# 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
# def load_graph(frozen_model_dir):
# def unfreeze_bot(frozen_model_path):
# def unfreeze_and_chat(frozen_model_path):
#     def as_words(sentence):
#         def respond_to(sentence):
# def get_frozen_vocab(config):
#     def __init__(self, frozen_model_dir, vocab_size):
#     def as_words(self, sentence):
#     def __call__(self, sentence):
# class FrozenBot:


import time
import logging
import unittest
import numpy as np
import tensorflow as tf
import pydoc
import data
import chatbot
from pydoc import locate
from utils import io_utils, bot_freezer
from tests.utils import *
        # ================================================
        flags = TEST_FLAGS
        flags = flags._replace(model_params=dict(
            ckpt_dir=os.path.join(TEST_DIR, 'out'),
            reset_model=True,
            steps_per_ckpt=20,
            max_steps=40))
        bot = create_bot(flags)
        self.assertEqual(bot.reset_model, True)
        # Simulate small train sesh on bot.
        bot.train()

        # ================================================
        # 2. Recreate a chattable bot.
        # ================================================
        # Recreate bot from scratch with decode set to true.
        logging.info("Resetting default graph . . . ")
        tf.reset_default_graph()
        flags = flags._replace(model_params={
            **flags.model_params,
            'reset_model': False,
            'decode': True,
            'max_steps': 100,
            'steps_per_ckpt': 50})
        self.assertTrue(flags.model_params.get('decode'))
        bot = create_bot(flags)
        self.assertTrue(bot.is_chatting)
        self.assertTrue(bot.decode)

        print("Testing quick chat sesh . . . ")

# 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
# def load_graph(frozen_model_dir):
# def unfreeze_bot(frozen_model_path):
# def unfreeze_and_chat(frozen_model_path):
#     def as_words(sentence):
#         def respond_to(sentence):
# def get_frozen_vocab(config):
#     def __init__(self, frozen_model_dir, vocab_size):
#     def as_words(self, sentence):
#     def __call__(self, sentence):
# class FrozenBot:


# Path: tests/test_dynamic_models.py
import time
import logging
import unittest
import numpy as np
import tensorflow as tf
import pydoc
import data
import chatbot
from pydoc import locate
from utils import io_utils, bot_freezer
from tests.utils import *

        # ================================================
        flags = TEST_FLAGS
        flags = flags._replace(model_params=dict(
            ckpt_dir=os.path.join(TEST_DIR, 'out'),
            reset_model=True,
            steps_per_ckpt=20,
            max_steps=40))
        bot = create_bot(flags)
        self.assertEqual(bot.reset_model, True)
        # Simulate small train sesh on bot.
        bot.train()

        # ================================================
        # 2. Recreate a chattable bot.
        # ================================================
        # Recreate bot from scratch with decode set to true.
        logging.info("Resetting default graph . . . ")
        tf.reset_default_graph()
        flags = flags._replace(model_params={
            **flags.model_params,
            'reset_model': False,
            'decode': True,
            'max_steps': 100,
            'steps_per_ckpt': 50})
        self.assertTrue(flags.model_params.get('decode'))
        bot = create_bot(flags)
        self.assertTrue(bot.is_chatting)
        self.assertTrue(bot.decode)

        print("Testing quick chat sesh . . . ")

        config = io_utils.parse_config(flags=flags)