from __future__ import division
from __future__ import print_function
import tensorflow as tf
import os
import numpy as np
tf.enable_eager_execution()
import transformer
import argparse
import pdb
import sys
import re
from collections import Counter
from tensorflow.python import debug as tf_debug
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import embedding_ops
import fastBPE
import platform

use_py3 = platform.python_version()[0] == '3'

parser = argparse.ArgumentParser(description='TensorFlow code for generating from CTRL')
parser.add_argument('--model_dir', type=str, required=True,
                                        help='location of model checkpoint')
parser.add_argument('--seed', type=int, default=1337,
                                        help='random seed for TensorFlow, numpy and PythonHash')
parser.add_argument('--generate_num', type=int, default=256,
                                        help='number of tokens to generate')
parser.add_argument('--temperature', type=float, default=0,
                                        help='temperature for sampling distribution; 0 means greedy')
parser.add_argument('--nucleus', type=float, default=0.,
                                        help='cumulative probability cutoff for nucleus sampling; 0 means no nucleus sampling')
parser.add_argument('--topk', type=int, default=0,
                                        help='topk value for sampling from the softmax distribution ; 0 means no topk preferred')
parser.add_argument('--penalty', type=float, default=1.2,
                                        help='repetition penalty for greedy sampling')

args = parser.parse_args()
tf.random.set_random_seed(args.seed)
os.environ['PYTHONHASHSEED'] = str(args.seed)
np.random.seed(args.seed)

# load the vocabulary from file
vocab = open('vocab').read().decode(encoding='utf-8').split('\n') if not use_py3 else open('vocab', encoding='utf-8').read().split('\n')
vocab = list(map(lambda x: x.split(' ')[0], vocab)) + ['<unk>'] + ['\n']
print ('{} unique words'.format(len(vocab)))

# length of the vocabulary
vocab_size = len(vocab)

# define the numericalization map
# idx2word maps the numericalized ID to the word
# word2idx maps the word to the numericalized ID
word2idx = {u:i for i, u in enumerate(vocab)}
idx2word = np.array(vocab)



# sequence length to use for the transformer
# the model is trained with a seq_length of 512
# so, any value <= 512 should work
seq_length = min(args.generate_num, 256)




# the dimension of the transformer
embedding_dim = 1280


# Now, we begin defining the model
# we defer the transformer definition to transformer.py
# here, we only define the tied softmax layer
# this layer ties the softmax weights to the input embeddings
class TiedEmbeddingSoftmax(tf.keras.layers.Layer):

  def __init__(self, vocab_size=vocab_size, embedding_size=embedding_dim, **kwargs):
    super(TiedEmbeddingSoftmax, self).__init__()
    self.w = self.add_weight(name='w', shape=(vocab_size, embedding_size),
                             initializer='random_normal',
                             trainable=True)
    self.b = self.add_weight(name='b', shape=(vocab_size,),
                             initializer='zeros',
                             trainable=True)

  def call(self, inputs, embed=True):
    if embed:
      dtype = tf.keras.backend.dtype(inputs)
      if dtype != 'int32' and dtype != 'int64':
        inputs = math_ops.cast(inputs, 'int32')
      return embedding_ops.embedding_lookup(self.w, inputs)
    else:
      return tf.tensordot(inputs, tf.transpose(self.w), 1) + self.b

# input for the keras model
tokens = tf.keras.layers.Input(shape=(seq_length,), dtype='int32')

# instantiates a tied softmax class
tied_embedding_softmax = TiedEmbeddingSoftmax()

# embedded tokens, before passing it to the transformer
embedded = tied_embedding_softmax(tokens, embed=True)

# the activations after passing it from the transformer
# for some odd reason, TPUs don't play well with specifying the arguments of the Encoder() function
# so you have to leave them at their defaults
transformed = transformer.Encoder()(embedded, training=False)


# pass the activations from our tiedsoftmax class
# this time with embed=False denoting that we are doing the softmax operation
# and not a lookup
logits = tied_embedding_softmax(transformed, embed=False)


# finally, define the Keras model with inputs as tokens and outputs as the logits we just computed
model = tf.keras.Model(inputs=tokens, outputs=logits)


# the loss function is a simple categorical crossentropy between the logits and the labels
def loss(labels, logits):
    return tf.keras.losses.sparse_categorical_crossentropy(labels, logits, from_logits=True)

# the optimizer is not used since this code only supports inference
# however, to compile the model, we still define it
optimizer = tf.contrib.tpu.CrossShardOptimizer(
    tf.contrib.estimator.clip_gradients_by_norm(
        tf.train.AdagradOptimizer(learning_rate=1e-2), 0.25)
    )        

# compile the model with the optimizer and loss            
model.compile(optimizer=optimizer, loss=loss)
print(model.summary())


# IMPORTANT
# this is where the saved model is presented to the code
# the model directory should have the model checkpoint and
# a checkpoint file
run_config = tf.contrib.tpu.RunConfig(
        model_dir=args.model_dir)


# this converts the Keras model to a TensorFlow estimator
# this step is critical
# remember to patch the TF 1.14 file before running the code, else you're going to see errors here
estimator_model = tf.keras.estimator.model_to_estimator(keras_model=model, config=run_config)

