utils.py

import torch
from transformers.models.bert.modeling_bert import BertModel, BertPreTrainedModel
from torch import nn
from itertools import chain
from torch.nn import MSELoss, CrossEntropyLoss
from cleantext import clean
from num2words import num2words
import re
import string

punct_chars = list((set(string.punctuation) | {'’', '‘', '–', '—', '~', '|', '“', '”', '…', "'", "`", '_'}))
punct_chars.sort()
punctuation = ''.join(punct_chars)
replace = re.compile('[%s]' % re.escape(punctuation))

MATH_PREFIXES = [ 
    "sum",
    "arc",
    "mass",
    "digit",
    "graph",
    "liter",
    "gram",
    "add",
    "angle",
    "scale",
    "data",
    "array",
    "ruler",
    "meter",
    "total",
    "unit",
    "prism",
    "median",
    "ratio",
    "area",
]

MATH_WORDS = [
    "absolute value",
    "area",
    "average",
    "base of",
    "box plot",
    "categorical",
    "coefficient",
    "common factor",
    "common multiple",
    "compose",
    "coordinate",
    "cubed",
    "decompose",
    "dependent variable",
    "distribution",
    "dot plot",
    "double number line diagram",
    "equivalent",
    "equivalent expression",
    "ratio",
    "exponent",
    "frequency",
    "greatest common factor",
    "gcd",
    "height of",
    "histogram",
    "independent variable",
    "interquartile range",
    "iqr",
    "least common multiple",
    "long division",
    "mean absolute deviation",
    "median",
    "negative number",
    "opposite vertex",
    "parallelogram",
    "percent",
    "polygon",
    "polyhedron",
    "positive number",
    "prism",
    "pyramid",
    "quadrant",
    "quadrilateral",
    "quartile",
    "rational number",
    "reciprocal",
    "equality",
    "inequality",
    "squared",
    "statistic",
    "surface area",
    "identity property",
    "addend",
    "unit",
    "number sentence",
    "make ten",
    "take from ten",
    "number bond",
    "total",
    "estimate",
    "hashmark",
    "meter",
    "number line",
    "ruler",
    "centimeter",
    "base ten",
    "expanded form",
    "hundred",
    "thousand",
    "place value",
    "number disk",
    "standard form",
    "unit form",
    "word form",
    "tens place",
    "algorithm",
    "equation",
    "simplif",
    "addition",
    "subtract",
    "array",
    "even number",
    "odd number",
    "repeated addition",
    "tessellat",
    "whole number",
    "number path",
    "rectangle",
    "square",
    "bar graph",
    "data",
    "degree",
    "line plot",
    "picture graph",
    "scale",
    "survey",
    "thermometer",
    "estimat",
    "tape diagram",
    "value",
    "analog",
    "angle",
    "parallel",
    "partition",
    "pentagon",
    "right angle",
    "cube",
    "digital",
    "quarter of",
    "tangram",
    "circle",
    "hexagon",
    "half circle",
    "half-circle",
    "quarter circle",
    "quarter-circle",
    "semicircle",
    "semi-circle",
    "rectang",
    "rhombus",
    "trapezoid",
    "triangle",
    "commutative",
    "equal group",
    "distributive",
    "divide",
    "division",
    "multipl",
    "parentheses",
    "quotient",
    "rotate",
    "unknown",
    "add",
    "capacity",
    "continuous",
    "endpoint",
    "gram",
    "interval",
    "kilogram",
    "volume",
    "liter",
    "milliliter",
    "approximate",
    "area model",
    "square unit",
    "unit square",
    "geometr",
    "equivalent fraction",
    "fraction form",
    "fractional unit",
    "unit fraction",
    "unit interval",
    "measur",
    "graph",
    "scaled graph",
    "diagonal",
    "perimeter",
    "regular polygon",
    "tessellate",
    "tetromino",
    "heptagon",
    "octagon",
    "digit",
    "expression",
    "sum",
    "kilometer",
    "mass",
    "mixed unit",
    "length",
    "measure",
    "simplify",
    "associative",
    "composite",
    "divisible",
    "divisor",
    "partial product",
    "prime number",
    "remainder",
    "acute",
    "arc",
    "collinear",
    "equilateral",
    "intersect",
    "isosceles",
    "symmetry",
    "line segment",
    "line",
    "obtuse",
    "perpendicular",
    "protractor",
    "scalene",
    "straight angle",
    "supplementary angle",
    "vertex",
    "common denominator",
    "denominator",
    "fraction",
    "mixed number",
    "numerator",
    "whole",
    "decimal expanded form",
    "decimal",
    "hundredth",
    "tenth",
    "customary system of measurement",
    "customary unit",
    "gallon",
    "metric",
    "metric unit",
    "ounce",
    "pint",
    "quart",
    "convert",
    "distance",
    "millimeter",
    "thousandth",
    "hundredths",
    "conversion factor",
    "decimal fraction",
    "multiplier",
    "equivalence",
    "multiple",
    "product",
    "benchmark fraction",
    "cup",
    "pound",
    "yard",
    "whole unit",
    "decimal divisor",
    "factors",
    "bisect",
    "cubic units",
    "hierarchy",
    "unit cube",
    "attribute",
    "kite",
    "bisector",
    "solid figure",
    "square units",
    "dimension",
    "axis",
    "ordered pair",
    "angle measure",
    "horizontal",
    "vertical",
    "categorical data",
    "lcm",
    "measure of center",
    "meters per second",
    "numerical",
    "solution",
    "unit price",
    "unit rate",
    "variability",
    "variable",
]

