# 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

    with tf.Session(graph=graph) as sess:

        def respond_to(sentence):
            """Outputs response sentence (string) given input (string)."""

            # Convert input sentence to token-ids.
            sentence_tokens = io_utils.sentence_to_token_ids(
                tf.compat.as_bytes(sentence), word_to_idx)
            sentence_tokens = np.array([sentence_tokens[::-1]])

            # Get output sentence from the chatbot.
            fetches = tensor_dict['outputs']
            feed_dict={tensor_dict['inputs']: sentence_tokens}
            response = sess.run(fetches=fetches, feed_dict=feed_dict)
            return as_words(response[0][:-1])

        sentence = io_utils.get_sentence()
        while sentence != 'exit':
            resp = respond_to(sentence)
            print("Robot:", resp)
            sentence = io_utils.get_sentence()
        print("Farewell, human.")


def get_frozen_vocab(config):
    """Helper function to get dictionaries for translating between tokens and words."""
    data_dir    = config['dataset_params']['data_dir']
    vocab_size  = config['dataset_params']['vocab_size']
    vocab_path = os.path.join(data_dir, 'vocab{}.txt'.format(vocab_size))

# 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


    with tf.Session(graph=graph) as sess:

        def respond_to(sentence):
            """Outputs response sentence (string) given input (string)."""

            # Convert input sentence to token-ids.
            sentence_tokens = io_utils.sentence_to_token_ids(
                tf.compat.as_bytes(sentence), word_to_idx)
            sentence_tokens = np.array([sentence_tokens[::-1]])

            # Get output sentence from the chatbot.
            fetches = tensor_dict['outputs']
            feed_dict={tensor_dict['inputs']: sentence_tokens}
            response = sess.run(fetches=fetches, feed_dict=feed_dict)
            return as_words(response[0][:-1])

        sentence = io_utils.get_sentence()
        while sentence != 'exit':
            resp = respond_to(sentence)
            print("Robot:", resp)
            sentence = io_utils.get_sentence()
        print("Farewell, human.")


def get_frozen_vocab(config):
    """Helper function to get dictionaries for translating between tokens and words."""
    data_dir    = config['dataset_params']['data_dir']
    vocab_size  = config['dataset_params']['vocab_size']
    vocab_path = os.path.join(data_dir, 'vocab{}.txt'.format(vocab_size))

    word_to_idx, idx_to_word = io_utils.get_vocab_dicts(vocab_path)