import os
import sys
import glob
import h5py
import numpy as np
import params
import json
import tensorflow as tf
import tensorflow_probability as tfp
import tensorflow_hub as hub
import time
import random
from scipy.stats import truncnorm
from PIL import Image
import visualize as vs
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../live-cortex/rpc/'))
from rpc import CortexRPC

from params import Params

FPS = 25

params = Params(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'params_dense-512.json'))

# --------------------------
# Make directories.
# --------------------------
tag = "test"
OUTPUT_DIR = os.path.join('output', tag)
if not os.path.exists(OUTPUT_DIR):
  os.makedirs(OUTPUT_DIR)

# --------------------------
# Load Graph.
# --------------------------
print("Loading module...")
generator = hub.Module(str(params.generator_path))
print("Loaded!")

gen_signature = 'generator'
if 'generator' not in generator.get_signature_names():
  gen_signature = 'default'

input_info = generator.get_input_info_dict(gen_signature)

BATCH_SIZE = 1
Z_DIM = input_info['z'].get_shape().as_list()[1]
N_CLASS = input_info['y'].get_shape().as_list()[1]

# --------------------------
# Initializers
# --------------------------

def label_sampler(num_classes=2, shape=(BATCH_SIZE, N_CLASS,)):
  label = np.zeros(shape)
  for i in range(shape[0]):
    for _ in range(random.randint(1, shape[1])):
      j = random.randint(0, shape[1]-1)
      label[i, j] = random.random()
    label[i] /= label[i].sum()
  return label

def truncated_z_sample(shape=(BATCH_SIZE, Z_DIM,), truncation=1.0):
  values = truncnorm.rvs(-2, 2, size=shape)
  return truncation * values

def normal_z_sample(shape=(BATCH_SIZE, Z_DIM,)):
  return np.random.normal(size=shape)

# --------------------------
# More complex ops
# --------------------------

def sin(opts, key, shape):
  noise = lerp(opts, key + '_noise', shape)
  scale = InterpolatorParam(name=key + '_scale', value=0.1)
  speed = InterpolatorParam(name=key + '_speed', value=1.0)
  time = opts['time'].variable
  out = tf.sin(time + noise) * scale.variable
  opts[key + '_scale'] = scale
  opts[key + '_speed'] = speed
  return out

def lerp(opts, key, shape):
  a = InterpolatorParam(name=key + '_a', shape=shape)
  b = InterpolatorParam(name=key + '_b', shape=shape)
  n = InterpolatorParam(name=key + '_n', value=0.0)
  speed = InterpolatorParam(name=key + '_speed', value=0.1)
  out = a.variable * (1 - n.variable) + b.variable * n.variable
  opts[key + '_a'] = a
  opts[key + '_b'] = b
  opts[key + '_n'] = n
  opts[key + '_speed'] = speed
  return out

# --------------------------
# Placeholder params
# --------------------------

class InterpolatorParam:
  def __init__(self, name, dtype=tf.float32, shape=(), value=None, type="noise"):
    self.scalar = shape == ()
    self.shape = shape
    self.type = type
    self.value = value if value is not None else np.zeros(shape)
    self.variable = tf.placeholder(dtype=dtype, shape=shape)

  def assign(self, value):
    self.value = value

  def randomize(self, num_classes):
    if self.type == 'noise':
      val = truncated_z_sample(shape=self.shape)
    elif self.type == 'label':
      val = label_sampler(shape=self.shape)
    self.assign(val)

# --------------------------
# Interpolator graph
# --------------------------

class Interpolator:
  def __init__(self):
    self.opts = {
      'time': InterpolatorParam(name='t', value=time.time()),
      'truncation' : InterpolatorParam(name='truncation', value=1.0),
    }

  def build(self):
    lerp_z = lerp(self.opts, 'latent', [BATCH_SIZE, Z_DIM])
    sin_z = sin(self.opts, 'orbit', [BATCH_SIZE, Z_DIM])
    lerp_label = lerp(self.opts, 'label', [BATCH_SIZE, N_CLASS])

    gen_in = {}
    gen_in['truncation'] = self.opts['truncation'].variable
    gen_in['z'] = lerp_z + sin_z
    gen_in['y'] = lerp_label
    gen_img = generator(gen_in, signature=gen_signature)

    # Convert generated image to channels_first.
    self.gen_img = tf.transpose(gen_img, [0, 3, 1, 2])

  def get_feed_dict(self):
    opt = {}
    for key, param in self.opts.items():
      opt[param.variable] = param.value
    return opt

  def get_state(self):
    opt = {}
    for key, param in self.opts.items():
      if param.scalar:
        if type(param.value) is np.ndarray:
          sys.stderr.write('{} is ndarray'.format(key))
          opt[key] = param.value.tolist()
        else:
          opt[key] = param.value
    return opt

  def set_value(self, key, value):
    self.opts[key].assign(value)

  def on_step(self, i, sess):
    gen_images = sess.run(self.gen_img, feed_dict=self.get_feed_dict())
    return gen_images

  def run(self, cmd, payload):
    # do things like create a new B and interpolate to it
    pass

# --------------------------
# RPC Listener
# --------------------------

class Listener:
  def __init__(self):
    self.interpolator = Interpolator()
    self.interpolator.build()

  def connect(self):
    self.rpc_client = CortexRPC(self.on_get, self.on_set, self.on_ready, self.on_cmd)

  def on_set(self, key, value):
    self.interpolator.set_value(key, value)

  def on_get(self):
    state = self.interpolator.get_state()
    sys.stderr.write(json.dumps(state))
    sys.stderr.flush()
    return state

  def on_cmd(self, cmd, payload):
    print("got command {}".format(cmd))
    self.interpolator.run(cmd, payload)

  def on_ready(self, rpc_client):
    self.sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
    self.sess.run(tf.global_variables_initializer())
    self.sess.run(tf.tables_initializer())
    print("Ready!")
    self.rpc_client = rpc_client
    self.rpc_client.send_status('processing', True)
    dt = 1 / FPS
    for i in range(99999):
      if (i % 100) == 0:
        print("Step {}".format(i))
      gen_time = time.time()
      self.interpolator.opts['time'].assign(gen_time)
      gen_images = self.interpolator.on_step(i, self.sess)
      if gen_images is None:
        print("Exiting...")
        break
      if (i % 100) == 0:
        print("Generation time: {:.1f}s".format(time.time() - gen_time))
      out_img = vs.data2pil(gen_images[0])
      if out_img is not None:
        #if out_img.resize_before_sending:
        img_to_send = out_img.resize((256, 256), Image.BICUBIC)
        meta = {
          'i': i,
          'sequence_i': i,
          'skip_i': 0,
          'sequence_len': 99999,
        }
        self.rpc_client.send_pil_image("frame_{:05d}.png".format(i+1), meta, img_to_send, 'jpg')
    self.rpc_client.send_status('processing', False)
    self.sess.close()

if __name__ == '__main__':
  listener = Listener()
  listener.connect()

# layer_name = 'module_apply_' + gen_signature + '/' + params.inv_layer
# gen_encoding = tf.get_default_graph().get_tensor_by_name(layer_name)
# ENC_SHAPE = gen_encoding.get_shape().as_list()[1:]
# encoding = tf.get_variable(name='encoding', dtype=tf.float32,
#                            shape=[BATCH_SIZE,] + ENC_SHAPE)
# tf.contrib.graph_editor.swap_ts(gen_encoding, tf.convert_to_tensor(encoding))