code: # coding: utf8
  from __future__ import print_function, unicode_literals
  
  import plac
  import random
  import numpy
  import time
  import re
  from collections import Counter
  from pathlib import Path
  from thinc.v2v import Affine, Maxout
  from thinc.misc import LayerNorm as LN
  from thinc.neural.util import prefer_gpu
  from wasabi import Printer
  import srsly
  
  from ..errors import Errors
  from ..tokens import Doc
  from ..attrs import ID, HEAD
  from .._ml import Tok2Vec, flatten, chain, create_default_optimizer
  from .._ml import masked_language_model, get_cossim_loss
  from .. import util
  from .train import _load_pretrained_tok2vec
  
  
  @plac.annotations(
      texts_loc=(
          "Path to JSONL file with raw texts to learn from, with text provided as the key 'text' or tokens as the "
          "key 'tokens'",
          "positional",
          None,
          str,
      ),
      vectors_model=("Name or path to spaCy model with vectors to learn from"),
      output_dir=("Directory to write models to on each epoch", "positional", None, str),
      width=("Width of CNN layers", "option", "cw", int),
      depth=("Depth of CNN layers", "option", "cd", int),
      cnn_window=("Window size for CNN layers", "option", "cW", int),
      cnn_pieces=("Maxout size for CNN layers. 1 for Mish", "option", "cP", int),
      use_chars=("Whether to use character-based embedding", "flag", "chr", bool),
      sa_depth=("Depth of self-attention layers", "option", "sa", int),
      bilstm_depth=("Depth of BiLSTM layers (requires PyTorch)", "option", "lstm", int),
      embed_rows=("Number of embedding rows", "option", "er", int),
      loss_func=(
          "Loss function to use for the objective. Either 'L2' or 'cosine'",
          "option",
          "L",
          str,
      ),
      use_vectors=("Whether to use the static vectors as input features", "flag", "uv"),
      dropout=("Dropout rate", "option", "d", float),
      batch_size=("Number of words per training batch", "option", "bs", int),
      max_length=(
          "Max words per example. Longer examples are discarded",
          "option",
          "xw",
          int,
      ),
      min_length=(
          "Min words per example. Shorter examples are discarded",
          "option",
          "nw",
          int,
      ),
      seed=("Seed for random number generators", "option", "s", int),
      n_iter=("Number of iterations to pretrain", "option", "i", int),
      n_save_every=("Save model every X batches.", "option", "se", int),
      init_tok2vec=(
          "Path to pretrained weights for the token-to-vector parts of the models. See 'spacy pretrain'. Experimental.",
          "option",
          "t2v",
          Path,
      ),
      epoch_start=(
          "The epoch to start counting at. Only relevant when using '--init-tok2vec' and the given weight file has been "
          "renamed. Prevents unintended overwriting of existing weight files.",
          "option",
          "es",
          int,
      ),
  )
  def pretrain(
      texts_loc,
      vectors_model,
      output_dir,
      width=96,
      depth=4,
      bilstm_depth=0,
      cnn_pieces=3,
      sa_depth=0,
      use_chars=False,
      cnn_window=1,
      embed_rows=2000,
      loss_func="cosine",
      use_vectors=False,
      dropout=0.2,
      n_iter=1000,
      batch_size=3000,
      max_length=500,
      min_length=5,
      seed=0,
      n_save_every=None,
      init_tok2vec=None,
      epoch_start=None,
  ):
      """
      Pre-train the 'token-to-vector' (tok2vec) layer of pipeline components,
      using an approximate language-modelling objective. Specifically, we load
      pretrained vectors, and train a component like a CNN, BiLSTM, etc to predict
      vectors which match the pretrained ones. The weights are saved to a directory
      after each epoch. You can then pass a path to one of these pretrained weights
      files to the 'spacy train' command.
  
      This technique may be especially helpful if you have little labelled data.
      However, it's still quite experimental, so your mileage may vary.
  
      To load the weights back in during 'spacy train', you need to ensure
      all settings are the same between pretraining and training. The API and
      errors around this need some improvement.
      """
      config = dict(locals())
      for key in config:
          if isinstance(config[key], Path):
              config[key] = str(config[key])
      msg = Printer()
      util.fix_random_seed(seed)
  
      has_gpu = prefer_gpu()
      if has_gpu:
          import torch
  
          torch.set_default_tensor_type("torch.cuda.FloatTensor")
      msg.info("Using GPU" if has_gpu else "Not using GPU")
  
      output_dir = Path(output_dir)
      if not output_dir.exists():
          output_dir.mkdir()
          msg.good("Created output directory")
      srsly.write_json(output_dir / "config.json", config)
      msg.good("Saved settings to config.json")
  