# we now create a serving function from this estimator
# this enables us to load the model once and easily query it multiple times
def serving_input_fn():
    inputs = {'input_1': tf.placeholder(tf.int32, [1,seq_length])}
    return tf.estimator.export.ServingInputReceiver(inputs, inputs)
predict_fn = tf.contrib.predictor.from_estimator(estimator_model, serving_input_fn)

# almost there, we now take the user prompt and tokenize with BPE
# load BPE codes
bpe = fastBPE.fastBPE('codes', 'vocab')

temperature = args.temperature
nucleusprob = args.nucleus
penalty = args.penalty
topk = args.topk

while True:
    prompt = raw_input('ENTER PROMPT: ') if not use_py3 else input('ENTER PROMPT: ')

    # tokenize provided prompt
    split_prompt = bpe.apply([prompt])[0].split()
    text = [word2idx[i] for i in split_prompt]

    # pad with 0s and create a mini-batch of 2 (arbitrary, for ease of code)
    padded_text = text + [0] * (args.generate_num - len(text))
    tokens_generated = np.tile(padded_text, (1,1))
    try:
        for token in range(len(text)-1, args.generate_num-1):
          # get the logits from the prediction function
          # the logic here is a bit convoluted because we are allowing generation past 512 tokens
          # this is done by sliding the window over (past 512 tokens) and continuing prediction
          # I'm sure this can be simplified (TODO)
          if token <= seq_length:
            prompt_logits = predict_fn({'input_1':tokens_generated[:, :seq_length]})['tied_embedding_softmax'].squeeze() / (temperature if temperature>0 else 1.)
            _token = token if token < seq_length else -1
          else:
            _token = -1
            end = token + 1
            start = token - seq_length + 2
            prompt_logits = predict_fn({'input_1':np.hstack((tokens_generated[:,0:1], tokens_generated[:,start:end]))})['tied_embedding_softmax'].squeeze() / (temperature if temperature>0 else 1.)


          # if penalty (for repetition) is non-zero,
          # discount the logits from already generated tokens
          if penalty>0:
              penalized_so_far = set()
              for _ in range(token+1):
                 generated_token = tokens_generated[0][_]
                 # don't penalize newlines
                 # you could also choose not to penalize frequent words
                 # (which incidentally are sorted in the vocab file)
                 # but I don't do that
                 # if it prints too many new lines instead of continuing generating text,
                 # you might want to comment this out
                 if idx2word[generated_token] == '\n':
                     continue
                 if generated_token in penalized_so_far:
                     continue
                 penalized_so_far.add(generated_token)
                 prompt_logits[_token][generated_token] /= penalty

          # disallow some tokens
          prompt_logits[_token][word2idx['<unk>']] = -1e8

          # sometimes, when generating from reddit,
          # it tries to generate the Score (reddit Karma) immediately after generating the Title:
          # to disallow this, we can just prevent it from generating Score
          prompt_logits[_token][word2idx['Sco@@']] = -1e8


          # compute probabilities from logits
          prompt_probs = np.exp(prompt_logits[_token])
          prompt_probs = prompt_probs / sum(prompt_probs)
          pruned_list = np.argsort(prompt_probs)[::-1]
          # if you are using nucleus prob, then compute the nucleus probability size
          if nucleusprob > 0.:
            minimum_topk = 1
            nucleus = max(np.where(np.cumsum(np.sort(prompt_probs)[::-1])>nucleusprob)[0][0], minimum_topk)
          elif topk > 0:
            # we are over-loading notation here
            # if you choose to specify a topk instead of a nucleus,
            # we will hardcode the nucleus to be just that
            nucleus = topk
          else:
            # if you specify neither nucleus or topk,
            # then we will use the whole list
            nucleus = len(pruned_list)
            
          # if you want to disallow more complex tokens, you can do so here
          # for instance, if you want to disallow anything with the phrase `http`,
          # you can delete theme from the pruned_list
          # you can comment this out, I'm keeping it in for demonstration purpose
          tokens_to_disallow = []
          for _ in range(len(pruned_list)):
              if 'http' in idx2word[pruned_list[_]]:
                  tokens_to_disallow.append(_)
          pruned_list = np.delete(pruned_list, tokens_to_disallow)

          # if temperature is 0
          # just pick the first (most probable) token
          if temperature==0:
              idx = pruned_list[0]
          else:
            # else,
            # sample from the pruned_list with the logits
            chosen_idx = int(tf.random.categorical(np.expand_dims(prompt_logits[0][_token][pruned_list],0), num_samples=1).numpy())
            idx = pruned_list[chosen_idx]


          # if you want to do some debugging,
          # like which one was chosen,
          # what the top25 were,
          # here is your opportunity. 
          print('chosen:', idx2word[idx])
          #print('top25 alternatives:', pruned_list[:25])

          # assign the token for generation
          tokens_generated[0][token+1] = idx

          # clear screen if you want to
          # os.system("clear")


          tokens_generated_so_far = ' '.join([idx2word[c] for c in tokens_generated[0].squeeze()[:token+2]])
          tokens_generated_so_far = re.sub('(@@ )', '', string=tokens_generated_so_far)
          tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
          tokens_generated_so_far = re.sub('(\n\n)', '', string=tokens_generated_so_far)
          print(tokens_generated_so_far)
          print()


    except KeyboardInterrupt: #Exception as e:
        print('Continuing')