spaCy/spacy/util.py

import os
import io
import json
import re
import os.path

import six
import sputnik
from sputnik.dir_package import DirPackage
from sputnik.package_list import (PackageNotFoundException,
                                  CompatiblePackageNotFoundException)

from . import about
from .attrs import TAG, HEAD, DEP, ENT_IOB, ENT_TYPE


LANGUAGES = {}


def set_lang_class(name, cls):
    global LANGUAGES
    LANGUAGES[name] = cls


def get_lang_class(name):
    lang = re.split('[^a-zA-Z0-9_]', name, 1)[0]
    if lang not in LANGUAGES:
        raise RuntimeError('Language not supported: %s' % lang)
    return LANGUAGES[lang]


def get_package(data_dir):
    if not isinstance(data_dir, six.string_types):
        raise RuntimeError('data_dir must be a string')
    return DirPackage(data_dir)


def get_package_by_name(name=None, via=None):
    package_name = name or about.__models__[about.__default_lang__]
    lang = get_lang_class(package_name)
    try:
        return sputnik.package(about.__title__, about.__version__,
            package_name, data_path=via)
    except PackageNotFoundException as e:
        raise RuntimeError("Model '%s' not installed. Please run 'python -m "
                           "%s.download' to install latest compatible "
                           "model." % (name, lang.__module__))
    except CompatiblePackageNotFoundException as e:
        raise RuntimeError("Installed model is not compatible with spaCy "
                           "version. Please run 'python -m %s.download "
                           "--force' to install latest compatible model." %
                           (lang.__module__))


def normalize_slice(length, start, stop, step=None):
    if not (step is None or step == 1):
        raise ValueError("Stepped slices not supported in Span objects."
                         "Try: list(tokens)[start:stop:step] instead.")
    if start is None:
       start = 0
    elif start < 0:
       start += length
    start = min(length, max(0, start))

    if stop is None:
       stop = length
    elif stop < 0:
       stop += length
    stop = min(length, max(start, stop))

    assert 0 <= start <= stop <= length
    return start, stop


def utf8open(loc, mode='r'):
    return io.open(loc, mode, encoding='utf8')


def read_lang_data(package):
    tokenization = package.load_json(('tokenizer', 'specials.json'))
    with package.open(('tokenizer', 'prefix.txt'), default=None) as file_:
        prefix = read_prefix(file_) if file_ is not None else None
    with package.open(('tokenizer', 'suffix.txt'), default=None) as file_:
        suffix = read_suffix(file_) if file_ is not None else None
    with package.open(('tokenizer', 'infix.txt'), default=None) as file_:
        infix = read_infix(file_) if file_ is not None else None
    return tokenization, prefix, suffix, infix


def read_prefix(fileobj):
    entries = fileobj.read().split('\n')
    expression = '|'.join(['^' + re.escape(piece) for piece in entries if piece.strip()])
    return expression


def read_suffix(fileobj):
    entries = fileobj.read().split('\n')
    expression = '|'.join([piece + '$' for piece in entries if piece.strip()])
    return expression


def read_infix(fileobj):
    entries = fileobj.read().split('\n')
    expression = '|'.join([piece for piece in entries if piece.strip()])
    return expression


# def read_tokenization(lang):
#     loc = path.join(DATA_DIR, lang, 'tokenization')
#     entries = []
#     seen = set()
#     with utf8open(loc) as file_:
#         for line in file_:
#             line = line.strip()
#             if line.startswith('#'):
#                 continue
#             if not line:
#                 continue
#             pieces = line.split()
#             chunk = pieces.pop(0)
#             assert chunk not in seen, chunk
#             seen.add(chunk)
#             entries.append((chunk, list(pieces)))
#             if chunk[0].isalpha() and chunk[0].islower():
#                 chunk = chunk[0].title() + chunk[1:]
#                 pieces[0] = pieces[0][0].title() + pieces[0][1:]
#                 seen.add(chunk)
#                 entries.append((chunk, pieces))
#     return entries


# def read_detoken_rules(lang): # Deprecated?
#     loc = path.join(DATA_DIR, lang, 'detokenize')
#     entries = []
#     with utf8open(loc) as file_:
#         for line in file_:
#             entries.append(line.strip())
#     return entries


def align_tokens(ref, indices): # Deprecated, surely?
    start = 0
    queue = list(indices)
    for token in ref:
        end = start + len(token)
        emit = []
        while queue and queue[0][1] <= end:
            emit.append(queue.pop(0))
        yield token, emit
        start = end
    assert not queue


def detokenize(token_rules, words): # Deprecated?
    """To align with treebanks, return a list of "chunks", where a chunk is a
    sequence of tokens that are separated by whitespace in actual strings. Each
    chunk should be a tuple of token indices, e.g.

    >>> detokenize(["ca<SEP>n't", '<SEP>!'], ["I", "ca", "n't", "!"])
    [(0,), (1, 2, 3)]
    """
    string = ' '.join(words)
    for subtoks in token_rules:
        # Algorithmically this is dumb, but writing a little list-based match
        # machine? Ain't nobody got time for that.
        string = string.replace(subtoks.replace('<SEP>', ' '), subtoks)
    positions = []
    i = 0
    for chunk in string.split():
        subtoks = chunk.split('<SEP>')
        positions.append(tuple(range(i, i+len(subtoks))))
        i += len(subtoks)
    return positions