def get_num_words(text):
    if not isinstance(text, str):
        print("%s is not a string" % text)
    text = replace.sub(' ', text)
    text = re.sub(r'\s+', ' ', text)
    text = text.strip()
    text = re.sub(r'\[.+\]', " ", text)
    return len(text.split())

def number_to_words(num):
    try:
        return num2words(re.sub(",", "", num))
    except:
        return num


clean_str = lambda s: clean(s,
                            fix_unicode=True,  # fix various unicode errors
                            to_ascii=True,  # transliterate to closest ASCII representation
                            lower=True,  # lowercase text
                            no_line_breaks=True,  # fully strip line breaks as opposed to only normalizing them
                            no_urls=True,  # replace all URLs with a special token
                            no_emails=True,  # replace all email addresses with a special token
                            no_phone_numbers=True,  # replace all phone numbers with a special token
                            no_numbers=True,  # replace all numbers with a special token
                            no_digits=False,  # replace all digits with a special token
                            no_currency_symbols=False,  # replace all currency symbols with a special token
                            no_punct=False,  # fully remove punctuation
                            replace_with_url="<URL>",
                            replace_with_email="<EMAIL>",
                            replace_with_phone_number="<PHONE>",
                            replace_with_number=lambda m: number_to_words(m.group()),
                            replace_with_digit="0",
                            replace_with_currency_symbol="<CUR>",
                            lang="en"
                            )

clean_str_nopunct = lambda s: clean(s,
                            fix_unicode=True,  # fix various unicode errors
                            to_ascii=True,  # transliterate to closest ASCII representation
                            lower=True,  # lowercase text
                            no_line_breaks=True,  # fully strip line breaks as opposed to only normalizing them
                            no_urls=True,  # replace all URLs with a special token
                            no_emails=True,  # replace all email addresses with a special token
                            no_phone_numbers=True,  # replace all phone numbers with a special token
                            no_numbers=True,  # replace all numbers with a special token
                            no_digits=False,  # replace all digits with a special token
                            no_currency_symbols=False,  # replace all currency symbols with a special token
                            no_punct=True,  # fully remove punctuation
                            replace_with_url="<URL>",
                            replace_with_email="<EMAIL>",
                            replace_with_phone_number="<PHONE>",
                            replace_with_number=lambda m: number_to_words(m.group()),
                            replace_with_digit="0",
                            replace_with_currency_symbol="<CUR>",
                            lang="en"
                            )



class MultiHeadModel(BertPreTrainedModel):
  """Pre-trained BERT model that uses our loss functions"""