      # Load texts from file or stdin
      if texts_loc != "-":  # reading from a file
          texts_loc = Path(texts_loc)
          if not texts_loc.exists():
              msg.fail("Input text file doesn't exist", texts_loc, exits=1)
          with msg.loading("Loading input texts..."):
              texts = list(srsly.read_jsonl(texts_loc))
          if not texts:
              msg.fail("Input file is empty", texts_loc, exits=1)
          msg.good("Loaded input texts")
          random.shuffle(texts)
      else:  # reading from stdin
          msg.text("Reading input text from stdin...")
          texts = srsly.read_jsonl("-")
  
      with msg.loading("Loading model '{}'...".format(vectors_model)):
          nlp = util.load_model(vectors_model)
      msg.good("Loaded model '{}'".format(vectors_model))
      pretrained_vectors = None if not use_vectors else nlp.vocab.vectors.name
      model = create_pretraining_model(
          nlp,
          Tok2Vec(
              width,
              embed_rows,
              conv_depth=depth,
              pretrained_vectors=pretrained_vectors,
              bilstm_depth=bilstm_depth,  # Requires PyTorch. Experimental.
              subword_features=not use_chars,  # Set to False for Chinese etc
              cnn_maxout_pieces=cnn_pieces,  # If set to 1, use Mish activation.
          ),
      )
      # Load in pretrained weights
      if init_tok2vec is not None:
          components = _load_pretrained_tok2vec(nlp, init_tok2vec)
          msg.text("Loaded pretrained tok2vec for: {}".format(components))
          # Parse the epoch number from the given weight file
          model_name = re.search(r"model\d+\.bin", str(init_tok2vec))
          if model_name:
              # Default weight file name so read epoch_start from it by cutting off 'model' and '.bin'
              epoch_start = int(model_name.group(0)[5:][:-4]) + 1
          else:
              if not epoch_start:
                  msg.fail(
                      "You have to use the '--epoch-start' argument when using a renamed weight file for "
                      "'--init-tok2vec'",
                      exits=True,
                  )
              elif epoch_start < 0:
                  msg.fail(
                      "The argument '--epoch-start' has to be greater or equal to 0. '%d' is invalid"
                      % epoch_start,
                      exits=True,
                  )
      else:
          # Without '--init-tok2vec' the '--epoch-start' argument is ignored
          epoch_start = 0
  
      optimizer = create_default_optimizer(model.ops)
      tracker = ProgressTracker(frequency=10000)
      msg.divider("Pre-training tok2vec layer - starting at epoch %d" % epoch_start)
      row_settings = {"widths": (3, 10, 10, 6, 4), "aligns": ("r", "r", "r", "r", "r")}
      msg.row(("#", "# Words", "Total Loss", "Loss", "w/s"), **row_settings)
  
      def _save_model(epoch, is_temp=False):
          is_temp_str = ".temp" if is_temp else ""
          with model.use_params(optimizer.averages):
              with (output_dir / ("model%d%s.bin" % (epoch, is_temp_str))).open(
                  "wb"
              ) as file_:
                  file_.write(model.tok2vec.to_bytes())
              log = {
                  "nr_word": tracker.nr_word,
                  "loss": tracker.loss,
                  "epoch_loss": tracker.epoch_loss,
                  "epoch": epoch,
              }
              with (output_dir / "log.jsonl").open("a") as file_:
                  file_.write(srsly.json_dumps(log) + "\n")
  
      skip_counter = 0
      for epoch in range(epoch_start, n_iter + epoch_start):
          for batch_id, batch in enumerate(
              util.minibatch_by_words(((text, None) for text in texts), size=batch_size)
          ):
              docs, count = make_docs(
                  nlp,
                  [text for (text, _) in batch],
                  max_length=max_length,
                  min_length=min_length,
              )
              skip_counter += count
              loss = make_update(
                  model, docs, optimizer, objective=loss_func, drop=dropout
              )
              progress = tracker.update(epoch, loss, docs)
              if progress:
                  msg.row(progress, **row_settings)
                  if texts_loc == "-" and tracker.words_per_epoch[epoch] >= 10 ** 7:
                      break
              if n_save_every and (batch_id % n_save_every == 0):
                  _save_model(epoch, is_temp=True)
          _save_model(epoch)
          tracker.epoch_loss = 0.0
          if texts_loc != "-":
              # Reshuffle the texts if texts were loaded from a file
              random.shuffle(texts)
      if skip_counter > 0:
          msg.warn("Skipped {count} empty values".format(count=str(skip_counter)))
      msg.good("Successfully finished pretrain")
  
  
  def make_update(model, docs, optimizer, drop=0.0, objective="L2"):
      """Perform an update over a single batch of documents.
  
