# Path: libml/utils.py
# _GPUS = None
# FLAGS = flags.FLAGS
#         _GPUS = tuple([x.name for x in local_device_protos if x.device_type == 'GPU'])
# class EasyDict(dict):
#     def __init__(self, *args, **kwargs):
# def get_config():
# def setup_main():
# def setup_tf():
# def smart_shape(x):
# def ilog2(x):
# def find_latest_checkpoint(dir, glob_term='model.ckpt-*.meta'):
# def get_latest_global_step(dir):
# def get_latest_global_step_in_subdir(dir):
# def getter_ema(ema, getter, name, *args, **kwargs):
# def model_vars(scope=None):
# def gpu(x):
# def get_available_gpus():
# def get_gpu():
# def average_gradients(tower_grads):
# def para_list(fn, *args):
# def para_mean(fn, *args):
# def para_cat(fn, *args):
# def interleave(x, batch):
# def de_interleave(x, batch):
# def combine_dicts(*args):


import json
import os
import numpy as np
import tensorflow as tf
from collections import defaultdict
from absl import app
from tqdm import trange, tqdm
from libml import utils
    npos = np.zeros(nclass, np.int64)
    class_data = [[] for _ in range(nclass)]
    unlabel = []
    tf.gfile.MakeDirs(os.path.dirname(target))
    with tf.python_io.TFRecordWriter(target + '-unlabel.tfrecord') as writer_unlabel:
        pos, loop = 0, trange(count, desc='Writing records')
        for input_file in input_files:
            for record in tf.python_io.tf_record_iterator(input_file):
                class_data[id_class[pos]].append((pos, record))
                while True:
                    c = np.argmax(train_stats - npos / max(npos.max(), 1))
                    if class_data[c]:
                        p, v = class_data[c].pop(0)
                        unlabel.append(p)
                        writer_unlabel.write(v)
                        npos[c] += 1
                    else:
                        break
                pos += 1
                loop.update()
        for remain in class_data:
            for p, v in remain:
                unlabel.append(p)
                writer_unlabel.write(v)
        loop.close()
    with tf.gfile.Open(target + '-unlabel.json', 'w') as writer:
        writer.write(json.dumps(dict(distribution=train_stats.tolist(), indexes=unlabel), indent=2, sort_keys=True))


if __name__ == '__main__':

# Path: libml/utils.py
# _GPUS = None
# FLAGS = flags.FLAGS
#         _GPUS = tuple([x.name for x in local_device_protos if x.device_type == 'GPU'])
# class EasyDict(dict):
#     def __init__(self, *args, **kwargs):
# def get_config():
# def setup_main():
# def setup_tf():
# def smart_shape(x):
# def ilog2(x):
# def find_latest_checkpoint(dir, glob_term='model.ckpt-*.meta'):
# def get_latest_global_step(dir):
# def get_latest_global_step_in_subdir(dir):
# def getter_ema(ema, getter, name, *args, **kwargs):
# def model_vars(scope=None):
# def gpu(x):
# def get_available_gpus():
# def get_gpu():
# def average_gradients(tower_grads):
# def para_list(fn, *args):
# def para_mean(fn, *args):
# def para_cat(fn, *args):
# def interleave(x, batch):
# def de_interleave(x, batch):
# def combine_dicts(*args):


# Path: scripts/create_unlabeled.py
import json
import os
import numpy as np
import tensorflow as tf
from collections import defaultdict
from absl import app
from tqdm import trange, tqdm
from libml import utils

    npos = np.zeros(nclass, np.int64)
    class_data = [[] for _ in range(nclass)]
    unlabel = []
    tf.gfile.MakeDirs(os.path.dirname(target))
    with tf.python_io.TFRecordWriter(target + '-unlabel.tfrecord') as writer_unlabel:
        pos, loop = 0, trange(count, desc='Writing records')
        for input_file in input_files:
            for record in tf.python_io.tf_record_iterator(input_file):
                class_data[id_class[pos]].append((pos, record))
                while True:
                    c = np.argmax(train_stats - npos / max(npos.max(), 1))
                    if class_data[c]:
                        p, v = class_data[c].pop(0)
                        unlabel.append(p)
                        writer_unlabel.write(v)
                        npos[c] += 1
                    else:
                        break
                pos += 1
                loop.update()
        for remain in class_data:
            for p, v in remain:
                unlabel.append(p)
                writer_unlabel.write(v)
        loop.close()
    with tf.gfile.Open(target + '-unlabel.json', 'w') as writer:
        writer.write(json.dumps(dict(distribution=train_stats.tolist(), indexes=unlabel), indent=2, sort_keys=True))


if __name__ == '__main__':

    utils.setup_tf()