  def __init__(self, config, head2size):
    super(MultiHeadModel, self).__init__(config, head2size)
    config.num_labels = 1
    self.bert = BertModel(config)
    self.dropout = nn.Dropout(config.hidden_dropout_prob)
    module_dict = {}
    for head_name, num_labels in head2size.items():
      module_dict[head_name] = nn.Linear(config.hidden_size, num_labels)
    self.heads = nn.ModuleDict(module_dict)

    self.init_weights()

  def forward(self, input_ids, token_type_ids=None, attention_mask=None,
              head2labels=None, return_pooler_output=False, head2mask=None,
              nsp_loss_weights=None):

    device = "cuda" if torch.cuda.is_available() else "cpu"

    # Get logits
    output = self.bert(
      input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask,
      output_attentions=False, output_hidden_states=False, return_dict=True)
    pooled_output = self.dropout(output["pooler_output"]).to(device)

    head2logits = {}
    return_dict = {}
    for head_name, head in self.heads.items():
      head2logits[head_name] = self.heads[head_name](pooled_output)
      head2logits[head_name] = head2logits[head_name].float()
      return_dict[head_name + "_logits"] = head2logits[head_name]


    if head2labels is not None:
      for head_name, labels in head2labels.items():
        num_classes = head2logits[head_name].shape[1]

        # Regression (e.g. for politeness)
        if num_classes == 1:

          # Only consider positive examples
          if head2mask is not None and head_name in head2mask:
            num_positives = head2labels[head2mask[head_name]].sum()  # use certain labels as mask
            if num_positives == 0:
              return_dict[head_name + "_loss"] = torch.tensor([0]).to(device)
            else:
              loss_fct = MSELoss(reduction='none')
              loss = loss_fct(head2logits[head_name].view(-1), labels.float().view(-1))
              return_dict[head_name + "_loss"] = loss.dot(head2labels[head2mask[head_name]].float().view(-1)) / num_positives
          else:
            loss_fct = MSELoss()
            return_dict[head_name + "_loss"] = loss_fct(head2logits[head_name].view(-1), labels.float().view(-1))
        else:
          loss_fct = CrossEntropyLoss(weight=nsp_loss_weights.float())
          return_dict[head_name + "_loss"] = loss_fct(head2logits[head_name], labels.view(-1))


    if return_pooler_output:
      return_dict["pooler_output"] = output["pooler_output"]

    return return_dict

class InputBuilder(object):
  """Base class for building inputs from segments."""

  def __init__(self, tokenizer):
      self.tokenizer = tokenizer
      self.mask = [tokenizer.mask_token_id]

  def build_inputs(self, history, reply, max_length):
      raise NotImplementedError

  def mask_seq(self, sequence, seq_id):
      sequence[seq_id] = self.mask
      return sequence

  @classmethod
  def _combine_sequence(self, history, reply, max_length, flipped=False):
      # Trim all inputs to max_length
      history = [s[:max_length] for s in history]
      reply = reply[:max_length]
      if flipped:
          return [reply] + history
      return history + [reply]


class BertInputBuilder(InputBuilder):
  """Processor for BERT inputs"""

  def __init__(self, tokenizer):
      InputBuilder.__init__(self, tokenizer)
      self.cls = [tokenizer.cls_token_id]
      self.sep = [tokenizer.sep_token_id]
      self.model_inputs = ["input_ids", "token_type_ids", "attention_mask"]
      self.padded_inputs = ["input_ids", "token_type_ids"]
      self.flipped = False


  def build_inputs(self, history, reply, max_length, input_str=True):
    """See base class."""
    if input_str:
        history = [self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize(t)) for t in history]
        reply = self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize(reply))
    sequence = self._combine_sequence(history, reply, max_length, self.flipped)
    sequence = [s + self.sep for s in sequence]
    sequence[0] = self.cls + sequence[0]

    instance = {}
    instance["input_ids"] = list(chain(*sequence))
    last_speaker = 0
    other_speaker = 1
    seq_length = len(sequence)
    instance["token_type_ids"] = [last_speaker if ((seq_length - i) % 2 == 1) else other_speaker
                                  for i, s in enumerate(sequence) for _ in s]
    return instance