# Path: dp_optimizer/dp_optimizer_sparse.py
# class SparseGaussianSumQuery(gaussian_query.GaussianSumQuery):
#   class DPOptimizerClass(cls):
#   class SparseDPGaussianOptimizerClass(make_optimizer_class(cls)):
#   def get_noised_result(self, sample_state, global_state):
#       def add_noise(v):
#       def add_noise(v):
# def make_optimizer_class(cls):
#     def __init__(
#         self,
#         dp_sum_query,
#         num_microbatches=None,
#         unroll_microbatches=False,
#         *args,  # pylint: disable=keyword-arg-before-vararg, g-doc-args
#         **kwargs):
#     def compute_gradients(self,
#                           loss,
#                           var_list,
#                           gate_gradients=GATE_OP,
#                           aggregation_method=None,
#                           colocate_gradients_with_ops=False,
#                           grad_loss=None,
#                           gradient_tape=None):
#         def process_microbatch(i, sample_state):
#         def normalize(v):
# def make_gaussian_optimizer_class(cls):
#     def __init__(
#         self,
#         l2_norm_clip,
#         noise_multiplier,
#         num_microbatches=None,
#         ledger=None,
#         unroll_microbatches=False,
#         *args,  # pylint: disable=keyword-arg-before-vararg
#         **kwargs):
#     def ledger(self):
#     GATE_OP = tf.train.Optimizer.GATE_OP  # pylint: disable=invalid-name
#     GATE_OP = None  # pylint: disable=invalid-name
# 
# Path: utils/common_utils.py
# def rigid_op_sequence(op_lambdas):
#   with ExitStack() as stack:
#     for op_func in op_lambdas:
#       op = op_func()
#       context = tf.control_dependencies([op])
#       stack.enter_context(context)
#   return op
# 
# Path: data/wiki9.py
# def read_wiki9_train_split(exp_id=0):
#   split_path = os.path.join(WIKI9_SPLIT_DIR, 'split{}.train'.format(exp_id))
#   if not os.path.exists(split_path):
#     train_docs, _ = split_wiki9_articles()
#     with open(split_path, 'wb') as f:
#       for doc in tqdm.tqdm(train_docs):
#         with open(os.path.join(WIKI9_DIR, doc)) as fd:
#           f.write(fd.read())
#         f.write(' ')
# 
#   return split_path
# 
# Path: data/common.py
# MODEL_DIR = '/path/to/model/'
# 
# Path: utils/common_utils.py
# def log(msg):
#   if msg[-1] != '\n':
#     msg += '\n'
#   sys.stderr.write(msg)
#   sys.stderr.flush()


from absl import flags
from absl import app
from dp_optimizer.dp_optimizer_sparse import SparseDPAdamGaussianOptimizer
from privacy.analysis.rdp_accountant import compute_rdp, get_privacy_spent
from utils.common_utils import rigid_op_sequence
from data.wiki9 import read_wiki9_train_split
from data.common import MODEL_DIR
from utils.common_utils import log
from collections import Counter
import tensorflow as tf
import numpy as np
import time
import smart_open
import os
  with tf.Session() as sess:
    iteration = 0
    train_loss = 0
    sess.run(tf.global_variables_initializer())

    for e in range(1, epochs + 1):
      start = time.time()
      for x, y in get_batches(train_words, batch_size, word_sample_int,
                              window_size):
        b = len(x)
        if num_microbatches > 0:
          offset = b - b % num_microbatches
          x, y = x[:offset], y[:offset]

        feed = {inputs: x, labels: np.array(y)[:, None]}
        err, _, _ = sess.run([scalar_loss, train_ops, update_curr_words],
                             feed_dict=feed)

        train_loss += err
        iteration += 1

        if iteration % print_every == 0:
          end = time.time()
          log("Iteration: {:.4f}%, Loss: {:.4f}, {:.4f} sec/batch".format(
            iteration / iterations * 100, train_loss / print_every,
            (end - start) / print_every))
          train_loss = 0
          start = time.time()
          if FLAGS.dpsgd:
            eps = compute_epsilon(iteration, len(train_words))