      docs (iterable): A batch of `Doc` objects.
      drop (float): The dropout rate.
      optimizer (callable): An optimizer.
      RETURNS loss: A float for the loss.
      """
      predictions, backprop = model.begin_update(docs, drop=drop)
      loss, gradients = get_vectors_loss(model.ops, docs, predictions, objective)
      backprop(gradients, sgd=optimizer)
      # Don't want to return a cupy object here
      # The gradients are modified in-place by the BERT MLM,
      # so we get an accurate loss
      return float(loss)
  
  
  def make_docs(nlp, batch, min_length, max_length):
      docs = []
      skip_count = 0
      for record in batch:
          if not isinstance(record, dict):
              raise TypeError(Errors.E137.format(type=type(record), line=record))
          if "tokens" in record:
              words = record["tokens"]
              if not words:
                  skip_count += 1
                  continue
              doc = Doc(nlp.vocab, words=words)
          elif "text" in record:
              text = record["text"]
              if not text:
                  skip_count += 1
                  continue
              doc = nlp.make_doc(text)
          else:
              raise ValueError(Errors.E138.format(text=record))
          if "heads" in record:
              heads = record["heads"]
              heads = numpy.asarray(heads, dtype="uint64")
              heads = heads.reshape((len(doc), 1))
              doc = doc.from_array([HEAD], heads)
          if len(doc) >= min_length and len(doc) < max_length:
              docs.append(doc)
      return docs, skip_count
  
  
  def get_vectors_loss(ops, docs, prediction, objective="L2"):
      """Compute a mean-squared error loss between the documents' vectors and
      the prediction.
  
      Note that this is ripe for customization! We could compute the vectors
      in some other word, e.g. with an LSTM language model, or use some other
      type of objective.
      """
      # The simplest way to implement this would be to vstack the
      # token.vector values, but that's a bit inefficient, especially on GPU.
      # Instead we fetch the index into the vectors table for each of our tokens,
      # and look them up all at once. This prevents data copying.
      ids = ops.flatten([doc.to_array(ID).ravel() for doc in docs])
      target = docs[0].vocab.vectors.data[ids]
      if objective == "L2":
          d_target = prediction - target
          loss = (d_target ** 2).sum()
      elif objective == "cosine":
          loss, d_target = get_cossim_loss(prediction, target)
      else:
          raise ValueError(Errors.E142.format(loss_func=objective))
      return loss, d_target
  
  
  def create_pretraining_model(nlp, tok2vec):
      """Define a network for the pretraining. We simply add an output layer onto
      the tok2vec input model. The tok2vec input model needs to be a model that
      takes a batch of Doc objects (as a list), and returns a list of arrays.
      Each array in the output needs to have one row per token in the doc.
      """
      output_size = nlp.vocab.vectors.data.shape[1]
      output_layer = chain(
          LN(Maxout(300, pieces=3)), Affine(output_size, drop_factor=0.0)
      )
      # This is annoying, but the parser etc have the flatten step after
      # the tok2vec. To load the weights in cleanly, we need to match
      # the shape of the models' components exactly. So what we cann
      # "tok2vec" has to be the same set of processes as what the components do.
      tok2vec = chain(tok2vec, flatten)
      model = chain(tok2vec, output_layer)
      model = masked_language_model(nlp.vocab, model)
      model.tok2vec = tok2vec
      model.output_layer = output_layer
      model.begin_training([nlp.make_doc("Give it a doc to infer shapes")])
      return model
  
  
  class ProgressTracker(object):
      def __init__(self, frequency=1000000):
          self.loss = 0.0
          self.prev_loss = 0.0
          self.nr_word = 0
          self.words_per_epoch = Counter()
          self.frequency = frequency
          self.last_time = time.time()
          self.last_update = 0
          self.epoch_loss = 0.0
  
      def update(self, epoch, loss, docs):
          self.loss += loss
          self.epoch_loss += loss
          words_in_batch = sum(len(doc) for doc in docs)
          self.words_per_epoch[epoch] += words_in_batch
          self.nr_word += words_in_batch
          words_since_update = self.nr_word - self.last_update
          if words_since_update >= self.frequency:
              wps = words_since_update / (time.time() - self.last_time)
              self.last_update = self.nr_word
              self.last_time = time.time()
              loss_per_word = self.loss - self.prev_loss
              status = (
                  epoch,
                  self.nr_word,
                  _smart_round(self.loss, width=10),
                  _smart_round(loss_per_word, width=6),
                  int(wps),
              )
              self.prev_loss = float(self.loss)
              return status
          else:
              return None
  
  
  def _smart_round(figure, width=10, max_decimal=4):
      """Round large numbers as integers, smaller numbers as decimals."""
      n_digits = len(str(int(figure)))
      n_decimal = width - (n_digits + 1)
      if n_decimal <= 1:
          return str(int(figure))
      else:
          n_decimal = min(n_decimal, max_decimal)
          format_str = "%." + str(n_decimal) + "f"
          return format_str % figure
  
apis: torch.set_default_tensor_type numpy.asarray