# Copyright 2016 Wojciech Zaremba
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Adapted from the original implementation by Wojciech Zaremba.
# Source: https://github.com/openai/improved-gan/blob/master/inception_score/model.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os.path
import tarfile

import numpy as np
from six.moves import urllib
import tensorflow as tf
import math
import sys

MODEL_DIR = '/tmp/imagenet'

DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
softmax = None

# Call this function with list of images. Each of elements should be a
# numpy array with values ranging from 0 to 255.
def get_inception_score(images, splits=10):
  assert(type(images) == list)
  assert(type(images[0]) == np.ndarray)
  assert(len(images[0].shape) == 3)
  #assert(np.max(images[0]) > 10) # EDIT: commented out
  #assert(np.min(images[0]) >= 0.0)
  inps = []
  for img in images:
    img = img.astype(np.float32)
    inps.append(np.expand_dims(img, 0))
  bs = 100
  # config = tf.ConfigProto(allow_soft_placement=True, device_count={'gpu': 3})
  # # config.gpu_options.per_process_gpu_memory_fraction = 0.1
  # config.gpu_options.allow_growth = True
  with tf.Session() as sess:
    preds = []
    n_batches = int(math.ceil(float(len(inps)) / float(bs)))
    for i in range(n_batches):
        #sys.stdout.write(".") # EDIT: commented out
        #sys.stdout.flush()
        inp = inps[(i * bs):min((i + 1) * bs, len(inps))]
        inp = np.concatenate(inp, 0)
        pred = sess.run(softmax, {'ExpandDims:0': inp})
        preds.append(pred)
    preds = np.concatenate(preds, 0)
    scores = []
    for i in range(splits):
      part = preds[(i * preds.shape[0] // splits):((i + 1) * preds.shape[0] // splits), :]
      kl = part * (np.log(part) - np.log(np.expand_dims(np.mean(part, 0), 0)))
      kl = np.mean(np.sum(kl, 1))
      scores.append(np.exp(kl))
    return np.mean(scores), np.std(scores)

# This function is called automatically.
def _init_inception():
  global softmax
  if not os.path.exists(MODEL_DIR):
    os.makedirs(MODEL_DIR)
  filename = DATA_URL.split('/')[-1]
  filepath = os.path.join(MODEL_DIR, filename)
  if not os.path.exists(filepath):
    def _progress(count, block_size, total_size):
      sys.stdout.write('\r>> Downloading %s %.1f%%' % (
          filename, float(count * block_size) / float(total_size) * 100.0))
      sys.stdout.flush()
    filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)
    print()
    statinfo = os.stat(filepath)
    print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')
  tarfile.open(filepath, 'r:gz').extractall(MODEL_DIR) # EDIT: increased indent
  with tf.gfile.FastGFile(os.path.join(MODEL_DIR, 'classify_image_graph_def.pb'), 'rb') as f:
      for i in range(3):
          with tf.device("/gpu:%d" % i):
                graph_def = tf.GraphDef()
                graph_def.ParseFromString(f.read())
                _ = tf.import_graph_def(graph_def, name='')
  # Works with an arbitrary minibatch size.
  # config = tf.ConfigProto(allow_soft_placement=True, device_count={'gpu': 3})
  # # config.gpu_options.per_process_gpu_memory_fraction = 0.1
  # config.gpu_options.allow_growth = True
  with tf.Session() as sess:
    pool3 = sess.graph.get_tensor_by_name('pool_3:0')
    ops = pool3.graph.get_operations()
    for op_idx, op in enumerate(ops):
        for o in op.outputs:
            shape = o.get_shape()
            shape = [s.value for s in shape]
            new_shape = []
            for j, s in enumerate(shape):
                if s == 1 and j == 0:
                    new_shape.append(None)
                else:
                    new_shape.append(s)
            try:
                o._shape = tf.TensorShape(new_shape)
            except ValueError:
                o._shape_val = tf.TensorShape(new_shape) # EDIT: added for compatibility with tensorflow 1.6.0
    w = sess.graph.get_operation_by_name("softmax/logits/MatMul").inputs[1]
    logits = tf.matmul(tf.squeeze(pool3, [1, 2]), w)
    softmax = tf.nn.softmax(logits)

if softmax is None: # EDIT: commented out
 _init_inception() # EDIT: commented out