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import itertools

import numpy
import tensorflow

class TokenizeLookupLayer(tensorflow.keras.layers.Layer):
    """
    Layer that encapsulates the following:
    - Tokenizing sentences by space (or given delimiter)
    - Looking up the words with a given vocabulary list / table

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import os                                                                                                               
                                                                                                                        
import pandas as pd                                                                                                     
import pyarrow.parquet as pq                                                                                            
import pyarrow as pa                                                                                                    
import numpy as np                                                                                                      
from tqdm import tqdm                                                                                                   
                                                                                                                        
TEST_DIR = 'jaggedbug_testpath'                         

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import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job

from pyspark.sql import functions as sf
from pyspark.sql import types as st
from awsglue.dynamicframe import DynamicFrame

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An error was encountered:
Session 0 unexpectedly reached final status 'dead'. See logs:
stdout: 

stderr: 
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/usr/share/aws/glue/etl/jars/glue-assembly.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/usr/lib/spark/jars/slf4j-log4j12-1.7.16.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]

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[2019-04-15 20:17:14 +0000] [22] [INFO] Starting gunicorn 19.9.0
[2019-04-15 20:17:14 +0000] [22] [INFO] Listening at: unix:/tmp/gunicorn.sock (22)
[2019-04-15 20:17:14 +0000] [22] [INFO] Using worker: gevent
[2019-04-15 20:17:14 +0000] [33] [INFO] Booting worker with pid: 33
[2019-04-15 20:17:14 +0000] [34] [INFO] Booting worker with pid: 34
[2019-04-15 20:17:14 +0000] [42] [INFO] Booting worker with pid: 42
[2019-04-15 20:17:14 +0000] [50] [INFO] Booting worker with pid: 50
[2019-04-15 20:17:15 +0000] [52] [INFO] Booting worker with pid: 52
[2019-04-15 20:17:15 +0000] [54] [INFO] Booting worker with pid: 54
[2019-04-15 20:17:15 +0000] [62] [INFO] Booting worker with pid: 62

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An error occurred while calling o4971.count.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 3 in stage 255.0 failed 4 times, most recent failure: Lost task 3.3 in stage 255.0 (TID 840, ip-172-32-98-36.ec2.internal, executor 1): java.lang.ClassCastException: java.lang.Boolean cannot be cast to java.lang.String
	at org.apache.spark.sql.catalyst.json.JSONOptions$$anonfun$27.apply(JSONOptions.scala:84)
	at scala.Option.map(Option.scala:146)
	at org.apache.spark.sql.catalyst.json.JSONOptions.<init>(JSONOptions.scala:84)
	at org.apache.spark.sql.catalyst.json.JSONOptions.<init>(JSONOptions.scala:43)
	at org.apache.spark.sql.catalyst.expressions.JsonToStructs.parser$lzycompute(jsonExpressions.scala:555)
	at org.apache.spark.sql.catalyst.expressions.JsonToStructs.parser(jsonExpressions.scala:552)
	at org.apache.spark.sql.catalyst.expressions.JsonToStructs.nullSafeEval(jsonExpressions.scala:585)
	at org.apache.spark.sql.catalyst.expressions.UnaryExpression.eval(Expression.scala:331)

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import tensorflow as tf                                                                  
                                                                                         
# Create a tf.keras model.                                                               
print(tf.version.VERSION)                                                                
model = tf.keras.Sequential()                                                            
model.add(tf.keras.layers.Dense(1, input_shape=[10]))                                    
model.summary()                                                                          
                                                                                         
# Save the tf.keras model in the SavedModel format.                                      
saved_to_path = tf.keras.experimental.export(                                            

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import keras                                                                           
import numpy as np                                                                     
                                                                                       
input_l = keras.Input(shape=(1,), name='input')                                        
layer_1 = keras.layers.Dense(1, activation='relu', name='layer_1')(input_l)            
output_l = keras.layers.Dense(1, activation='linear', name='output')(layer_1)          
model = keras.Model(inputs=[input_l], outputs=[output_l])                              
model.compile(loss='mean_absolute_error', optimizer='rmsprop')                         
                                                                                       
x = np.array(np.arange(1, 100000))                                                     

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{'error-code': 'INVALID_APPLICATION_PACKAGE',
 'message': 'Invalid application package: default.default: Error loading '
            'model: Could not import TensorFlow model from directory '
            "'/opt/vespa/var/db/vespa/config_server/serverdb/tenants/default/sessions/175/.preprocessed/models/plike_test/tf114_export': "
            "_output_shapes attribute of 'init_1' does not exist"}

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Traceback (most recent call last):
  File "jax_models.py", line 232, in <module>
    shuffle=True,
  File "jax_models.py", line 181, in fit
    voter_indices, target_indices, ratings, batch_size, batched_dataset_size)
  File "/home/u1/zach/proj/dataplayground3/lib/python3.5/site-packages/jax/api.py", line 150, in f_jitted
    out = xla.xla_call(flat_fun, *args_flat, device=device, backend=backend)
  File "/home/u1/zach/proj/dataplayground3/lib/python3.5/site-packages/jax/core.py", line 592, in call_bind
    outs = primitive.impl(f, *args, **params)
  File "/home/u1/zach/proj/dataplayground3/lib/python3.5/site-packages/jax/interpreters/xla.py", line 400, in _xla_call_impl