# 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: chatbot/globals.py
# DEFAULT_FULL_CONFIG = {
#     "model": "DynamicBot",
#     "dataset": "Cornell",
#     "model_params": {
#         "base_cell": "GRUCell",
#         "ckpt_dir": "out",  # Directory to store training checkpoints.
#         "decode": False,
#         "batch_size": 256,
#         "dropout_prob": 0.2,  # Drop rate applied at encoder/decoders output.
#         "decoder.class": "BasicDecoder",
#         "encoder.class": "BasicEncoder",
#         "embed_size": 128,
#         "learning_rate": 0.002,
#         "l1_reg": 1.0e-6,  # L1 regularization applied to word embeddings.
#         "lr_decay": 0.98,
#         "max_gradient": 5.0,
#         "max_steps": int(1e6),  # Max number of training iterations.
#         "num_layers": 1,  # Num layers for each of encoder, decoder.
#         "num_samples": 512,  # IF sampled_loss is true, default sample size.
#         "optimizer": "Adam",  # Options are those in OPTIMIZERS above.
#         "reset_model": True,
#         "sampled_loss": False,  # Whether to do sampled softmax.
#         "state_size": 512,
#         "steps_per_ckpt": 200,
#         "temperature": 0.0,  # Response temp for chat sessions. (default argmax)
#     },
#     "dataset_params": {
#         "data_dir": None,  # Require user to specify.
#         "vocab_size": 40000,
#         "max_seq_len": 10,  # Maximum length of sentence used to train bot.
#         "optimize_params": True  # Reduce vocab size if exceeds num unique words
#     },
# }
# 
# OPTIMIZERS = {
#     'Adagrad':  tf.train.AdagradOptimizer,
#     'Adam':     tf.train.AdamOptimizer,
#     'SGD':      tf.train.GradientDescentOptimizer,
#     'RMSProp':  tf.train.RMSPropOptimizer,
# }


import os
import copy
import yaml
import random
import subprocess
import numpy as np
import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector
from tensorflow.python.client import device_lib
from utils import io_utils
from chatbot.components import *
from chatbot.globals import DEFAULT_FULL_CONFIG, OPTIMIZERS
        from chatbot.legacy._train import train
        from chatbot.legacy._decode import decode
                dir_name = kwargs[key]
            new_ckpt_dir = os.path.join(new_ckpt_dir, dir_name)
        return new_ckpt_dir


class BucketModel(Model):
    """Abstract class. Any classes that extend BucketModel just need to customize their
        graph structure in __init__ and implement the step(...) function.
        The real motivation for making this was to be able to use the true Model
        abstract class for all classes in this directory, bucketed or not, r1.0 or r0.12.
    """

    def __init__(self, logger, buckets, dataset, params):
        self.buckets = buckets
        super(BucketModel, self).__init__(
            logger=logger,
            dataset=dataset,
            params=params)

    def compile(self):
        """ Configure training process. Name was inspired by Keras. <3 """

        if self.losses is None:
            raise ValueError("Tried compiling model before defining losses.")

        print("Configuring training operations. This may take some time . . . ")
        # Note: variables are trainable=True by default.
        params = tf.trainable_variables()
        # train_op will store the parameter (S)GD train_op.
        self.apply_gradients = []

# 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: chatbot/globals.py
# DEFAULT_FULL_CONFIG = {
#     "model": "DynamicBot",
#     "dataset": "Cornell",
#     "model_params": {
#         "base_cell": "GRUCell",
#         "ckpt_dir": "out",  # Directory to store training checkpoints.
#         "decode": False,
#         "batch_size": 256,
#         "dropout_prob": 0.2,  # Drop rate applied at encoder/decoders output.
#         "decoder.class": "BasicDecoder",
#         "encoder.class": "BasicEncoder",
#         "embed_size": 128,
#         "learning_rate": 0.002,
#         "l1_reg": 1.0e-6,  # L1 regularization applied to word embeddings.
#         "lr_decay": 0.98,
#         "max_gradient": 5.0,
#         "max_steps": int(1e6),  # Max number of training iterations.
#         "num_layers": 1,  # Num layers for each of encoder, decoder.
#         "num_samples": 512,  # IF sampled_loss is true, default sample size.
#         "optimizer": "Adam",  # Options are those in OPTIMIZERS above.
#         "reset_model": True,
#         "sampled_loss": False,  # Whether to do sampled softmax.
#         "state_size": 512,
#         "steps_per_ckpt": 200,
#         "temperature": 0.0,  # Response temp for chat sessions. (default argmax)
#     },
#     "dataset_params": {
#         "data_dir": None,  # Require user to specify.
#         "vocab_size": 40000,
#         "max_seq_len": 10,  # Maximum length of sentence used to train bot.
#         "optimize_params": True  # Reduce vocab size if exceeds num unique words
#     },
# }
# 
# OPTIMIZERS = {
#     'Adagrad':  tf.train.AdagradOptimizer,
#     'Adam':     tf.train.AdamOptimizer,
#     'SGD':      tf.train.GradientDescentOptimizer,
#     'RMSProp':  tf.train.RMSPropOptimizer,
# }


# Path: chatbot/_models.py
import os
import copy
import yaml
import random
import subprocess
import numpy as np
import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector
from tensorflow.python.client import device_lib
from utils import io_utils
from chatbot.components import *
from chatbot.globals import DEFAULT_FULL_CONFIG, OPTIMIZERS
        from chatbot.legacy._train import train
        from chatbot.legacy._decode import decode

                dir_name = kwargs[key]
            new_ckpt_dir = os.path.join(new_ckpt_dir, dir_name)
        return new_ckpt_dir


class BucketModel(Model):
    """Abstract class. Any classes that extend BucketModel just need to customize their
        graph structure in __init__ and implement the step(...) function.
        The real motivation for making this was to be able to use the true Model
        abstract class for all classes in this directory, bucketed or not, r1.0 or r0.12.
    """

    def __init__(self, logger, buckets, dataset, params):
        self.buckets = buckets
        super(BucketModel, self).__init__(
            logger=logger,
            dataset=dataset,
            params=params)

    def compile(self):
        """ Configure training process. Name was inspired by Keras. <3 """

        if self.losses is None:
            raise ValueError("Tried compiling model before defining losses.")

        print("Configuring training operations. This may take some time . . . ")
        # Note: variables are trainable=True by default.
        params = tf.trainable_variables()
        # train_op will store the parameter (S)GD train_op.
        self.apply_gradients = []

        optimizer = OPTIMIZERS[self.optimizer](self.learning_rate)