# Path: lamb/corpus.py
# class Corpus(object):
#   def __init__(self, data):
#   def data(self):
#   def size(self):
#   def __add__(self, other):
#   def instance_iterator(self, shuffle=False, start=None,
#                         max_instances=None, max_epochs=None):
#   def _effective_max_instances(self, max_instances, max_epochs):
#   def _share_of_iterator(self, n, num_iterators, i):
#   def ordered_iterators(self, num_iterators,
#                         max_instances=None, max_epochs=None):
#   def equidistant_iterators(self, num_iterators,
#                             random_starts=False, start_jitter=None,
#                             max_instances=None, max_epochs=None):
#           def start_fn(start=start):
#   def max_sentence_length(self):
#   def tokens(self):
# def maybe_truncate(seq, n):
# def read_character_based_corpus(filename, encoding='utf-8'):
# def read_word_based_corpus(filename, encoding='utf-8'):
# def get_episodic_batches(instance_generator, max_batch_size, vocab,
#                          num_steps, num_samples=1, max_batches=None,
#                          conditioning_separator=None):
#       def break_at_separator(seq):
#     def emit(batch_index):
# def get_non_episodic_batches(instance_generators, vocab,
#                              num_steps, num_samples=1, max_batches=None,
#                              add_eos=True):
#   def ensure_window(i):
#   def pop_window(i):
#   def emit_batch():
# def get_batches(corpus_, vocab, batch_size, num_steps, num_samples=1,
#                 episodic=None, deterministic=None, equidistant=True,
#                 max_instances=None, max_epochs=None, max_batches=None,
#                 conditioning_separator=None):


import math
import numpy as np
import six
import tensorflow.compat.v1 as tf
from absl import logging
from lamb import corpus
        eval_softmax_temperature = eval_softmax_temperature0
      # Stop if there was no improvement for two rounds.
      if best_i+1 < i:
        break

  # Use the best eval_softmax_temperature and do a longer
  # run on the validation set.
  def make_eval_iterator():
    return corpus.get_batches(
        data['valid'], vocab,
        eval_batch_size(data['valid']) if not dyneval else 1,
        max_time_steps,
        episodic=episodic,
        deterministic=True,
        max_epochs=1,
        max_batches=max_eval_eval_batches,
        conditioning_separator=conditioning_separator)
  xe = evaluate(
      model, make_eval_iterator, vocab, 'valid', episodic,
      num_samples=num_eval_samples,
      temperature=eval_softmax_temperature,
      power=eval_power_mean_power,
      dropout_multiplier=eval_dropout_multiplier,
      eval_method=eval_method,
      prediction_file=validation_prediction_file,
      dyneval=dyneval)

  # evaluate on test
  if data['test'].size():
    def make_test_iterator():

# Path: lamb/corpus.py
# class Corpus(object):
#   def __init__(self, data):
#   def data(self):
#   def size(self):
#   def __add__(self, other):
#   def instance_iterator(self, shuffle=False, start=None,
#                         max_instances=None, max_epochs=None):
#   def _effective_max_instances(self, max_instances, max_epochs):
#   def _share_of_iterator(self, n, num_iterators, i):
#   def ordered_iterators(self, num_iterators,
#                         max_instances=None, max_epochs=None):
#   def equidistant_iterators(self, num_iterators,
#                             random_starts=False, start_jitter=None,
#                             max_instances=None, max_epochs=None):
#           def start_fn(start=start):
#   def max_sentence_length(self):
#   def tokens(self):
# def maybe_truncate(seq, n):
# def read_character_based_corpus(filename, encoding='utf-8'):
# def read_word_based_corpus(filename, encoding='utf-8'):
# def get_episodic_batches(instance_generator, max_batch_size, vocab,
#                          num_steps, num_samples=1, max_batches=None,
#                          conditioning_separator=None):
#       def break_at_separator(seq):
#     def emit(batch_index):
# def get_non_episodic_batches(instance_generators, vocab,
#                              num_steps, num_samples=1, max_batches=None,
#                              add_eos=True):
#   def ensure_window(i):
#   def pop_window(i):
#   def emit_batch():
# def get_batches(corpus_, vocab, batch_size, num_steps, num_samples=1,
#                 episodic=None, deterministic=None, equidistant=True,
#                 max_instances=None, max_epochs=None, max_batches=None,
#                 conditioning_separator=None):


# Path: lamb/evaluation.py
import math
import numpy as np
import six
import tensorflow.compat.v1 as tf
from absl import logging
from lamb import corpus

        eval_softmax_temperature = eval_softmax_temperature0
      # Stop if there was no improvement for two rounds.
      if best_i+1 < i:
        break

  # Use the best eval_softmax_temperature and do a longer
  # run on the validation set.
  def make_eval_iterator():
    return corpus.get_batches(
        data['valid'], vocab,
        eval_batch_size(data['valid']) if not dyneval else 1,
        max_time_steps,
        episodic=episodic,
        deterministic=True,
        max_epochs=1,
        max_batches=max_eval_eval_batches,
        conditioning_separator=conditioning_separator)
  xe = evaluate(
      model, make_eval_iterator, vocab, 'valid', episodic,
      num_samples=num_eval_samples,
      temperature=eval_softmax_temperature,
      power=eval_power_mean_power,
      dropout_multiplier=eval_dropout_multiplier,
      eval_method=eval_method,
      prediction_file=validation_prediction_file,
      dyneval=dyneval)

  # evaluate on test
  if data['test'].size():
    def make_test_iterator():

      return corpus.get_batches(