# Path: dsenser/resources.py
# W2V = LoadOnDemand(load_W2V, DFLT_W2V_PATH)
# 
# Path: dsenser/utils.py
# def singleton(cls):
#     """Make `cls` instance unique across all calls.
# 
#     Args:
#       cls (class):
#         class to be decorated
# 
#     Retuns:
#       object:
#         singleton instance of the decorated class
# 
#     """
#     instance = cls()
#     instance.__call__ = lambda: instance
#     return instance


from dsenser.resources import W2V
from dsenser.utils import singleton
import numpy as np
#!/usr/bin/env python
# -*- coding: utf-8; mode: python; -*-

"""Module providing class for generic word embeddings.

Attributes:
  WEMB (class):
    class for fast retrieval and adjustment of the Google word embeddings

"""

##################################################################
# Imports
from __future__ import absolute_import, print_function




##################################################################
# Class

# Path: dsenser/resources.py
# W2V = LoadOnDemand(load_W2V, DFLT_W2V_PATH)
# 
# Path: dsenser/utils.py
# def singleton(cls):
#     """Make `cls` instance unique across all calls.
# 
#     Args:
#       cls (class):
#         class to be decorated
# 
#     Retuns:
#       object:
#         singleton instance of the decorated class
# 
#     """
#     instance = cls()
#     instance.__call__ = lambda: instance
#     return instance


# Path: dsenser/word2vec.py
from dsenser.resources import W2V
from dsenser.utils import singleton
import numpy as np

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

"""Module providing class for generic word embeddings.

Attributes:
  WEMB (class):
    class for fast retrieval and adjustment of the Google word embeddings

"""

##################################################################
# Imports
from __future__ import absolute_import, print_function




##################################################################
# Class

@singleton