# Code derived from
# tensorflow/tensorflow/models/image/imagenet/classify_image.py
from __future__ import absolute_import, division, print_function

import math
import os.path
import sys
import tarfile

import horovod.tensorflow as hvd
import numpy as np
import tensorflow as tf
from six.moves import urllib

MODEL_DIR = "/tmp/imagenet"
DATA_URL = (
    "http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz"
)
softmax = None

config = tf.ConfigProto()
config.gpu_options.visible_device_list = str(hvd.local_rank())
sess = tf.Session(config=config)


# 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):
    # For convenience
    if len(images[0].shape) != 3:
        return 0, 0

    # Bypassing all the assertions so that we don't end prematuraly'
    # assert(type(images) == list)
    # assert(type(images[0]) == np.ndarray)
    # assert(len(images[0].shape) == 3)
    # assert(np.max(images[0]) > 10)
    # 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 = 1
    preds = []
    n_batches = int(math.ceil(float(len(inps)) / float(bs)))
    for i in range(n_batches):
        sys.stdout.write(".")
        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)
    with tf.gfile.FastGFile(
        os.path.join(MODEL_DIR, "classify_image_graph_def.pb"), "rb"
    ) as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
        _ = tf.import_graph_def(graph_def, name="")
    # Works with an arbitrary minibatch size.
    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)
            o.set_shape(tf.TensorShape(new_shape))
    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:
    _init_inception()