# Path: cam/sgnmt/utils.py
# GO_ID = 1
# EOS_ID = 2
# UNK_ID = 0
# NOTAPPLICABLE_ID = 3
# NEG_INF = float("-inf")
# INF = float("inf")
# EPS_P = 0.00001
#     GO_ID = 0
#     EOS_ID = 2
#     UNK_ID = 3
#     GO_ID = 2 # Usually not used
#     EOS_ID = 1
#     UNK_ID = 3 # Don't rely on this: UNK not standardized in T2T
# TMP_FILENAME = '/tmp/sgnmt.%s.fst' % os.getpid()
# MESSAGE_TYPE_DEFAULT = 1
# MESSAGE_TYPE_POSTERIOR = 2
# MESSAGE_TYPE_FULL_HYPO = 3
# def switch_to_fairseq_indexing():
# def switch_to_t2t_indexing():
# def log_sum_tropical_semiring(vals):
# def log_sum_log_semiring(vals):
# def oov_to_unk(seq, vocab_size, unk_idx=None):
# def argmax_n(arr, n):
# def argmax(arr):
# def common_viewkeys(obj):
# def common_iterable(obj):
# def common_get(obj, key, default):
# def common_contains(obj, key):
# def w2f(fstweight):
# def load_fst(path):
# def get_path(tmpl, sub = 1):
# def split_comma(s, func=None):
#     def notify(self, message, message_type = MESSAGE_TYPE_DEFAULT):
#     def __init__(self):
#     def add_observer(self, observer):
#     def notify_observers(self, message, message_type = MESSAGE_TYPE_DEFAULT):
# class Observer(object):
# class Observable(object):
# 
# Path: cam/sgnmt/utils.py
# class Observer(object):
#     """Super class for classes which observe (GoF design patten) other
#     classes.
#     """
#     
#     @abstractmethod
#     def notify(self, message, message_type = MESSAGE_TYPE_DEFAULT):
#         """Get a notification from an observed object.
#         
#         Args:
#             message (object): the message sent by observed object
#             message_type (int): The type of the message. One of the
#                                 ``MESSAGE_TYPE_*`` variables
#         """
#         raise NotImplementedError
# 
# NEG_INF = float("-inf")
# 
# MESSAGE_TYPE_DEFAULT = 1


from abc import abstractmethod
from cam.sgnmt import utils
from cam.sgnmt.utils import Observer, NEG_INF, MESSAGE_TYPE_DEFAULT
                                 sentence without <S> or </S>
        """
        pass
    
    def initialize_heuristic(self, src_sentence):
        """This is called after ``initialize()`` if the predictor is
        registered as heuristic predictor (i.e. 
        ``estimate_future_cost()`` will be called in the future).
        Predictors can implement this function for initialization of 
        their own heuristic mechanisms.
        
        Args:
            src_sentence (list): List of word IDs which form the source
                                 sentence without <S> or </S>
        """
        pass
    
    def finalize_posterior(self, scores, use_weights, normalize_scores):
        """This method can be used to enforce the parameters use_weights
        normalize_scores in predictors with dict posteriors.
        
        Args:
            scores (dict): unnormalized log valued scores
            use_weights (bool): Set to false to replace all values in 
                                ``scores`` with 0 (= log 1)
            normalize_scores: Set to true to make the exp of elements 
                              in ``scores`` sum up to 1"""
        if not scores: # empty scores -> pass through
            return scores
        if not use_weights:

# Path: cam/sgnmt/utils.py
# GO_ID = 1
# EOS_ID = 2
# UNK_ID = 0
# NOTAPPLICABLE_ID = 3
# NEG_INF = float("-inf")
# INF = float("inf")
# EPS_P = 0.00001
#     GO_ID = 0
#     EOS_ID = 2
#     UNK_ID = 3
#     GO_ID = 2 # Usually not used
#     EOS_ID = 1
#     UNK_ID = 3 # Don't rely on this: UNK not standardized in T2T
# TMP_FILENAME = '/tmp/sgnmt.%s.fst' % os.getpid()
# MESSAGE_TYPE_DEFAULT = 1
# MESSAGE_TYPE_POSTERIOR = 2
# MESSAGE_TYPE_FULL_HYPO = 3
# def switch_to_fairseq_indexing():
# def switch_to_t2t_indexing():
# def log_sum_tropical_semiring(vals):
# def log_sum_log_semiring(vals):
# def oov_to_unk(seq, vocab_size, unk_idx=None):
# def argmax_n(arr, n):
# def argmax(arr):
# def common_viewkeys(obj):
# def common_iterable(obj):
# def common_get(obj, key, default):
# def common_contains(obj, key):
# def w2f(fstweight):
# def load_fst(path):
# def get_path(tmpl, sub = 1):
# def split_comma(s, func=None):
#     def notify(self, message, message_type = MESSAGE_TYPE_DEFAULT):
#     def __init__(self):
#     def add_observer(self, observer):
#     def notify_observers(self, message, message_type = MESSAGE_TYPE_DEFAULT):
# class Observer(object):
# class Observable(object):
# 
# Path: cam/sgnmt/utils.py
# class Observer(object):
#     """Super class for classes which observe (GoF design patten) other
#     classes.
#     """
#     
#     @abstractmethod
#     def notify(self, message, message_type = MESSAGE_TYPE_DEFAULT):
#         """Get a notification from an observed object.
#         
#         Args:
#             message (object): the message sent by observed object
#             message_type (int): The type of the message. One of the
#                                 ``MESSAGE_TYPE_*`` variables
#         """
#         raise NotImplementedError
# 
# NEG_INF = float("-inf")
# 
# MESSAGE_TYPE_DEFAULT = 1


# Path: cam/sgnmt/predictors/core.py
from abc import abstractmethod
from cam.sgnmt import utils
from cam.sgnmt.utils import Observer, NEG_INF, MESSAGE_TYPE_DEFAULT

                                 sentence without <S> or </S>
        """
        pass
    
    def initialize_heuristic(self, src_sentence):
        """This is called after ``initialize()`` if the predictor is
        registered as heuristic predictor (i.e. 
        ``estimate_future_cost()`` will be called in the future).
        Predictors can implement this function for initialization of 
        their own heuristic mechanisms.
        
        Args:
            src_sentence (list): List of word IDs which form the source
                                 sentence without <S> or </S>
        """
        pass
    
    def finalize_posterior(self, scores, use_weights, normalize_scores):
        """This method can be used to enforce the parameters use_weights
        normalize_scores in predictors with dict posteriors.
        
        Args:
            scores (dict): unnormalized log valued scores
            use_weights (bool): Set to false to replace all values in 
                                ``scores`` with 0 (= log 1)
            normalize_scores: Set to true to make the exp of elements 
                              in ``scores`` sum up to 1"""
        if not scores: # empty scores -> pass through
            return scores
        if not use_weights:

            scores = dict.fromkeys(scores, 0.0)