# Path: citation_extractor/io/iob.py
# def count_tokens(instances):
#     """Short summary.
# 
#     :param type instances: Description of parameter `instances`.
#     :return: Description of returned object.
#     :rtype: type
# 
#     """
#     return sum([1 for instance in instances for token in instance])
# 
# def file_to_instances(inp_file):
#     """Reads a IOB file a converts it into a list of instances.
# 
#     :param type inp_file: Path to the input IOB file.
#     :return: A list of tuples, where tuple[0] is the token and tuple[1]
#         contains its assigned label.
#     :rtype: list
# 
#     """
#     f = codecs.open(inp_file,"r","utf-8")
#     inp_text = f.read()
#     f.close()
#     out=[]
#     comment=re.compile(r'#.*?')
#     for i in inp_text.split("\n\n"):
#         inst=[]
#         for j in i.split("\n"):
#             if(not comment.match(j)):
#                 temp = tuple(j.split("\t"))
#                 if(len(temp)>1):
#                     inst.append(temp)
#         if(inst and len(inst)>1):
#             out.append(inst)
#     return out
# 
# def instance_to_string(instance):
#     """Converts a feature dictionary into a string representation.
# 
#     :param type instance: Description of parameter `instance`.
#     :return: Description of returned object.
#     :rtype: type
# 
#     """
#     def token_to_string(tok_dict):
#         return [tok_dict[k] for k in sorted(tok_dict.keys())]
# 
#     return [
#         "\t".join(token_to_string(feature_set))
#         for feature_set in instance
#     ]


import json
import logging
import os
import pickle
import re
import string
import types
import citation_extractor
        import re
    import glob
    import codecs
            import codecs
        import codecs
        import codecs
    import doctest
from sklearn_crfsuite import CRF
from stop_words import safe_get_stop_words
from citation_extractor.crfpp_wrap import *
from citation_extractor.io.iob import (count_tokens, file_to_instances,
                                       instance_to_string)
        from nltk.classify.scikitlearn import SklearnClassifier
        from sklearn.ensemble import AdaBoostClassifier
        from sklearn.naive_bayes import GaussianNB
        from sklearn.ensemble import RandomForestClassifier
        from citation_extractor.Utils.FastDict import LookupDictionary
                 'f_ngram_1': u'N',
                 'f_ngram_2': u'Ne',
                 'f_ngram_3': u'Nel',
                 'f_ngram_4': u'Nell',
                 'g_ngram_1': u'a',
                 'g_ngram_2': u'la',
                 'g_ngram_3': u'lla',
                 'g_ngram_4': u'ella',
                 'h_lowcase': u'nella',
                 'i_str-length': '5',
                 'l_pattern': 'Aaaaa',
                 'm_compressed-pattern': 'Aa',
                 'n_works_dictionary': 'OTHERS',
                 'z': '_'}, ... ]

        Returns:
            result:
                a list of dictionaries, where each dictionary corresponds
                to a token,

                [{'features': [],
                 'id': 37,
                 'label': 'O',
                 'probs': {'B-AAUTHOR':
                     {'alpha': '234.113833',
                     'beta': '-2.125040',
                     'prob': '0.000262'},
                   },
                 'token': '.'},...]
        """

# Path: citation_extractor/io/iob.py
# def count_tokens(instances):
#     """Short summary.
# 
#     :param type instances: Description of parameter `instances`.
#     :return: Description of returned object.
#     :rtype: type
# 
#     """
#     return sum([1 for instance in instances for token in instance])
# 
# def file_to_instances(inp_file):
#     """Reads a IOB file a converts it into a list of instances.
# 
#     :param type inp_file: Path to the input IOB file.
#     :return: A list of tuples, where tuple[0] is the token and tuple[1]
#         contains its assigned label.
#     :rtype: list
# 
#     """
#     f = codecs.open(inp_file,"r","utf-8")
#     inp_text = f.read()
#     f.close()
#     out=[]
#     comment=re.compile(r'#.*?')
#     for i in inp_text.split("\n\n"):
#         inst=[]
#         for j in i.split("\n"):
#             if(not comment.match(j)):
#                 temp = tuple(j.split("\t"))
#                 if(len(temp)>1):
#                     inst.append(temp)
#         if(inst and len(inst)>1):
#             out.append(inst)
#     return out
# 
# def instance_to_string(instance):
#     """Converts a feature dictionary into a string representation.
# 
#     :param type instance: Description of parameter `instance`.
#     :return: Description of returned object.
#     :rtype: type
# 
#     """
#     def token_to_string(tok_dict):
#         return [tok_dict[k] for k in sorted(tok_dict.keys())]
# 
#     return [
#         "\t".join(token_to_string(feature_set))
#         for feature_set in instance
#     ]


# Path: citation_extractor/core.py
import json
import logging
import os
import pickle
import re
import string
import types
import citation_extractor
        import re
    import glob
    import codecs
            import codecs
        import codecs
        import codecs
    import doctest
from sklearn_crfsuite import CRF
from stop_words import safe_get_stop_words
from citation_extractor.crfpp_wrap import *
from citation_extractor.io.iob import (count_tokens, file_to_instances,
                                       instance_to_string)
        from nltk.classify.scikitlearn import SklearnClassifier
        from sklearn.ensemble import AdaBoostClassifier
        from sklearn.naive_bayes import GaussianNB
        from sklearn.ensemble import RandomForestClassifier
        from citation_extractor.Utils.FastDict import LookupDictionary

                 'f_ngram_1': u'N',
                 'f_ngram_2': u'Ne',
                 'f_ngram_3': u'Nel',
                 'f_ngram_4': u'Nell',
                 'g_ngram_1': u'a',
                 'g_ngram_2': u'la',
                 'g_ngram_3': u'lla',
                 'g_ngram_4': u'ella',
                 'h_lowcase': u'nella',
                 'i_str-length': '5',
                 'l_pattern': 'Aaaaa',
                 'm_compressed-pattern': 'Aa',
                 'n_works_dictionary': 'OTHERS',
                 'z': '_'}, ... ]

        Returns:
            result:
                a list of dictionaries, where each dictionary corresponds
                to a token,

                [{'features': [],
                 'id': 37,
                 'label': 'O',
                 'probs': {'B-AAUTHOR':
                     {'alpha': '234.113833',
                     'beta': '-2.125040',
                     'prob': '0.000262'},
                   },
                 'token': '.'},...]
        """

        tagged_tokens_list = instance_to_string(feature_sets)