self.init_range = init_range
    self.init_std = init_std
    self.is_training = is_training
    self.dropout = dropout
    self.dropatt = dropatt
    self.use_tpu = use_tpu
    self.use_bfloat16 = use_bfloat16
    self.mem_len = mem_len
    self.reuse_len = reuse_len
    self.bi_data = bi_data
    self.clamp_len = clamp_len
    self.same_length = same_length


class XLNetModel(object):
  """A wrapper of the XLNet model used during both pretraining and finetuning."""

  def __init__(self, xlnet_config, run_config, input_ids, seg_ids, input_mask,
               mems=None, perm_mask=None, target_mapping=None, inp_q=None,
               **kwargs):
    """
    Args:
      xlnet_config: XLNetConfig,
      run_config: RunConfig,
      input_ids: int32 Tensor in shape [len, bsz], the input token IDs.
      seg_ids: int32 Tensor in shape [len, bsz], the input segment IDs.
      input_mask: float32 Tensor in shape [len, bsz], the input mask.
        0 for real tokens and 1 for padding.
      mems: a list of float32 Tensors in shape [mem_len, bsz, d_model], memory
        from previous batches. The length of the list equals n_layer.
        If None, no memory is used.
      perm_mask: float32 Tensor in shape [len, len, bsz].
        If perm_mask[i, j, k] = 0, i attend to j in batch k;
        if perm_mask[i, j, k] = 1, i does not attend to j in batch k.
        If None, each position attends to all the others.
      target_mapping: float32 Tensor in shape [num_predict, len, bsz].
        If target_mapping[i, j, k] = 1, the i-th predict in batch k is
        on the j-th token.
        Only used during pretraining for partial prediction.
        Set to None during finetuning.
      inp_q: float32 Tensor in shape [len, bsz].
        1 for tokens with losses and 0 for tokens without losses.
        Only used during pretraining for two-stream attention.
        Set to None during finetuning.
    """

    initializer = _get_initializer(run_config)

    tfm_args = dict(
        n_token=xlnet_config.n_token,
        initializer=initializer,
        attn_type="bi",
        n_layer=xlnet_config.n_layer,
        d_model=xlnet_config.d_model,
        n_head=xlnet_config.n_head,
        d_head=xlnet_config.d_head,
        d_inner=xlnet_config.d_inner,
        ff_activation=xlnet_config.ff_activation,
        untie_r=xlnet_config.untie_r,

        is_training=run_config.is_training,
        use_bfloat16=run_config.use_bfloat16,
        use_tpu=run_config.use_tpu,
        dropout=run_config.dropout,
        dropatt=run_config.dropatt,

        mem_len=run_config.mem_len,
        reuse_len=run_config.reuse_len,
        bi_data=run_config.bi_data,
        clamp_len=run_config.clamp_len,
        same_length=run_config.same_length
    )

    input_args = dict(
        inp_k=input_ids,
        seg_id=seg_ids,
        input_mask=input_mask,
        mems=mems,
        perm_mask=perm_mask,
        target_mapping=target_mapping,
        inp_q=inp_q)
    tfm_args.update(input_args)

    with tf.variable_scope("model", reuse=tf.AUTO_REUSE):
      (self.output, self.new_mems, self.lookup_table
          ) = modeling.transformer_xl(**tfm_args)

    self.input_mask = input_mask
    self.initializer = initializer
    self.xlnet_config = xlnet_config
    self.run_config = run_config

  def get_pooled_out(self, summary_type, use_summ_proj=True):
    """
    Args:
      summary_type: str, "last", "first", "mean", or "attn". The method
        to pool the input to get a vector representation.
      use_summ_proj: bool, whether to use a linear projection during pooling.
    Returns:
      float32 Tensor in shape [bsz, d_model], the pooled representation.
    """

    xlnet_config = self.xlnet_config
    run_config = self.run_config

    with tf.variable_scope("model", reuse=tf.AUTO_REUSE):
      summary = modeling.summarize_sequence(
          summary_type=summary_type,
          hidden=self.output,
          d_model=xlnet_config.d_model,
          n_head=xlnet_config.n_head,
          d_head=xlnet_config.d_head,
          dropout=run_config.dropout,
          dropatt=run_config.dropatt,
          is_training=run_config.is_training,
          input_mask=self.input_mask,
          initializer=self.initializer,
          use_proj=use_summ_proj)

    return summary

  def get_sequence_output(self):
    """
    Returns:
      float32 Tensor in shape [len, bsz, d_model]. The last layer hidden
      representation of XLNet.
    """

    return self.output

  def get_new_memory(self):
    """
    Returns:
      list of float32 Tensors in shape [mem_len, bsz, d_model], the new
      memory that concatenates the previous memory with the current input
      representations.
      The length of the list equals n_layer.
    """
    return self.new_mems

  def get_embedding_table(self):
    """
    Returns:
      float32 Tensor in shape [n_token, d_model]. The embedding lookup table.
      Used for tying embeddings between input and output layers.
    """
    return self.lookup_table

  def get_initializer(self):
    """
    Returns:
      A tf initializer. Used to initialize variables in layers on top of XLNet.
    """
    return self.initializer