# 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 os
import data
import chatbot
import logging
import tensorflow as tf
from pydoc import locate
from utils import io_utils
        print("Not very adventurous, are we?")
    elif bot.temperature < 0.7:
        print("This should be interesting . . . ")
    else:
        print("Enjoy your gibberish!")
    bot.chat()


def main(argv):

    if FLAGS.debug:
        # Setting to '0': all tensorflow messages are logged.
        os.environ['TF_CPP_MIN_LOG_LEVEL'] = '0'
        logging.basicConfig(level=logging.INFO)

    # Extract the merged configs/dictionaries.
    config = io_utils.parse_config(flags=FLAGS)
    if config['model_params']['decode'] and config['model_params']['reset_model']:
        print("Woops! You passed {decode: True, reset_model: True}." 
              " You can't chat with a reset bot! I'll set reset to False.")
        config['model_params']['reset_model'] = False

    # If loading from pretrained, double-check that certain values are correct.
    # (This is not something a user need worry about -- done automatically)
    if FLAGS.pretrained_dir is not None:
        assert config['model_params']['decode'] \
               and not config['model_params']['reset_model']

    # Print out any non-default parameters given by user, so as to reassure
    # them that everything is set up properly.

# 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: main.py
import os
import data
import chatbot
import logging
import tensorflow as tf
from pydoc import locate
from utils import io_utils

        print("Not very adventurous, are we?")
    elif bot.temperature < 0.7:
        print("This should be interesting . . . ")
    else:
        print("Enjoy your gibberish!")
    bot.chat()


def main(argv):

    if FLAGS.debug:
        # Setting to '0': all tensorflow messages are logged.
        os.environ['TF_CPP_MIN_LOG_LEVEL'] = '0'
        logging.basicConfig(level=logging.INFO)

    # Extract the merged configs/dictionaries.
    config = io_utils.parse_config(flags=FLAGS)
    if config['model_params']['decode'] and config['model_params']['reset_model']:
        print("Woops! You passed {decode: True, reset_model: True}." 
              " You can't chat with a reset bot! I'll set reset to False.")
        config['model_params']['reset_model'] = False

    # If loading from pretrained, double-check that certain values are correct.
    # (This is not something a user need worry about -- done automatically)
    if FLAGS.pretrained_dir is not None:
        assert config['model_params']['decode'] \
               and not config['model_params']['reset_model']

    # Print out any non-default parameters given by user, so as to reassure
    # them that everything is set up properly.

    io_utils.print_non_defaults(config)