# Path: classifier/competitive.py
# class SimStringInternalClassifier(LibLinearClassifier):
#     def __init__(self):
#         LibLinearClassifier.__init__(self)
#         self.feature_class = SimStringInternalFeature
# 
# Path: resources.py
# class Document(object):
#     def __init__(self, id, title, abstract, path):
#         self.id = id
#         self.title = title
#         self.abstract = abstract
#         self.path = path
# 
#     def __iter__(self):
#         return chain(self.title, self.abstract)
# 
#     def __len__(self):
#         return len(self.title) + len(self.abstract)
# 
# class Sentence(object):
#     def __init__(self, text, annotations):
#         self.text = text
#         self.annotations = annotations
# 
#     def __getitem__(self, val):
#         return self.text.__getitem__(val)
# 
#     def __iter__(self):
#         return iter(self.annotations)
# 
#     def __str__(self):
#         return self.text
# 
#     def add_annotation(self, annotation):
#         self.annotations.append(annotation)
# 
#     def annotation_text(self, annotation):
#         ret = self.text[annotation.start:annotation.end]
#         assert ret, '{0} len({1}) "{2}"'.format(annotation,
#                 len(self.text), self.text)
#         return ret
# 
# class Annotation(object):
#     def __init__(self, start, end, type):
#         self.start = start
#         self.end = end
#         self.type = type
# 
#     def __str__(self):
#         return 'Annotation(type={}, start={}, end={})'.format(
#                 self.type, self.start, self.end)
# 
# Path: experiment/common.py
# def cache_simstring(datasets, verbose=False, ann_modulo=1000,
#         queries_modulo=1000):
#     from simstring import reader as simstring_reader
# 
#     if verbose:
#         print >> stderr, 'Caching SimString:'
# 
#         print >> stderr, 'Pre-caching queries...',
#         queries_seen = 0
# 
#     # For most cases we are better off caching every single query instead of
#     # iterating over them, this also makes sure that each query is unique when
#     # we finally hit the SimString database
#     queries = set()
#     for dataset in datasets:
#         for document in dataset:
#             for sentence in document:
#                 for annotation in sentence:
#                     queries.add(sentence.annotation_text(annotation))
#                     if verbose:
#                         queries_seen += 1
#                         if queries_seen % queries_modulo == 0:
#                             print >> stderr, queries_seen, '...',
#     if verbose:
#         print >> stderr, ('Done! (reduced from {} to {})'
#                 ).format(queries_seen, len(queries))
# 
#     for db_i, db_path in enumerate(SIMSTRING_DB_PATHS, start=1):
#         if verbose:
#             print >> stderr, 'Caching for db: {0} ({1}/{2}) ...'.format(db_path, db_i,
#                     len(SIMSTRING_DB_PATHS)),
#         
#         if verbose:
#             ann_cnt = 0
#         db_reader = None
#         try:
#             db_reader = simstring_reader(db_path)
#             for query in queries:
#                 query_simstring_db(query, db_path,
#                     reader_arg=db_reader)
# 
#                 if verbose:
#                     ann_cnt += 1
#                     if ann_cnt % ann_modulo == 0:
#                         print >> stderr, ann_cnt, '...',
# 
#         finally:
#             if db_reader is not None:
#                 db_reader.close()
#         if verbose:
#             print >> stderr, 'Done!'
# 
# Path: train.py
# def _tab_separated_input_to_doc(input):
#     # Create a dataset out of the input
#     doc = Document(input.name, [], [], '<%s>' % input.name)
#     for _string, _type in (l.rstrip('\n').split('\t') for l in input):
#         doc.abstract.append(Sentence(_string,
#             [Annotation(0, len(_string), _type), ]))
#     return doc


from argparse import ArgumentParser, FileType
    from cPickle import load as pickle_load
    from pickle import load as pickle_load
from classifier.competitive import SimStringInternalClassifier
from resources import Document, Sentence, Annotation
from experiment.common import cache_simstring
from train import _tab_separated_input_to_doc
    from sys import argv
# XXX: Assumes no context and <STRING>\t<TYPE> format

try:
except ImportError:


# TODO: Should not be done this way, API for caching

### Constants
ARGPARSER = ArgumentParser(description='XXX')#XXX: TODO:
ARGPARSER.add_argument('model_path')
ARGPARSER.add_argument('-v', '--verbose', action='store_true')
ARGPARSER.add_argument('-n', '--no-cache', action='store_true')
ARGPARSER.add_argument('-i', '--input', default='-', type=FileType('r'),
        help='input source (DEFAULT: stdin)')
###

def _string_to_ann_sent(_string):
    return Sentence(_string, [Annotation(0, len(_string), None)])

def main(args):
    argp = ARGPARSER.parse_args(args[1:])

    if not argp.no_cache:
        # We can't do it iteratively listening to stdin, read it all
        doc = Document('<classify>', [], [], '<classify>')
        for _string in (l.rstrip('\n') for l in argp.input):
            doc.abstract.append(_string_to_ann_sent(_string))
        docs = (doc, )
    else:

# Path: classifier/competitive.py
# class SimStringInternalClassifier(LibLinearClassifier):
#     def __init__(self):
#         LibLinearClassifier.__init__(self)
#         self.feature_class = SimStringInternalFeature
# 
# Path: resources.py
# class Document(object):
#     def __init__(self, id, title, abstract, path):
#         self.id = id
#         self.title = title
#         self.abstract = abstract
#         self.path = path
# 
#     def __iter__(self):
#         return chain(self.title, self.abstract)
# 
#     def __len__(self):
#         return len(self.title) + len(self.abstract)
# 
# class Sentence(object):
#     def __init__(self, text, annotations):
#         self.text = text
#         self.annotations = annotations
# 
#     def __getitem__(self, val):
#         return self.text.__getitem__(val)
# 
#     def __iter__(self):
#         return iter(self.annotations)
# 
#     def __str__(self):
#         return self.text
# 
#     def add_annotation(self, annotation):
#         self.annotations.append(annotation)
# 
#     def annotation_text(self, annotation):
#         ret = self.text[annotation.start:annotation.end]
#         assert ret, '{0} len({1}) "{2}"'.format(annotation,
#                 len(self.text), self.text)
#         return ret
# 
# class Annotation(object):
#     def __init__(self, start, end, type):
#         self.start = start
#         self.end = end
#         self.type = type
# 
#     def __str__(self):
#         return 'Annotation(type={}, start={}, end={})'.format(
#                 self.type, self.start, self.end)
# 
# Path: experiment/common.py
# def cache_simstring(datasets, verbose=False, ann_modulo=1000,
#         queries_modulo=1000):
#     from simstring import reader as simstring_reader
# 
#     if verbose:
#         print >> stderr, 'Caching SimString:'
# 
#         print >> stderr, 'Pre-caching queries...',
#         queries_seen = 0
# 
#     # For most cases we are better off caching every single query instead of
#     # iterating over them, this also makes sure that each query is unique when
#     # we finally hit the SimString database
#     queries = set()
#     for dataset in datasets:
#         for document in dataset:
#             for sentence in document:
#                 for annotation in sentence:
#                     queries.add(sentence.annotation_text(annotation))
#                     if verbose:
#                         queries_seen += 1
#                         if queries_seen % queries_modulo == 0:
#                             print >> stderr, queries_seen, '...',
#     if verbose:
#         print >> stderr, ('Done! (reduced from {} to {})'
#                 ).format(queries_seen, len(queries))
# 
#     for db_i, db_path in enumerate(SIMSTRING_DB_PATHS, start=1):
#         if verbose:
#             print >> stderr, 'Caching for db: {0} ({1}/{2}) ...'.format(db_path, db_i,
#                     len(SIMSTRING_DB_PATHS)),
#         
#         if verbose:
#             ann_cnt = 0
#         db_reader = None
#         try:
#             db_reader = simstring_reader(db_path)
#             for query in queries:
#                 query_simstring_db(query, db_path,
#                     reader_arg=db_reader)
# 
#                 if verbose:
#                     ann_cnt += 1
#                     if ann_cnt % ann_modulo == 0:
#                         print >> stderr, ann_cnt, '...',
# 
#         finally:
#             if db_reader is not None:
#                 db_reader.close()
#         if verbose:
#             print >> stderr, 'Done!'
# 
# Path: train.py
# def _tab_separated_input_to_doc(input):
#     # Create a dataset out of the input
#     doc = Document(input.name, [], [], '<%s>' % input.name)
#     for _string, _type in (l.rstrip('\n').split('\t') for l in input):
#         doc.abstract.append(Sentence(_string,
#             [Annotation(0, len(_string), _type), ]))
#     return doc


# Path: classify.py
from argparse import ArgumentParser, FileType
    from cPickle import load as pickle_load
    from pickle import load as pickle_load
from classifier.competitive import SimStringInternalClassifier
from resources import Document, Sentence, Annotation
from experiment.common import cache_simstring
from train import _tab_separated_input_to_doc
    from sys import argv

# XXX: Assumes no context and <STRING>\t<TYPE> format

try:
except ImportError:


# TODO: Should not be done this way, API for caching

### Constants
ARGPARSER = ArgumentParser(description='XXX')#XXX: TODO:
ARGPARSER.add_argument('model_path')
ARGPARSER.add_argument('-v', '--verbose', action='store_true')
ARGPARSER.add_argument('-n', '--no-cache', action='store_true')
ARGPARSER.add_argument('-i', '--input', default='-', type=FileType('r'),
        help='input source (DEFAULT: stdin)')
###

def _string_to_ann_sent(_string):
    return Sentence(_string, [Annotation(0, len(_string), None)])

def main(args):
    argp = ARGPARSER.parse_args(args[1:])

    if not argp.no_cache:
        # We can't do it iteratively listening to stdin, read it all
        doc = Document('<classify>', [], [], '<classify>')
        for _string in (l.rstrip('\n') for l in argp.input):
            doc.abstract.append(_string_to_ann_sent(_string))
        docs = (doc, )
    else:

        docs = (Document('Line: %s' % i, [], [_string_to_ann_sent(_string)],