# Path: dp_optimizer/dp_optimizer_sparse.py
# class SparseGaussianSumQuery(gaussian_query.GaussianSumQuery):
#   class DPOptimizerClass(cls):
#   class SparseDPGaussianOptimizerClass(make_optimizer_class(cls)):
#   def get_noised_result(self, sample_state, global_state):
#       def add_noise(v):
#       def add_noise(v):
# def make_optimizer_class(cls):
#     def __init__(
#         self,
#         dp_sum_query,
#         num_microbatches=None,
#         unroll_microbatches=False,
#         *args,  # pylint: disable=keyword-arg-before-vararg, g-doc-args
#         **kwargs):
#     def compute_gradients(self,
#                           loss,
#                           var_list,
#                           gate_gradients=GATE_OP,
#                           aggregation_method=None,
#                           colocate_gradients_with_ops=False,
#                           grad_loss=None,
#                           gradient_tape=None):
#         def process_microbatch(i, sample_state):
#         def normalize(v):
# def make_gaussian_optimizer_class(cls):
#     def __init__(
#         self,
#         l2_norm_clip,
#         noise_multiplier,
#         num_microbatches=None,
#         ledger=None,
#         unroll_microbatches=False,
#         *args,  # pylint: disable=keyword-arg-before-vararg
#         **kwargs):
#     def ledger(self):
#     GATE_OP = tf.train.Optimizer.GATE_OP  # pylint: disable=invalid-name
#     GATE_OP = None  # pylint: disable=invalid-name
# 
# Path: utils/common_utils.py
# def rigid_op_sequence(op_lambdas):
#   with ExitStack() as stack:
#     for op_func in op_lambdas:
#       op = op_func()
#       context = tf.control_dependencies([op])
#       stack.enter_context(context)
#   return op
# 
# Path: data/wiki9.py
# def read_wiki9_train_split(exp_id=0):
#   split_path = os.path.join(WIKI9_SPLIT_DIR, 'split{}.train'.format(exp_id))
#   if not os.path.exists(split_path):
#     train_docs, _ = split_wiki9_articles()
#     with open(split_path, 'wb') as f:
#       for doc in tqdm.tqdm(train_docs):
#         with open(os.path.join(WIKI9_DIR, doc)) as fd:
#           f.write(fd.read())
#         f.write(' ')
# 
#   return split_path
# 
# Path: data/common.py
# MODEL_DIR = '/path/to/model/'
# 
# Path: utils/common_utils.py
# def log(msg):
#   if msg[-1] != '\n':
#     msg += '\n'
#   sys.stderr.write(msg)
#   sys.stderr.flush()


# Path: train_word_embedding_dp.py
from absl import flags
from absl import app
from dp_optimizer.dp_optimizer_sparse import SparseDPAdamGaussianOptimizer
from privacy.analysis.rdp_accountant import compute_rdp, get_privacy_spent
from utils.common_utils import rigid_op_sequence
from data.wiki9 import read_wiki9_train_split
from data.common import MODEL_DIR
from utils.common_utils import log
from collections import Counter
import tensorflow as tf
import numpy as np
import time
import smart_open
import os

  with tf.Session() as sess:
    iteration = 0
    train_loss = 0
    sess.run(tf.global_variables_initializer())

    for e in range(1, epochs + 1):
      start = time.time()
      for x, y in get_batches(train_words, batch_size, word_sample_int,
                              window_size):
        b = len(x)
        if num_microbatches > 0:
          offset = b - b % num_microbatches
          x, y = x[:offset], y[:offset]

        feed = {inputs: x, labels: np.array(y)[:, None]}
        err, _, _ = sess.run([scalar_loss, train_ops, update_curr_words],
                             feed_dict=feed)

        train_loss += err
        iteration += 1

        if iteration % print_every == 0:
          end = time.time()
          log("Iteration: {:.4f}%, Loss: {:.4f}, {:.4f} sec/batch".format(
            iteration / iterations * 100, train_loss / print_every,
            (end - start) / print_every))
          train_loss = 0
          start = time.time()
          if FLAGS.dpsgd:
            eps = compute_epsilon(iteration, len(train_words))

            log('The current epsilon is: {:.2f} for delta={}'.format(