# Path: deepnlp/segmenter.py
# DEFAULT_MODEL = str(os.path.join(pkg_path, "segment/models/zh/crf_model"))
# class Tokenizer(object):
#     def __init__(self, model_path = DEFAULT_MODEL):
#     def seg(self, text):
# def load_model(name = 'zh'):
# def load_user_model(model_path):
# 
# Path: deepnlp/pos_tagger.py
# class ModelLoader(object):
#     def __init__(self, name, data_path, ckpt_path, conf_path):
#     def predict(self, words):
#     def _init_pos_model(self, session):
#     def _predict_pos_tags(self, session, model, words, data_path):
# def load_model(name = 'zh'):
# 
# Path: deepnlp/ner_tagger.py
# TAG_NONE_ENTITY = "nt"
# ENTITY_TAG_DICT = "entity_tags.dic"
# ENTITY_TAG_DICT_PICKLE = "entity_tags.dic.pkl"
# DEFAULT_DICT_ZH_NAME = "zh"
# def udf_default(word, tags, *args):
# def udf_disambiguation_cooccur(word, tags, context, tag_feat_dict, *args):
# def ensemble_udf(udfs, word, tags, *args):
#     def __init__(self, name, data_path, ckpt_path, conf_path):
#     def predict(self, words, tagset = []):
#     def _init_ner_model(self, session):
#     def _predict_ner_tags_model(self, session, model, words, data_path):
#     def set_tag_feat_dict(self, tag_feat_dict):
#     def _predict_ner_tags_dict(self, words, merge = False, tagset = [],udfs = [udf_default]):
#     def _get_context_words(self, words, i, window = 4):
#     def _preprocess_segment(self, words):
#     def _merge_tagging(self, model_tagging, dict_tagging):
#     def _load_default_dict(self, name = 'zh'):
#     def _load_dict(self, dict_name):
#     def load_dict(self, dict_name):
#     def load_user_dict(self, path):
# def load_model(name = 'zh'):
# class ModelLoader(object):
# 
# Path: deepnlp/nn_parser.py
# UNKNOWN = "*"
#             X = np.array([features])                            # convert 1-D list to ndarray size [1, dim]
#             Y = np.array([[0] * target_num])
# class ModelLoader(object):
#     def __init__(self, name, data_path, ckpt_path, conf_path):
#     def predict(self, words, tags = None):
#     def _init_model(self, session):
#     def _predict(self, session, model, words, tags):
#     def _predict_tree(self, session, model, sent, debug = False):
# def load_model(name = 'zh'):


import deepnlp
from deepnlp import segmenter
from deepnlp import pos_tagger
from deepnlp import ner_tagger
from deepnlp import nn_parser
#!/usr/bin/python
# -*- coding:utf-8 -*-


try:
    deepnlp.download("segment", "zh_finance")
except Exception as e:
    print (e)

deepnlp.register_model("segment", "zh_finance")
deepnlp.download("segment", "zh_finance")
try:

# Path: deepnlp/segmenter.py
# DEFAULT_MODEL = str(os.path.join(pkg_path, "segment/models/zh/crf_model"))
# class Tokenizer(object):
#     def __init__(self, model_path = DEFAULT_MODEL):
#     def seg(self, text):
# def load_model(name = 'zh'):
# def load_user_model(model_path):
# 
# Path: deepnlp/pos_tagger.py
# class ModelLoader(object):
#     def __init__(self, name, data_path, ckpt_path, conf_path):
#     def predict(self, words):
#     def _init_pos_model(self, session):
#     def _predict_pos_tags(self, session, model, words, data_path):
# def load_model(name = 'zh'):
# 
# Path: deepnlp/ner_tagger.py
# TAG_NONE_ENTITY = "nt"
# ENTITY_TAG_DICT = "entity_tags.dic"
# ENTITY_TAG_DICT_PICKLE = "entity_tags.dic.pkl"
# DEFAULT_DICT_ZH_NAME = "zh"
# def udf_default(word, tags, *args):
# def udf_disambiguation_cooccur(word, tags, context, tag_feat_dict, *args):
# def ensemble_udf(udfs, word, tags, *args):
#     def __init__(self, name, data_path, ckpt_path, conf_path):
#     def predict(self, words, tagset = []):
#     def _init_ner_model(self, session):
#     def _predict_ner_tags_model(self, session, model, words, data_path):
#     def set_tag_feat_dict(self, tag_feat_dict):
#     def _predict_ner_tags_dict(self, words, merge = False, tagset = [],udfs = [udf_default]):
#     def _get_context_words(self, words, i, window = 4):
#     def _preprocess_segment(self, words):
#     def _merge_tagging(self, model_tagging, dict_tagging):
#     def _load_default_dict(self, name = 'zh'):
#     def _load_dict(self, dict_name):
#     def load_dict(self, dict_name):
#     def load_user_dict(self, path):
# def load_model(name = 'zh'):
# class ModelLoader(object):
# 
# Path: deepnlp/nn_parser.py
# UNKNOWN = "*"
#             X = np.array([features])                            # convert 1-D list to ndarray size [1, dim]
#             Y = np.array([[0] * target_num])
# class ModelLoader(object):
#     def __init__(self, name, data_path, ckpt_path, conf_path):
#     def predict(self, words, tags = None):
#     def _init_model(self, session):
#     def _predict(self, session, model, words, tags):
#     def _predict_tree(self, session, model, sent, debug = False):
# def load_model(name = 'zh'):


# Path: test/test_register.py
import deepnlp
from deepnlp import segmenter
from deepnlp import pos_tagger
from deepnlp import ner_tagger
from deepnlp import nn_parser

#!/usr/bin/python
# -*- coding:utf-8 -*-


try:
    deepnlp.download("segment", "zh_finance")
except Exception as e:
    print (e)

deepnlp.register_model("segment", "zh_finance")
deepnlp.download("segment", "zh_finance")
try:

    seg_tagger = segmenter.load_model("zh_finance")