# Path: utils/io_utils.py
# _PAD = b"_PAD"      # Append to unused space for both encoder/decoder.
# _GO = b"_GO"       # Prepend to each decoder input.
# _EOS = b"_EOS"      # Append to outputs only. Stopping signal when decoding.
# _UNK = b"_UNK"      # For any symbols not in our vocabulary.
# _START_VOCAB = [_PAD, _GO, _EOS, _UNK]
# PAD_ID = 0
# GO_ID = 1
# EOS_ID = 2
# UNK_ID = 3
# _WORD_SPLIT = re.compile(b"([.,!?\"':;)(])")
# _DIGIT_RE = re.compile(br"\d")
# _FLAGS = Flags(pretrained_dir=None,
#                config=None,
#                debug=None,
#                model='{}',
#                dataset='{}',
#                model_params='{}',
#                dataset_params='{}')
# def save_hyper_params(hyper_params, fname):
# def get_sentence(lower=True):
# def update_config(config=None,
#                   config_path=None,
#                   return_config=True,
#                   **kwargs):
# def get_yaml_config(path, save_path=True):
# def load_pretrained_config(pretrained_dir):
# def print_non_defaults(config):
# def flags_to_dict(flags):
# def merge_dicts(default_dict, preference_dict):
# def parse_config(flags=None, pretrained_dir=None, config_path=None):
# def basic_tokenizer(sentence):
# def num_lines(file_path):
# def get_word_freqs(path, counter, norm_digits=True):
# def create_vocabulary(vocab_path, from_path, to_path, max_vocab_size, norm_digits=True):
# def get_vocab_dicts(vocabulary_path):
# def sentence_to_token_ids(sentence, vocabulary, normalize_digits=True):
# def data_to_token_ids(data_path, target_path, vocabulary_path, normalize_digits=True):
# def prepare_data(data_dir,
#                  vocab_size,
#                  from_train_path=None,
#                  to_train_path=None,
#                  from_valid_path=None,
#                  to_valid_path=None,
#                  optimize=True,
#                  config_path=None):
#     def maybe_set_param(param, file_name):
#     def get_vocab_path(vocab_size):
#     def append_to_paths(s, **paths):


import numpy as np
import tensorflow as tf
import os
import re
from utils import io_utils
from pydoc import locate
            graph_def,
            input_map=None,
            return_elements=None,
            op_dict=None,
            producer_op_list=None
        )
    return graph


def unfreeze_bot(frozen_model_path):
    """Restores the frozen graph from file and grabs input/output tensors needed to
    interface with a bot for conversation.

    Args:
        frozen_model_path: location of protobuf file containing frozen graph.

    Returns:
        outputs: tensor that can be run in a session.
    """

    bot_graph   = load_graph(frozen_model_path)
    tensors = {'inputs': bot_graph.get_tensor_by_name('import/input_pipeline/user_input:0'),
               'outputs': bot_graph.get_tensor_by_name('import/outputs:0')}
    return tensors, bot_graph


def unfreeze_and_chat(frozen_model_path):
    """Summon a bot back from the dead and have a nice lil chat with it."""

    tensor_dict, graph = unfreeze_bot(frozen_model_path)

# Path: utils/io_utils.py
# _PAD = b"_PAD"      # Append to unused space for both encoder/decoder.
# _GO = b"_GO"       # Prepend to each decoder input.
# _EOS = b"_EOS"      # Append to outputs only. Stopping signal when decoding.
# _UNK = b"_UNK"      # For any symbols not in our vocabulary.
# _START_VOCAB = [_PAD, _GO, _EOS, _UNK]
# PAD_ID = 0
# GO_ID = 1
# EOS_ID = 2
# UNK_ID = 3
# _WORD_SPLIT = re.compile(b"([.,!?\"':;)(])")
# _DIGIT_RE = re.compile(br"\d")
# _FLAGS = Flags(pretrained_dir=None,
#                config=None,
#                debug=None,
#                model='{}',
#                dataset='{}',
#                model_params='{}',
#                dataset_params='{}')
# def save_hyper_params(hyper_params, fname):
# def get_sentence(lower=True):
# def update_config(config=None,
#                   config_path=None,
#                   return_config=True,
#                   **kwargs):
# def get_yaml_config(path, save_path=True):
# def load_pretrained_config(pretrained_dir):
# def print_non_defaults(config):
# def flags_to_dict(flags):
# def merge_dicts(default_dict, preference_dict):
# def parse_config(flags=None, pretrained_dir=None, config_path=None):
# def basic_tokenizer(sentence):
# def num_lines(file_path):
# def get_word_freqs(path, counter, norm_digits=True):
# def create_vocabulary(vocab_path, from_path, to_path, max_vocab_size, norm_digits=True):
# def get_vocab_dicts(vocabulary_path):
# def sentence_to_token_ids(sentence, vocabulary, normalize_digits=True):
# def data_to_token_ids(data_path, target_path, vocabulary_path, normalize_digits=True):
# def prepare_data(data_dir,
#                  vocab_size,
#                  from_train_path=None,
#                  to_train_path=None,
#                  from_valid_path=None,
#                  to_valid_path=None,
#                  optimize=True,
#                  config_path=None):
#     def maybe_set_param(param, file_name):
#     def get_vocab_path(vocab_size):
#     def append_to_paths(s, **paths):


# Path: utils/bot_freezer.py
import numpy as np
import tensorflow as tf
import os
import re
from utils import io_utils
from pydoc import locate

            graph_def,
            input_map=None,
            return_elements=None,
            op_dict=None,
            producer_op_list=None
        )
    return graph


def unfreeze_bot(frozen_model_path):
    """Restores the frozen graph from file and grabs input/output tensors needed to
    interface with a bot for conversation.

    Args:
        frozen_model_path: location of protobuf file containing frozen graph.

    Returns:
        outputs: tensor that can be run in a session.
    """

    bot_graph   = load_graph(frozen_model_path)
    tensors = {'inputs': bot_graph.get_tensor_by_name('import/input_pipeline/user_input:0'),
               'outputs': bot_graph.get_tensor_by_name('import/outputs:0')}
    return tensors, bot_graph


def unfreeze_and_chat(frozen_model_path):
    """Summon a bot back from the dead and have a nice lil chat with it."""

    tensor_dict, graph = unfreeze_bot(frozen_model_path)

    config  = io_utils.parse_config(pretrained_dir=frozen_model_path)