import os
import tempfile
import json

import tensorflow as tf
from tensorflow.contrib.layers import fully_connected as fc
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.python.client import timeline


class TimeLiner:
    _timeline_dict = None

    def update_timeline(self, chrome_trace):
        # convert crome trace to python dict
        chrome_trace_dict = json.loads(chrome_trace)
        # for first run store full trace
        if self._timeline_dict is None:
            self._timeline_dict = chrome_trace_dict
        # for other - update only time consumption, not definitions
        else:
            for event in chrome_trace_dict['traceEvents']:
                # events time consumption started with 'ts' prefix
                if 'ts' in event:
                    self._timeline_dict['traceEvents'].append(event)

    def save(self, f_name):
        with open(f_name, 'w') as f:
            json.dump(self._timeline_dict, f)


batch_size = 100

inputs = tf.placeholder(tf.float32, [batch_size, 784])
targets = tf.placeholder(tf.float32, [batch_size, 10])

with tf.variable_scope("layer_1"):
    fc_1_out = fc(inputs, num_outputs=500, activation_fn=tf.nn.sigmoid)
with tf.variable_scope("layer_2"):
    fc_2_out = fc(fc_1_out, num_outputs=784, activation_fn=tf.nn.sigmoid)
with tf.variable_scope("layer_3"):
    logits = fc(fc_2_out, num_outputs=10)

loss = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=targets))
train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

if __name__ == '__main__':
    mnist_save_dir = os.path.join(tempfile.gettempdir(), 'MNIST_data')
    mnist = input_data.read_data_sets(mnist_save_dir, one_hot=True)

    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True
    with tf.Session(config=config) as sess:
        sess.run(tf.global_variables_initializer())

        options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
        run_metadata = tf.RunMetadata()
        many_runs_timeline = TimeLiner()
        runs = 5
        for i in range(runs):
            batch_input, batch_target = mnist.train.next_batch(batch_size)
            feed_dict = {inputs: batch_input,
                         targets: batch_target}

            sess.run(train_op,
                     feed_dict=feed_dict,
                     options=options,
                     run_metadata=run_metadata)

            fetched_timeline = timeline.Timeline(run_metadata.step_stats)
            chrome_trace = fetched_timeline.generate_chrome_trace_format()
            many_runs_timeline.update_timeline(chrome_trace)
        many_runs_timeline.save('timeline_03_merged_%d_runs.json' % runs)