# Path: wordseg/utils.py
# def str2type(t):
# def type2str(t):
# def strip(string):
# def null_logger():
# def get_logger(name=None, level=logging.WARNING):
#     def __init__(self, elements):
#     def __iter__(self):
#     def __next__(self):
#     def __init__(self, function):
#     def __call__(self):
#     def exit(msg):
# def get_binary(binary):
# def get_config_files(algorithm, extension=None):
#     def __init__(self, short_name=None, name=None, type=None,
#                  default=None, help=''):
#     def add_to_parser(self, parser):
#     def parsed_name(self):
# def get_parser(description=None, separator=Separator(), train_file=False):
# def prepare_main(name=None,
#                  description=None,
#                  separator=Separator(phone=None, syllable=None, word=None),
#                  add_arguments=None,
#                  train_file=False):
# class CountingIterator:
# class CatchExceptions:
# class Argument:
# 
# Path: wordseg/folding.py
# def _permute(l):
# def _flatten(l):
# def _cumsum(l):
# def boundaries(text, nfolds):
# def fold(text, nfolds, fold_boundaries=None):
# def unfold(folds, index):


import codecs
import collections
import os
import joblib
from wordseg import utils, folding

- Training ans segmentation on separate files:
  Specify <input-text> and --train-file <training-file>. Both texts must be in
  phonologized form. The PUDDLE model is trained offline on <training-file>,
  before the segmentation of <input-text>. In this mode --nfolds and --njobs
  options are not valid.

"""





class Puddle:
    """Train and segmenttext with a PUDDLE modelling

    Implementation of a PUDDLE model with `train()` and `segment()` methods.

    Parameters
    ----------
    window : int, optional
        Number of phonemes to be taken into account for boundary constraint.
        Default to 2.
    by_frequency : bool, optional
        When True choose the word candidates by filterring them by frequency.
        Default to False.
    log : logging.Logger, optional
        The logger instance where to send messages.

    """

# Path: wordseg/utils.py
# def str2type(t):
# def type2str(t):
# def strip(string):
# def null_logger():
# def get_logger(name=None, level=logging.WARNING):
#     def __init__(self, elements):
#     def __iter__(self):
#     def __next__(self):
#     def __init__(self, function):
#     def __call__(self):
#     def exit(msg):
# def get_binary(binary):
# def get_config_files(algorithm, extension=None):
#     def __init__(self, short_name=None, name=None, type=None,
#                  default=None, help=''):
#     def add_to_parser(self, parser):
#     def parsed_name(self):
# def get_parser(description=None, separator=Separator(), train_file=False):
# def prepare_main(name=None,
#                  description=None,
#                  separator=Separator(phone=None, syllable=None, word=None),
#                  add_arguments=None,
#                  train_file=False):
# class CountingIterator:
# class CatchExceptions:
# class Argument:
# 
# Path: wordseg/folding.py
# def _permute(l):
# def _flatten(l):
# def _cumsum(l):
# def boundaries(text, nfolds):
# def fold(text, nfolds, fold_boundaries=None):
# def unfold(folds, index):


# Path: wordseg/algos/puddle.py
import codecs
import collections
import os
import joblib
from wordseg import utils, folding


- Training ans segmentation on separate files:
  Specify <input-text> and --train-file <training-file>. Both texts must be in
  phonologized form. The PUDDLE model is trained offline on <training-file>,
  before the segmentation of <input-text>. In this mode --nfolds and --njobs
  options are not valid.

"""





class Puddle:
    """Train and segmenttext with a PUDDLE modelling

    Implementation of a PUDDLE model with `train()` and `segment()` methods.

    Parameters
    ----------
    window : int, optional
        Number of phonemes to be taken into account for boundary constraint.
        Default to 2.
    by_frequency : bool, optional
        When True choose the word candidates by filterring them by frequency.
        Default to False.
    log : logging.Logger, optional
        The logger instance where to send messages.

    """

    def __init__(self, window=2, by_frequency=False, log=utils.null_logger()):