# 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
    with tf.gfile.GFile(prediction_file, 'w') as f:
      def callback(target, length, xe):
        for i in six.moves.range(length[0]):
          token = vocab.decode([target[i][0]])[0]
          log_prob = -xe[i][0]
          print('{!r}'.format(token), file=f)
          print('{}'.format(log_prob), file=f)
      return dispatch(callback)
  else:
    return dispatch(None)


def evaluate_all(model, data, vocab, batch_size, max_time_steps,
                 min_non_episodic_eval_examples_per_stripe,
                 max_training_eval_batches,
                 max_eval_eval_batches,
                 max_test_eval_batches,
                 episodic,
                 eval_softmax_temperature,
                 eval_softmax_temperature_estimation_num_tokens,
                 eval_method,
                 num_eval_samples,
                 eval_power_mean_power,
                 eval_dropout_multiplier,
                 validation_prediction_file,
                 dyneval,
                 conditioning_separator=None):
  """Evaluate on training/validation and maybe on test."""
  # Evaluate on training set.
  def make_train_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

    with tf.gfile.GFile(prediction_file, 'w') as f:
      def callback(target, length, xe):
        for i in six.moves.range(length[0]):
          token = vocab.decode([target[i][0]])[0]
          log_prob = -xe[i][0]
          print('{!r}'.format(token), file=f)
          print('{}'.format(log_prob), file=f)
      return dispatch(callback)
  else:
    return dispatch(None)


def evaluate_all(model, data, vocab, batch_size, max_time_steps,
                 min_non_episodic_eval_examples_per_stripe,
                 max_training_eval_batches,
                 max_eval_eval_batches,
                 max_test_eval_batches,
                 episodic,
                 eval_softmax_temperature,
                 eval_softmax_temperature_estimation_num_tokens,
                 eval_method,
                 num_eval_samples,
                 eval_power_mean_power,
                 eval_dropout_multiplier,
                 validation_prediction_file,
                 dyneval,
                 conditioning_separator=None):
  """Evaluate on training/validation and maybe on test."""
  # Evaluate on training set.
  def make_train_iterator():

    return corpus.get_batches(